how to cite research paper scientific

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• SCIENTIFIC STYLE AND FORMAT CITATION QUICK GUIDE

Scientific Style and Format Citation Quick Guide

Scientific Style and Format presents three systems for referring to references (also known as citations) within the text of a journal article, book, or other scientific publication: 1) citation–sequence; 2) name–year; and 3) citation–name. These abbreviated references are called in-text references. They refer to a list of references at the end of the document.

The system of in-text references that you use will determine the order of references at the end of your document. These end references have essentially the same format in all three systems, except for the placement of the date of publication in the name–year system.

Though Scientific Style and Format now uses citation–sequence for its own references, each system is widely used in scientific publishing. Consult your publisher to determine which system you will need to follow.

Click on the tabs below for more information and to see some common examples of materials cited in each style, including examples of electronic sources. For numerous specific examples, see Chapter 29 of the 8th edition of Scientific Style and Format .

Citation–Sequence and Citation–Name

The following examples illustrate the citation–sequence and citation–name systems. The two systems are identical except for the order of references. In both systems, numbers within the text refer to the end references.

In citation–sequence, the end references are listed in the sequence in which they first appear within the text. For example, if a reference by Smith is the first one mentioned in the text, then the complete reference to the Smith work will be number 1 in the end references. The same number is used for subsequent in-text references to the same document.

In citation–name, the end references are listed alphabetically by author. Multiple works by the same author are listed alphabetically by title. The references are numbered in that sequence, such that a work authored by Adam is number 1, Brown is number 2, and so on. Numbers assigned to the end references are used for the in-text references regardless of the sequence in which they appear in the text of the work. For example, if a work by Zielinski is number 56 in the reference list, each in-text reference to Zielinski will be number 56 also.

List authors in the order in which they appear in the original text, followed by a period. Periods also follow article and journal title and volume or issue information. Separate the date from volume and issue by a semicolon. The location (usually the page range for the article) is preceded by a colon.

Author(s). Article title. Journal title. Date;volume(issue):location.

Journal titles are generally abbreviated according to the List of Title Word Abbreviations maintained by the ISSN International Centre. See Appendix 29.1 in Scientific Style and Format for more information.

For articles with more than 1 author, names are separated by a comma.

Smart N, Fang ZY, Marwick TH. A practical guide to exercise training for heart failure patients. J Card Fail. 2003;9(1):49–58.

For articles with more than 10 authors, list the first 10 followed by “et al.”

Pizzi C, Caraglia M, Cianciulli M, Fabbrocini A, Libroia A, Matano E, Contegiacomo A, Del Prete S, Abbruzzese A, Martignetti A, et al. Low-dose recombinant IL-2 induces psychological changes: monitoring by Minnesota Multiphasic Personality Inventory (MMPI). Anticancer Res. 2002;22(2A):727–732.

Volume with no issue or other subdivision

Laskowski DA. Physical and chemical properties of pyrethroids. Rev Environ Contam Toxicol. 2002;174:49–170.

Volume with issue and supplement

Gardos G, Cole JO, Haskell D, Marby D, Paine SS, Moore P. The natural history of tardive dyskinesia. J Clin Pharmacol. 1988;8(4 Suppl):31S–37S

Volume with supplement but no issue

Heemskerk J, Tobin AJ, Ravina B. From chemical to drug: neurodegeneration drug screening and the ethics of clinical trials. Nat Neurosci. 2002;5 Suppl:1027–1029.

Multiple issue numbers

Ramstrom O, Bunyapaiboonsri T, Lohmann S, Lehn JM. Chemical biology of dynamic combinatorial libraries. Biochim Biophys Acta. 2002;1572(2–3):178–186.

Issue with no volume

Sabatier R. Reorienting health and social services. AIDS STD Health Promot Exch. 1995;(4):1–3.

Separate information about author(s), title, edition, and publication by periods. The basic format is as follows:

Author(s). Title. Edition. Place of publication: publisher; date. Extent. Notes.

Extent can include information about pagination or number of volumes and is considered optional. Notes can include information of interest to the reader, such as language of publication other than English; such notes are optional.

Essential notes provide information about location, such as a URL for online works. See Chapter 29 for more information.

For books with more than 1 author, names are separated by a comma.

Ferrozzi F, Garlaschi G, Bova D. CT of metastases. New York (NY): Springer; 2000.

For books with more than 10 authors, list the first 10 followed by “et al.”

Wenger NK, Sivarajan Froelicher E, Smith LK, Ades PA, Berra K, Blumenthal JA, Certo CME, Dattilo AM, Davis D, DeBusk RF, et al. Cardiac rehabilitation. Rockville (MD): Agency for Health Care Policy and Research (US); 1995.

Organization as author

Advanced Life Support Group. Acute medical emergencies: the practical approach. London (England): BMJ Books; 2001.

Author(s) plus editor(s) or translator(s)

Klarsfeld A, Revah F. The biology of death: origins of mortality. Brady L, translator. Ithaca (NY): Cornell University Press; 2003.

Luzikov VN. Mitochondrial biogenesis and breakdown. Galkin AV, translator; Roodyn DB, editor. New York (NY): Consultants Bureau; 1985.

Chapter or other part of a book, same author(s)

Gawande A. The checklist manifesto: how to get things right. New York (NY): Metropolitan Books; 2010. Chapter 3, The end of the master builder; p. 48–71.

Chapter or other part of a book, different authors

Rapley R. Recombinant DNA and genetic analysis. In: Wilson K, Walker J, editors. Principles and techniques of biochemistry and molecular biology. 7th ed. New York (NY): Cambridge University Press; 2010. p. 195–262.

Multivolume work as a whole

Alkire LG, editor. Periodical title abbreviations. 16th ed. Detroit (MI): Thompson Gale; 2006. 2 vol. Vol. 1, By abbreviation; vol. 2, By title.

Dissertations and Theses

Lutz M. 1903: American nervousness and the economy of cultural change [dissertation]. [Stanford (CA)]: Stanford University; 1989.

Blanco EE, Meade JC, Richards WD, inventors; Ophthalmic Ventures, assignee. Surgical stapling system. United States patent US 4,969,591. 1990 Nov 13.

Weiss R. Study shows problems in cloning people: researchers find replicating primates will be harder than other mammals. Washington Post (Home Ed.). 2003 Apr 11;Sect. A:12 (col. 1).

Indicate a copyright date with a lowercase “c”.

Johnson D, editor. Surgical techniques in orthopaedics: anterior cruciate ligament reconstruction [DVD]. Rosemont (IL): American Academy of Orthopaedic Surgeons; c2002. 1 DVD.

Websites and Other Online Formats

References to websites and other online formats follow the same general principles as for printed references, with the addition of a date of update/revision (if available) along with an access date and a URL.

Title of Homepage. Edition. Place of publication: publisher; date of publication [date updated; date accessed]. Notes.

If no date of publication can be determined, use a copyright date (if available), preceded by “c”. Include the URL in the notes.

APSnet: plant pathology. St Paul (MN): American Phytopathological Association; c1994–2005 [accessed 2005 Jun 20]. http://www.apsnet.org/.

Online journal article

Author(s) of article. Title of article. Title of journal (edition). Date of publication [date updated; date accessed];volume(issue):location. Notes.

A DOI (Digital Object Identifier) may be included in the notes in addition to a URL, if available:

Savage E, Ramsay M, White J, Beard S, Lawson H, Hunjan R, Brown D. Mumps outbreaks across England and Wales in 2004: observational study. BMJ. 2005 [accessed 2005 May 31];330(7500):1119–1120. http://bmj.bmjjournals.com/cgi/reprint/330/7500/1119. doi:10.1136/bmj.330.7500.1119.

Author(s). Title of book. Edition. Place of publication: publisher; date of publication [date updated; date accessed]. Notes.

Brogden KA, Guthmille JM, editors. Polymicrobial diseases. Washington (DC): ASM Press; 2002 [accessed February 28, 2014]. http://www.ncbi.nlm.nih.gov/books/NBK2475/.

Author’s name. Title of post [descriptive word]. Title of blog. Date of publication. [accessed date]. URL.

Fogarty M. Formatting titles on Twitter and Facebook [blog]. Grammar Girl: Quick and Dirty Tips for Better Writing. 2012 Aug 14. [accessed 2012 Oct 19]. http://grammar.quickanddirtytips.com/formatting-titles-on-twitter-and-facebook.aspx.

Forthcoming or Unpublished Material

Not all forthcoming or unpublished sources are suitable for inclusion in reference lists. Check with your publisher if in doubt.

Forthcoming journal article or book

Journal article:

Farley T, Galves A, Dickinson LM, Perez MJ. Stress, coping, and health: a comparison of Mexican immigrants, Mexican-Americans, and non-Hispanic whites. J Immigr Health. Forthcoming 2005 Jul.

Goldstein DS. Adrenaline and the inner world: an introduction to scientific integrative medicine. Baltimore (MD): Johns Hopkins University Press. Forthcoming 2006.

Paper or poster presented at meeting

Unpublished presentations are cited as follows:

Antani S, Long LR, Thoma GR, Lee DJ. Anatomical shape representation in spine x-ray images. Paper presented at: VIIP 2003. Proceedings of the 3rd IASTED International Conference on Visualization, Imaging and Image Processing; 2003 Sep 8–10; Benalmadena, Spain.

Charles L, Gordner R. Analysis of MedlinePlus en Español customer service requests. Poster session presented at: Futuro magnifico! Celebrating our diversity. MLA ’05: Medical Library Association Annual Meeting; 2005 May 14–19; San Antonio, TX.

References to published presentations are cited much like contributions to books, with the addition of information about the date and place of the conference. See Chapter 29 for more information.

Personal communication

References to personal communication are placed in running text rather than as formal end references.

Permission is usually required and should be acknowledged in an “Acknowledgment” or “Notes” section at the end of the document.

. . . and most of these meningiomas proved to be inoperable (2003 letter from RS Grant to me; unreferenced, see “Notes”) while a few were not.

Name–Year

The following examples illustrate the name–year system. In this system (sometimes called the Harvard system), in-text references consist of the surname of the author or authors and the year of publication of the document. End references are unnumbered and appear in alphabetical order by author and year of publication, with multiple works by the same author listed in chronological order.

Each example of an end reference is accompanied here by an example of a corresponding in-text reference. For more details and many more examples, see Chapter 29 of Scientific Style and Format .

For the end reference, list authors in the order in which they appear in the original text. The year of publication follows the author list. Use periods to separate each element, including author(s), date of publication, article and journal title, and volume or issue information. Location (usually the page range for the article) is preceded by a colon.

Author(s). Date. Article title. Journal title. Volume(issue):location.

For the in-text reference, use parentheses and list author(s) by surname followed by year of publication.

(Author(s) Year)

For articles with 2 authors, names are separated by a comma in the end reference but by “and” in the in-text reference.

Mazan MR, Hoffman AM. 2001. Effects of aerosolized albuterol on physiologic responses to exercise in standardbreds. Am J Vet Res. 62(11):1812–1817.

(Mazan and Hoffman 2001)

For articles with 3 to 10 authors, list all authors in the end reference; in the in-text reference, list only the first, followed by “et al.”

Smart N, Fang ZY, Marwick TH. 2003. A practical guide to exercise training for heart failure patients. J Card Fail. 9(1):49–58.

(Smart et al. 2003)

For articles with more than 10 authors, list the first 10 in the end reference, followed by “et al.”

Pizzi C, Caraglia M, Cianciulli M, Fabbrocini A, Libroia A, Matano E, Contegiacomo A, Del Prete S, Abbruzzese A, Martignetti A, et al. 2002. Low-dose recombinant IL-2 induces psychological changes: monitoring by Minnesota Multiphasic Personality Inventory (MMPI). Anticancer Res. 22(2A):727–732.

(Pizzi et al. 2002)

Laskowski DA. 2002. Physical and chemical properties of pyrethroids. Rev Environ Contam Toxicol. 174:49–170.

(Laskowski 2002)

Gardos G, Cole JO, Haskell D, Marby D, Paine SS, Moore P. 1988. The natural history of tardive dyskinesia. J Clin Pharmacol. 8(4 Suppl):31S–37S.

(Gardos et al. 1988)

Heemskerk J, Tobin AJ, Ravina B. 2002. From chemical to drug: neurodegeneration drug screening and the ethics of clinical trials. Nat Neurosci. 5 Suppl:1027–1029.

(Heemskerk et al. 2002)

Ramstrom O, Bunyapaiboonsri T, Lohmann S, Lehn JM. 2002. Chemical biology of dynamic combinatorial libraries. Biochim Biophys Acta. 1572(2–3):178–186.

(Ramstrom et al. 2002)

Sabatier R. 1995. Reorienting health and social services. AIDS STD Health Promot Exch. (4):1–3.

(Sabatier 1995)

In the end reference, separate information about author(s), date, title, edition, and publication by periods. The basic format is as follows:

Author(s). Date. Title. Edition. Place of publication: publisher. Extent. Notes.

Extent can include information about pagination or number of volumes and is considered optional. Notes can include information of interest to the reader, such as language of publication other than English; such notes are optional. Essential notes provide information about location, such as a URL for online works. See Chapter 29 for more information.

For books with 2 authors, names are separated by a comma in the end reference but by “and” in the in-text reference.

Leboffe MJ, Pierce BE. 2010. Microbiology: laboratory theory and application. Englewood (CO): Morton Publishing Company.

(Leboffe and Pierce 2010)

For books with 3 to 10 authors, list all authors in the end reference; in the in-text reference, list only the first, followed by “et al.”

Ferrozzi F, Garlaschi G, Bova D. 2000. CT of metastases. New York (NY): Springer.

(Ferrozzi et al. 2000)

For books with more than 10 authors, list the first 10 in the end reference, followed by “et al.”

Wenger NK, Sivarajan Froelicher E, Smith LK, Ades PA, Berra K, Blumenthal JA, Certo CME, Dattilo AM, Davis D, DeBusk RF, et al. 1995. Cardiac rehabilitation. Rockville (MD): Agency for Health Care Policy and Research (US).

(Wenger et al. 1995)

[ALSG] Advanced Life Support Group. 2001. Acute medical emergencies: the practical approach. London (England): BMJ Books.

(ALSG 2001)

Klarsfeld A, Revah F. 2003. The biology of death: origins of mortality. Brady L, translator. Ithaca (NY): Cornell University Press.

Luzikov VN. 1985. Mitochondrial biogenesis and breakdown. Galkin AV, translator; Roodyn DB, editor. New York (NY): Consultants Bureau.

(Klarsfeld and Revah 2003)

(Luzikov 1985)

Gawande A. 2010. The checklist manifesto: how to get things right. New York (NY): Metropolitan Books. Chapter 3, The end of the master builder; p. 48–71.

(Gawande 2010)

Rapley R. 2010. Recombinant DNA and genetic analysis. In: Wilson K, Walker J, editors. Principles and techniques of biochemistry and molecular biology. 7th ed. New York (NY): Cambridge University Press. p. 195–262.

(Rapley 2010)

Alkire LG, editor. 2006. Periodical title abbreviations. 16th ed. Detroit (MI): Thompson Gale. 2 vol. Vol. 1, By abbreviation; vol. 2, By title.

(Alkire 2006)

Lutz M. 1989. 1903: American nervousness and the economy of cultural change [dissertation]. [Stanford (CA)]: Stanford University.

(Lutz 1989)

Blanco EE, Meade JC, Richards WD, inventors; Ophthalmic Ventures, assignee. 1990 Nov 13. Surgical stapling system. United States patent US 4,969,591.

(Blanco et al. 1990)

Weiss R. 2003 Apr 11. Study shows problems in cloning people: researchers find replicating primates will be harder than other mammals. Washington Post (Home Ed.). Sect. A:12 (col. 1).

(Weiss 2003)

Johnson D, editor. c2002. Surgical techniques in orthopaedics: anterior cruciate ligament reconstruction [DVD]. Rosemont (IL): American Academy of Orthopaedic Surgeons. 1 DVD.

(Johnson c2002)

Format for end reference:

Title of Homepage. Date of publication. Edition. Place of publication: publisher; [date updated; date accessed]. Notes.

APSnet: plant pathology online. c1994–2005. St Paul (MN): American Phytopathological Association; [accessed 2005 Jun 20]. http://www.apsnet.org/.

For the in-text reference, include only the first word or two of the title (enough to distinguish it from other titles in the reference list), followed by an ellipsis.

(APSnet . . . c1994–2005)

Author(s) of article. Date of publication. Title of article. Title of journal (edition). [date updated; date accessed];Volume(issue):location. Notes.

Savage E, Ramsay M, White J, Beard S, Lawson H, Hunjan R, Brown D. 2005. Mumps outbreaks across England and Wales in 2004: observational study. BMJ. [accessed 2005 May 31];330(7500):1119–1120. http://bmj.bmjjournals.com/cgi/reprint/330/7500/1119. doi:10.1136/bmj.330.7500.1119.

(Savage et al. 2005)

Author(s). Date of publication. Title of book. Edition. Place of publication: publisher; [date updated; date accessed]. Notes.

Brogden KA, Guthmille JM, editors. 2002. Polymicrobial diseases. Washington (DC): ASM Press; [accessed February 28, 2014]. http://www.ncbi.nlm.nih.gov/books/NBK2475/.

(Brogden and Guthmille 2002)

Author’s name. Date of publication. Title of post [descriptive word]. Title of blog. [accessed date]. URL.

Fogarty M. 2012 Aug 14. Formatting titles on Twitter and Facebook [blog]. Grammar Girl: Quick and Dirty Tips for Better Writing. [accessed 2012 Oct 19]. http://grammar.quickanddirtytips.com/formatting-titles-on-twitter-and-facebook.aspx.

(Fogarty 2012)

Farley T, Galves A, Dickinson LM, Perez MJ. Forthcoming 2005 Jul. Stress, coping, and health: a comparison of Mexican immigrants, Mexican-Americans, and non-Hispanic whites. J Immigr Health.

(Farley et al. 2005)

Goldstein DS. Forthcoming 2006. Adrenaline and the inner world: an introduction to scientific integrative medicine. Baltimore (MD): Johns Hopkins University Press.

(Goldstein 2006)

Antani S, Long LR, Thoma GR, Lee DJ. 2003. Anatomical shape representation in spine x-ray images. Paper presented at: VIIP 2003. Proceedings of the 3rd IASTED International Conference on Visualization, Imaging and Image Processing; Benalmadena, Spain.

Charles L, Gordner R. 2005. Analysis of MedlinePlus en Español customer service requests. Poster session presented at: Futuro magnifico! Celebrating our diversity. MLA ’05: Medical Library Association Annual Meeting; San Antonio, TX.

(Atani et al. 2003)

(Charles and Gordner 2005)

References to personal communication are placed in running text rather than as formal end references. Permission is usually required and should be acknowledged in an “Acknowledgment” or “Notes” section at the end of the document.

Scientific Style and Format, 8th Edition text © 2014 by the Council of Science Editors. Scientific Style and Format Online © 2014 by the Council of Science Editors.

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• Citation Styles

What citation style to use for science [Updated 2023]

What citation style should you use for a science paper? In this post, we explore the most frequently used citation styles for science. We cover APA, IEEE, ACS, and others and provide examples of each style.

APA (American Psychological Association) style is a citation format used in the social sciences, education, and engineering, as well as in the sciences. APA consists of two elements: in-text citations and a reference list.

It uses an author-date system, in which the author’s last name and year of publication are put in parentheses (e.g. Smith 2003). These parenthetical citations refer the reader to a list at the end of the paper, which includes information about each source.

APA style resources

🌐 Official APA style guidelines

🗂 APA style guide

📝 APA citation generator

APA style examples

Here is an example of an in-text citation in APA style:

In recent years, much debate has been stirred regarding volcanic soil (Avşar et al., 2018) .

Here is a bibliography entry in APA style:

Avşar, E., Ulusay, R., Aydan, Ö., & Mutlutürk, M . ( 2015 ). On the Difficulties of Geotechnical Sampling and practical Estimates of the Strength of a weakly bonded Volcanic Soil . Bulletin of Engineering Geology and the Environment , 74 ( 4 ), 1375–1394 . https://doi.org/10.1007/s10064-014-0710-9

Chicago style is another form of citation used for science papers and journals. It has two formats: a notes and bibliography system and an author-date system.

The notes and bibliography system is mostly used for the humanities, whereas the author-date system is used in science and business. The latter uses in-text citations formed by the author's last name and date of publication. A bibliography at the end of the paper lists the full information for all references.

Chicago style resources

🌐 Official Chicago style guidelines

🗂 Chicago style guide

📝 Chicago citation generator

Chicago style examples

Here is an in-text citation in Chicago style:

However, a research proved this theory right (Hofman and Rick 2018, 65-115) .

Here is a bibliography entry in Chicago style:

Hofman, Courtney A., and Torben C. Rick . “ Ancient Biological Invasions and Island Ecosystems: Tracking Translocations of Wild Plants and Animals .” Journal of Archaeological Research 26 , no. 1 ( 2018 ): 65–115 . doi.org/10.1007/s10814-017-9105-3 .

CSE style is the standard format used in the physical and life sciences. This style features three types of citation systems: citation-sequence, name-year, and citation-name.

• Name-Year : In-text citations of this type feature the author’s last name and the year of publication in brackets. A bibliography at the end lists all references in full.

• Citation-Sequence : Every source is assigned a superscript number that is used as an in-text reference. The bibliography at the end lists all numbers with their references in the order in which they appeared in the text.

• Citation-Name : The reference list is organized alphabetically by authors’ last names; each name is assigned a number which can be placed in superscript as an in-text reference.

CSE style resources

🌐 Official CSE style guidelines

📝 CSE citation generator

CSE style examples

Here is an example of an in-text citation in CSE name-year style:

Therefore, the translocation of wild plants was tracked (Hofman and Rick 2018) .

Here is a bibliography entry in CSE name-year style:

Hofman CA, Rick TC. 2018. Ancient Biological Invasions and Island Ecosystems: Tracking Translocations of Wild Plants and Animals. J. Archaeol. [accessed 2019 Mar 11]; 26(1): 65–11. doi.org/10.1007/s10814-017-9105-3.

AIP style, as its title suggests, is commonly applied in physics and astronomy papers. This style has a numbered citation system , which uses superscript numbers to show in-text citations. These numbers correspond to a list of sources at the end of the paper.

AIP style resources

🌐 Official AIP style guidelines

🗂 AIP style guide

📝 AIP citation generator

AIP style examples

Here is an in-text citation in AIP style:

A similar study was carried out in 2015 ¹ .

Here is a bibliography entry in AIP style:

¹ H.D. Young and R.A. Freedman, Sears & Zemansky's University Physics (Addison-Wesley, San Francisco, CA, 2015) p. 160

ACS style is the standard citation style for chemistry. This style uses both numeric and author-date citations systems. The numbered in-text citations can have either a superscript number or a number in italics. Full references for each source are listed at the end of the paper.

ACS style resources

🌐 Official ACS style guidelines

🗂 ACS style guide

📝 ACS citation generator

ACS style examples

Here is an in-text citation in ACS author-date style:

The opposing side was given first (Brown et al., 2017) .

Here is a bibliography entry in ACS author-date style:

Brown, T.E.; LeMay H.E.; Bursten, B.E.; Murphy, C.; Woodward, P.; Stoltzfus M.E. Chemistry: The Central Science in SI Units . Pearson: New York, 2017.

IEEE style is used for engineering and science papers. This style uses a numeric, in-text citation format, with a number in square brackets. This number corresponds to a reference list entry at the end of the paper.

IEEE style resources

🌐 Official IEEE style guidelines

🗂 IEEE style guide

📝 IEEE citation generator

IEEE style examples

Here is an example of an in-text citation in IEEE style:

As seen in a multi-camera study [1] ...

Here is a bibliography entry in IEEE style:

[1] E. Nuger and B. Benhabib, “Multi-Camera Active-Vision for Markerless Shape Recovery of Unknown Deforming Objects,” J. Intell. Rob. Syst. , vol. 92, no. 2, pp. 223–264, Oct. 2018.

Frequently Asked Questions about citation styles used in science

The most frequently used citation style in the sciences is APA (American Psychological Association) style.

There are two major types of citation systems you can use: author-date or numeric. Numeric citation styles tend to be preferred for science disciplines.

Yes, you have to add a bibliography or reference list citing all sources mentioned in your scientific paper.

Some of the most popular scientific journals are: Science Magazine , Nature , and The Lancet .

Title pages for science papers must follow the format of the citation style that you’re using. For example, in APA style you need to include a title, running head, a name, and other details. Visit our guide on title pages to learn more.

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Home » How to Cite Research Paper – All Formats and Examples

How to Cite Research Paper – All Formats and Examples

Table of Contents

Research Paper Citation

Research paper citation refers to the act of acknowledging and referencing a previously published work in a scholarly or academic paper . When citing sources, researchers provide information that allows readers to locate the original source, validate the claims or arguments made in the paper, and give credit to the original author(s) for their work.

The citation may include the author’s name, title of the publication, year of publication, publisher, and other relevant details that allow readers to trace the source of the information. Proper citation is a crucial component of academic writing, as it helps to ensure accuracy, credibility, and transparency in research.

How to Cite Research Paper

There are several formats that are used to cite a research paper. Follow the guide for the Citation of a Research Paper:

Last Name, First Name. Title of Book. Publisher, Year of Publication.

Example : Smith, John. The History of the World. Penguin Press, 2010.

Journal Article

Last Name, First Name. “Title of Article.” Title of Journal, vol. Volume Number, no. Issue Number, Year of Publication, pp. Page Numbers.

Example : Johnson, Emma. “The Effects of Climate Change on Agriculture.” Environmental Science Journal, vol. 10, no. 2, 2019, pp. 45-59.

Research Paper

Last Name, First Name. “Title of Paper.” Conference Name, Location, Date of Conference.

Example : Garcia, Maria. “The Importance of Early Childhood Education.” International Conference on Education, Paris, 5-7 June 2018.

Author’s Last Name, First Name. “Title of Webpage.” Website Title, Publisher, Date of Publication, URL.

Example : Smith, John. “The Benefits of Exercise.” Healthline, Healthline Media, 1 March 2022, https://www.healthline.com/health/benefits-of-exercise.

News Article

Last Name, First Name. “Title of Article.” Name of Newspaper, Date of Publication, URL.

Example : Robinson, Sarah. “Biden Announces New Climate Change Policies.” The New York Times, 22 Jan. 2021, https://www.nytimes.com/2021/01/22/climate/biden-climate-change-policies.html.

Author, A. A. (Year of publication). Title of book. Publisher.

Example: Smith, J. (2010). The History of the World. Penguin Press.

Author, A. A., Author, B. B., & Author, C. C. (Year of publication). Title of article. Title of Journal, volume number(issue number), page range.

Example: Johnson, E., Smith, K., & Lee, M. (2019). The Effects of Climate Change on Agriculture. Environmental Science Journal, 10(2), 45-59.

Author, A. A. (Year of publication). Title of paper. In Editor First Initial. Last Name (Ed.), Title of Conference Proceedings (page numbers). Publisher.

Example: Garcia, M. (2018). The Importance of Early Childhood Education. In J. Smith (Ed.), Proceedings from the International Conference on Education (pp. 60-75). Springer.

Author, A. A. (Year, Month Day of publication). Title of webpage. Website name. URL

Example: Smith, J. (2022, March 1). The Benefits of Exercise. Healthline. https://www.healthline.com/health/benefits-of-exercise

Author, A. A. (Year, Month Day of publication). Title of article. Newspaper name. URL.

Example: Robinson, S. (2021, January 22). Biden Announces New Climate Change Policies. The New York Times. https://www.nytimes.com/2021/01/22/climate/biden-climate-change-policies.html

Chicago/Turabian style

Please note that there are two main variations of the Chicago style: the author-date system and the notes and bibliography system. I will provide examples for both systems below.

Author-Date system:

• In-text citation: (Author Last Name Year, Page Number)
• Reference list: Author Last Name, First Name. Year. Title of Book. Place of publication: Publisher.
• In-text citation: (Smith 2005, 28)
• Reference list: Smith, John. 2005. The History of America. New York: Penguin Press.

Notes and Bibliography system:

• Footnote/Endnote citation: Author First Name Last Name, Title of Book (Place of publication: Publisher, Year), Page Number.
• Bibliography citation: Author Last Name, First Name. Title of Book. Place of publication: Publisher, Year.
• Footnote/Endnote citation: John Smith, The History of America (New York: Penguin Press, 2005), 28.
• Bibliography citation: Smith, John. The History of America. New York: Penguin Press, 2005.

JOURNAL ARTICLES:

• Reference list: Author Last Name, First Name. Year. “Article Title.” Journal Title Volume Number (Issue Number): Page Range.
• In-text citation: (Johnson 2010, 45)
• Reference list: Johnson, Mary. 2010. “The Impact of Social Media on Society.” Journal of Communication 60(2): 39-56.
• Footnote/Endnote citation: Author First Name Last Name, “Article Title,” Journal Title Volume Number, Issue Number (Year): Page Range.
• Bibliography citation: Author Last Name, First Name. “Article Title.” Journal Title Volume Number, Issue Number (Year): Page Range.
• Footnote/Endnote citation: Mary Johnson, “The Impact of Social Media on Society,” Journal of Communication 60, no. 2 (2010): 39-56.
• Bibliography citation: Johnson, Mary. “The Impact of Social Media on Society.” Journal of Communication 60, no. 2 (2010): 39-56.

RESEARCH PAPERS:

• Reference list: Author Last Name, First Name. Year. “Title of Paper.” Conference Proceedings Title, Location, Date. Publisher, Page Range.
• In-text citation: (Jones 2015, 12)
• Reference list: Jones, David. 2015. “The Effects of Climate Change on Agriculture.” Proceedings of the International Conference on Climate Change, Paris, France, June 1-3, 2015. Springer, 10-20.
• Footnote/Endnote citation: Author First Name Last Name, “Title of Paper,” Conference Proceedings Title, Location, Date (Place of publication: Publisher, Year), Page Range.
• Bibliography citation: Author Last Name, First Name. “Title of Paper.” Conference Proceedings Title, Location, Date. Place of publication: Publisher, Year.
• Footnote/Endnote citation: David Jones, “The Effects of Climate Change on Agriculture,” Proceedings of the International Conference on Climate Change, Paris, France, June 1-3, 2015 (New York: Springer, 10-20).
• Bibliography citation: Jones, David. “The Effects of Climate Change on Agriculture.” Proceedings of the International Conference on Climate Change, Paris, France, June 1-3, 2015. New York: Springer, 10-20.
• In-text citation: (Author Last Name Year)
• Reference list: Author Last Name, First Name. Year. “Title of Webpage.” Website Name. URL.
• In-text citation: (Smith 2018)
• Reference list: Smith, John. 2018. “The Importance of Recycling.” Environmental News Network. https://www.enn.com/articles/54374-the-importance-of-recycling.
• Footnote/Endnote citation: Author First Name Last Name, “Title of Webpage,” Website Name, URL (accessed Date).
• Bibliography citation: Author Last Name, First Name. “Title of Webpage.” Website Name. URL (accessed Date).
• Footnote/Endnote citation: John Smith, “The Importance of Recycling,” Environmental News Network, https://www.enn.com/articles/54374-the-importance-of-recycling (accessed April 8, 2023).
• Bibliography citation: Smith, John. “The Importance of Recycling.” Environmental News Network. https://www.enn.com/articles/54374-the-importance-of-recycling (accessed April 8, 2023).

NEWS ARTICLES:

• Reference list: Author Last Name, First Name. Year. “Title of Article.” Name of Newspaper, Month Day.
• In-text citation: (Johnson 2022)
• Reference list: Johnson, Mary. 2022. “New Study Finds Link Between Coffee and Longevity.” The New York Times, January 15.
• Footnote/Endnote citation: Author First Name Last Name, “Title of Article,” Name of Newspaper (City), Month Day, Year.
• Bibliography citation: Author Last Name, First Name. “Title of Article.” Name of Newspaper (City), Month Day, Year.
• Footnote/Endnote citation: Mary Johnson, “New Study Finds Link Between Coffee and Longevity,” The New York Times (New York), January 15, 2022.
• Bibliography citation: Johnson, Mary. “New Study Finds Link Between Coffee and Longevity.” The New York Times (New York), January 15, 2022.

Harvard referencing style

Format: Author’s Last name, First initial. (Year of publication). Title of book. Publisher.

Example: Smith, J. (2008). The Art of War. Random House.

Journal article:

Format: Author’s Last name, First initial. (Year of publication). Title of article. Title of journal, volume number(issue number), page range.

Example: Brown, M. (2012). The impact of social media on business communication. Harvard Business Review, 90(12), 85-92.

Research paper:

Format: Author’s Last name, First initial. (Year of publication). Title of paper. In Editor’s First initial. Last name (Ed.), Title of book (page range). Publisher.

Example: Johnson, R. (2015). The effects of climate change on agriculture. In S. Lee (Ed.), Climate Change and Sustainable Development (pp. 45-62). Springer.

Format: Author’s Last name, First initial. (Year, Month Day of publication). Title of page. Website name. URL.

Example: Smith, J. (2017, May 23). The history of the internet. Encyclopedia Britannica. https://www.britannica.com/topic/history-of-the-internet

News article:

Format: Author’s Last name, First initial. (Year, Month Day of publication). Title of article. Title of newspaper, page number (if applicable).

Example: Thompson, E. (2022, January 5). New study finds coffee may lower risk of dementia. The New York Times, A1.

IEEE Format

Author(s). (Year of Publication). Title of Book. Publisher.

Smith, J. K. (2015). The Power of Habit: Why We Do What We Do in Life and Business. Random House.

Journal Article:

Author(s). (Year of Publication). Title of Article. Title of Journal, Volume Number (Issue Number), page numbers.

Johnson, T. J., & Kaye, B. K. (2016). Interactivity and the Future of Journalism. Journalism Studies, 17(2), 228-246.

Author(s). (Year of Publication). Title of Paper. Paper presented at Conference Name, Location.

Jones, L. K., & Brown, M. A. (2018). The Role of Social Media in Political Campaigns. Paper presented at the 2018 International Conference on Social Media and Society, Copenhagen, Denmark.

• Website: Author(s) or Organization Name. (Year of Publication or Last Update). Title of Webpage. Website Name. URL.

Example: National Aeronautics and Space Administration. (2019, August 29). NASA’s Mission to Mars. NASA. https://www.nasa.gov/topics/journeytomars/index.html

• News Article: Author(s). (Year of Publication). Title of Article. Name of News Source. URL.

Example: Johnson, M. (2022, February 16). Climate Change: Is it Too Late to Save the Planet? CNN. https://www.cnn.com/2022/02/16/world/climate-change-planet-scn/index.html

Vancouver Style

In-text citation: Use superscript numbers to cite sources in the text, e.g., “The study conducted by Smith and Johnson^1 found that…”.

Reference list citation: Format: Author(s). Title of book. Edition if any. Place of publication: Publisher; Year of publication.

Example: Smith J, Johnson L. Introduction to Molecular Biology. 2nd ed. New York: Wiley-Blackwell; 2015.

In-text citation: Use superscript numbers to cite sources in the text, e.g., “Several studies have reported that^1,2,3…”.

Reference list citation: Format: Author(s). Title of article. Abbreviated name of journal. Year of publication; Volume number (Issue number): Page range.

Example: Jones S, Patel K, Smith J. The effects of exercise on cardiovascular health. J Cardiol. 2018; 25(2): 78-84.

In-text citation: Use superscript numbers to cite sources in the text, e.g., “Previous research has shown that^1,2,3…”.

Reference list citation: Format: Author(s). Title of paper. In: Editor(s). Title of the conference proceedings. Place of publication: Publisher; Year of publication. Page range.

Example: Johnson L, Smith J. The role of stem cells in tissue regeneration. In: Patel S, ed. Proceedings of the 5th International Conference on Regenerative Medicine. London: Academic Press; 2016. p. 68-73.

In-text citation: Use superscript numbers to cite sources in the text, e.g., “According to the World Health Organization^1…”.

Reference list citation: Format: Author(s). Title of webpage. Name of website. URL [Accessed Date].

Example: World Health Organization. Coronavirus disease (COVID-19) advice for the public. World Health Organization. https://www.who.int/emergencies/disease/novel-coronavirus-2019/advice-for-public [Accessed 3 March 2023].

In-text citation: Use superscript numbers to cite sources in the text, e.g., “According to the New York Times^1…”.

Reference list citation: Format: Author(s). Title of article. Name of newspaper. Year Month Day; Section (if any): Page number.

Example: Jones S. Study shows that sleep is essential for good health. The New York Times. 2022 Jan 12; Health: A8.

Author(s). Title of Book. Edition Number (if it is not the first edition). Publisher: Place of publication, Year of publication.

Example: Smith, J. Chemistry of Natural Products. 3rd ed.; CRC Press: Boca Raton, FL, 2015.

Journal articles:

Author(s). Article Title. Journal Name Year, Volume, Inclusive Pagination.

Example: Garcia, A. M.; Jones, B. A.; Smith, J. R. Selective Synthesis of Alkenes from Alkynes via Catalytic Hydrogenation. J. Am. Chem. Soc. 2019, 141, 10754-10759.

Research papers:

Author(s). Title of Paper. Journal Name Year, Volume, Inclusive Pagination.

Example: Brown, H. D.; Jackson, C. D.; Patel, S. D. A New Approach to Photovoltaic Solar Cells. J. Mater. Chem. 2018, 26, 134-142.

Author(s) (if available). Title of Webpage. Name of Website. URL (accessed Month Day, Year).

Example: National Institutes of Health. Heart Disease and Stroke. National Heart, Lung, and Blood Institute. https://www.nhlbi.nih.gov/health-topics/heart-disease-and-stroke (accessed April 7, 2023).

News articles:

Author(s). Title of Article. Name of News Publication. Date of Publication. URL (accessed Month Day, Year).

Example: Friedman, T. L. The World is Flat. New York Times. April 7, 2023. https://www.nytimes.com/2023/04/07/opinion/world-flat-globalization.html (accessed April 7, 2023).

In AMA Style Format, the citation for a book should include the following information, in this order:

• Title of book (in italics)
• Edition (if applicable)
• Place of publication
• Year of publication

Lodish H, Berk A, Zipursky SL, et al. Molecular Cell Biology. 4th ed. New York, NY: W. H. Freeman; 2000.

In AMA Style Format, the citation for a journal article should include the following information, in this order:

• Title of article
• Abbreviated title of journal (in italics)
• Year of publication; volume number(issue number):page numbers.

Chen H, Huang Y, Li Y, et al. Effects of mindfulness-based stress reduction on depression in adolescents and young adults: a systematic review and meta-analysis. JAMA Netw Open. 2020;3(6):e207081. doi:10.1001/jamanetworkopen.2020.7081

In AMA Style Format, the citation for a research paper should include the following information, in this order:

• Title of paper
• Name of journal or conference proceeding (in italics)
• Volume number(issue number):page numbers.

Bredenoord AL, Kroes HY, Cuppen E, Parker M, van Delden JJ. Disclosure of individual genetic data to research participants: the debate reconsidered. Trends Genet. 2011;27(2):41-47. doi:10.1016/j.tig.2010.11.004

In AMA Style Format, the citation for a website should include the following information, in this order:

• Title of web page or article
• Name of website (in italics)
• Date of publication or last update (if available)
• URL (website address)
• Date of access (month day, year)

Centers for Disease Control and Prevention. How to protect yourself and others. CDC. Published February 11, 2022. Accessed February 14, 2022. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html

In AMA Style Format, the citation for a news article should include the following information, in this order:

• Name of newspaper or news website (in italics)
• Date of publication

Gorman J. Scientists use stem cells from frogs to build first living robots. The New York Times. January 13, 2020. Accessed January 14, 2020. https://www.nytimes.com/2020/01/13/science/living-robots-xenobots.html

Bluebook Format

One author: Daniel J. Solove, The Future of Reputation: Gossip, Rumor, and Privacy on the Internet (Yale University Press 2007).

Two or more authors: Martha Nussbaum and Saul Levmore, eds., The Offensive Internet: Speech, Privacy, and Reputation (Harvard University Press 2010).

Journal article

One author: Daniel J. Solove, “A Taxonomy of Privacy,” University of Pennsylvania Law Review 154, no. 3 (January 2006): 477-560.

Two or more authors: Ethan Katsh and Andrea Schneider, “The Emergence of Online Dispute Resolution,” Journal of Dispute Resolution 2003, no. 1 (2003): 7-19.

One author: Daniel J. Solove, “A Taxonomy of Privacy,” GWU Law School Public Law Research Paper No. 113, 2005.

Two or more authors: Ethan Katsh and Andrea Schneider, “The Emergence of Online Dispute Resolution,” Cyberlaw Research Paper Series Paper No. 00-5, 2000.

WebsiteElectronic Frontier Foundation, “Surveillance Self-Defense,” accessed April 8, 2023, https://ssd.eff.org/.

News article

One author: Mark Sherman, “Court Deals Major Blow to Net Neutrality Rules,” ABC News, January 14, 2014, https://abcnews.go.com/Politics/wireStory/court-deals-major-blow-net-neutrality-rules-21586820.

Two or more authors: Siobhan Hughes and Brent Kendall, “AT&T Wins Approval to Buy Time Warner,” Wall Street Journal, June 12, 2018, https://www.wsj.com/articles/at-t-wins-approval-to-buy-time-warner-1528847249.

In-Text Citation: (Author’s last name Year of Publication: Page Number)

Example: (Smith 2010: 35)

Reference List Citation: Author’s last name First Initial. Title of Book. Edition. Place of publication: Publisher; Year of publication.

Example: Smith J. Biology: A Textbook. 2nd ed. New York: Oxford University Press; 2010.

Example: (Johnson 2014: 27)

Reference List Citation: Author’s last name First Initial. Title of Article. Abbreviated Title of Journal. Year of publication;Volume(Issue):Page Numbers.

Example: Johnson S. The role of dopamine in addiction. J Neurosci. 2014;34(8): 2262-2272.

Example: (Brown 2018: 10)

Reference List Citation: Author’s last name First Initial. Title of Paper. Paper presented at: Name of Conference; Date of Conference; Place of Conference.

Example: Brown R. The impact of social media on mental health. Paper presented at: Annual Meeting of the American Psychological Association; August 2018; San Francisco, CA.

Example: (World Health Organization 2020: para. 2)

Reference List Citation: Author’s last name First Initial. Title of Webpage. Name of Website. URL. Published date. Accessed date.

Example: World Health Organization. Coronavirus disease (COVID-19) pandemic. WHO website. https://www.who.int/emergencies/disease-coronavirus-2019. Updated August 17, 2020. Accessed September 5, 2021.

Example: (Smith 2019: para. 5)

Reference List Citation: Author’s last name First Initial. Title of Article. Title of Newspaper or Magazine. Year of publication; Month Day:Page Numbers.

Example: Smith K. New study finds link between exercise and mental health. The New York Times. 2019;May 20: A6.

Purpose of Research Paper Citation

The purpose of citing sources in a research paper is to give credit to the original authors and acknowledge their contribution to your work. By citing sources, you are also demonstrating the validity and reliability of your research by showing that you have consulted credible and authoritative sources. Citations help readers to locate the original sources that you have referenced and to verify the accuracy and credibility of your research. Additionally, citing sources is important for avoiding plagiarism, which is the act of presenting someone else’s work as your own. Proper citation also shows that you have conducted a thorough literature review and have used the existing research to inform your own work. Overall, citing sources is an essential aspect of academic writing and is necessary for building credibility, demonstrating research skills, and avoiding plagiarism.

Advantages of Research Paper Citation

There are several advantages of research paper citation, including:

• Giving credit: By citing the works of other researchers in your field, you are acknowledging their contribution and giving credit where it is due.
• Strengthening your argument: Citing relevant and reliable sources in your research paper can strengthen your argument and increase its credibility. It shows that you have done your due diligence and considered various perspectives before drawing your conclusions.
• Demonstrating familiarity with the literature : By citing various sources, you are demonstrating your familiarity with the existing literature in your field. This is important as it shows that you are well-informed about the topic and have done a thorough review of the available research.
• Providing a roadmap for further research: By citing relevant sources, you are providing a roadmap for further research on the topic. This can be helpful for future researchers who are interested in exploring the same or related issues.
• Building your own reputation: By citing the works of established researchers in your field, you can build your own reputation as a knowledgeable and informed scholar. This can be particularly helpful if you are early in your career and looking to establish yourself as an expert in your field.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Citing sources: Overview

• Citation style guides

Manage your references

Use these tools to help you organize and cite your references:

• Citation Management and Writing Tools

If you have questions after consulting this guide about how to cite, please contact your advisor/professor or the writing and communication center .

Why citing is important

It's important to cite sources you used in your research for several reasons:

• To show your reader you've done proper research by listing sources you used to get your information
• To be a responsible scholar by giving credit to other researchers and acknowledging their ideas
• To avoid plagiarism by quoting words and ideas used by other authors
• To allow your reader to track down the sources you used by citing them accurately in your paper by way of footnotes, a bibliography or reference list

About citations

Citing a source means that you show, within the body of your text, that you took words, ideas, figures, images, etc. from another place.

Citations are a short way to uniquely identify a published work (e.g. book, article, chapter, web site).  They are found in bibliographies and reference lists and are also collected in article and book databases.

Citations consist of standard elements, and contain all the information necessary to identify and track down publications, including:

• author name(s)
• titles of books, articles, and journals
• date of publication
• page numbers
• volume and issue numbers (for articles)

Citations may look different, depending on what is being cited and which style was used to create them. Choose an appropriate style guide for your needs.  Here is an example of an article citation using four different citation styles.  Notice the common elements as mentioned above:

Author - R. Langer

Article Title - New Methods of Drug Delivery

Source Title - Science

Volume and issue - Vol 249, issue 4976

Publication Date - 1990

Page numbers - 1527-1533

American Chemical Society (ACS) style:

Langer, R. New Methods of Drug Delivery. Science 1990 , 249 , 1527-1533.

IEEE Style:

R. Langer, " New Methods of Drug Delivery," Science , vol. 249 , pp. 1527-1533 , SEP 28, 1990 .

American Psychological Association   (APA) style:

Langer, R. (1990) . New methods of drug delivery. Science , 249 (4976), 1527-1533.

Modern Language Association (MLA) style:

Langer, R. " New Methods of Drug Delivery." Science 249.4976 (1990) : 1527-33.

What to cite

You must cite:

• Facts, figures, ideas, or other information that is not common knowledge

Publications that must be cited include:  books, book chapters, articles, web pages, theses, etc.

Another person's exact words should be quoted and cited to show proper credit 

When in doubt, be safe and cite your source!

Avoiding plagiarism

Plagiarism occurs when you borrow another's words (or ideas) and do not acknowledge that you have done so. In this culture, we consider our words and ideas intellectual property; like a car or any other possession, we believe our words belong to us and cannot be used without our permission.

Plagiarism is a very serious offense. If it is found that you have plagiarized -- deliberately or inadvertently -- you may face serious consequences. In some instances, plagiarism has meant that students have had to leave the institutions where they were studying.

The best way to avoid plagiarism is to cite your sources - both within the body of your paper and in a bibliography of sources you used at the end of your paper.

Some useful links about plagiarism:

• MIT Academic Integrity Overview on citing sources and avoiding plagiarism at MIT.
• Avoiding Plagiarism From the MIT Writing and Communication Center.
• Plagiarism: What It is and How to Recognize and Avoid It From Indiana University's Writing Tutorial Services.
• Plagiarism- Overview A resource from Purdue University.
• Next: Citation style guides >>
• Last Updated: Jan 16, 2024 7:02 AM
• URL: https://libguides.mit.edu/citing

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How to Cite a Research Paper

Last Updated: March 29, 2024 Fact Checked

This article was reviewed by Gerald Posner and by wikiHow staff writer, Jennifer Mueller, JD . Gerald Posner is an Author & Journalist based in Miami, Florida. With over 35 years of experience, he specializes in investigative journalism, nonfiction books, and editorials. He holds a law degree from UC College of the Law, San Francisco, and a BA in Political Science from the University of California-Berkeley. He’s the author of thirteen books, including several New York Times bestsellers, the winner of the Florida Book Award for General Nonfiction, and has been a finalist for the Pulitzer Prize in History. He was also shortlisted for the Best Business Book of 2020 by the Society for Advancing Business Editing and Writing. There are 8 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 411,359 times.

When writing a paper for a research project, you may need to cite a research paper you used as a reference. The basic information included in your citation will be the same across all styles. However, the format in which that information is presented is somewhat different depending on whether you're using American Psychological Association (APA), Modern Language Association (MLA), Chicago, or American Medical Association (AMA) style.

Referencing a Research Paper

• In APA style, cite the paper: Last Name, First Initial. (Year). Title. Publisher.
• In Chicago style, cite the paper: Last Name, First Name. “Title.” Publisher, Year.
• In MLA style, cite the paper: Last Name, First Name. “Title.” Publisher. Year.

Citation Help

• For example: "Kringle, K., & Frost, J."

• For example: "Kringle, K., & Frost, J. (2012)."
• If the date, or any other information, are not available, use the guide at https://blog.apastyle.org/apastyle/2012/05/missing-pieces.html .

• For example: "Kringle, K., & Frost, J. (2012). Red noses, warm hearts: The glowing phenomenon among North Pole reindeer."
• If you found the research paper in a database maintained by a university, corporation, or other organization, include any index number assigned to the paper in parentheses after the title. For example: "Kringle, K., & Frost, J. (2012). Red noses, warm hearts: The glowing phenomenon among North Pole reindeer. (Report No. 1234)."

• For example: "Kringle, K., & Frost, J. (2012). Red noses, warm hearts: The glowing phenomenon among North Pole reindeer. (Report No. 1234). Retrieved from Alaska University Library Archives, December 24, 2017."

• For example: "(Kringle & Frost, 2012)."
• If there was no date on the research paper, use the abbreviation n.d. : "(Kringle & Frost, n.d.)."

• For example: "Kringle, Kris, and Jack Frost."

• For example: "Kringle, Kris, and Jack Frost. "Red Noses, Warm Hearts: The Glowing Phenomenon among North Pole Reindeer." Master's thesis."

• For example: "Kringle, Kris, and Jack Frost. "Red Noses, Warm Hearts: The Glowing Phenomenon among North Pole Reindeer." Master's thesis, Alaska University, 2012."

• For example: "Kringle, Kris, and Jack Frost. "Red Noses, Warm Hearts: The Glowing Phenomenon among North Pole Reindeer." Master's thesis, Alaska University, 2012. Accessed at https://www.northpolemedical.com/raising_rudolf."

• Footnotes are essentially the same as the full citation, although the first and last names of the authors aren't inverted.
• For parenthetical citations, Chicago uses the Author-Date format. For example: "(Kringle and Frost 2012)."

• For example: "Kringle, Kris, and Frost, Jack."

• For example: "Kringle, Kris, and Frost, Jack. "Red Noses, Warm Hearts: The Glowing Phenomenon Among North Pole Reindeer.""

• For example, suppose you found the paper in a collection of paper housed in university archives. Your citation might be: "Kringle, Kris, and Frost, Jack. "Red Noses, Warm Hearts: The Glowing Phenomenon Among North Pole Reindeer." Master's Theses 2000-2010. University of Alaska Library Archives. Accessed December 24, 2017."

• For example: "(Kringle & Frost, p. 33)."

• For example: "Kringle K, Frost J."

• For example: "Kringle K, Frost J. Red noses, warm hearts: The glowing phenomenon among North Pole reindeer."

• For example: "Kringle K, Frost J. Red noses, warm hearts: The glowing phenomenon among North Pole reindeer. Nat Med. 2012; 18(9): 1429-1433."

• For example, if you're citing a paper presented at a conference, you'd write: "Kringle K, Frost J. Red noses, warm hearts: The glowing phenomenon among North Pole reindeer. Oral presentation at Arctic Health Association Annual Summit; December, 2017; Nome, Alaska."
• To cite a paper you read online, you'd write: "Kringle K, Frost J. Red noses, warm hearts: The glowing phenomenon among North Pole reindeer. https://www.northpolemedical.com/raising_rudolf"

• For example: "According to Kringle and Frost, these red noses indicate a subspecies of reindeer native to Alaska and Canada that have migrated to the North Pole and mingled with North Pole reindeer. 1 "

Community Q&A

• If you used a manual as a source in your research paper, you'll need to learn how to cite the manual also. Thanks Helpful 0 Not Helpful 0
• If you use any figures in your research paper, you'll also need to know the proper way to cite them in MLA, APA, AMA, or Chicago. Thanks Helpful 0 Not Helpful 0

You Might Also Like

• ↑ https://askus.library.wwu.edu/faq/116659
• ↑ https://guides.libraries.psu.edu/apaquickguide/intext
• ↑ https://owl.purdue.edu/owl/research_and_citation/chicago_manual_17th_edition/cmos_formatting_and_style_guide/general_format.html
• ↑ https://libanswers.snhu.edu/faq/48009
• ↑ https://www.chicagomanualofstyle.org/tools_citationguide/citation-guide-2.html
• ↑ https://owl.purdue.edu/owl/research_and_citation/mla_style/mla_formatting_and_style_guide/mla_in_text_citations_the_basics.html
• ↑ https://morningside.libguides.com/MLA8/location
• ↑ https://owl.purdue.edu/owl/research_and_citation/ama_style/index.html

About This Article

To cite a paper APA style, start with the author's last name and first initial, and the year of publication. Then, list the title of the paper, where you found it, and the date that you accessed it. In a paper, use a parenthetical reference with the last name of the author and the publication year. For an MLA citation, list the author's last name and then first name and the title of the paper in quotations. Include where you accessed the paper and the date you retrieved it. In your paper, use a parenthetical reference with the author's last name and the page number. Keep reading for tips on Chicago and AMA citations and exceptions to the citation rules! Did this summary help you? Yes No

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How do I cite a ScienceDirect article?

An article citation is typically made up of the author(s), article title, publication title, date of publication, and page numbers (or article number). Consult the journal’s reference style for the exact appearance of the article citation elements, abbreviation of the journal name, and the use of punctuation.

General principles for citing articles

There are some general principles for citing articles:

• Regardless of reference style, always include the article number where the page number (or page range) would otherwise go.
• Never cite the internal page numbering starting at ‘1’.
• Include the DOI if known.

Citing articles which are not final

Articles which are not final (e.g., a journal pre-proof) or do not contain all the typical elements of an article citation can be cited using the year of online publication and the DOI, as follows: author(s), article title, publication (year), DOI. For more information on creating and citing DOI links, visit the DOI website .

• A.U. Thor, Title of article, Favorite Journal (2020), https://dx.doi.org/10.1016/doi-suffix, or
• A.U. Thor, Title of article, Favorite Journal, https://dx.doi.org/10.1016/doi-suffix.
• A.U. Thor, Title of article, Favorite Journal 5 (1) (2020) 37-65, https://dx.doi.org/10.1016/doi-suffix.
• A.U. Thor, Title of article, Favorite Journal 5 (1) (2020) 101357, https://dx.doi.org/10.1016/doi-suffix.

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Scientific Papers and Lab Reports: Citing in APA

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Citing in APA

Use this page to create the References portion of your scientific paper and/or lab report in the APA citation style. Why cite? Check out our Academic Honesty/Plagiarism online tutorial for the whys and hows of avoiding plagiarism.

APA Video Walkthrough

Use the following video  and sites to learn about proper citation in APA. In addition, learn about cross-checking your citations (this video uses mostly MLA citations but gives a good idea of the practice of cross-checking). 

Covers the basic citation rules and provides citation examples of the commonly used source types.

A guide from Purdue University on using APA guidelines in research papers and and citing all sources. A good resource for more complex APA sources. 

You can also create citations using generators.  Remember : it's your responsibility to double check the citations for accuracy!

A citation generator for MLA, APA, Turabian and Chicago styles. Popular with students.

Citation generator created by the Hekman Library of Calvin College, it assists with creating citations in MLA, APA, and Chicago. Popular with me, the librarian.

A Firefox only extension that helps with the collection, management, and citation of sources.

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When you use ideas that are not your own, it is important to credit or cite the author(s) or source, even if you do not quote their idea or words exactly as written. Citing your sources allows your reader to identify the works you have consulted and to understand the scope of your research. There are many different citation styles available. You may be required to use a particular style or you may choose one.

One of the commonly used styles is the APA (American Psychological Association) Style.

APA style stipulates that authors use brief references in the text of a work with full bibliographic details supplied in a Reference List (typically at the end of your document). In text, the reference is very brief and usually consists simply of the author's last name and a date.For example:

...Sheep milk has been proved to contain more nutrients than cow milk (Johnson, 2005).

In a Reference list, the reference contains full bibliographic details written in a format that depends on the type of reference. Examples of formats for some common types of references are listed below. For additional information, visit the University of Arkansas libraries webpage on citing your sources . Another useful web-site on this topic is here.

Author last name, Author First Initial. Author Second Initial. (Publication Year). Title of article. Title of Journal, volume(issue) (if issue numbered), pages.

Bass, M. A., Enochs, W. K., & DiBrezzo, R. (2002). Comparison of two exercise programs on general well-being of college students. Psychological Reports, 91(3), 1195-1201.

Author Last Name, Author First Initial. Author Second Initial. (if there is no author move entry title to first position) (Publication year). Title of article or entry. In Work title. (Vol. number, pp. pages). Place: Publisher.

"Ivory-billed woodpecker." (2002). In The new encyclopædia britannica. (Vol. 5, p. ). 15th ed. Chicago: Encyclopædia Britannica.

Author Last Name, Author First Initial. Author Second Initial. (if there is no author move entry title to first position) (Publication year). Title of article or entry. In Work title. Retrieved from (database name or URL).

Ivory-billed woodpecker. (2006). In Encyclopædia britannica online. Retrieved from http://search.eb.com/eb/article-9043081

Author last name, Author First Initial. Author Second Initial. (Publication Year, Month Day). Title of article. Title of Magazine,volume, pages.

Holloway, M. (2005, August). When extinct isn't. Scientific American, 293, 22-23.

Author last name, Author First Initial. Author Second Initial. (Publication Year, Month Day). Title of article. Title of Magazine. volume, pages. Retrieved from (database name or URL).

Holloway, M. (2005, August). When extinct isn't. Scientific American, 293, 22-23. Retrieved from Academic Search Premier database.

Page Author Last Name, Page Author First Initial. Page Author Second Initial. Page title [nature of work - web site, blog, forum posting, etc.]. (Publication Year). Retrieved from (URL)

Sabo, G., et al. Rock art in Arkansas [Web site]. (2001). Retrieved from http://arkarcheology.uark.edu/rockart/index.html

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Writing a scientific paper.

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• INTRODUCTION

Literature Cited Section

Guides from other schools, citation styles & writing guides, "literature cited checklist" from: how to write a good scientific paper. chris a. mack. spie. 2018..

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This is the last section of the paper. Here you should provide an alphabetical listing of all the published work you cited in the text of the paper. This does not mean every article you found in your research; only include the works you actually cited in the text of your paper. A standard format is used both to cite literature in the text and to list these studies in the Literature Cited section.  Hypothetical examples of the format used in the journal Ecology are below:     Djorjevic, M., D.W. Gabriel and B.G. Rolfe. 1987. Rhizobium: Refined parasite of legumes. Annual Review of Phytopathology 25: 145-168.     Jones, I. J. and B. J. Green. 1963. Inhibitory agents in walnut trees. Plant Physiology 70:101-152.     MacArthur, R.H. and E.O. Wilson. 1967. The Theory of Island Biogeography. Princeton University Press, Princeton, N.J.     Smith, E. A. 1949. Allelopathy in walnuts. American Journal of Botany 35:1066-1071. Here is a dissection of the first entry, in the format for Ecology :       Firstauthor, M., D.W. Secondauthor and B.G. Thirdauthor. Year. Article title with only the first letter capitalized. Journal Article Title with Important Words in Caps  volume#(issue# if there is one): firstpage-lastpage. Notice some of the following details:       - the list is alphabetized;     - no first or middle names are listed (the author's first and middle initials are used instead);     - only the first word in the title of the journal article (except for proper nouns) is capitalized;     - different journals use different styles for Literature Cited sections.   You should pay careful attention to details of formatting when you write your own Literature Cited section. For papers published in journals you must provide the date, title, journal name, volume number, and page numbers. For books you need the publication date, title, publisher, and place of publication.

• Bates College Guide to Citing Sources
• American Psychological Association (APA) style A guide to formatting papers using APA from Purdue University.
• APA 2007 Revision of Citation Styles An online revision of the information presented in the fifth edition of the Publication Manual of the American Psychological Association . more... less... This guide serves a resource for citation styles and uniform means of referencing authoritative works.
• APA Documentation (University of Wisconsin-Madison) A quick resource for citing references in papers using the 5th edition of the Publication Manual of the American Psychological Association (2001). Provided by The Writing Center at the University of Wisconsin-Madison.
• American Anthropological Association Style Manual Prepared for and preferred by the American Anthropological Association (AAA) using the Chicago Manual of Style. Citation examples listed from pages 10-14. Also recommend consulting the Chicago Manual of Style Online.

• Elements of Style This classic work by William Strunk is intended for use in which the practice of composition is combined with the study of literature. It gives the main requirements of plain English style and concentrates on the rules of usage most often abused.
• IEEE Editorial Style Manual This link will take you to a downloadable version of the IEEE Editorial Style Manual.
• Modern Language Association (MLA) style
• Purdue OWL (Online Writing Lab) Easy-to-use site that provides information and examples for using the American Psychological Association (APA) citation and format style and the Modern Language Association (MLA) citation and format style. Also included information about the Chicago Manual of Style (16th ed.)
• Include citations that provide sufficient context to allow for critical analysis of this
• work by others.
• Include citations that give the reader sources of background and related material so
• that the current work can be understood by the target audience.
• Include citations that provide examples of alternate ideas, data, or conclusions to
• compare and contrast with this work, if they exist. Do not exclude contrary evidence.
• Include citations that acknowledge and give credit to sources relied upon for this
• Are the citations up to date, referencing that latest work on this topic?
• It is the job of the authors to verify the accuracy of the references.
• Avoid: spurious citations (citations that are not needed but are included anyway);

biased citations (references added or omitted for reasons other than meeting the above goals of citations); excessive self-cites (citations to one’s own work). 

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Writing and Citing Resources: How to Write Scientific Papers: Scientific Writing Resources

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Scientific and Medical Writing Books

Writing assistance from unc chapel hill.

• How to Cite
• Citation Tools: EndNote, SciWheel...

View a UNC Libraries Catalog search for books (electronic and print) on the subject of Medical Writing. Selected electronic books are listed below:

UNC Writing Center

• About the Writing Center Information about Writing Center services for students, faculty, and staff in the professional schools.
• Writing Center Handouts Collections handouts and multimedia demos for all aspects of academic writing. See especially the handouts for Annotated Bibliographies and Literature Reviews .
• English as a Second Lanuage Services Writing groups, mini-courses, videos and workshops focused on the ESL needs of full-time UNC undergraduate and graduate students.

UNC Research Funding Information Portal

• Proposal Writing Resources From UNC Research Funding Information Portal.

UNC Libraries Tutorials

• Copyright Basics Provides a basic explanation of how US Copyright law impacts reuse of published resources. Includes a brief quiz to test your knowledge.
• Plagiarism and Citing Sources (Health Affairs) This guide explains what plagiarism is and provides a short quiz to help you test your knowledge.
• Plagiarism Tutorial (Multidisciplinary) The Plagiarism Tutorial is a series of web-based, self-paced modules designed to help you learn about the broad issues surrounding plagiarism and how to best avoid this academic issue.
• Structure of Scholarly Articles and Peer Review Explains the standard parts of a medical research article, compares characteristics of types of journals, and shows how to find peer reviewed articles and journals.
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Cited Reference Search

Search for records that have cited a published work, and discover how a known idea or innovation has been confirmed, applied, improved, extended, or corrected. Find out who’s citing your research and the impact your work is having on other researchers in the world.

In the Arts & Humanities Citation Index, you can use cited reference search to find articles that refer to or include an illustration of a work of art or a music score; these references are called implicit citations .

• You may also search on Cited Year(s), Cited Volume, Cited Issue, Cited Pages, Cited Title, or Cited DOI
• Click Search; results from the cited reference index that include the work you’re searching appears on a table. Every reference on the cited reference index has been cited by at least one article indexed in the Web of Science. The first author of a cited work always displays in the Cited Author column. If the cited author you specified in step 1 is not the primary author, then the name of author you specified follows the name of the first author (click Show all authors). If you retrieve too many hits, return to the cited reference search page and add criteria for Cited Year, Cited Volume, Cited Issue, or Cited Page.
• cited reference is not a source article in the Web of Science
• reference may contain incomplete or inaccurate information, and can’t be linked to a source article
• reference may refer to a document from a publication outside the timespan of your subscription; for example, if the article was published in 1992, but your subscription only gives you access to 20 years of data
• cited item may refer to a document from a publication not covered by a database in your subscription
• Click Search to view your results.

Click View abbreviation list to see the abbreviations of journal and conference proceedings titles used as cited works; this list will open in a new browser tab.

When you complete a cited reference search, the number of citing items you retrieve may be smaller than the number listed in the Citing Articles column if your institution's subscription does not include all years of the database. In other words, the count in the Citing Articles column is not limited by your institution's subscription. However, your access to records in the product is limited by your institution's subscription.

• Enter the name of the first author of a multi-authored article or book
• Enter an abbreviated journal title followed by an asterisk or the first one or two significant words of a book title followed by an asterisk.
• Try searching for the cited reference without entering a cited year in order to retrieve variations of the same cited reference. You can always return to the Cited Reference Search page and enter a cited year if you get too many references.
• When searching for biblical references, enter Bible in the Cited Author field and the name of the book ( Corinthinans* , Matthew* Leviticus *, etc.) in the Cited Work field. Ensure that you use the asterisk (*) wildcard in your search.

Follow these steps to find articles that have cited Brown, M.E. and Calvin, W.M. Evidence for crystalline water and ammonia ices on Pluto's satellite Charon. Science . 287 (5450): 107-109. January 7, 2000:

• On the Cited Reference Search page, enter Brown M* in the Cited Author field.
• Enter Science* in the Cited Work field.
• Click Search to go to the Cited Reference Search table. This page shows all the results from the Web of Science cited reference index that matched the query.
• Page through the results to find this reference:

• Select the check box to the left of the reference.
• Click the See Results button to go to the Cited Reference Search Results page to see the list of articles that cite the article by Brown and Calvin.

Every cited reference in the Cited Reference Index contains enough information to uniquely identify the document. Because only essential bibliographic information is captured, and because author names and source publication titles are unified as much as possible, the same reference cited in two different records should appear the same way in the database. This unification is what makes possible the Times Cited number on the Full Record page.

However, not all references to the same publication can be unified. As a consequence, a cited reference may have variations in the product.

For example, consider these variations of a reference to an article by A.J. Bard published in volume 374 of Nature:

The first reference contains the correct volume number and other bibliographic information. The View Record link takes you to the Full Record, which has a Times Cited count of 31.

The second reference contains a different volume number and it does not have a View Record link. Because a journal cannot have two different volume numbers in the same publication year, it is obvious that this is an incorrect reference to the same article.

Click Export at the top of the Cited Reference Search table to export the cited reference search results to Excel.

Articles indexed in the Science Citation Index Expanded cite books, patents, and other types of publications in addition to other articles. You can do a cited reference search for a patent to find journal articles that have cited it.

If you know the patent number, enter it in the Cited Work field. If you do not know the patent number, try entering the name of the first listed inventor or patent assignee in the Cited Author field. For example, to find references to U.S. patent 4096196-A, enter 4096196 in the Cited Work field. If you also subscribe to Derwent Innovations Index and the patent is included in the Derwent database, the patents you find in the citation index will be linked to the corresponding full patent records in Derwent Innovations Index.

Self-citations refer to cited references that contain an author name that matches the name of the author of a citing article.

You may want to eliminate self-citations from the results of a Cited Reference Search by combining a Cited Reference Search with a search by the source author.

• Perform a Cited Reference Search to find items that cite the works of a particular author. Ensure that you complete both steps of a Cited Reference Search.
• Go to the search page. Enter the name of the same author in the Author field. Click the Search button.
• Go to the advanced search page.
• Combine the two searches you just completed in a Boolean NOT expression (for example, #1 NOT #2 ). The results of the Search (the items written by the author) should be the set on the right-hand side of the operator.

Articles indexed in the product cite books, patents, and other types of publications in addition to other articles. You can do a cited reference search on a book to find journal articles that have cited it.

You should identify a book by entering the name of the first listed author in the Cited Author field and the first word or words of the title in the Cited Work field. Many cited works are abbreviated. If you are not sure how a word has been spelled or abbreviated, enter the first few letters of the word, followed by an asterisk. For example, to search for records of articles that cite Edith Hamilton's book Mythology , you would enter Hamilton E* in the Cited Author field and Myth* in the Cited Work field.

Do not enter a year in the Cited Year field. Authors often cite a particular edition of a book, and the cited year is the year of the edition they are citing. Generally, you want to find all articles that cite a book, regardless of the particular edition cited.

For example, enter the following data on the Cited Reference Search page, and then click Search .

CITED AUTHOR Tuchman BW

CITED WORK Guns*

CITED YEAR 1962

Note the number of references that are retrieved. Now repeat the search using the following data:

CITED AUTHOR Tuchman B*

See how many more references you retrieved? Notice that the author has been cited as Tuchman B as well as Tuchman BW. Also, notice how many different cited years and cited page numbers there are for the same work.

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• v.25(3); 2014 Oct

How to Write a Scientific Paper: Practical Guidelines

Edgard delvin.

1 Centre de recherche, CHU Sainte-Justine

2 Département de Biochimie, Université de Montréal, Montréal, Canada

Tahir S. Pillay

3 Department of Chemical Pathology, Faculty of Health Sciences, University of Pretoria

4 Division of Chemical Pathology, University of Cape Town

5 National Health Laboratory Service, CTshwane Academic Division, Pretoria, South Africa

Anthony Newman

6 Life Sciences Department, Elsevier, Amsterdam, The Netherlands

Precise, accurate and clear writing is essential for communicating in health sciences, as publication is an important component in the university criteria for academic promotion and in obtaining funding to support research. In spite of this, the development of writing skills is a subject infrequently included in the curricula of faculties of medicine and allied health sciences. Therefore clinical investigators require tools to fill this gap. The present paper presents a brief historical background to medical publication and practical guidelines for writing scientific papers for acceptance in good journals.

INTRODUCTION

A scientific paper is the formal lasting record of a research process. It is meant to document research protocols, methods, results and conclusions derived from an initial working hypothesis. The first medical accounts date back to antiquity. Imhotep, Pharaoh of the 3 rd Dynasty, could be considered the founder of ancient Egyptian medicine as he has been credited with being the original author of what is now known as the Edwin Smith Papyrus ( Figure 1 ). The Papyrus, by giving some details on cures and anatomical observations, sets the basis of the examination, diagnosis, treatment, and prognosis of numerous diseases. Closer to the Common Era, in 460 BCE, Hippocrates wrote 70 books on medicine. In 1020, the Golden age of the Muslim Culture, Ibn Sina, known as Avicenna ( Figure 2a ), recorded the Canon of medicine that was to become the most used medical text in Europe and Middle East for almost half a millennium. This was followed in the beginning of the 12 th Century bytheextensivetreatiseofMaimonides( Figure 2b ) (Moses ben Maimon) on Greek and Middle Eastern medicine. Of interest, by the end of the 11 th Century Trotula di Ruggiero, a woman physician, wrote several influential books on women’s ailment. A number of other hallmark treatises also became more accessible, thanks to the introduction of the printing press that allowed standardization of the texts. One example is the De Humani Corporis Fabrica by Vesalius which contains hundreds of illustrations of human dissection. Thomas A Lang provides an excellent concise history of scientific publications [ 1 ]. These were the days when writing and publishing scientific or philosophical works were the privilege of the few and hence there was no or little competition and no recorded peer reviewing system. Times have however changed, and contemporary scientists have to compose with an increasingly harsh competition in attracting editors and publishers attention. As an example, the number of reports and reviews on obesity and diabetes has increased from 400 to close to 4000/year and 50 to 600/year respectively over a period of 20 years ( Figure 3 ). The present article, essentially based on TA Lang’s guide for writing a scientific paper [ 1 ], will summarize the steps involved in the process of writing a scientific report and in increasing the likelihood of its acceptance.

The Edwin Smith Papyrus (≈3000 BCE)

Avicenna and Maimonides

Annual publication load in the field of obesity and diabetes over 20 years.

Reasons for publishing are varied. One may write to achieve a post-graduate degree, to obtain funding for pursuing research or for academic promotion. While all 3 reasons are perfectly legitimate, one must ask whether they are sufficient to be considered by editors, publishers and reviewers. Why then should the scientist write? The main reason is to provide to the scientific community data based on hypotheses that are innovative and thus to advance the understanding in a specific domain. One word of caution however, is that if a set of experiments has not been done or reported, it does not mean that it should be. It may simply reflect a lack of interest in it.

DECIDING ON PUBLISHING AND TARGETING THE JOURNAL

In order to assist with the decision process, pres-ent your work orally first to colleagues in your field who may be more experienced in publishing. This step will help you in gauging whether your work is publishable and in shaping the paper.

Targeting the journal, in which you want to present your data, is also a critical step and should be done before starting to write. One hint is to look for journals that have published similar work to yours, and that aims readers most likely to be interested in your research. This will allow your article to be well read and cited. These journals are also those that you are most likely to read on a regular basis and to cite abundantly. The next step is to decide whether you submit your manuscript to a top-ranking impact factor journal or to a journal of lower prestige. Although it is tempting to test the waters, or to obtain reviewers comments, be realistic about the contribution your work provides and submit to a journal with an appropriate rank.

Do not forget that each rejection delays publication and that the basin of reviewers within your specialty is shallow. Thus repeated submission to different journals could likely result in having your work submitted for review to the same re-viewer.

DECIDING ON THE TYPE OF MANUSCRIPT

There are several types of scientific reports: observational, experimental, methodological, theoretical and review. Observational studies include 1) single-case report, 2) collective case reports on a series of patients having for example common signs and symptoms or being followed-up with similar protocols, 3) cross-sectional, 4) cohort studies, and 5) case-control studies. The latter 3 could be perceived as epidemiological studies as they may help establishing the prevalence of a condition, and identify a defined population with and without a particular condition (disease, injury, surgical complication). Experimental reports deal with research that tests a research hypothesis through an established protocol, and, in the case of health sciences, formulate plausible explanations for changes in biological systems. Methodological reports address for example advances in analytical technology, statistical methods and diagnostic approach. Theoretical reports suggest new working hypotheses and principles that have to be supported or disproved through experimental protocols. The review category can be sub-classified as narrative, systematic and meta-analytic. Narrative reviews are often broad overviews that could be biased as they are based on the personal experience of an expert relying on articles of his or her own choice. Systematic reviews and meta-analyses are based on reproducible procedures and on high quality data. Researchers systematically identify and analyze all data collected in articles that test the same working hypothesis, avoiding selection bias, and report the data in a systematic fashion. They are particularly helpful in asking important questions in the field of healthcare and are often the initial step for innovative research. Rules or guidelines in writing such report must be followed if a quality systematic review is to be published.

For clinical research trials and systematic reviews or meta-analyses, use the Consort Statement (Consolidated Standards Of Reporting Trials) and the PRISMA Statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses) respectively [ 2 , 3 ]. This assures the editors and the reviewers that essential elements of the trials and of the reviews were tackled. It also speeds the peer review process. There are several other Statements that apply to epidemiological studies [ 4 ], non-randomized clinical trials [ 5 ], diagnostic test development ( 6 ) and genetic association studies ( 7 ). The Consortium of Laboratory Medicine Journal Editors has also published guidelines for reporting industry-sponsored laboratory research ( 8 ).

INITIAL STEPS IN THE PROCESS OF WRITING A SCIENTIFIC DOCUMENT

Literature review is the initial and essential step before starting your study and writing the scientific report based on it. In this process use multiple databases, multiple keyword combinations. It will allow you to track the latest development in your field and thus avoid you to find out that someone else has performed the study before you, and hence decrease the originality of your study. Do not forget that high-ranking research journals publish results of enough importance and interest to merit their publication.

Determining the authorship and the order of authorship, an ethical issue, is the second essential step, and is unfortunately often neglected. This step may avoid later conflicts as, despite existing guidelines, it remains a sensitive issue owing to personal biases and the internal politics of institutions. The International Committee of Medical Editors has adopted the following guidelines for the biomedical sciences ( 9 ).

“Authorship credit should be based only on: 1) Substantial contributions to the conception and design, or acquisition of data, or analysis and interpretation of data; 2) Drafting the article or revising it critically for important intellectual content; and 3) Final approval of the version to be published. Conditions 1, 2 and 3 must be all met. Acquisition of funding, the collections of data, or general supervision of the research group, by themselves, do not justify authorship.” ( 9 , 10 )

The order of authorship should reflect the individual contribution to the research and to the publication, from most to least ( 11 ). The first author usually carries out the lead for the project reported. However the last author is often mistakenly perceived as the senior author. This is perpetuated from the European tradition and is discouraged. As there are divergent conventions among journals, the order of authorship order may or may not reflect the individual contributions; with the exception that the first author should be the one most responsible for the work.

WRITING EFFECTIVELY

Effective writing requires that the text helps the readers 1) understand the content and the context, 2) remember what the salient points are, 3) find the information rapidly and, 4) use or apply the information given. These cardinal qualities should be adorned with the precise usage of the language, clarity of the text, inclu-siveness of the information, and conciseness. Effective writing also means that you have to focus on the potential readers’ needs. Readers in science are informed individuals who are not passive, and who will formulate their own opinion of your writing whether or not the meaning is clear. Therefore you need to know who your audience is. The following 4 questions should help you writing a reader-based text, meaning written to meet the information needs of readers [ 12 ].

What do you assume your readers already know? In other words, which terms and concepts can you use without explanation, and which do you have to define?

What do they want to know? Readers in science will read only if they think they will learn something of value.

What do they need to know? Your text must contain all the information necessary for the reader to understand it, even if you think this information id obvious to them.

What do they think they know that is not so? Correcting misconceptions can be an important function of communication, and persuading readers to change their minds can be a challenging task.

WRITING THE SCIENTIFIC PAPER

Babbs and Tacker ’ s advice to write as much of the paper before performing the research project or experimental protocol may, at first sight, seem unexpected and counterintuitive [ 13 ], but in fact it is exactly what is being done when writing a research grant application. It will allow you to define the authorship alluded to before. The following section will briefly review the structure of the different sections of a manuscript and describe their purpose.

Reading the instructions to authors of the Journal you have decided to submit your manuscript is the first important step. They provide you with the specific requirements such as the way of listing the authors, type of abstract, word, figure or table limits and citation style. The Mulford Library of University of Toledo website contains instructions to authors for over 3000 journals ( http://mulford.meduoiho.edu/instr/ ).

The general organization of an article follows the IMRAD format (Introduction, Methods, Results, and Discussion). These may however vary. For instance, in clinical research or epidemiology studies, the methods section will include details on the subjects included, and there will be a statement of the limitation of the study. Although conclusions may not always be part of the structure, we believe that it should, even in methodological reports.

The tile page provides essential information so that the editor, reviewers, and readers will identify the manuscript and the authors at a glance as well as enabling them to classify the field to which the article pertains.

The title page must contain the following:

• The tile of the article – it is an important part of the manuscript as it is the most often read and will induce the interested readers to pursue further. Therefore the title should be precise, accurate, specific and truthful;
• Each author’s given name (it may be the full name or initials) and family name;
• Each author’s affiliation;
• Some journals ask for highest academic degree;
• A running title that is usually limited to a number of characters. It must relate to the full title;
• Key words that will serve for indexing;
• For clinical studies, the trial’s registration number;
• The name of the corresponding author with full contact information.

The abstract is also an important section of your manuscript. Importantly, the abstract is the part of the article that your peers will see when consulting publication databases such as PubMed. It is the advertisement to your work and will strongly influence the editor deciding whether it will be submitted to reviewers or not. It will also help the readers decide to read the full article. Hence it has to be comprehensible on its own. Writing an abstract is challenging. You have to carefully select the content and, while being concise, assure to deliver the essence of your manuscript.

Without going into details, there are 3 types of abstracts: descriptive, informative and structured. The descriptive abstract is particularly used for theoretical, methodological or review articles. It usually consists of a single paragraph of 150 words or less. The informative abstract, the most common one, contains specific information given in the article and, are organized with an introduction (background, objectives), methods, results and discussion with or without conclusion. They usually are 150 to 250 words in length. The structured abstract is in essence an informative abstract with sections labeled with headings. They may also be longer and are limited to 250 to 300 words. Recent technology also allows for graphical or even video abstracts. The latter are interesting in the context of cell biology as they enable the investigator to illustrate ex vivo experiment results (phagocytosis process for example).

Qualities of abstracts:

• Understood without reading the full paper. Shoul dcontain no abbreviations.lf abbreviations are used, they must be defined. This however removes space for more important information;
• Contains information consistent with the full report. Conclusions in the abstract must match those given in the full report;
• Is attractive and contains information needed to decide whether to read the full report.

Introduction

The introduction has 3 main goals: to establish the need and importance of your research, to indicate how you have filled the knowledge gap in your field and to give your readers a hint of what they will learn when reading your paper. To fulfil these goals, a four-part introduction consisting of a background statement, a problem statement, an activity statement and a forecasting statement, is best suited. Poorly defined background information and problem setting are the 2 most common weaknesses encountered in introductions. They stem from the false perception that peer readers know what the issue is and why the study to solve it is necessary. Although not a strict rule, the introduction in clinical science journals should target only references needed to establish the rationale for the study and the research protocol. This differ from more basic science or cell biology journals, for which a longer and elaborate introduction may be justified because the research at hand consists of several approaches each requiring background and justification.

The 4-part introduction consists of:

• A background statement that provides the context and the approach of the research;
• A problem statement that describes the nature, scope and importance of the problem or the knowledge gap;
• An activity statement, that details the research question, sets the hypothesis and actions undertaken for the investigation;
• A forecasting statement telling the readers whattheywillfìndwhen readingyourarticle [ 14 ].

Methods section

This section may be named “Materials and Methods”, “Experimental section” or “Patients and Methods” depending upon the type of journal. Its purpose to allow your readers to provide enough information on the methods used for your research and to judge on their adequacy. Although clinical and “basic” research protocols differ, the principles involved in describing the methods share similar features. Hence, the breadth of what is being studied and how the study can be performed is common to both. What differ are the specific settings. For example, when a study is conducted on humans, you must provide, up front, assurance that it has received the approval of you Institution Ethics Review Board (IRB) and that participants have provided full and informed consent. Similarly when the study involves animals, you must affirm that you have the agreement from your Institutional Animal Care and Use Committee (IACUC). These are too often forgotten, and Journals (most of them) abiding to the rules of the Committee on Publication Ethics (COPE) and World Association of Medical Editors (WAME) will require such statement. Although journals publishing research reports in more fundamental science may not require such assurance, they do however also follow to strict ethics rules related to scientific misconduct or fraud such as data fabrication, data falsification. For clinical research papers, you have to provide information on how the participants were selected, identify the possible sources of bias and confounding factors and how they were diminished.

In terms of the measurements, you have to clearly identify the materials used as well as the suppliers with their location. You should also be unambiguous when describing the analytical method. If the method has already been published, give a brief account and refer to the original publication (not a review in which the method is mentioned without a description). If you have modified it, you have to provide a detailed account of the modifications and you have to validate its accuracy, precision and repeatability. Mention the units in which results are reported and, if necessary, include the conversion factors [mass units versus “système international” (S.I.)]. In clinical research, surrogate end-points are often used as biomarkers. Under those circumstances, you must show their validity or refer to a study that has already shown that are valid.

In cases of clinical trials, the Methods section should include the study design, the patient selection mode, interventions, type of outcomes.

Statistics are important in assuring the quality of the research project. Hence, you should consult a biostatistician at the time of devising the research protocol and not after having performed the experiments or the clinical trial.

The components of the section on statistics should include:

• The way the data will be reported (mean, median, centiles for continuous data);
• Details on participant assignments to the different groups (random allocation, consecutive entry);
• Statistical comparison tools (parametric or non parametric statistics, paired or unpaired t-tests for normally distributed data and so on);
• The statistical power calculation when determining the sample size to obtain valid and significant comparisons together with the a level;
• The statistical software package used in the analysis.

Results section

The main purpose of the results section is to report the data that were collected and their relationship. It should also provide information on the modifications that have taken place because of unforeseen events leading to a modification of the initial protocol (loss of participants, reagent substitution, loss of data).

• Report results as tables and figures whenever possible, avoid duplication in the text. The text should summarize the findings;
• Report the data with the appropriate descriptive statistics;
• Report any unanticipated events that could affect the results;
• Report a complete account of observations and explanations for missing data (patient lost).

The discussion should set your research in context, reinforce its importance and show how your results have contributed to the further understanding of the problem posed. This should appear in the concluding remarks. The following organization could be helpful.

• Briefly summarize the main results of your study in one or two paragraphs, and how they support your working hypothesis;
• Provide an interpretation of your results and show how they logically fit in an overall scheme (biological or clinical);
• Describe how your results compare with those of other investigators, explain the differences observed;
• Discuss how your results may lead to a new hypothesis and further experimentation, or how they could enhance the diagnostic procedures.
• Provide the limitations of your study and steps taken to reduce them. This could be placed in the concluding remarks.

Acknowledgements

The acknowledgements are important as they identify and thank the contributors to the study, who do not meet the criteria as co-authors. They also include the recognition of the granting agency. In this case the grant award number and source is usually included.

Declaration of competing interests

Competing interests arise when the author has more than one role that may lead to a situation where there is a conflict of interest. This is observed when the investigator has a simultaneous industrial consulting and academic position. In that case the results may not be agreeable to the industrial sponsor, who may impose a veto on publication or strongly suggest modifications to the conclusions. The investigator must clear this issue before starting the contracted research. In addition, the investigator may own shares or stock in the company whose product forms the basis of the study. Such conflicts of interest must be declared so that they are apparent to the readers.

Acknowledgments

The authors thank Thomas A Lang, for his advice in the preparation of this manuscript.

There is unequivocal evidence that Earth is warming at an unprecedented rate. Human activity is the principal cause.

• While Earth’s climate has changed throughout its history , the current warming is happening at a rate not seen in the past 10,000 years.
• According to the Intergovernmental Panel on Climate Change ( IPCC ), "Since systematic scientific assessments began in the 1970s, the influence of human activity on the warming of the climate system has evolved from theory to established fact." 1
• Scientific information taken from natural sources (such as ice cores, rocks, and tree rings) and from modern equipment (like satellites and instruments) all show the signs of a changing climate.
• From global temperature rise to melting ice sheets, the evidence of a warming planet abounds.

The rate of change since the mid-20th century is unprecedented over millennia.

Earth's climate has changed throughout history. Just in the last 800,000 years, there have been eight cycles of ice ages and warmer periods, with the end of the last ice age about 11,700 years ago marking the beginning of the modern climate era — and of human civilization. Most of these climate changes are attributed to very small variations in Earth’s orbit that change the amount of solar energy our planet receives.

The current warming trend is different because it is clearly the result of human activities since the mid-1800s, and is proceeding at a rate not seen over many recent millennia. 1 It is undeniable that human activities have produced the atmospheric gases that have trapped more of the Sun’s energy in the Earth system. This extra energy has warmed the atmosphere, ocean, and land, and widespread and rapid changes in the atmosphere, ocean, cryosphere, and biosphere have occurred.

Earth-orbiting satellites and new technologies have helped scientists see the big picture, collecting many different types of information about our planet and its climate all over the world. These data, collected over many years, reveal the signs and patterns of a changing climate.

Scientists demonstrated the heat-trapping nature of carbon dioxide and other gases in the mid-19th century. 2 Many of the science instruments NASA uses to study our climate focus on how these gases affect the movement of infrared radiation through the atmosphere. From the measured impacts of increases in these gases, there is no question that increased greenhouse gas levels warm Earth in response.

Scientific evidence for warming of the climate system is unequivocal.

Intergovernmental Panel on Climate Change

Ice cores drawn from Greenland, Antarctica, and tropical mountain glaciers show that Earth’s climate responds to changes in greenhouse gas levels. Ancient evidence can also be found in tree rings, ocean sediments, coral reefs, and layers of sedimentary rocks. This ancient, or paleoclimate, evidence reveals that current warming is occurring roughly 10 times faster than the average rate of warming after an ice age. Carbon dioxide from human activities is increasing about 250 times faster than it did from natural sources after the last Ice Age. 3

The Evidence for Rapid Climate Change Is Compelling:

Global Temperature Is Rising

The planet's average surface temperature has risen about 2 degrees Fahrenheit (1 degrees Celsius) since the late 19th century, a change driven largely by increased carbon dioxide emissions into the atmosphere and other human activities. 4 Most of the warming occurred in the past 40 years, with the seven most recent years being the warmest. The years 2016 and 2020 are tied for the warmest year on record. 5 Image credit: Ashwin Kumar, Creative Commons Attribution-Share Alike 2.0 Generic.

The Ocean Is Getting Warmer

The ocean has absorbed much of this increased heat, with the top 100 meters (about 328 feet) of ocean showing warming of 0.67 degrees Fahrenheit (0.33 degrees Celsius) since 1969. 6 Earth stores 90% of the extra energy in the ocean. Image credit: Kelsey Roberts/USGS

The Ice Sheets Are Shrinking

The Greenland and Antarctic ice sheets have decreased in mass. Data from NASA's Gravity Recovery and Climate Experiment show Greenland lost an average of 279 billion tons of ice per year between 1993 and 2019, while Antarctica lost about 148 billion tons of ice per year. 7 Image: The Antarctic Peninsula, Credit: NASA

Glaciers Are Retreating

Glaciers are retreating almost everywhere around the world — including in the Alps, Himalayas, Andes, Rockies, Alaska, and Africa. 8 Image: Miles Glacier, Alaska Image credit: NASA

Snow Cover Is Decreasing

Satellite observations reveal that the amount of spring snow cover in the Northern Hemisphere has decreased over the past five decades and the snow is melting earlier. 9 Image credit: NASA/JPL-Caltech

Sea Level Is Rising

Global sea level rose about 8 inches (20 centimeters) in the last century. The rate in the last two decades, however, is nearly double that of the last century and accelerating slightly every year. 10 Image credit: U.S. Army Corps of Engineers Norfolk District

Arctic Sea Ice Is Declining

Both the extent and thickness of Arctic sea ice has declined rapidly over the last several decades. 11 Credit: NASA's Scientific Visualization Studio

Extreme Events Are Increasing in Frequency

The number of record high temperature events in the United States has been increasing, while the number of record low temperature events has been decreasing, since 1950. The U.S. has also witnessed increasing numbers of intense rainfall events. 12 Image credit: Régine Fabri,  CC BY-SA 4.0 , via Wikimedia Commons

Ocean Acidification Is Increasing

Since the beginning of the Industrial Revolution, the acidity of surface ocean waters has increased by about 30%. 13 , 14 This increase is due to humans emitting more carbon dioxide into the atmosphere and hence more being absorbed into the ocean. The ocean has absorbed between 20% and 30% of total anthropogenic carbon dioxide emissions in recent decades (7.2 to 10.8 billion metric tons per year). 1 5 , 16 Image credit: NOAA

1. IPCC Sixth Assessment Report, WGI, Technical Summary . B.D. Santer et.al., “A search for human influences on the thermal structure of the atmosphere.” Nature 382 (04 July 1996): 39-46. https://doi.org/10.1038/382039a0. Gabriele C. Hegerl et al., “Detecting Greenhouse-Gas-Induced Climate Change with an Optimal Fingerprint Method.” Journal of Climate 9 (October 1996): 2281-2306. https://doi.org/10.1175/1520-0442(1996)009<2281:DGGICC>2.0.CO;2. V. Ramaswamy, et al., “Anthropogenic and Natural Influences in the Evolution of Lower Stratospheric Cooling.” Science 311 (24 February 2006): 1138-1141. https://doi.org/10.1126/science.1122587. B.D. Santer et al., “Contributions of Anthropogenic and Natural Forcing to Recent Tropopause Height Changes.” Science 301 (25 July 2003): 479-483. https://doi.org/10.1126/science.1084123. T. Westerhold et al., "An astronomically dated record of Earth’s climate and its predictability over the last 66 million years." Science 369 (11 Sept. 2020): 1383-1387. https://doi.org/10.1126/science.1094123

2. In 1824, Joseph Fourier calculated that an Earth-sized planet, at our distance from the Sun, ought to be much colder. He suggested something in the atmosphere must be acting like an insulating blanket. In 1856, Eunice Foote discovered that blanket, showing that carbon dioxide and water vapor in Earth's atmosphere trap escaping infrared (heat) radiation. In the 1860s, physicist John Tyndall recognized Earth's natural greenhouse effect and suggested that slight changes in the atmospheric composition could bring about climatic variations. In 1896, a seminal paper by Swedish scientist Svante Arrhenius first predicted that changes in atmospheric carbon dioxide levels could substantially alter the surface temperature through the greenhouse effect. In 1938, Guy Callendar connected carbon dioxide increases in Earth’s atmosphere to global warming. In 1941, Milutin Milankovic linked ice ages to Earth’s orbital characteristics. Gilbert Plass formulated the Carbon Dioxide Theory of Climate Change in 1956.

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7. I. Velicogna, Yara Mohajerani, A. Geruo, F. Landerer, J. Mouginot, B. Noel, E. Rignot, T. Sutterly, M. van den Broeke, M. Wessem, D. Wiese, "Continuity of Ice Sheet Mass Loss in Greenland and Antarctica From the GRACE and GRACE Follow-On Missions." Geophysical Research Letters 47, Issue 8 (28 April 2020): e2020GL087291. https://doi.org/10.1029/2020GL087291.

8. National Snow and Ice Data Center World Glacier Monitoring Service

9. National Snow and Ice Data Center D.A. Robinson, D. K. Hall, and T. L. Mote, "MEaSUREs Northern Hemisphere Terrestrial Snow Cover Extent Daily 25km EASE-Grid 2.0, Version 1 (2017). Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0530.001 . http://nsidc.org/cryosphere/sotc/snow_extent.html Rutgers University Global Snow Lab. Data History

10. R.S. Nerem, B.D. Beckley, J. T. Fasullo, B.D. Hamlington, D. Masters, and G.T. Mitchum, "Climate-change–driven accelerated sea-level rise detected in the altimeter era." PNAS 15, no. 9 (12 Feb. 2018): 2022-2025. https://doi.org/10.1073/pnas.1717312115.

11. https://nsidc.org/cryosphere/sotc/sea_ice.html Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS, Zhang and Rothrock, 2003) http://psc.apl.washington.edu/research/projects/arctic-sea-ice-volume-anomaly/ http://psc.apl.uw.edu/research/projects/projections-of-an-ice-diminished-arctic-ocean/

12. USGCRP, 2017: Climate Science Special Report: Fourth National Climate Assessment, Volume I [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, 470 pp, https://doi.org/10.7930/j0j964j6 .

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16. Special Report on the Ocean and Cryosphere in a Changing Climate , Technical Summary, Chapter TS.5, Changing Ocean, Marine Ecosystems, and Dependent Communities, Section 5.2.2.3. https://www.ipcc.ch/srocc/chapter/technical-summary/

Header image shows clouds imitating mountains as the sun sets after midnight as seen from Denali's backcountry Unit 13 on June 14, 2019. Credit: NPS/Emily Mesner Image credit in list of evidence: Ashwin Kumar, Creative Commons Attribution-Share Alike 2.0 Generic.

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Technological innovations for sustainable transformation towards carbon neutrality

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Sustainable transformation towards carbon neutrality can be achieved by the development of innovative low-carbon energy technologies. Despite the promulgation and subsequent implementation of various policies, many obstacles and challenges lie ahead in the long transition to the achievement of a sustainable transition towards carbon neutrality, including government behaviour, the market environment, and enterprise capacity. Seeking to overcome such substantial barriers to such development, this special issue focuses on urgent issues, with the theme of “Technological Innovations for Sustainable Transformation Towards Carbon Neutrality”. The call welcomes researchers worldwide to contribute to extensive discussions on this theme and share their original thinking and high-quality research output. Its primary focus is to determine how stakeholders who have developed new technologies and designed green transformation paths can attain their carbon neutrality goals.

The guest editors of this special issue (Prof. Dr. Xiongfeng Pan, Prof. Dr. Jia Liu, Dr. Nader Atawnah, Prof. Dr. Chew Tin Lee, Dr. Muhammad Imran Qureshi, and Dr. Rashid Zaman) have worked diligently to bring together a diverse range of papers that address some of the most pressing low-carbon technological innovation challenges of our time. For example, He et al. (2023) quantitatively studied the implications of the dynamic decision behaviour of stakeholders on the prefabricated construction market; Lu et al. (2023) proposed a three-step method to guide the establishment of an extensible carbon emission factor database for the construction industry; Kayani et al. (2023) investigated the intricate interplay between carbon emissions and foreign direct investment within the context of BRICS; Yu et al. (2023) examined the feedback mechanism of critical peak pricing on coal consumption of power generation side units; Pan and Wang (2023) evaluated the impact of marine ecological compensation policy on marine carbon emission efficiency; and Skrzypczak et al. (2023) explored the development of sustainable fertilizers from waste materials of a biogas plant and a brewery. This special issue presents the latest research and developments in low-carbon technology and deepens our understanding of how to promote and accelerate the development of low-carbon technologies and sustainable transformation.

We, the guest editors, would like to thank the Editor-in-Chief of the ESPR journal, the editorial assistant, and all the supporting staff for giving us this opportunity. We would also like to extend our sincere thanks to all the contributors and reviewers who have made this special issue possible. We hope that the papers in this special issue will inspire further research and innovation in this important field.

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Title: uncovering misattributed suicide causes through annotation inconsistency detection in death investigation notes.

Abstract: Data accuracy is essential for scientific research and policy development. The National Violent Death Reporting System (NVDRS) data is widely used for discovering the patterns and causes of death. Recent studies suggested the annotation inconsistencies within the NVDRS and the potential impact on erroneous suicide-cause attributions. We present an empirical Natural Language Processing (NLP) approach to detect annotation inconsistencies and adopt a cross-validation-like paradigm to identify problematic instances. We analyzed 267,804 suicide death incidents between 2003 and 2020 from the NVDRS. Our results showed that incorporating the target state's data into training the suicide-crisis classifier brought an increase of 5.4% to the F-1 score on the target state's test set and a decrease of 1.1% on other states' test set. To conclude, we demonstrated the annotation inconsistencies in NVDRS's death investigation notes, identified problematic instances, evaluated the effectiveness of correcting problematic instances, and eventually proposed an NLP improvement solution.

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• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

• Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
• Supinya Piampongsant 1 , 2 , 3   na1 ,
• Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
• Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
• Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
• Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
• Florian A. Theßeling 1 , 2 , 3 ,
• Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
• Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
• Philippe Malcorps 6 ,
• Luk Daenen 6 ,
• Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
• Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

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• Metabolomics
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The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Kushagra Saini & Venkatnarayan Ramanathan

Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

Authors and Affiliations

VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

Philippe Malcorps & Luk Daenen

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Contributions

S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

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Correspondence to Kevin J. Verstrepen .

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K.J.V. is affiliated with bar.on. The other authors declare no competing interests.

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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Received : 30 October 2023

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Published : 26 March 2024

DOI : https://doi.org/10.1038/s41467-024-46346-0

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