sensory evaluation of food research paper

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Perspective
• Open access
• Published: 19 June 2018

Example food: What are its sensory properties and why is that important?

• Herbert Stone 1  

npj Science of Food volume  2 , Article number:  11 ( 2018 ) Cite this article

56k Accesses

20 Citations

5 Altmetric

Metrics details

• Human behaviour

Today’s marketplace for food and beverages is diverse and competitive in ways not imagined a few decades ago. It is driven, in part, by consumers and consumer blogs asking questions about the safety of ingredients and processed foods, in general. Some of these questions are about GMOs and/or the need for gluten free, organic, and natural foods. It also has led to the development of products by non-traditional food companies that offer alternatives vs. foods currently available. Regardless of the product, source of raw materials, and potential benefits offered, success is determined ultimately by the consumer. History tells us that a unique ingredient or a food formulated that meets some or all of the aforementioned characteristics does not guarantee market success. Although there are numerous steps between development and consumption by the consumer where a product can fail, its sensory properties are critical in establishing its potential and continued success.

Where product success is achieved, changes happen reflecting the dynamics of the marketplace and include such issues as the shift to larger scale production, availability and cost of raw materials, competition, and so forth. Any change results in a different product and management needs to know whether the change is important, i.e., is it recognized by the consumer and does it change purchase intent? What product characteristics or combination of characteristics have changed? What can technology do to compensate if this change has negative effects? Answers to these kinds of questions are best obtained through use of sensory evaluation, a science that has long been used for the evaluation of products but not as well-known as other consumer testing resources, e.g., market research.

What is sensory evaluation?

Sensory evaluation is a science that measures, analyzes, and interprets the reactions of people to products as perceived by the senses. It is a means of determining whether product differences are perceived, the basis for the differences, and whether one product is liked more than another. The value of the science lies in its use of limited numbers of consumers to reach decisions that can be extrapolated to larger populations with confidence. This means that the subjects are representative of the consumer population for whom the product is intended and have the necessary sensory skills. In practical terms, it enables one to evaluate products in a relatively short time and at low cost. In the discussion that follows, key sensory resources are described; however, a detailed discussion about the science can be found in the reference list at the end of this article.

There are four types of resources that serve as the basis for the science. These are subjects, methods, test plans and analyses, and facilities and support services. Each is summarized below.

Sensory evaluation relies on consumers to provide the data on which decisions are based. Although there are consumers everywhere to provide responses, there are considerable differences in any population. They look different and not surprisingly, their sensory skills are different, by as much as 100% or more in sensitivity to differences among products. Depending on the type of information needed, analytical or affective, different skill levels or qualifications are required.

For analytical methods (discrimination and descriptive), subjects need to be above average users of a product/product category and demonstrate ability to discriminate differences at better than chance among those products.

Empirical evidence from more than 50 years of testing around the world has shown that about 30% of any population cannot discriminate differences at better than chance. This is independent of age, frequency of product use, gender, and other typical criteria. It means that those who do not meet this requirement should not participate as sensitivity is decreased and variability is greater. Otherwise, there is a high risk of concluding there is no difference when, in fact, there is (β risk). For more on risk, see Cohen 1 .

Qualifying subjects for analytical tests is a straightforward process taking about 4–5 h in a series of three or four 90 min test sessions. Consumers have to learn how to use their senses, how to take a test, and familiarize themselves with the products. Based on numerous experiments, it was concluded that having a consumer participate in about 30–40 difference test trials was necessary before determining an individual is qualified. It is surprising how often this process is ignored and recommendations based on test results are not supported in the marketplace. See Stone et al. 2 , 3 for a more extensive discussion on this topic.

For affective tests, i.e., liking or preference, the key qualification is product and brand use. Above average product use is necessary, as those consumers are generally found to be more sensitive than the infrequent user.

There are many test methods available for use; all have specific applications and can be categorized as analytical or affective.

Analytical methods include discrimination/difference and descriptive analysis. Results from a difference test tell us whether product differences are perceived. Results from a descriptive test identify the specific kinds of differences, e.g., a fruit aroma for a series of samples and their intensities. An example of results from the latter is shown in Fig. 1 , a sensory map showing results from a test of competitive wines.

A sensory map of aroma and appearance attributes obtained from a descriptive analysis of an competitive chardonnay wines. The intensity values are measured from the center to that point where a line crosses and are the computed means obtained from a panel of 12 subjects and 4 replicates ( N  = 48)

Analytical tests rely on limited numbers of qualified subjects but all require replication. This is no different than a chemical analysis of a product which is done more than once to have confidence in the reported result. For discrimination, between 20 and 30 subjects are recommended. With replication this yields sufficient judgments to have confidence in the conclusions. More discussion about this recommendation, see Stone 2 .

For descriptive analysis, the optimal number of subjects is 12 and not < 10. Empirically, it has been observed that using more than 12 does not result in more information, only greater statistical significances. Using fewer than 10 makes it more difficult to obtain expected significances. With a 6-product test, 30–40 attributes (typical for foods and beverages), and 4 replicates for each product, there are sufficient data to enable a thorough analysis of subject skill and specific product differences.

Affective methods include hedonic and preference. Results from a hedonic test tell us the degree of liking for series of products. It is used primarily for determining which product is best liked from an array of options. Preference methods determine which product is preferred. About 75–100 subjects are recommended for an affective sensory test.

No sensory method is more sensitive than another. The choice of method is based on the test objective and the nature and number of products to be tested. If products have a lingering taste, then methods requiring multiple sampling within a test session would not be appropriate because of sensory fatigue.

A few comments are warranted here about the use of the internet as a means for obtaining information from consumers. Various methods have been described in the literature and have attracted considerable attention. One can collect large amounts of information in a relatively short time period; a process no different than surveys using landlines to recruit for a test or measure attitudes about social or political issues. One of the advantages today is the ability to provide pictures and descriptions of products which add focus to the consumer’s responses. In early stage development of ideas, this approach has value to brand managers and technical staff; however, without knowledge of a person’s sensory skills, it does not, as yet, appear to be a substitute for more traditional sensory procedures.

Test plans and analyses

The importance of using appropriate design and analysis cannot be underestimated. Plans must reflect behavioral and physiological elements in the design which means that balanced designs are preferred and designs that rely on randomized serving orders should be avoided. Otherwise, risk is high that one sample will be served in one position several times, whereas another product is never served in that position. First sample effects cannot be eliminated so one must be sure that each product appears equally often in each position. Although this is relatively easy to achieve in a test of four or fewer products, it is more difficult in a test of ten products. A solution is to balance the design across all subjects. With replication, the serving order challenges are minimized but not eliminated. A variety of test designs have been published while others are connected with software packages; however, all need to be reviewed before use.

In situations where products are scored, a common feature of descriptive analysis and liking, the most useful analysis is the analysis of variance. As with any statistical procedure, it is important that the data collection process fit the model on which the test plan is based. Much has been written about statistical methods and the interested reader is directed to Stone 2 .

Facilities and support services

Traditionally, sensory tests were fielded in a purpose built facility with a controlled environment and partitions to minimize visual contact between subjects and servers. Responses were captured on paper ballots and then transcribed for analysis. Today’s facility has touch screens and the ability to provide real time analyses regardless of where the data were obtained. In addition this has enabled the actual data collection to no longer be restricted to a sensory facility. Use of a tablet and the web has made it possible to test products anywhere, based on the purpose for the information. This means that managers can obtain information and transmit instructions to others regardless of their location.

Application, an example

As previously noted, a sensory map is very useful in identifying the effects of formulation changes on sensory attributes from a developer’s perspective. Maps also have value for marketing and brand managers when results are connected with preferences and other consumer measures.

Figure 2 shows results from a plot of sensory attributes and consumer preferences. The circled groupings show the connection between specific sensory attributes and preferences enabling marketing to identify which word groupings are best used in communicating to which consumers. Additional discussion about this and other applications are described in the aforementioned references.

A bi-plot of the sensory attributes and preferences obtained from wine consumers. The attributes that are most associated with specific wines are circled

Sensory evaluation is all about people using their senses vs. simply eating a meal without appreciating the experience. Learning to use one’s senses has many rewards especially as it relates to a greater appreciation for the food and beverages we consume. Companies invest substantial sums of money in developing and marketing products without fully appreciating the consumer’s ability to identify what makes one product a success and another just getting by. Sensory information should be an integral part of any product effort, whether at the formulation stage or at the marketing stage if one expects to be more successful in satisfying consumer expectations.

Cohen, J. Statistical Power Analysis for the Behavioral Sciences . 2nd edn, (Taylor & Frances Group: NY, 1988).

Google Scholar  

Stone, H. in Rapid Sensory Profiling Techniques and Related Methods (eds Delarue J., Lawlor J. B., & Rogeaux M.) pp 27–50 (Woodhead Publishing, Cambridge, 2015).

Stone, H., Bleibaum, R. M., & Thomas, H. Sensory Evaluation Practices. 4th edn (Academic Press: San Diego, 2012).

Download references

Acknowledgements

There were no funds received by the author for this research.

Author information

Authors and affiliations.

Sensory Consulting Services, Menlo Park, CA, USA

Herbert Stone

You can also search for this author in PubMed   Google Scholar

Contributions

This manuscript was prepared solely by the author.

Corresponding author

Correspondence to Herbert Stone .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Stone, H. Example food: What are its sensory properties and why is that important?. npj Sci Food 2 , 11 (2018). https://doi.org/10.1038/s41538-018-0019-3

Download citation

Received : 24 July 2017

Revised : 02 January 2018

Accepted : 26 April 2018

Published : 19 June 2018

DOI : https://doi.org/10.1038/s41538-018-0019-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Exploitation of seaweed functionality for the development of food products.

• Mandakini Devi Hanjabam
• Chandragiri Nagarajarao Ravishankar

Food and Bioprocess Technology (2023)

Sensory lexicon and aroma volatiles analysis of brewing malt

npj Science of Food (2022)

The Pros and Cons of Incorporating Bioactive Compounds Within Food Networks and Food Contact Materials: a Review

• Moslem Sabaghi
• Sedighe Tavasoli
• Afshin Faridi Esfanjani

Food and Bioprocess Technology (2022)

Hedonic evaluation and check-all-that-apply (CATA) question for sensory characterisation of stewed vegetable Amaranthus

• Lucil Hiscock
• Carina Bothma
• Willem Sternberg Jansen van Rensburg

Journal of Food Science and Technology (2020)

Critical functional properties of defatted peanut meal produced by aqueous extraction and conventional methods

Journal of Food Science and Technology (2019)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Sensory Analysis and Consumer Research in New Product Development

Sensory analysis examines the properties (texture, flavor, taste, appearance, smell, etc.) of a product or food through the senses (sight, smell, taste, touch and hearing) of the panelists. This type of analysis has been used for centuries for the purpose of accepting or rejecting food products. Historically, it was considered a methodology that complements technological and microbiological safety when assessing the quality of food. However, its important evolution and impact in recent decades has placed it as one of the most important methodologies for innovation and application to ensure final product acceptance by consumers. Traditional sensory techniques, such as discriminatory, descriptive evaluations, preference and hedonic tests, which are still widely used today, have evolved into newer, faster and more complete techniques: check-all-that-apply (CATA), napping (N), flash profile (FP), temporal dominance of sensations (TDS), etc., together with an important and adequate statistical analysis. All of these techniques, with their advantages and disadvantages, are very useful in the development of new foods. However, it is not only sensory characteristics that determine the acceptance or success of a new product. Factors such as social aspects, the environment, nutritional knowledge, specific diets, emotions, health, the nature of the products, packaging, etc., also have a very important influence. New food product developers should take into account the attitudes and expectations of potential consumers. Consumers describe a product’s benefits by perceived intrinsic and extrinsic characteristics. For example, focus groups could be planned to identify different consumer expectations for new products. Properly measuring these factors and emotions will also have a very decisive influence on the success or failure of new product developments. A better understanding of the sensory experiences of potential consumers opens up a space and the inspiration for innovation. For all these reasons, sensory analysis, together with consumer research, is currently considered by the industry and researchers to be one of the most useful tools at the different stages of new product development, from design to commercialization, to improve the quality of products and to guarantee the success of innovation in market uptake among consumers. All these aspects are collected by Ruiz-Capillas et al. [ 1 ] in their review, which helps to better elucidate these techniques and enhance knowledge in this field, in order to facilitate the choice of the most appropriate aspect at the time of its application in the different stages of new product development, particularly regarding meat products.

Furthermore, this Special Issue (SI) also includes different sensory and consumer studies used for their application in the development of very different new products. Kalumbi et al. [ 2 ] evaluated consumer acceptability of new enriching maize-based stiff porridge with flour made from hydrothermally treated soybeans. This development could significantly contribute towards reducing the burden of energy–protein under-nutrition in populations in sub-Saharan Africa. On the other hand, Szymandera-Buszka et al. [ 3 ] employed consumer tests and sensory profiling to assess the impact of ethanol extracts of spices (lovage, marjoram, thyme, oregano, rosemary and basil) on the sensory quality of new pork meatballs and hamburgers. This work noted the usefulness of these techniques in the development of products with clean labeling, replacing synthetic preservatives with natural plant extracts. Tao and Cho [ 4 ] evaluated the sensory characteristics of Rebaudioside (Reb) A, D and M compared with sucrose, using a consumer panel, and explored the relationship between 6-n-Propylthiouracil (PROP) taster status (i.e., non-tasters, medium tasters, supertasters) and the perceived intensity of sweet and bitter tastes of the three steviol glycosides. Consumers were instructed to rate the sweetness and bitterness intensities of different solutions and a check-all-that-apply (CATA) question was used to evaluate the taste, which are considered to be more flexible and less time-consuming methodologies than traditional ones.

Other authors have analyzed the association between consumer perceptions of food quality and their acceptance of enhanced meat products and novel packaging [ 5 ]. This study was conducted using the Computer-Assisted Personal Interview (CAPI) method in a random group of 1009 respondents. The results suggested that educating consumers may improve their acceptance of product enhancement, as concerns about the addition of food preservatives may lead them to reject enhanced products. Biro et al. [ 6 ] also study sensory evaluation (CATA) together with technological parameters (color, hardness, etc.) to assess consumer acceptance of insects as food. These authors valued the acceptance or rejection of oat biscuits enriched with insect powders [ Acheta domesticus (house cricket)]. An important part of this study was to discover how the insect content of the products affects the overall liking (OAL) and which attributes are the drivers of liking.

The potential to design natural tea-infused set yoghurt was also investigated using quantitative descriptive profile analysis and a consumer hedonic test together with technological properties (texture analysis, yield stress, physical stability and color) [ 7 ]. Three types of tea ( Camellia sinensis )—black, green and oolong tea—as well as lemon balm ( Melissa officinalis L.), were used to produce set yoghurt and compare this with plain yoghurt. Both types of yoghurt were also characterized by a high consumer willingness to buy. Principal component analysis (PCA) was used to analyze differences between samples and the correlation of selected variables.

Studies of different consumers from different countries have also been presented in this SI. Grasso and Jaworska [ 8 ] studied the presence of hybrid meat products in the UK market, extracted UK online consumer reviews on hybrid meat products and gathered preliminary consumer insights, utilizing the tools and techniques of corpus linguistics. These studies are of great importance since hybrid meat products could open up new business opportunities for the food industry and a greater diversity of products for consumers. Silva et al. [ 9 ] also carried out a preliminary inquiry with 155 consumers from Região de Lisboa and Vale do Tejo (Center of Portugal) to assess fish consumption, the applicability of fish product innovation and the importance of valorizing discarded fish (blue jack mackerel, black seabream, piper gurnard, etc.). Five products (black seabream ceviche, smoked blue jack mackerel pâté, dehydrated piper gurnard, fried boarfish and comber pastries) were developed and investigated for their sensory characteristics and consumer liking by hedonic tests by 90 consumers. The knowledge of consumers’ interests acquired with the data from the initial survey allowed for the development of new fish products, with the addition or substitution of the fish species under study through the reformulation of existing products that are familiar to the consumer. Głuchowski et al. [ 10 ] also used descriptive quantitative analyses and consumer tests to explore sensory characteristics, consumer liking of key attributes, their declared sensations and emotions, as well as consumers’ facial expressions when responding to the six dishes prepared using lemon or tomatoes and made in the traditional (classical), molecular and Note by Note (NbN) versions. Tests included a nine-point hedonic scale for degree of liking a dish, check-all-that-apply (CATA) for declared sensations and FaceReader for facial expressions. The influence of factors associated with consumer attitudes toward new food and willingness to try the dishes in the future were also determined. Such an approach was valuable in modifying features such as the taste, flavor and texture of dishes according to consumers’ points of view. The goal of the Kumar et al. [ 11 ] study was to highlight one strategic framework to find white spaces in the marketplace and then develop new snack texture concepts to fit the sensory concepts identified as white spaces. This paper shows one method of how new product concepts can be developed using such sensory science tools as product categorization, projective mapping and descriptive profiling. This research approach for novel and distinctive market opportunities displays an innovative, practical side of NPD research as a complement. The methodology produced in this study can be used by food product developers to explore new opportunities in the global marketplace.

Finally, Świąder and Marczewska [ 12 ] explored the current trends of using sensory evaluation in NPD in the food industry in countries that belong to the EIT Regional Innovation Scheme (RIS). A computer-assisted self-interviewing (CASI) technique for survey data collection was used. The research results showed that almost 70% of companies apply sensory evaluation methods in NPD and more so the bigger the company. Here, it was also noted that most companies prefer consumer (affective) tests to expert tests.

Conclusions and Future Outlook

To summarize, the works collected in this Special Issue serve to offer an interesting contribution to the field and a better understanding of sensorial techniques and consumer research as a tool for innovation in new product development in order to satisfy the demands of consumers who increasingly seek new flavors, pleasure and fun and for companies to gain a better market position. Therefore, this SI is very useful for both present and future use for the different players involved in this kind of product development (industry, companies, researchers, scientists, marketing, merchandising, consumers, etc.). However, although these techniques have evolved substantially in recent years, the potential of using sensory evaluation methods is not yet fully exploited. A great future is predicted for them and a significant development is expected in the coming years. Finding new opportunities in food product development is a challenging assignment.

Acknowledgments

We thank to all the authors who have collaborated and have made this special issue possible.

Author Contributions

C.R.-C.; A.M.H. writing—original draft preparation; C.R.-C.; A.M.H. writing—review and editing; C.R.-C.; A.M.H. funding acquisition. All authors have read and agreed to the published version of the manuscript.

This research was funded by the Spanish Ministry of Science and Innovation (PID2019-107542RB-C21), by the CSIC Intramural projects (grant numbers 201470E073 and 202070E242), CYTED (Reference 119RT0568; Healthy Meat Network).

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A comprehensive review of food rheology: analysis of experimental, computational, and machine learning techniques

• Review Article
• Published: 28 October 2023
• Volume 35 , pages 279–306, ( 2023 )

Cite this article

• Osita Sunday Nnyigide 1 &
• Kyu Hyun   ORCID: orcid.org/0000-0001-5129-5169 1  

449 Accesses

Explore all metrics

The main objective of food rheology is to identify food structure and texture by rheological measurements, thereby reducing the requirement for sensory analysis in evaluating food products. However, determining food texture and structure exclusively from rheological measurements can be challenging because of the complicated composition and structure of food, as well as the complexities of factoring in the changes that occur during food mastication. This article provides a comprehensive review of the current experimental, computational and machine learning techniques used in food rheology to probe the structure and texture of food products. The textural attributes and structural information that can be inferred from each measurement technique is discussed and recent studies that carried out the measurements are highlighted. Also presented in this review are the recent progress in the experimental techniques and challenges.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA) Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Introduction: Measuring Rheological Properties of Foods

The Possibility of Applying a Universal Testing Machine for Evaluating Food Textures

Rheological and Thickening Properties

Nnyigide OS (2019) A Study of Bovine Serum Albumin (BSA) Hydrogel with Various Chemical Denaturants by Rheological Measurements and Molecular Dynamics Simulation, Ph.D. Thesis. Pusan National University.

Borwankar RP (1992) Food texture and rheology: a tutorial review. J Food Eng 16:1–16. https://doi.org/10.1016/0260-8774(92)90016-Y

Article   Google Scholar  

Melito HS, Daubert CR (2011) Rheological innovations for characterizing food material properties. Annu Rev Food Sci Technol 2:153–179. https://doi.org/10.1146/annurev-food-022510-133626

Article   CAS   Google Scholar  

Tabilo-Munizaga G, Barbosa-Cánovas GV (2005) Rheology for the food industry. J Food Eng 67:147–156. https://doi.org/10.1016/j.jfoodeng.2004.05.062

Ipsen R (2008 ) Food rheology: A personal view of the past and future. Annu trans Nord Rheol Soc 16

Zhu Y, Gao H, Liu W, Zou L, McClements DJ (2020) A review of the rheological properties of dilute and concentrated food emulsions. J Texture Stud 51:45–55. https://doi.org/10.1111/jtxs.12444

Diamante L, Umemoto M (2015) Rheological properties of fruits and vegetables: a review. Int J Food Prop 18(6):1191–1210. https://doi.org/10.1080/10942912.2014.898653

Wang Y, Selomulya C (2022) Food rheology applications of large amplitude oscillation shear (LAOS). Trends Food Sci Technol 127:221–244. https://doi.org/10.1016/j.tifs.2022.05.018

Joyner HS (2021) Nonlinear (large-amplitude oscillatory shear) rheological properties and their impact on food processing and quality. Annu Rev Food Sci Technol 12:591–609. https://doi.org/10.1146/annurev-food-061220-100714

Nnyigide OS, Hyun K (2018) Effects of anionic and cationic surfactants on the rheological properties and kinetics of bovine serum albumin hydrogel. Rheol Acta 57:563–573. https://doi.org/10.1007/s00397-018-1100-1

Nnyigide OS, Hyun K (2020) The rheological properties and gelation kinetics of corn starch/bovine serum albumin blend. Korea Aust Rheol J 32:71–78. https://doi.org/10.1007/s13367-020-0008-3

Nnyigide OS, Hyun K (2018) Rheo-kinetics of bovine serum albumin in catanionic surfactant systems. Korean J Chem Eng 35:1969–1978. https://doi.org/10.1007/s11814-018-0128-3

Faber TJ, Van Breemen LCA, McKinley GH (2017) From firm to fluid—structure-texture relations of filled gels probed under large amplitude oscillatory shear. J Food Eng 210:1–18. https://doi.org/10.1016/j.jfoodeng.2017.03.028

Fancey KS (2005) A mechanical model for creep, recovery and stress relaxation in polymeric materials. J Mater Sci 40(18):4827–4831. https://doi.org/10.1007/s10853-005-2020-x

Freer EM, Yim KS, Fuller GG, Radke CJ (2004) Interfacial rheology of globular and flexible proteins at the hexadecane/water interface: comparison of shear and dilatation deformation. J Phys Chem B 108(12):3835–3844. https://doi.org/10.1021/jp037236k

Heyer P, Läuger J (2008 ) Interfacial Shear Rheology of Films Formed by Coffee. Annu trans Nord Rheol Soc 16.

Joyner HS (2018) Explaining food texture through rheology. Curr Opin Food Sci 21:7–14. https://doi.org/10.1016/j.cofs.2018.04.003

Hyun K, Wilhelm M, Klein CO, Cho KS, Nam JG, Ahn KH, Lee SJ, Ewoldt RH, McKinley GH (2011) A review of nonlinear oscillatory shear tests: analysis and application of large amplitude oscillatory shear (LAOS). Prog Polym Sci 36:1697–1753. https://doi.org/10.1016/j.progpolymsci.2011.02.002

Du J, Ohtani H, Owens CE, Zhang L, Ellwood K, McKinley GH (2021) An improved capillary breakup extensional rheometer to characterize weakly rate-thickening fluids: applications in synthetic automotive oils. J Nonnewton Fluid Mech 291:104496. https://doi.org/10.1016/j.jnnfm.2021.104496

Huang Q (2022) When polymer chains are highly aligned: a perspective on extensional rheology. Macromolecules 55(3):715–727. https://doi.org/10.1021/acs.macromol.1c02262

Lee CW, Rogers SA (2017) A sequence of physical processes quantified in LAOS by continuous local measures. Korea-Aust Rheol J 29(4):269–279. https://doi.org/10.1007/s13367-017-0027-x

Melito HS, Daubert CR, Foegeding EA (2013) Relating large amplitude oscillatory shear and food behavior: correlation of nonlinear viscoelastic, rheological, sensory and oral processing behavior of whey protein isolate/κ-carrageenan gels: relating LAOS and food behavior. J Food Process Eng 36(4):521–534. https://doi.org/10.1111/jfpe.12015

Yue Q, Li M, Liu C, Li L, Zheng X, Bian K (2020) Comparison of uniaxial/biaxial extensional rheological properties of mixed dough with traditional rheological test results: relationship with the quality of steamed bread. Int J Food Sci Technol 55(7):2751–2761. https://doi.org/10.1111/ijfs.14528

Del Nobile MA, Chillo S, Mentana A, Baiano A (2007) Use of the generalized maxwell model for describing the stress relaxation behavior of solid-like foods. J Food Eng 78(3):978–983. https://doi.org/10.1016/j.jfoodeng.2005.12.011

Vithanage CR, Grimson MJ, Smith BG, Wills PR (2011) Creep test observation of viscoelastic failure of edible fats. J Phys Conf Ser 286:012008. https://doi.org/10.1088/1742-6596/286/1/012008

Yu C, Gunasekaran S (2001) Correlation of dynamic and steady flow viscosities of food materials. Appl Rheol 11(3):134–140. https://doi.org/10.1515/arh-2001-0008

Song HY, Nnyigide OS, Salehiyan R, Hyun K (2016) Investigation of nonlinear rheological behavior of linear and 3-arm star 1,4-Cis-polyisoprene (PI) under medium amplitude oscillatory shear (MAOS) flow via FT-rheology. Polymer 104:268–278. https://doi.org/10.1016/j.polymer.2016.04.052

Hyun K, Kim SH, Ahn KH, Lee SJ (2002) Large amplitude oscillatory shear as a way to classify the complex fluids. J Nonnewton Fluid Mech 107(1):51–65. https://doi.org/10.1016/S0377-0257(02)00141-6

Song HY, Park SY, Kim S, Youn HJ, Hyun K (2022) Linear and nonlinear oscillatory rheology of chemically pretreated and non-pretreated cellulose nanofiber suspensions. Carbohydr Polym 275:118765. https://doi.org/10.1016/j.carbpol.2021.118765

Van Den Berg L, Jan Klok H, Van Vliet T, Van Der Linden E, Van Boekel MAJS, Van De Velde F (2008) Quantification of a 3D structural evolution of food composites under large deformations using microrheology. Food Hydrocoll 22(8):1574–1583. https://doi.org/10.1016/j.foodhyd.2007.11.002

Rogers SA (2017) In search of physical meaning: defining transient parameters for nonlinear viscoelasticity. Rheol Acta 56(5):501–525. https://doi.org/10.1007/s00397-017-1008-1

Wilhelm M (2002) Fourier-Transform Rheology. Macromol Mater Eng 287:83–105. https://doi.org/10.1002/1439-2054(20020201)287:2%3c83::AID-MAME83%3e3.0.CO;2-B

Kohyama K, Ishihara S, Nakauma M, Funami T (2021) Fracture phenomena of soft gellan gum gels during compression with artificial tongues. Food Hydrocoll 112:106283. https://doi.org/10.1016/j.foodhyd.2020.106283

Ishihara S, Isono M, Nakao S, Nakauma M, Funami T, Hori K, Ono T, Kohyama K, Nishinari K (2014) Instrumental uniaxial compression test of gellan gels of various mechanical properties using artificial tongue and its comparison with human oral strategy for the first size reduction. J Texture Stud 45:354–366. https://doi.org/10.1111/jtxs.12080

Zhou B, Tobin JT, Drusch S, Hogan SA (2021) Dynamic adsorption and interfacial rheology of whey protein isolate at oil-water interfaces: effects of protein concentration. PH and Heat Treatment Food Hydrocoll 116:106640. https://doi.org/10.1016/j.foodhyd.2021.106640

Moore PB, Langley K, Wilde PJ, Fillery-Travis A, Mela DJ (1998) Effect of emulsifier type on sensory properties of oil-in-water emulsions. J Sci Food Agric 76:469–476. https://doi.org/10.1002/(SICI)1097-0010(199803)76:3%3c469::AID-JSFA974%3e3.0.CO;2-Y

El Omari Y, Yousfi M, Duchet-Rumeau J, Maazouz A (2022) Recent advances in the interfacial shear and dilational rheology of polymer systems: from fundamentals to applications. Polymers 14(14):2844. https://doi.org/10.3390/polym14142844

Murray BS (2002) Interfacial rheology of food emulsifiers and proteins. Curr Opin Colloid Interface 7:426–431. https://doi.org/10.1016/S1359-0294(02)00077-8

Zhou Y, Mei Y, Luo T, Chen W, Zhong Q, Chen H, Chen W (2021) Study on the relationship between emulsion properties and interfacial rheology of sugar beet pectin modified by different enzymes. Molecules 26(9):2829. https://doi.org/10.3390/molecules26092829

Wang S, Zhou B, Yang X, Niu L, Li S (2022) Tannic acid enhanced the emulsion stability, rheology and interface characteristics of clanis bilineata tingtauica mell protein stabilised oil-in-water emulsion. Int J of Food Sci Tech 57(8):5228–5238. https://doi.org/10.1111/ijfs.15839

Wang S, Yang J, Shao G, Qu D, Zhao H, Yang L, Zhu L, He Y, Liu H, Zhu D (2020) Soy protein isolated-soy hull polysaccharides stabilized O/W emulsion: effect of polysaccharides concentration on the storage stability and interfacial rheological properties. Food Hydrocoll 101:105490. https://doi.org/10.1016/j.foodhyd.2019.105490

Yang J, Thielen I, Berton-Carabin CC, van der Linden E, Sagis LMC (2020) Nonlinear interfacial rheology and atomic force microscopy of air-water interfaces stabilized by whey protein beads and their constituents. Food Hydrocoll 101:105466. https://doi.org/10.1016/j.foodhyd.2019.105466

Geonzon LC, Kobayashi M, Tassieri M, Bacabac RG, Adachi Y, Matsukawa S (2023) Microrheological properties and local structure of ι-carrageenan gels probed by using optical tweezers. Food Hydrocoll 137:108325. https://doi.org/10.1016/j.foodhyd.2022.108325

Mettu S, Zhou M, Tardy BL, Ashokkumar M, Dagastine RR (2016) Temperature dependent mechanical properties of air, oil and water filled microcapsules studied by atomic force microscopy. Polymer 102:333–341. https://doi.org/10.1016/j.polymer.2016.02.046

Papagiannopoulos A, Sotiropoulos K, Pispas S (2016) Particle tracking microrheology of the power-law viscoelasticity of xanthan solutions. Food Hydrocoll 61:201–210. https://doi.org/10.1016/j.foodhyd.2016.05.020

Jin H, Chen J, Zhang J, Sheng L (2021) Impact of phosphates on heat-induced egg white gel properties: Texture, water state, micro-rheology and microstructure. Food Hydrocoll 110:106200. https://doi.org/10.1016/j.foodhyd.2020.106200

Bakhsh A, Elobeid T, Avci E, Demirci M, Taylan O, Ozmen D, Meral R, Yilmaz MT (2022) A tracer microrheology for determination of viscoelasticity of dilute ovalbumin colloids. Emerg Mater Res 11(1):98–104. https://doi.org/10.1680/jemmr.20.00282

Xu J, Boddu VM, Liu SX (2023) Microrheological investigation of low-viscosity barley β-glucan (LVBBG) solutions by diffusion wave spectroscopy (DWS). Am J Food Technol 18:8–15. https://doi.org/10.3923/ajft.2023.8.15

Yang N, Lv R, Jia J, Nishinari K, Fang Y (2017) Application of microrheology in food science. Annu Rev Food Sci Technol 8(1):493–521. https://doi.org/10.1146/annurev-food-030216-025859

Nath PC, Debnath S, Sridhar K, Inbaraj BS, Nayak PK, Sharma M (2022) A comprehensive review of food hydrogels: principles, formation mechanisms, microstructure, and its applications. Gels 9(1):1. https://doi.org/10.3390/gels9010001

Lodge JFM, Heyes DM (1999) Rheology of transient colloidal gels by brownian dynamics computer simulation. J Rheol 4(1):219–244. https://doi.org/10.1122/1.550984

Whittle M, Dickinso E (1997) Brownian Dynamics simulation of gelation in soft sphere systems with irreversible bond formation. Mol Phys 90(5):739–758. https://doi.org/10.1080/002689797172101

Carpen IC, Brady JF (2005) Microrheology of colloidal dispersions by brownian dynamics simulations. J Rheol 49(6):1483–1502. https://doi.org/10.1122/1.2085174

Santos PHS, Campanella OH, Carignano MA (2010) Brownian dynamics study of gel-forming colloidal particles. J Phys Chem B 114(41):13052–13058. https://doi.org/10.1021/jp105711y

Liu L, Zhang Y, Dao L, Huang X, Qiu R, Pang P, Wu S (2023) Efficient and accurate multi-scale simulation for viscosity mechanism of konjac glucomannan colloids. Int J Biol Macromol 236:123992. https://doi.org/10.1016/j.ijbiomac.2023.123992

Azevedo TN, Rizzi LG (2020) Microrheology of filament networks from brownian dynamics simulations. J Phys Conf Ser 1483(1):012001. https://doi.org/10.1088/1742-6596/1483/1/012001

Chen JY, Li Z, Szlufarska I, Klingenberg DJ (2021) Rheology and structure of suspensions of spherocylinders via brownian dynamics simulations. J Rheol 65(2):273–288. https://doi.org/10.1122/8.0000155

Park JD, Rogers SA (2020) Rheological manifestation of microstructural change of colloidal gel under oscillatory shear flow. Phys Fluids 32(6):063102. https://doi.org/10.1063/5.0006792

Sánchez-Diáz LE, Iwashita T, Egami T, Chen WR (2019) Connection between the anisotropic structure and nonlinear rheology of sheared colloidal suspensions investigated by brownian dynamics simulations. J Phys Commun 3(5):055018. https://doi.org/10.1088/2399-6528/ab1e79

Liu L, Zhou N, Yang Y, Huang X, Qiu R, Pang J, Wu S (2022) Rheological properties of konjac glucomannan composite colloids in strong shear flow affected by mesoscopic structures: Multi-scale simulation and experiment. Colloids Surf A Physicochem Eng Asp 652:129850. https://doi.org/10.1016/j.colsurfa.2022.129850

Nnyigide OS, Lee SG, Hyun K (2018) Exploring the differences and similarities between urea and thermally driven denaturation of bovine serum albumin: intermolecular forces and solvation preferences. J Mol Model 24:75. https://doi.org/10.1007/s00894-018-3622-y

Nnyigide OS, Hyun K (2021) Molecular dynamics studies of the protective and destructive effects of sodium dodecyl sulfate in thermal denaturation of hen egg-white lysozyme and bovine serum albumin. J Biomol Struct Dyn 39:1106–1120. https://doi.org/10.1080/07391102.2020.1726209

Nnyigide OS, Lee SG, Hyun K (2019) In Silico Characterization of the binding modes of surfactants with bovine serum albumin. Sci Rep 9:10643. https://doi.org/10.1038/s41598-019-47135-2

Nnyigide OS, Nnyigide TO, Lee SG, Hyun K (2022) Protein repair and analysis server: a web server to repair PDB structures, add missing heavy atoms and hydrogen atoms, and assign secondary structures by amide interactions. J Chem Inf Model 62:4232–4246. https://doi.org/10.1021/acs.jcim.2c00571

Ore Areche F, Flores DDC, Quispe-Solano MA, Nayik GA, Cruz-Porta EADL, Rodríguez AR, Roman AV, Chweya R (2023) Formulation, characterization, and determination of the rheological profile of loquat compote Mespilus Germánica L. through sustenance artificial intelligence. J Food Qual 2023:1–12. https://doi.org/10.1155/2023/3344539

Lee S, Kim SR, Lee HJ, Kim BS, Oh H, Lee JB, Park K, Yi YJ, Park CH, Park JD (2022) Predictive model for the spreadability of cosmetic formulations based on large amplitude oscillatory shear (LAOS) and machine learning. Phys Fluids 34(10):103109. https://doi.org/10.1063/5.0117989

Schmidt J, Marques MRG, Botti S, Marques MAL (2019) Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater 5(1): 83. https://doi.org/10.1038/s41524-019-0221-0

Guiné, RPF (2019) The use of artificial neural networks (ANN) in food process engineering. Int J Food Eng 5: 15–21. https://doi.org/10.18178/ijfe.5.1.15-21

Abang Zaidel DN, Chin NL, Yusof YA (2010) A Review on rheological properties and measurements of dough and gluten. J Appl Sci 10:2478–2490. https://doi.org/10.3923/jas.2010.2478.2490

Xia W, Siu WK, Sagis LMC (2021) Linear and non-linear rheology of heat-set soy protein gels: effects of selective proteolysis of β-conglycinin and glycinin. Food Hydrocoll 120:106962. https://doi.org/10.1016/j.foodhyd.2021.106962

Deblais A, Hollander ED, Boucon C, Blok AE, Veltkamp B, Voudouris P, Versluis P, Kim HJ, Mellema M, Stieger M, Bonn D, Velikov KP (2021) Predicting thickness perception of liquid food products from their non-newtonian rheology. Nat Commun 12(1):6328. https://doi.org/10.1038/s41467-021-26687-w

Agoda-Tandjawa G, Le Garnec C, Boulenguer P, Gilles M, Langendorff V (2017) Rheological behavior of starch/carrageenan/milk proteins mixed systems: role of each biopolymer type and chemical characteristics. Food Hydrocoll 73:300–312. https://doi.org/10.1016/j.foodhyd.2017.07.012

Contador L, Díaz M, Hernández E, Shinya P, Infante R ( 2017 ) The relationship between instrumental tests and sensory determinations of peach and nectarine texture. Eur J Hoticultural Sci 81:189 196. https://doi.org/10.17660/eJHS.2016/81.4.1

Nnyigide OS, Oh Y, Song HY, Park E, Choi SH, Hyun K (2017) Effect of urea on heat-induced gelation of bovine serum albumin (BSA) studied by rheology and small angle neutron scattering (SANS). Korea Aust Rheol J 29:101–113. https://doi.org/10.1007/s13367-017-0012-4

Kohyama K, Ishihara S, Nakauma M, Funami T (2019) Compression test of soft food gels using a soft machine with an artificial tongue. Foods 8(6):182. https://doi.org/10.3390/foods8060182

Ishihara S, Nakao S, Nakauma M, Funami T, Hori K, Ono T, Kohyama K, Nishinari K (2013) Compression test of food gels on artificial tongue and its comparison with human test. J Texture Stud 44(2):104–114. https://doi.org/10.1111/jtxs.12002

Melito HS, Daubert CR, Foegeding EA (2013) Relating Large Amplitude Oscillatory Shear and Food Behavior: Correlation of Nonlinear Viscoelastic, Rheological, Sensory and Oral Processing Behavior of Whey Protein Isolate/κ-Carrageenan Gels. J Food Process Eng 36: 521–534. https://doi.org/10.1111/jfpe.12015

Morell P, Hernando I, Llorca E, Fiszman S (2015) Yogurts with an increased protein content and physically modified starch: rheological, structural, oral digestion and sensory properties related to enhanced satiating capacity. Food Res Int 70:64–73. https://doi.org/10.1016/j.foodres.2015.01.024

Varela P, Pintor A, Fiszman S (2014) How hydrocolloids affect the temporal oral perception of ice cream. Food Hydrocoll 36:220–228. https://doi.org/10.1016/j.foodhyd.2013.10.005

Pereira EPR, Cavalcanti RN, Esmerino EA, Silva R, Guerreiro LRM, Cunha RL, Bolini HMA, Meireles MA, Faria JAF, Cruz AG (2016) Effect of incorporation of antioxidants on the chemical, rheological, and sensory properties of probiotic petit suisse cheese. J Dairy Sci 99:1762–1772. https://doi.org/10.3168/jds.2015-9701

Basu S, Shivhare US (2013) Rheological, textural, microstructural, and sensory properties of sorbitol-substituted mango jam. Food Bioprocess Technol 6:1401–1413. https://doi.org/10.1007/s11947-012-0795-8

Li Z, Yang Z, Zhang Y, Lu T, Zhang X, Qi Y, Wang P, Xu X (2021) Innovative characterization based on stress relaxation and creep to reveal the tenderizing effect of ultrasound on wooden breast. Foods 10(1):195. https://doi.org/10.3390/foods10010195

Jakubczyk E, Kamińska-Dwórznicka A, Kot A (2022) The rheological properties and texture of agar gels with canola oil—effect of mixing rate and addition of lecithin. Gels 8(11):738. https://doi.org/10.3390/gels8110738

Okonkwo VC, Kwofie EM, Mba OI, Ngadi MO (2021) Impact of thermos sonication on quality indices of starch-based sauces. Ultrason Sonochem 73:105473. https://doi.org/10.1016/j.ultsonch.2021.105473

Nnyigide OS, Hyun K (2020) The protection of bovine serum albumin against thermal denaturation and gelation by sodium dodecyl sulfate studied by rheology and molecular dynamics simulation. Food Hydrocoll 103:105656. https://doi.org/10.1016/j.foodhyd.2020.105656

Stading M (2021) Bolus rheology of texture-modified food: effect of degree of modification. J Texture Stud 52:540–551. https://doi.org/10.1111/jtxs.12598

Melito HS, Foegeding DCREA (2013) Relating LAOS and food behavior. J Food Process Eng 36:521–534. https://doi.org/10.1111/jfpe.12015

Jung H, Oyinloye TM, Yoon WB (2022) Evaluating the mechanical response of agarose-xanthan mixture gels using tensile testing, numerical simulation, and a large amplitude oscillatory shear (LAOS) approach. Foods 11(24):4042. https://doi.org/10.3390/foods11244042

Nnyigide OS, Nnyigide TO, Hyun K (2021) The degradation of xanthan gum in ionic and non-ionic denaturants studied by rheology and molecular dynamics simulation. Carbohydr Polym 251:117061. https://doi.org/10.1016/j.carbpol.2020.117061

Schreuders FKG, Schlangen M, Bodnár I, Erni P, Boom RM, Jan van der Goot A (2022) Structure formation and non-linear rheology of blends of plant proteins with pectin and cellulose. Food Hydrocoll 124:107327. https://doi.org/10.1016/j.foodhyd.2021.107327

Hyun K, Wilhelm M (2009) Establishing a new mechanical nonlinear coefficient Q from FT-rheology: first investigation of entangled linear and comb polymer model systems. Macromolecules 42(1):411–422. https://doi.org/10.1021/ma8017266

Pearson DS, Rochefort WE (1982) Behavior of concentrated polystyrene solutions in large-amplitude oscillating shear fields. J polym sci 20(1):83–98. https://doi.org/10.1002/pol.1982.180200107

Cho KS, Hyun K, Ahn KH, Lee SJ (2005) A geometrical interpretation of large amplitude oscillatory shear response. J Rheol 49(3):747–758. https://doi.org/10.1122/1.1895801

Ewoldt RH, Hosoi AE, McKinley GH (2008) New measures for characterizing nonlinear viscoelasticity in large amplitude oscillatory shear. J Rheol 52(6):1427–1458. https://doi.org/10.1122/1.2970095

Erturk MY, Rogers SA, Kokini J (2022) Comparison of sequence of physical processes (SPP) and fourier transform coupled with chebyshev polynomials (FTC) methods to interpret large amplitude oscillatory shear (LAOS) response of viscoelastic doughs and viscous pectin solution. Food Hydrocoll 128:107558. https://doi.org/10.1016/j.foodhyd.2022.107558

Jimenez LN, Martínez Narváez CDV, Sharma V (2020) Capillary breakup and extensional rheology response of food thickener cellulose gum (NaCMC) in salt-free and excess salt solutions. Phys Fluids 32(1):012113. https://doi.org/10.1063/1.5128254

Hadde EK, Cichero JAY, Zhao S, Chen W, Chen J (2019) The importance of extensional rheology in bolus control during swallowing. Sci Rep 9(1):16106. https://doi.org/10.1038/s41598-019-52269-4

Dinic J, Jimenez LN, Sharma V (2017) Pinch-off dynamics and dripping-onto-substrate (DoS) rheometry of complex fluids. Lab Chip 17(3):460–473. https://doi.org/10.1039/C6LC01155A

Renardy M (1995) A numerical study of the asymptotic evolution and breakup of Newtonian and viscoelastic jets. J Non-Newton Fluid Mech 59:267–282. https://doi.org/10.1016/0377-0257(95)01375-6

Marconati M, Ramaioli M (2020) The role of extensional rheology in the oral phase of swallowing: an in vitro study. Food Funct 11(5):4363–4375. https://doi.org/10.1039/C9FO02327E

Hadde EK, Nicholson TM, Cichero JAY (2020) Evaluation of thickened fluids used in dysphagia management using extensional rheology. Dysphagia 35:242–252. https://doi.org/10.1007/s00455-019-10012-1

Yue Q, Li M, Liu C, Li L, Zheng X, Bian K (2020) Extensional rheological properties in mixed and fermented/rested dough and relationships with steamed bread quality. J Cereal Sci 93:102968. https://doi.org/10.1016/j.jcs.2020.102968

Derkanosova NM, Stakhurlova AA, Vukic M, Vujadinovic D (2022) Rheological properties of composite mixtures from wheat and amaranth flour. IOP Conf Ser Earth Environ Sci 954(1):012079. https://doi.org/10.1088/1755-1315/954/1/012079

Ravera F, Loglio G, Kovalchuk VI (2010) Interfacial dilational rheology by oscillating bubble/drop methods. Curr Opin Colloid Interface 15(4):217–228. https://doi.org/10.1016/j.cocis.2010.04.001

Wijmans CM, Dickinson E (1998) Simulation of interfacial shear and dilatational rheology of an adsorbed protein monolayer modeled as a network of spherical particles. Langmuir 14(25):7278–7286. https://doi.org/10.1021/la980687p

Meleties M, Martineau RL, Gupta MK, Montclare JK (2022) Particle-based microrheology as a tool for characterizing protein-based materials. ACS Biomater Sci Eng 8(7):2747–2763. https://doi.org/10.1021/acsbiomaterials.2c00035

Nnyigide TO, Nnyigide OS, Hyun K (2023) Rheological and molecular dynamics simulation studies of the gelation of human serum albumin in anionic and cationic surfactants. Korean J Chem Eng. https://doi.org/10.1007/s11814-023-1513-0

Lee YJ, Jin H, Kim S, Myung JS, Ahn KH (2021) Brownian dynamics simulation on orthogonal superposition rheology: time-shear rate superposition of colloidal gel. J Rheol 65(3):337–354. https://doi.org/10.1122/8.0000161

Nnyigide OS, Hyun K (2016) Effect of urea and temperature on the molecular dynamics of bovine serum albumin in heavy and light water. J Chem Technol Metall 51:147–149

CAS   Google Scholar  

Nnyigide OS, Hyun K (2023) Charge-induced low-temperature gelation of mixed proteins and the effect of pH on the gelation: a spectroscopic, rheological and coarse-grained molecular dynamics study. Colloids Surf B 230:113527. https://doi.org/10.1016/j.colsurfb.2023.113527

Saeidirad MH, Rohani A, Zarifneshat S (2013) Predictions of viscoelastic behavior of pomegranate using artificial neural network and maxwell model. Comput Electron Agric 98:1–7. https://doi.org/10.1016/j.compag.2013.07.009

Torkashvand AM, Ahmadi A, Nikravesh NL (2017) Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR). J Integr Agric 16(7):1634–1644. https://doi.org/10.1016/S2095-3119(16)61546-0

Toker OS, Dogan M (2013) Effect of temperature and starch concentration on the creep/recovery behaviour of the grape molasses: modelling with ANN, ANFIS and response surface methodology. Eur Food Res Technol 236(6):1049–1061. https://doi.org/10.1007/s00217-013-1959-0

Al-Mahasneh M, Aljarrah M, Rababah T, Alu’datt M, (2016) Application of hybrid neural fuzzy system (ANFIS) in food processing and technology. Food Eng Rev 8(3):351–366. https://doi.org/10.1007/s12393-016-9141-7

Al-Mahasneh MA, Rababah TM, Ma’Abreh AS ( 2013 ) Evaluating the Combined Effect of Temperature, Shear Rate and Water Content on Wild-Flower Honey Viscosity Using Adaptive Neural Fuzzy Inference System and Artificial Neural Networks. J Food Process Eng 36(4): 510–520. https://doi.org/10.1111/jfpe.12014

Jeong S, Kim H, Lee S (2021) Rheology-based classification of foods for the elderly by machine learning analysis. Appl Sci 11(5):2262. https://doi.org/10.3390/app11052262

Download references

Funded by Pusan National University (2-Year Research Grant)

Author information

Authors and affiliations.

School of Chemical Engineering, Pusan National University, Busan, 46241, Korea

Osita Sunday Nnyigide & Kyu Hyun

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Kyu Hyun .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Nnyigide, O.S., Hyun, K. A comprehensive review of food rheology: analysis of experimental, computational, and machine learning techniques. Korea-Aust. Rheol. J. 35 , 279–306 (2023). https://doi.org/10.1007/s13367-023-00075-w

Download citation

Received : 26 July 2023

Revised : 10 September 2023

Accepted : 12 September 2023

Published : 28 October 2023

Issue Date : November 2023

DOI : https://doi.org/10.1007/s13367-023-00075-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Computer simulation
• Machine learning
• Find a journal
• Publish with us
• Track your research

ORIGINAL RESEARCH article

Evaluation of vegetable green logistics in lanling county of china based on dematel-anp-fce model provisionally accepted.

• 1 Qingdao Agricultural University, China

The final, formatted version of the article will be published soon.

Introduction: Due to the problems of greenhouse gas emissions, noise pollution, and vegetable waste pollution during the transportation of vegetables, it is not conducive to the sustainable development of the environment. To mitigate the pollution of the environment during transportation, vegetable green logistics plays a pivotal role in promoting both environmental sustainability and high-quality economic development. Therefore, it is crucial to evaluate the development of vegetable green logistics.Methods: This research builds a DEMATEL-ANP-FCE model to scientifically assess the development of vegetable green logistics in Lanling County. In the first place, the model uses DEMATEL to verify the relationship and degree of influence between the primary indexes. In the second place, the ANP approach with Super Decisions software to determine the weights of the indexes at each level. Lastly, evaluating and scoring vegetable green logistics in Lanling County based on FCE.Results and Discussion: The results of the study show that there is an interaction relationship between the primary evaluation indexes, and its evaluation score is low, the vegetable logistics in Lanling County has not reached the degree of greening. Accordingly, the evaluation results obtained by the DEMATEL-ANP-FCE model in this work are in line with the actual situation of vegetable green logistics in Lanling County, which verifies that the model has good applicability. Moreover, managerial contributions for promoting the development of vegetable green logistics in Lanling County are put forward in response to the evaluation situation. expecting to enhance the greening level of vegetable logistical development and advance agricultural sustainability. Finally, all four questions raised in this paper are well addressed. This study also provides a new perspective and evaluation model for future related research.

Keywords: Lanling County1, vegetables2, green logistics3, DEMATEL-ANP-FCE model4, evaluation5

Received: 08 Mar 2024; Accepted: 26 Apr 2024.

Copyright: © 2024 Wang, Liu, Lu and Cui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. hongzhi Wang, Qingdao Agricultural University, Qingdao, Shandong Province, China Miss. Zhaoli Liu, Qingdao Agricultural University, Qingdao, Shandong Province, China Dr. Hailong Cui, Qingdao Agricultural University, Qingdao, Shandong Province, China

People also looked at