sensory evaluation of food research paper

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Emerging Methods for the Evaluation of Sensory Quality of Food: Technology at Service

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• Published: 29 January 2024
• Volume 2 , pages 77–90, ( 2024 )

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• Sandra S. Q. Rodrigues 1 , 2 ,
• Luís G. Dias 1 , 2 &
• Alfredo Teixeira 1 , 2  

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Purpose of Review

Sensory evaluation holds vital significance in the food sector. Typically, humans conduct sensory analysis. Humans, being the ultimate consumers, assess food traits effectively. However, human judgment is influenced by various factors. Hence, countering subjectivity is crucial for objective evaluation while retaining hedonic insights.

Recent Findings

Food’s sensory assessment primarily employs humans. Various techniques differentiate, depict, or rank food. Modern sensory tools, aiming to enhance objectivity and reliability, are emerging to supplement or supplant human assessment. This advance can bolster quality, consistency, and safety by mimicking human senses such as smell, taste, and vision, mitigating risks tied to human assessors.

This paper provides a review about sensory analysis of food using technological methodologies. A review of different technological tools to analyze sensory characteristics of food, as well as a discussion of how those technological tools can relate to humans’ perception of food is presented.

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Introduction

Sensory analysis is a scientific method used to evaluate and understand the human perception of food, drink, and other consumer products. The sensory evaluation of foods presupposes the analysis of their intrinsic and extrinsic characteristics. While the intrinsic characteristics are related to how the physicochemical characteristics of food are perceived by the sense organs, such as its appearance, aroma, texture, and flavor, using the senses of sight, smell, taste, touch, and sometimes sound, the extrinsic characteristics have a more subjective character, relating to the way consumers react to the former. We speak of sensory science in the first case and consumer science in the second. Traditional sensory evaluation methods (discriminative, descriptive, and hedonic [ 1 ]) rely on human senses to assess the quality and characteristics of a product. While these traditional methods have been effective, they can be time-consuming, expensive, and subjective due to their reliance on human evaluators. Moreover, they may not fully capture the complete range of sensory experiences associated with complex products like multi-component foods or beverages.

Recognizing the considerable time and economic investments required for training assessment panels in descriptive analysis, numerous innovative methodologies for sensory characterization emerged in the early 2000s [ 2 ]. These methodologies prove to be less time-intensive and more adaptable and can involve partially trained assessors and even consumers. They generate sensory maps that closely resemble the outcomes of traditional descriptive analysis conducted with highly skilled panels. These novel techniques, mentioned and used by various authors, employ diverse approaches, such as methods centered on the assessment of specific attributes (such as intensity scales [ 3 ], check-all-that-apply questions or CATA [ 4 , 5 , 6 , 7 , 8 ], flash profiling [ 9 ], and paired comparisons [ 10 , 11 ]), methods focused on evaluating overall differences (sorting [ 12 ], projective mapping or Napping® [ 5 , 13 , 14 , 15 ]), methods involving the comparison with product references (polarized sensory positioning [ 5 , 16 ]), and methods based on an open, comprehensive evaluation of individual products (Open-ended questions [ 17 ]). Additionally, hedonic methods such as just about right (JAR), ideal profile method (IPM), relative preference mapping (RPM), and temporal dominance of sensations (TDS), among others, were reported [ 18 ]. Nonetheless, the above methodologies still exhibit certain limitations.

In response to the need for more objective and comprehensive assessments of sensory attributes of food, and perceive how consumers react to them, emerging methods have gained attention. Novel techniques such as the use of biometric measurements (including facial expressions, heart rate, skin conductance, body temperature, and eye-tracking) [ 19 , 20 •, 21 , 22 ], virtual environments (virtual and augmented reality) [ 23 , 24 ], and artificial senses (e-nose and e-tongue [ 25 •, 26 ]) are being explored as tools to understand the complex nature of human responses in sensory tests [ 27 , 28 ••, 29 ]. Other methods include chromatography [ 30 ] and spectroscopy [ 29 , 31 ], which employ sensors to detect and quantify specific compounds associated with flavor, aroma, and texture. Digital imaging (E-eye) is another emerging method that uses cameras and algorithms to analyze the visual characteristics of products [ 25 •, 32 ]. Additionally, consumer-based methods such as social media analysis and online surveys have become valuable tools [ 33 ]. These emerging methods offer advantages such as increased objectivity, faster data collection, and the ability to capture a broader range of sensory experiences. However, they also have limitations, such as high costs and the need for specialized equipment or expertise.

Both traditional and emerging sensory evaluation methods have their strengths and weaknesses. The choice of method depends on the specific needs and goals of the food industry. By combining different methods, the industry can obtain a more comprehensive understanding of the sensory attributes of their products and make informed decisions regarding product development, marketing, and quality control. Precise sensory evaluation methods are crucial in the food industry for ensuring consistency, safety, efficiency, and compliance with regulatory standards. Failure to implement appropriate sensory analysis can lead to negative consequences such as customer dissatisfaction, compromised public health, production inefficiencies, and legal repercussions(Fig. 1 ).

The integration of technology enhances objectivity and efficiency in food sensory evaluation

The purpose of this paper is to provide a comprehensive review of emerging methods in sensory analysis that can enhance our understanding of sensory attributes in the food industry. The review focuses on various techniques, including spectroscopy, artificial senses, and biometric measurements. These methods offer innovative approaches to obtain more objective and comprehensive assessments of sensory attributes. Despite some limitations, ongoing advancements and research continue to address these challenges, making emerging methods valuable tools in enhancing product development, consumer understanding, and quality control.

• Spectroscopy

In this section, the significance of diverse spectroscopy methodologies in food analysis is elucidated, affirming their pivotal role in upholding stringent quality and safety control measures, ultimately contributing to consumer satisfaction and safety. The emergence of spectroscopic techniques presents objective, swift, and non-destructive tools for assessing food quality [ 34 ]. Within the most common spectroscopic techniques used in food science, visible and near-infrared spectroscopy, Fourier-transform infrared spectroscopy, and Raman spectroscopy in combination with chemometric methods have been used in assessing the characteristics of several products such as dairy and honey products [ 35 , 36 , 37 ], meat and seafood [ 38 , 39 , 40 , 41 ], cereals [ 42 , 43 , 44 , 45 ], vegetable oils [ 46 , 47 , 48 ], and coffee [ 49 , 50 ]. In recent years, hyperspectral imaging has emerged as a valuable tool in food science, enabling the determination of composition parameters such as moisture and protein content. A rapid, nondestructive, and noncontact analytical method is useful to assess the quality and safety of meat and meat products, vegetables and fruits, cereals, aquatic products, and others [ 51 ].

Moreover, it has been successfully utilized to study the optical properties of various food products including oils, juices, milk, yogurts, and eggs [ 52 ]. Similarly, nuclear magnetic resonance (NMR) has found significant applications in food science, food analysis, and food quality control [ 53 ]. NMR has proven particularly effective in the analysis of milk and milk products [ 54 , 55 , 56 ], enabling characterization based on geographic origin and feeding diet [ 57 ]. It has also been employed in studying the effects of freezing on pasta filata and non-pasta filata cheeses [ 58 ], as well as in the analysis of meat [ 59 , 60 ], edible oils [ 61 ], cereals and beer [ 62 , 63 ], and fruits and vegetables [ 64 ]. These advanced techniques (hyperspectral imaging and NMR) have demonstrated their efficacy in providing valuable insights into the composition, physical characteristics, and quality of various food products. Their successful applications in diverse areas of food science contribute to improved food analysis, quality control, and understanding of food properties.

The assessment of most physicochemical parameters in food is typically linked to sensory properties. When it comes to determining the ultimate quality of a product based on consumer preference, sensory analysis by trained sensory panels serves as the key. However, it is important to note that maintaining sensory-trained panels can be challenging, costly, and time-consuming. Considering these challenges, the utilization of spectroscopic techniques as non-destructive, fast, and precise methods has been explored as an alternative to traditional sensory panels. While there have been numerous studies published in this area, only a few directly relate spectroscopic and chemometric techniques to sensory traits. Table 1 shows a selection of studies that related the spectroscopy techniques and chemometrics techniques with sensory attributes in various food products.

Indirectly related but largely influencing the food sensory quality is the fat content, particularly the intramuscular fat or marbling, which is one of the most important characteristics of meat quality. It is often associated with the meat’s color, another determining factor for consumers when purchasing meat. Hyperspectral imaging has been successfully applied to characterize intramuscular fat distribution in beef and classify beef marbling with great accuracy [ 80 , 81 ], and in pork [ 82 ]. It has also been applied in lambs [ 83 ] with promising results.

For predicting the intramuscular fat, NIR spectroscopy has also been used [ 84 ] showing the potential of Vis–NIR to predict moisture and IMF using homogenized pork muscles [ 85 ] and for example to predict chemical composition in goats [ 31 ], and in beef [ 86 ]. In sheep and goats, an extensive revision on the use of non-destructive imaging and spectroscopy techniques for the assessment of meat quality was made [ 87 ].

The detection of fat content, an important factor related to fish quality, in salmon or grass carp is also achievable using hyperspectral imaging [ 88 , 89 , 90 ]. Additionally, a multispectral model has been developed to detect changes in docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) levels in fish fillets [ 91 ]. These two n-3 polyunsaturated fatty acids have been proven to offer beneficial health effects, particularly for cardiovascular and inflammatory conditions. Also, in fruits, hyperspectral technology is used to detect characteristics related to sensory quality as in apples for predicting bruise susceptibility [ 92 ]. The recent advances and applications of hyperspectral Imaging in detecting, classifying, and visualizing quality and safety attributes of fruits and vegetables were summarized in a revision by Lu et al. [ 93 ].

With the advancement of computer image processing technology, various procedures such as ultrasounds and computed tomography have been employed to obtain muscle images for assessing intramuscular fat through computer image analysis. The computer vision–based marbling assessment has been performed in beef [ 94 , 95 , 96 ], pork [ 97 , 98 ], and lamb [ 99 ] and also in cheese quality evaluation [ 100 ] or even in fruit classification [ 101 ]. Additionally, computer vision has been employed for the assessment of color grading in beef fat [ 102 ] and the color of salmon fillets [ 103 ].

Spectroscopy techniques have emerged as powerful tools for assessing food quality attributes in a rapid, non-destructive, and objective manner. While their applications in food science and sensory analysis have already shown great promise, the future holds even greater potential for these techniques to revolutionize the assessment of sensory food quality. Continued advancements in technology, data analysis, and integration with other disciplines will propel these techniques to new heights. By leveraging these tools, the food industry can optimize product development, ensure consistent quality, and meet the ever-evolving demands and preferences of consumers. Spectroscopy techniques are poised to revolutionize sensory food quality assessment and drive innovation in the field of food science.

Artificial Senses

Electronic noses and tongues.

The electronic tongue (E-tongue) and electronic nose (E-nose) are recent tools that can redefine the traditional methods of evaluating food attributes, bringing a data-driven and objective dimension to the intricate world of taste and aroma analysis. Both align with the imperative for precise and swift quality assessment of food products, driven by the paramount importance of safety considerations within the food supply chain.

The electronic tongue, known as the E-tongue, serves as a versatile instrument for decoding taste profiles and can be constructed using diverse measurement principles, including optical, electrochemical (potentiometric, impedimetric, voltammetric, or amperometric), mass-based, and spectroscopic detection techniques [ 104 ]. The potentiometric E-tongue with polymeric lipid sensors stands out as the most extensively employed option. [ 105 ]. Resembling an artificial palate, it is a multisensory apparatus designed to mimic the human gustatory system. Comprising an array of chemical sensors that respond to various taste compounds, the electronic tongue generates unique response patterns, or sensor “fingerprints,” for each food sample that is analyzed. These fingerprints are subjected to advanced statistical analyses and machine learning algorithms to decode taste attributes such as sweetness, bitterness, saltiness, sourness, and umami. The E-tongue bridges the gap between technology and human evaluation. Its ability to swiftly discern complex taste profiles showcases its potential in various food sectors, such as in the meat area. Some different examples of potentiometric electronic tongue applications in meat, poultry, and fish are pork/chicken adulteration in minced mutton [ 106 ]; salt taste intensity effect of saltiness-enhancing peptides in meat products [ 107 ]; flavor profiles of sheep breeds [ 108 ]; crayfish flesh flavor evaluation due to different dietary protein sources [ 109 ]; flavor compounds in dry-cured pork with different salt content [ 110 ]; and physical–chemical and microbiological changes in fresh pork meat under cold storage [ 111 ].

Parallelly, the E-nose mimics the human olfactory prowess by identifying and distinguishing diverse odors and aromas. This device is also an emerging approach capable of detecting and differentiating between various aromas through an array of electronic sensors (usually, semiconductor gas sensors). The data harnessed from these sensors also undergoes sophisticated algorithms to craft distinct aroma patterns, revealing the nuanced scent signatures of diverse food samples. The E-nose is also a non-destructive and low-cost system that can be applied to evaluate food quality, safety, and adulterations since it is capable of characterizing food quality factors [ 112 ].

Numerous instances exemplify its application in the realm of highly perishable muscle-based foods, including meat, poultry, and fish. These instances demonstrate its potential as a promising tool for evaluating quality attributes such as freshness, spoilage detection, and the identification of adulteration in meat products. For instance, the presence of pork in meat and meat sausages [ 113 ], the adulteration of beef meat involving varying proportions of pork meat [ 114 , 115 ], and the blending of minced mutton with duck [ 116 ] were subjected to analysis using an E-nose. The outcomes of these analyses revealed acceptable accuracy, markedly shortened detection time, and good detection efficiency. Similar results were obtained in studies centered on beef spoilage [ 117 ], pork spoilage [ 118 ], and fish meal spoilage [ 119 ], as well as the freshness of chicken [ 120 ], freshness of pork [ 121 ], and shelf-life evaluation of meat and fish products [ 122 , 123 ].

Aroma Tests

Aroma tests involve evaluating how consumers perceive food aromas by exposing them to various scents and rating their preferences and intensities. These tests utilize specialized sensory evaluation methods like olfactometry or gas chromatography–olfactometry, allowing precise presentation and analysis of aromas. Human evaluation remains essential despite using technology. While sensory approaches provide valuable data on the overall nature and intensity of aroma mixtures, they may not fully capture the intricate interactions during odor perception. To address this, researchers use models based on complex mixture compositions to understand odor nature and intensity [ 124 ]. Similarly, headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry was employed [ 125 , 126 ] to study the aroma components of dark tea varieties. Odor activity value calculation and aroma profile tests were conducted to understand the aroma characteristics of “aged fragrance” and “fungi flower aroma.” Moreover, Hawko et al. [ 124 ] utilized an experimental mixture design with sensory analysis to develop numerical models converting chemical data into sensory data. They used Langage des Nez® (LdN), an objective odor-nature description method, to characterize the odor nature for each mixture and modeled the variation in odor nature based on mixture composition. It is worth noting that aroma tests can also be associated with electronic noses (E-nose).

Electronic Eye

An electronic eye (E-eye) is a computer vision technology that converts optical images using an image sensor, eliminating subjective human vision [ 127 ]. It finds applications in food quality evaluation [ 128 ], providing fast, accurate, and non-destructive assessment of product shape, size, color, and texture [ 129 ]. This versatile technology integrates mechanics, optics, electromagnetic detection, colorimetry, spectrophotometry (discussed in detail previously), digital video, and image processing, making it valuable for monitoring visual quality changes during production [ 130 , 131 ]. Appearance and color are vital factors in consumers’ quality experience, and the E-eye ensures reliable and consistent monitoring [ 132 ]. It offers objectivity, reproducible measurements, and data storage for product traceability and does not affect product consistency or texture. The E-eye allows in-depth analysis and can correlate with sensory panel assessments [ 133 ]. Its applications extend to agricultural and food industry processes, monitoring product aging, detecting foreign substances, verifying color changes during food processing, and assisting the brewing industry in automation for optimizing product quality [ 134 , 135 , 136 , 137 ].

Conventional image analysis is highly valuable for studying meat products’ appearance characteristics due to its cost-effectiveness, consistency, speed, and accuracy in automated applications [ 138 , 139 , 140 ]. Extensive research has been conducted on the use of the E-eye for quality evaluation in various fresh meat and meat product applications. Its versatility includes assessing color [ 141 , 142 ] and monitoring color changes [ 143 , 144 ], grading marbling level [ 145 , 146 , 147 ], quality prediction [ 148 ] and control [ 32 ], defect detection [ 149 , 150 , 151 ], and sorting operations [ 152 , 153 , 154 ]. Color is a critical attribute in meat and meat products, closely linked to freshness, and color discrepancies may lead to the rejection of meat cuts [ 155 ]. Studies have shown favorable correlations between E-eye and colorimeters, with good results for lightness and reasonable regression coefficients for redness and yellowness in chicken meat [ 156 ]. Globally, E-eye technology holds the potential to bring advantages to the recent trends in automation and online control in food production [ 157 ].

Biometric Measurements

The use of neuro-physiological data in models of consumer choice is gaining popularity. Eye tracking, facial expressions, and electroencephalography (EEG) are some examples [ 19 , 29 ]. Food experiences are shaped not only by the inherent qualities of the food such as its appearance, taste, texture, and flavor but also by external factors like visual branding and the consumers’ past encounters with the food. Advancements in automated facial expression analysis and heart rate detection, utilizing remote photoplethysmography (RPPG) [ 20 •] based on changes in skin color, have made it possible to monitor food experiences through video images of the face. This type of methodology/technology can be applied remotely using video images, opening opportunities for large-scale testing in consumer science, and allowing researchers to conduct studies with a broader reach [ 20 •, 158 ].

Facial Expressions

Numerous researchers globally have extensively documented the recent sensory method of measuring facial expressions [ 20 •, 21 , 22 , 159 ]. When exposed to stimuli, humans unconsciously display emotions, often through involuntary facial movements, which researchers use to understand emotional states. While psychologists have employed this approach for a considerable time, its popularity among sensory scientists has grown due to the integration of automated mechanisms for quick response processing [ 160 ]. Software solutions like FaceReader™, developed by Noldus Information Technology in Wageningen, The Netherlands, or Affectiva Affdex®, created by Affectiva Inc. in Waltham, MA, USA, utilize built-in algorithms to detect and measure various facial movements. These algorithms then translate the captured signals into emotional responses [ 161 ]. Facial expressions have proven to be valuable in assessing consumers’ emotional reactions to a variety of products, including chocolate [ 158 , 162 ], beers [ 163 , 164 ], sports drinks [ 22 ], meat products [ 165 ], yogurt [ 166 ], and soy sauce [ 20 •]. Table 2 provides a summary of the results obtained in studies where facial expressions were utilized to evaluate food products, showing great potential for the use of this technology.

Furthermore, the findings from a study exploring whether facial expressions during food consumption could provide additional insights into temporally dynamic, implicit responses to foods beyond self-reported conscious measures [ 167 ] suggest that facial electromyography (EMG) has the potential to aid in understanding consumer responses to food in future research. However, while it showed a connection to the hedonic liking of commercially available chocolate samples, the sensory variations in these samples made it challenging to use facial EMG to distinguish samples based on mean liking, which is better achieved through self-reporting methods.

Exploration of how sensorial perceptions change with age and whether biometric analysis can help uncover unconscious consumer responses was made [ 168 ], focusing the investigation on the effects of consumer age on facial expression responses (FER) while consuming beef patties with varying firmness and taste. Two age groups were considered—younger (22 to 52) and older (60 to 76). Video images were recorded during the consumption, and the FERs were analyzed using the FaceReader™ software. Younger participants exhibited higher intensity for happy, sad, and scared expressions but lower intensity for neutral and disgusted expressions compared to older participants. Additionally, interactions between age and texture/sauce showed minimal FER variation in older individuals, while younger participants showed significant FER variation. Notably, younger participants displayed the lowest intensity of happy FER and the highest intensity of angry FER when consuming the hard patty. The addition of sauce led to a higher intensity of happy and contempt expressions in younger consumers but not in older consumers. Results demonstrated a successful differentiation between the unconscious responses of younger and older consumers by analysis of FER using FaceReader™. By utilizing automatic facial coding (face reader) and skin conductance response (SCR) measurements along with context information during the observation, olfaction, manipulation, and consumption of liquid foods by children [ 169 ], the study observed that the methodology employed successfully distinguished the three samples throughout these stages. The most effective discrimination between samples occurred during the manipulation task.

Other Autonomic Nervous System Responses

Apart from analyzing facial expressions, several other biometric techniques can be employed to evaluate the emotional responses of participants or panels toward different stimuli. These techniques, implicit measurements of food experience [ 170 ], include measuring heart rate [ 20 •], body temperature [ 159 ], and skin conductance [ 171 ]. Furthermore, a recent review focused on the application of specific neuroscientific methods in consumer sensory analysis, particularly highlighting the use of EEG and eye movements [ 172 ]. Techniques mainly used for collecting brain signals (EEG, electroencephalography; fMRI, functional magnetic resonance; and MEG, magnetoencephalography), active muscle fibers electric signals (EMG, electromyography), and heart-beat rates (ECG, electrocardiogram) [ 173 ] are referred as new non-invasive sensory approaches in food sensory analysis and market survey. The application of this novel technology has shown to be appealing in the context of sensory and consumer sciences, complementing information from explicit measures of the sensory properties themselves obtained by objective evaluation of a taste panel. As referred by Viejo et al. [ 164 ], the combination of sensory and biometric responses in consumer acceptance tests proved to be a dependable tool for beer tasting, enabling the extraction of valuable information from consumers’ physiology, behavior, and cognitive responses.

Electrophysiology, specifically using EEG, measures brain electrical activity in response to sensory stimuli, providing insights into neural patterns linked to different sensory experiences, such as sweetness or bitterness. This information helps in understanding how consumers perceive food and how sensory perception affects preferences and behavior. EEG enables predictive models for sensory perception, optimizing food formulation and packaging to cater to diverse consumer groups. Several studies have utilized electrophysiology for this purpose [ 174 , 175 , 176 , 177 , 178 ].

When applying biometric techniques to food-related studies, the results can differ based on the type of product being evaluated and the cultural background of the participants or panel involved. Table 3 resumes the results from studies made with the application of biometric measures when consuming food.

Virtual Reality and Immersive Techniques

The environment in which consumers taste their foods or beverages can significantly affect their sensory responses [ 24 ]. Traditional sensory tests use isolated booths to eliminate external interference, but some argue that this lacks real-life context [ 181 ]. To address this, researchers conduct consumer tests in real-world settings like restaurants and kitchens [ 182 ], though this can be time-consuming and costly. Immersive virtual reality offers a promising solution, allowing the simulation of various contexts in controlled laboratory facilities. This approach has been valuable in studying the sensory impact of different environments, such as tasting wines and chocolates [ 183 , 184 ]. Augmented reality is another option, integrating virtual elements into the real world to assess consumer perceptions and emotional responses, as demonstrated in tasting yogurt products [ 185 ].

In a preliminary study [ 186 ], the impact of immersive consumption contexts on food-evoked emotions was investigated using facial expressions and subjective ratings. The findings revealed the following three key points: (1) recreating physical and social consumption contexts in the laboratory influenced general and food-evoked emotions, as evident from both self-reported emotions and facial expressions; (2) both the type of food and the context independently influenced food-evoked self-reported emotions and facial expressions; (3) while there were similarities between self-reported, food-evoked emotions and facial expressions, some differences were also observed, highlighting the additional value of measuring facial expressions in understanding emotional responses to food.

Never underestimating sensory evaluation performed by humans, in a world propelled by innovation, these technologies beckon a paradigm shift in sensory analysis, fusing cutting-edge prowess with culinary finesse. As they continue to evolve, the new techniques promise a new frontier where objectivity and data-driven insights heighten our appreciation of food attributes.

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The authors are grateful to the Laboratory of Carcass and Meat Quality, Agriculture School of Polytechnic Institute of Bragança “Cantinho do Alfredo’Alfredo”.

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Rodrigues, S.S.Q., Dias, L.G. & Teixeira, A. Emerging Methods for the Evaluation of Sensory Quality of Food: Technology at Service. Curr Food Sci Tech Rep 2 , 77–90 (2024). https://doi.org/10.1007/s43555-024-00019-7

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Sensory Analysis and Consumer Research in New Meat Products Development

Claudia ruiz-capillas, ana m herrero, tatiana pintado, gonzalo delgado-pando.

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Received 2020 Dec 31; Accepted 2021 Feb 12; Collection date 2021 Feb.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/ ).

This review summarises the main sensory methods (traditional techniques and the most recent ones) together with consumer research as a key part in the development of new products, particularly meat products. Different types of sensory analyses (analytical and affective), from conventional methods (Quantitative Descriptive Analysis) to new rapid sensory techniques (Check All That Apply, Napping, Flash Profile, Temporal Dominance of Sensations, etc.) have been used as crucial techniques in new product development to assess the quality and marketable feasibility of the novel products. Moreover, an important part of these new developments is analysing consumer attitudes, behaviours, and emotions, in order to understand the complex consumer–product interaction. In addition to implicit and explicit methodologies to measure consumers’ emotions, the analysis of physiological responses can also provide information of the emotional state a food product can generate. Virtual reality is being used as an instrument to take sensory analysis out of traditional booths and configure conditions that are more realistic. This review will help to better understand these techniques and to facilitate the choice of the most appropriate at the time of its application at the different stages of the new product development, particularly on meat products.

Keywords: sensory analysis, food quality, sensory attributes, new meat product development, healthier meat products, Quantitative Descriptive Analysis (QDA), Check All That Apply (CATA), Napping, Flash Profile, Temporal Dominance of Sensations (TDS), consumer research

1. Introduction

Sensory evaluation has been used since ancient times with the purpose of accepting or rejecting food products. However, it started developing as a hard science in the last century, when sensory analysis grew rapidly together with the growth of industry and processed food. It boomed during the second world war when the food industry began to prepare food rations for soldiers and there was a need for them to be palatable. This promoted the development of different sensory techniques, and progress was made on the knowledge of human perception [ 1 , 2 ].

Sensory analysis is a scientific specialty used to assess, study, and explain the response of the particularities of food that are observed and interpreted by the panellists using their senses of sight, smell, taste, touch, and hearing [ 3 , 4 ]. This human-panellist reply is quantitatively assessed. Sensory analysis has a subjective connotation due to human involvement. In general, data collected from human perception shows great variability among the participants (cultural, educational, environmental, habits, weaknesses, variability in sensory capacities and predilection, etc.). A lot of the answers from individuals cannot be mastered in this type of analysis. Therefore, in order to limit the subjectivity of the test, the circumstances during its development have to be attentively carried out. In this way, the sensory evaluation results will be more objective [ 5 ]. Many factors have to be taken into account to address these variations and increase the accuracy of the analysis: Adequate selection of personnel, training, preparation, and information to the panel, the place where the sensorial analysis will be carried out (tasting room with individual test booths), preparation and serving of samples, labelling the samples with random numbers, etc. [ 6 , 7 ]. Moreover, and due to the potential variability, proper data analysis and interpretation is a key part of the sensory techniques. Therefore, evaluation of the results and statistical analysis are a critical part of sensory testing. This requires advanced and diverse statistical skills both from the quantitative and qualitative fields [ 8 , 9 ].

On the other hand, sensory analysis is a very useful tool for the elaboration of new products. Apart from technological and safety analysis, foods stand out for their organoleptic properties (taste, smell, texture, etc.), and they must be taken into account when innovating, since they are the properties that will determine if the consumer will purchase the product and if it will choose the same product again. More studies focused on the stakeholder requirements in the final products’ demands, such as analysis of sensory analysis and the consumers’ research, can significantly improve the quality of products and their success in the market. All these sensory studies involve human participants. Therefore, they should be performed according to the indications of the Declaration of Helsinki of 1975, checked in 2013 [ 10 ].

Based on the importance of these sensorial techniques and their great potential at the different stages of new product development, from design to commercialisation, this manuscript aims to give an overview of the sensory and consumer techniques. From the traditional sensorial techniques to the most recent ones that have been used in sensory analysis, together with studies on consumers and their fundamental importance as an analysis stage in the development of new products, particularly meat products. These include a classification, their bases, importance, and advantages and disadvantages at the different stages of new product development. The review aims to consolidate the knowledge in order to help both industry and sensory scientists.

2. Traditional Sensory Analysis

Initially, the quality control of industrial productions was carried out by one person or a small number of people. They would assess the goodness or not of a production process and its resulting product quality through precarious sensory tests. The conducted tests were changed progressively by others more disciplined and directed, which were more quantifiable and exact, more reliable, less risky, and with eliminated segmentation [ 1 , 3 ].

In general, traditional sensory analysis can be divided in two: Analytical and affective. Analytical tests, which include discriminatory and descriptive evaluations, try to describe and differentiate the products. On the other hand, affective tests try to evaluate the acceptance of the product and are divided into preference and hedonic tests [ 7 , 11 ] ( Table 1 ).

Different traditional and novel sensory tests used to evaluate food.

QDA: Quantitative Descriptive Analysis; CATA: Check-all-that-apply; FP: Flash profile; RATA: Rate-all-that-apply; PM: Projective mapping; PSP: Polarized sensory positioning.

2.1. Analytical Tests

Analytical tests can address analysis such as discrimination or differentiation between new products (are the new products different?) or product description (how different are the new products?). This will provide information that can be employed with different purposes in the optimisation of technological developments.

Discrimination (difference tests) are the simplest sensory analysis that try to dilute if the panellists are able to detect any difference between two samples, as well as the magnitude of the perceived difference between two confounding stimuli. Attributes are not valued. It is important to eliminate the component due to chance in the analysis, so an important number of evaluators must appreciate the differences between the products for them to be significant. The panellists require a certain degree of training. The most commonly used discrimination techniques are: The paired-comparison method, duo-trio, and triangular test ( Table 1 ). For example, the duo-trio presents a selection between 2 samples (A and B) establishing similarity or difference of a known pattern (R). In the triangular, the panellist must identify between 3 samples, (A, B, R), which are the same and which one is different [ 1 ].

Descriptive tests consist of a full sensory description of the products and need a trained sensory panel; the results can be quantified ( Table 1 ). For these analyses, it is necessary to establish and find descriptors that could provide maximum information about the sensory properties of the product [ 1 ]. The panellists have to evaluate their perception with quantitative values proportional to an intensity. To obtain a significant and meaningful result, the panellists must have gone through thorough training. Some of these techniques, mostly novel sensory techniques, can also be carried out by semi-trained panellists [ 5 , 8 ].

Different descriptive methods, such as flavour profile method, or the texture profile method, use trained judges [ 1 , 12 ]. For example, texture profile has been used to identify particular intensities in a product using control products. An improvement of these methods that can be applied not only to taste and texture was achieved with the Quantitative Descriptive Analysis (QDA) [ 3 ]. Free choice profiling, flash descriptive, and spectrum method are other descriptive procedures [ 6 ].

Structured and equidistant scales are usually used for descriptive analysis, where the panellists through these scales assess his/her perception assigned to a particular attribute with a determined intensity. The strength of the attribute is indicated on the horizontal scale with a generally vertical mark, so that its numerical assignment is easier to assess. These scales can be of a single attribute or multiple attributes or descriptors, which represent the descriptive profile of the products as in the QDA. In these scales, the descriptors are arranged according to a logical order of perception: sight, smell and sensation in mouth. Descriptors are a critical point in these analyses and must be accurately chosen to describe the impulse. They must be specific and clear about the sensation they describe and they must have certain relevance and discrimination power in the products to be analysed [ 13 ]. In general, these scales benefit from the use of fewer tasting samples and fewer trained tasters, although fatigue errors can also occur [ 14 ]. The excess of parameters that are subjected to evaluation is one of the main problems when using semi-trained tasters, and this fact can negatively affect the final results, since differences between very similar parameters are a difficulty for them losing interest in the analysis [ 8 ].

In general, descriptive analysis are presented as one of the most adequate sensory tests, they provide the greatest amount of information and are easily interpreted in the elaboration of new products [ 5 ].

2.2. Affective Tests

Affective tests assess the preference or choice of a product (preferences analysis and consumers’ willingness to pay) and the level of acceptance (hedonic evaluation) using the subjective criteria of the tasters. In most cases, the panellists correspond to naïve consumers not trained in the description of preferences, where their evaluation is based on taste and focused on the purchase decision and general acceptance [ 3 , 5 ]. There are two types of affective techniques: Preference and hedonic ( Table 1 ).

The preference or choice tests allow us to ascertain the preference (or not) for a new product based on the majoritarian response of a panel. Traditionally, they are applied to different products in pairs [ 3 ]. It is also recommended to include the “no preference” option, as it will provide more information to facilitate the interpretation of the results. These preference techniques are very useful and are usually employed for market research of new products. They allow us to obtain important information regarding different population targets. However, the main drawback is that this methodology does not give any information about the magnitude of the liking or disliking from the respondents, as panellists only choose whether they like a product or not. To obtain more information about it, hedonic tests can be utilised:

The hedonic method offers an assessment of the liking of the product being tested, using hedonic scales (9-pt hedonic) [ 15 ] ( Figure 1 ). In this scale, the panellists have to choose the expression more in relation to their perception and acceptance of the product. The use of this type of scale allows us to transform this answer into a numerical value, for example, 1 = dislike extremely to 9 = like extremely. This type of evaluation provides quick information on the capacity and potential for success of the new developed product. Hedonic tests can also provide information of the various cluster of consumers for different products, different textures, different composition, etc. These results would help to better understand the justification for liking or disliking a product [ 5 ]. However, this technique also has some limitations, such as: The number of necessary panellists (representative consumers), and the atmosphere and circumstances, that should be similar to the real situations in which consumers would find themselves. Usually, more than 60 representative consumers are used. It should be taken into account that the result of this type of test is not indicative of the consumer purchase intention, as other types of factors, apart from the linking, influence it. Assessing the purchase intention requires a greater number of participants (usually more than 100).

An example of a 9-point hedonic scale useful for evaluating the acceptance of a new products [ 16 ].

Currently, a combination of affective and descriptive sensory technologies is applied during the processing and elaboration of new products. This allows us to take advantage of each technique’s convenience limiting the disadvantages and helps in understanding, through acceptance or consumer preferences (affective), what qualities should be improved, maintained (descriptive), or formulated during the development of new products. However, some of these sensory analyses have shown their limitations. Some aspects in relation to the whole complexity of the consumer-product interactions are often forgotten in traditional sensory techniques. These interactions go further than the conscious response stamped on a liking scale, as external stimuli are also affecting the decision and the degree of acceptance of a food product. To understand the consumers’ preferences for a product, it is also necessary to understand their needs and restrictions, purchasing power, prices of fresh or processed products, product quality, the connotation of healthiness (fat content, salt additives, etc.), the environment of its consumption, etc. In order to solve some of these limitations, new sensory and consumer research techniques have been developed.

3. New Sensory Methods

During the past decades, efforts have been put into developing new methodologies for sensory characterization of food with the aim of gaining speed and simplicity in relation to the traditional ones ( Table 1 ). These new techniques try to provide complete information in innovation and product development and in proper approach of their marketing campaigns, to ensure success. These new alternatives have been categorized into three types depending on the nature of the evaluation task assigned to the panellists [ 17 ]:

3.1. Methods Based on Written Descriptions of the Products

Check-All-That-Apply (CATA) is a method that traditionally has been used with trained assessors, however, its use has recently become popular for food products’ sensory analysis with consumers. CATA is a versatile multiple-choice questionnaire where different options of words or sentences are shown for the panellists to give their free opinion of without any type of limitation [ 18 ]. Consumers could use terms related with sensory attributes, hedonic responses, or other non-sensory properties such as: When are the products consumed? In which situation and atmosphere? What are the emotions or feelings while consuming? etc. One important thing to consider in this analysis is that the attributes are chosen by the consumer.

Flash Profiling (FP) is a method that in the first step develops the descriptive terms together with the participants and on a second step uses these descriptive terms to rank (e.g., from low to high, or least to most, etc.) the tasted products. Panellists are forced to generate discriminative attributes of the whole sample set, which is more important than the individual attributes of the products. This test allows combining free-choice profiling with a comparative evaluation of the set of products [ 19 ]. The number of needed panellists will depend on the objective and dissimilarities among the products. Even though panellists could be untrained, there is a need for at least familiarisation with the products. That is why semi-trained panellists are recommended. Moreover, FP can be more discriminating than conventional profiling for similar product categories [ 19 ]. Some limitations of FP are the need of presenting all the products at the same time and the difficulties when trying to compare results from this methodology and more traditional ones. FP is considered one of the more agile and malleable sensory methodologies to characterise food products.

Rate-All-That-Apply (RATA) is a type of CATA that is based querying consumers to classify the level of strength of descriptors that are applicable for defining/labelling samples [ 20 ]. This test has an increased ability to differentiate between samples which have a similar sensory response in terms of attributes, and is able to differentiate them based on the intensity of that response [ 21 ]. Although RATA has been tested on a different range of products, methodological studies on their reliability are still limited [ 20 ].

3.2. Methods Based on the Measurements of the Similarity or the Differences between Products

Napping is an evolved version of projective mapping a methodology developed to solve the limitations showed by the traditional techniques [ 22 ]. Untrained participants evaluate the samples taking into account their similarities (close to each other) and differences (further apart). The test allows for a comparison between all the samples presented at the same time, but it is not suitable if the samples have to be previously prepared [ 23 ]. Napping is usually combined with other sensory tests, for example, with Ultra Flash Profile, where participants can write down the properties that they consider best describe the samples, in this way, extra qualitative information is provided to the analysis [ 24 ].

3.3. Methods Based on the Comparison of Individual Products with a Reference

Polarized Sensory Positioning (PSP) uses reference products (poles) to determine the similarities or differences between samples to be evaluated. The reference poles must be different from the products to be evaluated, but they must represent the main characteristics in the products they represent [ 25 ].

3.4. Dynamic Sensory Methods

The aforementioned sensory techniques assess the perception of attributes as a “static” phenomenon. However, sensory perception is a dynamic practice, so its assessment, intensity, etc., changes with time while consuming a food product. In that sense, dynamic sensory techniques allow us to describe these changes in sensory perception during the test. Some examples are:

The Time-Intensity (TI), first to be developed, and temporal dominance of sensations (TDS) are the main dynamic sensory evaluation techniques currently used [ 26 ]. TI presented the modification of strength the one single appreciation over time; however, TDS assesses multiple attributes, trying to elucidate the sequence of dominant attributes throughout the test. The choice of one or the other method mainly depends on the objective of the analysis: Qualitative, quantitative, evolution of the quality and perception along testing, etc.

Temporal Check-All-That-Apply (TCATA) is a temporal addition of CATA. Currently, evaluating the multidimensional sensory characteristics in food products as they evolve over time during consumption has gained a lot of attention. For this technique, trained panellists must select sensory attributes (less than 10) freely and continuously, resulting in a temporal classification of the products. However, TCATA does not offer data on the dominant impressions, and none of them calculate consumers’ hedonic insights of the products. Combining TCATA and TDS has shown good results [ 18 ].

4. Complementary Measures for Consumer Research

It has been studied that there is more to eating behaviour than sensory liking: External context, social factors, nutritional status, emotional state, etc., all have an impact on how a consumer interacts with a food product [ 27 ]. For this reason, in consumer research, more tools than affective testing are needed to understand and measure the attitudes, emotions, and behaviours for the successful development of new products. Some authors indicated [ 28 ] that non-verbal emotion punctuation enhanced food choice prediction when employed in conjunction with hedonic scales. Measuring emotions after food ingestion or food purchase seems to be an important step to take when developing new products. However, emotions are usually disregarded by food companies when launching new products.

Several tools have been developed to assess the consumers’ emotions based on both explicit and implicit methods. Explicit means that the methods are based on self-reporting, and thus implies a direct and conscious measurement of the emotions, whereas in the implicit methods, there is no self-reporting and the emotions are measured indirectly.

A verbal self-reporting question sheet is the greatest employed tool for emotion measurements due to their rapidness, discrimination power, and ease of application [ 29 ]. These questionnaires consist of an emotional lexicon the consumers select while consuming the products. Some examples of these are already predefined, like EsSense Profile ® , EsSense25, PANAS, Food Experience’ Scale, etc., but some others are defined by the consumers during different sessions. As pointed out by Kaneko et al. [ 30 ], these verbal self-reporting questionnaires have some associated shortcomings: a) Difficulties to verbalise emotions, b) language dependence of the lexicon, c) interference with food experience, and d) only capturing conscious emotions. In an attempt to improve and facilitate the capture of emotions with little impact on the food testing, a self-reported questionnaire called PrEmo was developed based only on images and animations.

On the other hand, implicit methods are based either on physiological and/or visual measurements, or on behavioural tasks measurements. The latter are based on psychological tools such as the Implicit Association Test (IAT) and the Affective Priming Paradigm (APP). IAT consists of measuring the speed at which words are associated with one of two pairs of concepts. For example, we could have four categories (two products and pleasant and unpleasant words) the consumer has to recognise by clicking a certain key. Monnery-Patris et al. [ 31 ] have used an IAT to assess children’s food choices. In APP, consumers undertake a categorisation task with target words preceded by food primes. The APP has been confirmed as a robust indirect measure of food enjoyment, although there is not enough evidence of its utility to measure eating behaviours [ 32 ].

Measuring involuntary physiological responses governed by the Autonomic Nervous System (ANS) and other physiological characteristics, such as face recognition, heart rate, eye-movement, body temperature, skin temperature and conductivity, etc., can provide information of the emotional state a food product can generate. Gunaratne et al. [ 33 ] used measurements of skin temperature, facial manifestations, and heart speed to analyse the relationships between short and unconscious answers to different chocolate testing. The authors found that sweet chocolate was contrariwise related with displeased emotion and salted chocolate was positively connected with sadness. Another interesting application has been proposed by Fuentes et al. [ 34 ], where the authors were able to derive models from heart speed, blood pressure, facial manifestations, and skin-temperature modifications to predict the liking of insect-based foods with the help of machine learning.

There has been significant interest in an enhanced comprehension of the position of the context in consumer sensory testing as it is widely accepted that context participates in how emotional and hedonic responses are shaped. Hathaway and Simons [ 35 ] found that the distinguishable and consistency of consumer acceptance information increases with the level of immersion the consumer experiences. The use of VR to increase the immersion level has proved to be successful on a few food products such as cookies, vegetables, and coffee. Another recent application of this technology in sensory science has been the possibility of transporting the consumer to virtual stores. The more realistic the setting was—e.g., consumers able to walk in a virtual supermarket—the better the evaluation of the purchase decision [ 36 ].

In addition to emotions and context, there are other factors that have a significant effect on food choice. These factors are product and person-dependent, as they deviate from the intrinsic quality attributes to be more external ones. Some examples of these are: Healthiness, price, familiarity, pleasure, convenience, ethical issues (e.g., vegans), cultural disgust (e.g., entomophagy), etc. In 1995, Steptoe et al. [ 37 ] developed the Food Choice Question sheet (FCQ) as an instrument to assess the reasons for accepting a food. This questionnaire was later improved on its ethical dimension with the addition of animal welfare, environmental defence, and political and religious principles [ 38 ]. The original questionnaire comprises 36 four-point matters (e.g., “It is significant to me that the food I eat on a usual day maintains me healthy”, where 1= not at all important, and 4= very important), and has been used extensively. Another extensively used test has been the Food Neophobia Scale (FNS). Food neophobia is the unwillingness to eat unusual foods, such as insects in occidental culture. Pliner and Hobden [ 39 ] developed the FNS, consisting on a 10-item test, and it was validated through confirmatory factor analysis. The FNS is a completely balanced neophobia analysis and has been frequently exposed to predict real replies to novel food.

Qualitative investigation is widely employed to study consumer behaviour and extract ideas for the development of new products. One of the qualitative techniques more used in consumer research is focus groups. Focus groups were traditionally used in social sciences with the aim to help the researcher to find questions for future questionnaires. Focus groups are one of the most appropriate methods to obtain qualitative data while boosting the participants’ interaction to interchange ideas, establishing a non-aggressive environment to encourage dialogue among them [ 40 , 41 ]. Focus groups are formed with a small number of individuals and set in a closed environment, although online meetings are now also used, where participants have an informal discussion about a specific issue or several established topics. The advances in specialised software to analyse the results as well as the possibility of combining them with other exploratory and projective techniques has made focus groups an interesting tool for consumer research. Ethnography is another qualitative tool that has gained popularity in consumer research. It aims to provide a cultural comprehension of consumers through sharing events, moving from the lab to their homes as a method to make more useful communication procedures [ 42 ]. The scientist must become a member of the community, but should also maintain distance and objectivity while observing.

5. Sensory Analysis as Tool for the Development of New Meat Products

Sensory analyses are important tools used by sensory scientists and food companies to achieve data applicable to technology, quality assessment, consumer insights, marketing, and the development of new products. Sensory analysis involves consumers, offering a relationship with technology and the market strategies [ 43 , 44 , 45 ].

Sensory analysis methods can be used at many stages of the process to assess the quality of the new product, but also the consumers’ expectations and reactions to the product. However, traditionally, the development of new products appears to be disconnected between the understandings of consumers and the different stages (research, design, process, packaging, labelling, etc.) in the productions and commercialisation of these new products. These phases are critical, and it has been demonstrated that more studies and more participation of sensory panels and consumers in the products’ design and development processes affect products success in their commercialisation.

In general, the growth of the global market for food, meat, and meat products especially, is a good opportunity for the development of new products that satisfy the demands of consumers around the world.

The development of novel products passes strict quality controls (physico-chemical, microbiological, and sensory) to guarantee their safety and their success among consumers. Sensory analysis to assess a product’s quality are a significant part of a quality control program, since the consumer is the final evaluator of the quality of a new product [ 46 ].

Although we can find different meanings of quality in the scientific literature, we can say that one of the most used describes quality as the entirety of features and characteristics of a product that bear on its capability to please a given need. Some of them included also some quality properties such as safety, nutritional quality, availability, convenience and integrity, and freshness quality. Other definitions incorporate an extensive variety of other features such as value for money, legal value, technological importance, socio-ecological value, and even psychological, political, and ecological abilities according to their specific expertise and interests [ 47 ]. With this regard, the perception of food quality should be based on the manufacturer’s, the consumer’s, and the surveillance and legislative bodies’ diverse requests. Then, there is both an objective and a subjective understanding of the quality [ 48 , 49 ]. The objective understanding is connected to the material characteristics that can be explained and objectively calculated. The subjective definitions depend on the consumer’s view and assessment, being criteria implicated in consumer approval, mostly sensorial parameters such as colour, odour, flavour, etc. ( Figure 2 ) [ 50 ].

An example of a scale of sensorial analysis applied for development of new meat products [ 50 ].

The sensory analysis plays a very significant role in the successful elaboration of new meat products in the whole production process, from research and development to quality control and marketing. These sensory analyses bring important information to the different sectors involved in the production and commercialisation chain (industry, commerce, R+D, consumers, consumer agencies, etc.). The success or failure of the new meat products in the market will depend to some extent on these analyses, their correct application, and the adequate interpretation of the results. One of the key points is to choose the most adequate analysis depending on the type of product and target population. An optimised design of the analyses at the different stages of the processing and commercialisation steps can entail great savings of both time and money. To this regard, it is noteworthy that the meat product sector encompasses a huge variety of products with different manufacturers, processing conditions, packaging, flavours, composition, etc., and thus, sensory analysis should consider these specificities as well as the appropriate panellist selection (purchase capacity, eating routines, special requirements, etc.).

On the other hand, an important part of the development of meat products is the one addressing the design and development of new healthier meat products. The elaboration of these healthier products involves changed composition and/or processing settings to reduce the presence of specific possibly harmful compounds, and/or the option of incorporating specific appropriate substances, either naturally or by incorporation, with the consequent additional benefits to health status. The aim of this development is to enhance the nutritional profile and the health characteristics of the product, while maintaining acceptable taste and flavour [ 51 ].

Healthier meat products are a response to the increasing demand from the consumers of safer and healthier products. One of the most studied healthier meat products has been the optimisation of the lipid content [ 51 , 52 ], mainly due to the relationship between the animal fat in the meat products and the risk of certain diseases.

Traditional sensory tests, mainly discrimination tests, are the most used for the evaluation of the organoleptic properties of new healthier meat products at the industrial level. However, in research studies, it is the hedonic or descriptive tests that are used the most. Tenderness and juiciness have been the most sensory analysed attributes in meat product research. However, it was observed that using only these two parameters limited the overall assessment of the products, and extra relevant attributes were considered: Appearance, colour, tenderness, juiciness, aroma, and flavour [ 50 , 53 ] ( Figure 2 ). This allowed for a more objective and accurate judgment, which can give a better indication of consumer acceptance [ 12 ].

Different healthier meat products with improved lipid profile (frankfurters, fresh sausages, dry fermented sausages, burger patties, etc.) have been developed with the support of sensory analysis results [ 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. In dry fermented sausages, such as chorizo, reformulated with healthier lipid content, a hedonic scale rating test was performed where panellists evaluated appearance, flavour, firmness, juiciness, and overall acceptability, which refers to a general point of view of the product [ 59 ]. Although, the panel considered that the organoleptic properties of the new healthier dry fermented sausages in general were acceptable, the greatest sensorial limitation was the firmness score, which was considered as mainly responsible for the reduction in the general acceptability of the new products ( Figure 3 a). In other types of meat products with enhanced fat content, such as frankfurters and fresh sausages, a sensory panel were instructed to evaluate some parameters such as texture, colour, flavour, and general acceptability [ 54 , 56 , 58 , 60 ]. Generally, the panellists considered that all products were acceptable at moderately high scores ( Figure 3 b,c).

Example of sensory analysis results obtained in studies based on the improvement of the lipid content in meat products: ( a ) Dry fermented sausages (adapted from Jiménez-Colmenero et al. [ 59 ]; ( b ) fresh sausages (adapted from Pintado et al. [ 58 ]; ( c ) cooked sausages, frankfurter type (adapted from Pintado et al. [ 54 ]).

In the formulation of other healthy meat products based on minimising the presence of deleterious compounds, such as sodium or nitrites, sensory analysis has also been utilised. In this context, non-structured descriptive scales with fixed extremes have been employed in the elaboration of low-fat sodium reduced fresh merguez sausage, observing that the reduction of salt did not undesirably affected the sensory evaluation [ 57 ]. Moreover, sensory analysis has also been incorporated in the formulation of healthier meat products such as hot dog without nitrites, and the panellists considered all products acceptable [ 61 , 62 ].

On the other hand, an important part in the formulation of new products is the correlation of these sensorial results with the instrumental measures for the different attributes by means of statistical methods such as regression and correlation, thus achieving greater objectivity in sensory analyses [ 50 , 63 ]. However, the main problem is the lack of homogeneity in the attributes and descriptors, as well as establishing which attribute is the main one in an analysis. Since for each taster it may be different parameters (juiciness, hardness, favour, etc.) the ones that determine their acceptance or rejection of a product.

Despite this, the correlation results are an important measurement of new products quality. Colour is an important attribute of acceptance or rejection and constitutes a direct and efficient measure of the commercial acceptance of meat. Different studies have correlated instrumental measures of L *, a *, and b * (colour parameters—CIELAB) with the results of descriptive sensory analysis [ 50 , 54 , 57 ]. Moreover, lower juiciness values from a sensorial analysis were correlated with greater weight loss during processing in dry fermented sausages [ 50 ]. Similar studies have carried out the correlation between instrumental and sensory hardness [ 64 ].

Spectroscopic techniques combined with chemometric analysis in the sensory analysis of meat and meat products and the elaboration of healthy meat products have been a recent novel approach. Near infrared spectroscopy (NIR) has been used as a method to quickly determine some organoleptic characteristics of meat such as appearance (colour, marbling, etc.), odour, flavour, juiciness, tenderness, or firmness [ 65 , 66 , 67 ]. On the other hand, Raman spectra from cooked beef samples has been correlated with organoleptic properties (juiciness and texture) using PLSR [ 68 ]. Attenuated total reflectance–Fourier transform infrared spectroscopy (ATR-FTIR) has been used in the development of healthier meat products for the evaluation of both their technological and sensory properties. Results showed that these healthier products involved more lipid–protein interactions, but their sensory properties were not affected and the new products were judged acceptable [ 54 , 60 ].

Novel sensory techniques have also been employed in the sensory characterisation and development of traditional and healthier meat products. In this sense, flash profiling has also been applied for the sensory analysis of meat products such as hams or hot dogs. The results derived from Flash Profile were comparable to those obtained applying quantitative descriptive analysis (QDA) [ 24 ]. Flash Profile demonstrated an efficient discriminant ability between a traditional Madagascar meat product elaborated with pork and beef and a traditional Portuguese sausage [ 69 ]. Lorido et al. [ 70 ] applied Flash Profile to differentiate between dry-cured loins made with various quantities of NaCl. These works also combined Flash Profile with other sensory analysis such as napping or dynamic sensory techniques [ 69 , 70 ]. Alves et al. [ 71 ] through a CATA analysis chose the expressions to bologna-type sausages from a previous dialogue with a team of 15 consumers, and consumers were requested to conclude the CATA questionnaire with 19 descriptors connected to the organoleptic characteristics of the Bologna-type sausage. In another study, a total of 32 sensory descriptors were developed on the adapted “Kelly Repertory Grid Method”. These terms were clustered (appearance, colour, favour/taste, texture, and odour) and were used to determinate the organoleptic properties of healthier bolognas (enriched with ω3 fatty acids) by CATA [ 72 ]. Both studies concluded that the employment of CATA showed some significant considerations in the formulation of healthier bolognas since it was capable to explain relevant characteristics. Other authors have compared CATA analysis with trained panellists’ results [ 73 ], Descriptive Analysis (DA) and their relationship with overall liking (OL) [ 74 ], acceptance testing [ 75 ], etc. According to these authors, the CATA questions successfully distinguished between the meat products regarding their organoleptic properties. In addition, these attributes were connected to chemical and instrumental quality parameters.

The use of CATA has been applied to commercial and healthier reformulated meat products to analyse the acceptance and the impact that some modifications (partial protein replacement, lipid content improvement, etc.) have on consumers [ 24 ]. This method was able to indicate some relevant considerations in the elaboration of meat products and was able to describe important characteristics.

Many recent works have indicated the application of napping-UFP in assorted meat products. The method allowed for a good discrimination among pork tested samples in relation to different cooking methods and conditions [ 76 ]. Napping-UFP successfully characterised bacon samples smoked with different woods, discerning the woods employed for smoking. The samples characterisation of samples and the results were correlated with volatile compounds [ 74 ]. Moreover, the great discrimination ability of Napping-UFP in healthier reformulated products has been proven: With bioactive components (e.g., fibres, prebiotics), with different fat or salt levels [ 24 ].

With respect to dynamic sensory analysis on meat products, TI was applied to determine the temporal opinion of tenderness in cooked pork and beef [ 77 , 78 ]. TDS was performed by Paulsen et al. [ 79 ], who considered the influence of NaCl replacement on the temporal perception of flavour and texture on sausages. TDS indicated unidentified sensory descriptions of NaCl replacement in meat products when it was compared with the results obtained from the classic QDA. TI and TDS have been applied to dry-cured hams elaborated from pigs with diverse feeding backgrounds and varying in NaCl content. TDS allowed a more effective discernment between different types of ham [ 80 ]. TI and TDS were found to offer complementary results, and thus using both temporal methods are recommended when a thorough sensory evaluation of the samples is expected. Paglarini et al. [ 81 ] evaluated the effect of salt and fat reduction on Bologna sausage with incorporation of emulsion gel in the dynamic sensorial perception by using TDS and TCATA methods contemplating overall enjoyment. The TDS and TCATA curves indicated that texture attributes were relevant at the beginning of the estimation for all samples, and TCATA also exhibited that juiciness was prevailing in the first 15 s of the eating period.

6. Conclusions

Sensory analysis and consumer research are a relevant tool in the development of health-enhanced meat products. Although sensory analysis techniques have evolved greatly in the last decades, these advances must continue. It should be noted that sensory analysis is a science that determines, analyses, and interprets the replies of people to products as perceived by the human senses, which implicate many factors and variability. The different sensory techniques that are applied in the development of new products must reduce and control the variability due to human involvement for these new developments to be successful. This is in line with the objective 9 (industries, innovation, and infrastructure) of the UN Agenda 2030, as it will modernize and innovate the sector while increasing efficiency. In order to do this, novel technologies and methodologies have to be further explored and implemented in a more holistic way, not only taking into account likeness and affectivity, but also emotions, context, and preference factors.

Author Contributions

Conceptualization C.R.-C., and A.M.H.; formal analysis C.R.-C., A.M.H., T.P., G.D.-P.; investigation C.R.-C., A.M.H., T.P., G.D.-P., project administration, C.R.-C. and A.M.H.; resources, C.R.-C. and A.M.H.; writing—original draft, C.R.-C., A.M.H., T.P., G.D.-P.; writing—review and editing, C.R.-C., A.M.H., T.P., G.D.-P. 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 number 201470E073 and 202070E242), CYTED (grant number reference 119RT0568; HealthyMeat network) and the EIT Food Project 20206.

Conflicts of Interest

There is no conflict of interest.

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

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