Overview of Descriptive Analysis

The aim of all descriptive techniques is to generate quantitative data which describes the similarities and differences among a set of products. Each method has a different approach; however the basic framework of all the techniques is the same:

• selection of panel members
• term generation (or selection of appropriate lexicon)
• concept formation
• testing of panel consonance
• evaluation of products

Descriptive techniques include Free Choice Profiling (FCP), the Spectrum™ method, Quantitative Descriptive Analysis™ (QDA), Flavor Profile Method, Texture Profile Method, Flash Profiling and generic descriptive analysis. Generic descriptive analysis generally takes pieces from QDA and Spectrum™ methods, but is modified to suit the goals of the project and limitations of the product being tested. Of the methods mentioned here, FCP and Flash Profiling involved the use of untrained consumers rather than a trained panel (although a trained panel can be used). This main point of differentiation makes these techniques faster and cheaper to conduct as there is no training involved.¹²

Panel Selection

The selection of panel members is very important to the quality of the data obtained. Potential members need to be screened for their ability to discriminate between similar samples, rate products for intensity and identify tastes and aromas. Equally, or possibly more, important than a panelists’ sensory acuity is their motivation. A panelist who feels they are required to particiapte may not perform as well as and equally skilled panelist who feels motivated to participate.

Panel Training

Panel training encompasses term generation, concept alignment and panel testing phases. The amount of training required is dependent upon the method used as well as the product(s) to be tested.³ A company with an in-house descriptive panel may spend several months or more training a panel over a wide range of products, rather than training the panel specifically for each product as needed.

Notes

¹ Lawless, H. and H. Heymann (1998). Sensory Evaluation of Food: Principles and Practices. New York, Kluwer Academic/Plenum Publishers.

² Murray, J. M., C. M. Delahunty and I. A. Baxter (2001). Descriptive Sensory Analysis: Past, Present and Future. Food Research International, 34: 461-471.

³ Chambers, D. H., A.-M. A. Allison and E. I. Chambers (2004). Training Effects on Performance of Descriptive Panelists. Journal of Sensory Studies, 19: 486-499.

⁴ Stone, H. and Sidel, J.L. (2004). Sensory Evaluation Practices. Elsevier Academic Press. San Diego, CA, pp. 201-244.

Duo-Trio Test

The duo-trio test, developed by Peryam and Swartz (1950), represented an alternative to the triangle test that, for some, was a more complex test psychologically. The duo-trio test was found to be useful for products that had relatively intense taste, odor, and/or kinestethetic effects such that may impact sensitivity.

A Duo-Trio Test is an overall difference test which will determine whether or not a sensory difference exists between two samples. This method is particularly useful:

1. To determine whether product differences result from a change in ingredients, processing, packaging, or storage
2. To determine whether an overall difference exists, where no specific attributes can be identified as having been affected

Compared to Other Overall Difference Tests

The Duo-trio test is equally sensitive to the triangle test and is simple and easily understood. Compared with the Paired Comparison test, it has the advantage that a reference sample is presented which avoids confusion with respect to what constitutes a difference, but a disadvantage is that three samples, rather than two, must be tasted.

Test Principal

Present to each subject an identified reference sample, followed by two coded samples, one of which matches the reference sample. Ask subjects to indicate which coded sample matches the reference. Count the number of correct replies and refer to a table for interpretation. Two design options are available for a duo-trio test. The conventional approach is a balanced the reference between the control and test products; however in some situations, the reference may be kept constant.

Test Subjects

All should be familiar with the format, task, and evaluation procedure for the Duo-Trio Test. An orientation session is recommended prior to the actual test to familiarize subjects with the test procedures and product characteristics.

As a general rule, the minimum is 16 subjects, but for less than 28, the beta-error is high. Discrimination is much improved if 32, 40, or a larger number can be employed.

Test Procedure

Offer samples simultaneously, if possible, or else present samples sequentially. Prepare equal numbers of the possible combinations (control and tests) and allocate the sets in a balanced design among the subjects. Space for multiple Duo-trio tests may be provided on the scoresheet, but do not ask supplementary questions (e.g., the degree or type of difference or the subject’s preference) as the subject’s choice of matching sample may bias his or her response to these additional questions. Count the number of correct responses and the total number of responses. Do not count “no difference” responses; subjects must guess if in doubt.

References

¹ Meilgaard, M., G. V. Civille and B. T. Carr (2007). Sensory Evaluation Techniques, 4th Ed. New Boca Raton, FL: CRC Press, 6: 72 – 79.

² Stone, H., and Sidel., J.L. (2004). Sensory Evaluation Practices, 3rd edition. Elsevier Academic Press, San Diego, CA, 5: 152-153.

Split-Plot Designs

The split-plot design is an experimental design that is used when a factorial treatment structure has two levels of experimental units. In the case of the split-plot design, two levels of randomization are applied to assign experimental units to treatments¹. The first level of randomization is applied to the whole plot and is used to assign experimental units to levels of treatment factor A. The whole plot is split into subplots, and the second level of randomization is used to assign the subplot experimental units to levels of treatment factor B.^1,2 Since the split-plot design has two levels of experimental units, the whole plot and subplot portions have separate experimental errors².
h2. Application to Descriptive Analysis
The split-plot design is used to analyze descriptive data when applying Analysis of Variance (ANOVA). This design tests significant differences among samples and also estimates variation due to panelist inconsistencies³. Samples evaluated by judges are considered to be the “whole-plot effect” and are placed at the top of the ANOVA table (Table 1). Judges themselves make up the “subplot effect” and are placed at the bottom of the table.^4,5 Trained descriptive judges are taught to evaluate products in the same way, and therefore, judges are generally treated as a random effect⁵.
If judges rate their perceptions of attributes differently from sample-to-sample, then there may be a potential for judge-by-sample interaction. If there is potential for this type of interaction, it can be included in the model as a source of variability separate from error. An F-test (F3) can show whether or not this interaction is significant⁴. If it is assumed that there is no panelist-by-sample interaction, this term can be pooled with error⁵.
If various judges scale attributes in different ways, then there may be an overall judge main effect. An F-test (F2) can show if the judge main effect is significant⁴.
Judge-by-sample interactions and judge effects should be minimized by effective panel training and maintenance. If judges do not fully understand how to scale terms, then they may scale in an inconsistent manner across samples. If each judge is using a different portion of the scale to rate attributes, then results will be inconsistent across panelists. Proper training and orientation can help clarify term definitions and evaluation methods in order to avoid these inconsistencies. Data obtained from well-trained panels can accurately show small differences among samples.

References

¹ Ott, R.L. and Longnecker, M. 2010. An introduction to Statistical Methods and Data Analysis, 6th Ed., pp. 1095-1101, Brooks/Cole, Pacific Grove, California.

² Kuehl, R.O. 2000. Design of Experiments: Statistical Principles of Research Design and Analysis, 2nd Ed., pp. 469-472, Brooks/Cole, Pacific Grove, California.

³ Lawless, H.T. and Heymann, H. 1998. Sensory Evaluation of Food: Principles and Practices, pp. 714-715, Aspen Publishers, Inc., Gaithersburg, Maryland.

⁴ Stone, H. and Sidel, J.L., 2004. Sensory Evaluation Practices, 3rd edition, pp. 128-133, Elsvier Academic Press, London.

⁵ Meilgaard, M.C., Civille, G.V. and Carr, B.T. 1991. Sensory Evaluation Techniques, 2nd edition, pp. 266-267, CRC Press, Boca Raton, Florida.

⁶ Chambers, E. Course notes in Descriptive Sensory Analysis, Kansas State University, Manhattan, KS, 2011.

The Sour-Bitter Confusion

There is a phenomenon in the sensory world widely referred to as the sour-bitter confusion that commonly occurs among untrained assessors. This occurrence involves the assessor describing a sour sensation as bitter and/or a bitter sensation as sour, with the former being more predominant. This practice appears to be limited to predominantly English-speaking countries such as the United States, Great Britain, and New Zealand⁶. Debate in the past has centered on whether this confusion stems from a physiological disorder or simply a deficit in exposure to and training with sour and bitter tastes^4,5,7.
Bitter and sour are two of the basic tastes and are found in a wide variety of foods and beverages to help balance the products flavor profiles. Compounds such as amino acids, peptides, esters, lactones, phenols and polyphenols, methylxanthines, flavonoids, terpenes, sulfimides, and organic and inorganic salts contribute to the bitter tastes in products such as coffee, tea, chocolate, and some fruits and vegetables². Sour tastes are associated with hydrogen ions and organic acids and are found in such sour foods as jams and jellies, buttermilk, processed meats, sauerkraut, and other products¹.
Despite different compounds contributing to the sour and bitter tastes in foods, several studies have recorded subjects frequently confusing the two terms when attempting to describe simple solutions made with sour and bitter substances. In a study conducted by Meiselman and Dzendolet⁵, 80 subjects tasted 10 mL aliquots of 15 mM sucrose (sweet), 50 mM NaCl (salty), 2 mM HCl (sour), and 20 mM KCl (bitter) and asked to describe the basic taste perceived for each solution. While all types of confusions were made, the sour-bitter confusion was the most common error made, occurring in 21.25% of the subjects (sour being called bitter more frequent than vice versa). The scientists then instilled a correction procedure in an attempt to train the subjects on the different tastes, but 35% of these subjects still made the sour-bitter error. These results led the researchers to attribute the sour-bitter confusion to a physiological defect analogous to abnormal color !
vision.
OMahony et al.⁶ conducted a large series of experiments in an effort to better understand the sour-bitter confusion. Some of the experiments were modifications of past work conducted^3,4,5,7, while others were new designs. Of the new experiments, variations included using students in both the United States and Great Britain, inclusion of correction procedures when naming errors occurred, and varying the concentration levels of the simple solutions used in testing: sucrose (sweet), NaCl (salty), citric acid (sour), and quinine sulphate (bitter).
The results of these experiments clearly demonstrated the sour-bitter confusion with 13.3% of all 1629 responses for sour and bitter stimuli involving citric acid being called bitter and 7.7% of the responses involving quinine sulfphate being called sour. The authors offered several explanations as to why the subjects had difficulty distinguishing between sour and bitter tastes. One hypothesis is that the subjects have more cultural experience with sweet and salty foods than sour and bitter foods, allowing their perception of sweet and salty to be more clearly developed than sour and bitter. A second hypothesis is that subjects are more familiar with sucrose and salt in their pure forms than citric acid and quinine sulphate, again allowing the subjects to better develop their own personal concepts of sweet and salty versus sour and bitter. A third hypothesis involves the incorrect cultural labeling of typically!
sour foods as bitter, as in the case of bitter lemon. In regards to these hypotheses, the authors concluded that the sour-bitter confusion can be attributed to a lack in the clear understanding of the definitions of sour and bitter rather than a physiological defect⁶.

References

¹ Da Conceicao Neta, ER, Johanningsmeier, SD, and McFeeters, RF. 2007. The chemistry and physiology of sour taste a review. Journal of Food Science. 72(2): R33 R38.

² Drewnowski, A. 2001. The science and complexity of bitter taste. Nutrition Reviews. 59(6): 163 169.

³ Gregson, RAM and Baker, AFH. 1973. Sourness and bitterness: confusions over sequences of taste judgments. British Journal of Psychology. 64: 71 76.

⁴ McAuliffe, WK and Meiselman, HL. 1974. The roles of practice and correction I the categorization of sour and bitter taste qualities. Perception and Psychophysics. 16: 242 244.

⁵ Meiselman, HL and Dzendolet, E. 1967. Variability in gustatory quality identification. Perception and Psychophysics. 2: 496 498.

⁶ OMahony, M, Goldenberg, M, Stedmon, J, and Alford, J. 1979. Confusion in the use of the taste adjectives sour and bitter. Chemical Senses and Flavour. 4(4): 77 94.

⁷ Robinson, JO. 1970. The misuse of taste names by untrained observers. British Journal of Psychology. 61: 375 378.

Descriptive Skin Feel Analysis

Consumer acceptability of products, such as foods and personal care products, often depends largely on sensory properties perceived during use. Descriptive Analysis is a technique that was developed to quantify perceptual properties of samples so that their sensory profiles can be directly compared.

Descriptive analysis, although historically applied to food products, has been used to evaluate personal care products, such as lotions, creams, and cosmetics, since the 1970’s. This application was first published in 1974 by Naomi Oshinsky Schwartz of General Foods Corporation in the Journal of Texture Studies¹; “Adaptation of the Sensory Texture Profile Method to Skin Care Products” was based on the Texture Profile method developed by the General Foods Research Center in the 1960’s².

A descriptive analysis technique used to evaluate skin care products is now a standard practice in the American Society for Testing and Materials entitled “Standard Practice for Descriptive Skinfeel Analysis of Creams and Lotions” (ASTM E 1490 – 03). ASTM E 1490 – 03 outlines each step of a descriptive analysis procedure, including methods for panelist selection and training, as well as providing terms, references, and evaluation methods. The ASTM skin feel analysis is separated into three main evaluation sections: evaluation of the product in a petri dish, evaluation of the product while being rubbed between a finger and thumb (pick-up evaluation), and evaluation of the product being rubbed on the forearm (rub out evaluation)³.

Many other studies have evaluated skin feel of assorted personal care products and their ingredients using variations on the ASTM E 1490 – 03 methodology. Common attributes across these studies included gloss, stickiness, spreadability, and residue. Common references included items such as petroleum jelly, lanolin, and mineral oil^3,4,5,6,7.

Descriptive skin feel analysis is fundamentally the same as a descriptive analysis of a food product in terms of selection of panelists, term generation, concept formation, and sample evaluation. Obviously, however, different criteria must be applied to select suitable panelists for skin feel analysis compared to oral evaluation. Skin feel analysis panelists must demonstrate tactile acuity (primarily a function of finger size⁸) and apply to other criteria in terms of skin allergies, skin condition, and skin type³.

References

¹ Stone, H.; Sidel, J., Sensory Evaluation Practices. 3rd ed.; Academic Press: London, 2004.

² Schwartz, N. O., Adaptation of the Sensory Texture Profile Method to Skin Care Products. J Texture Stud 1974, 6 (1), 33-42.

³ ASTM, International, ASTM Standard E1490 – 03: Standard Practice fo Descriptive Skinfeel Analysis of Creams and Lotions. ASTM International: West Conshohocken, PA, 2003; p 16.

⁴ Parente, M. E.; Gambaro, A.; Ares, G., Sensory Characterization Of Emollients. Journal of Sensory Studies (2008) 23, 149–161.

⁵ Lee, I.; et al, Terminology Development And Panel Training For Sensory Evaluation Of Skin Care Products. Journal of Sensory Studies (2005) 20, 421–433.

⁶ Aust, L. B.; et al, The Descriptive Analysis Of Skin Care Products By A Trained Panel Of Judges. J. Soc. Cosmet Chem (1987) 38, 443-449.

⁷ Almeida, I. F.; Gaio, A. R.; Bahia, M. F., Hedonic And Descriptive Skinfeel Analysis Of Two Oleogels: Comparison With Other Topical Formulations. Journal of Sensory Studies (2008) 23, 92–113.

⁸ Peters, R. M.; Hackeman, E.; Goldreich, D., Diminutive Digits Discern Delicate Details: Fingertip Size and the Sex Difference in Tactile Spatial Acuity. The Journal of Neuroscience (2009) 29(50), 15756-15761.
 

The difference from control test is classified as an overall difference test. It is similar to the degree of difference test, in that it is used to determine if there is, in fact, a difference between one or more test samples and a control. And, more importantly, if there is a difference, its size can also be measured with this test.

This test is most useful in situations such as quality assurance and control, where there is a heterogeneous quality to the products being tested. In this case, some difference is to be expected between batches, and it is the size of those differences that determines a decision. A duo-trio, triangle, or other difference test would not be as effective as they do not measure the size of the difference. The difference from control test is also useful when fatigue and carryover are an issue, as it can be used as a two-sample, rather than a multi-sample, test¹.

When conducting this test, 20-50 subjects should be used to provide meaningful results. It does not matter if the panel is trained or untrained, but there should not be both. Each subject is given a labeled control sample and one or more test samples. Within the test samples, the control can also be present as a blind-control, and the subjects should be alerted to this. The blind control helps to establish a base line for the rest of the test samples, as most blind controls will get a non-zero score due to individual variability². They are then to rate the size of the difference between each test sample and the control. This rating can be done on a line or category scale.

To interpret the results, determine the means of the observed size of the difference from control, both for each type of sample, and for the blind-control samples. Each mean can be compared using analysis of variance, or, if only using one test sample, a paired t-test can be used¹.

There are some issues that can be involved with this test. One issue is not having enough subjects participating. This is seen in many quality control/assurance situations. Another issue is that statistically failing to reject the null hypothesis of there being no difference between test and control, does not necessarily mean that there is no sensory difference between the samples.

References

¹ Meilgaard, M, Civille, CV, Carr, BT. 2007. Sensory Evaluation Techniques. 4th ed. CRC Press. 448 p.

² Lawless, HT, Heymann, H. 2010. Sensory Evaluation of Food: Principles and Practices. 2nd ed. Springer. 850 p.
 

Synesthesia is the phenomena of when sensory input is crossed in the brain resulting in such sensations as smelling colors and seeing scents. “Unlike color generated from light waves or odors generated by chemical compounds, color, smell, sound, taste and touch that is experiences by synesthetes is generated by a physical stimulus that for most of us is entirely unconnected to its induced sensation,”¹. Synesthetes are usually highly intelligent people who use their difference to help them learn, thus making them a very good candidate for sensory evaluation. However, this different brain wiring can be a particular problem in sensory evaluation because it is a phenomenon that will influence responses for panelists. Though it seems that synesthetes would be unable to give valid input into studies for sensory analysis; their brain paths, like that of other people, do not readily change. Thus a synesthete can be incorporated into testing by simply using their ability and working with it. Say they taste cinnamon whenever there are red lights, this would be an issue because when testing color sensitive samples the lighting will usually be red, thus the subject will always taste cinnamon in the sample, the participant could be blindfolded and test the samples that way instead.

References

¹ Robertson, Lynn C. and Naom Sagiv. Synesthesia: perspectives from cognitive neuroscience. New York: Oxford University Press, 2005.

- The exact probability test to use with Likert-type data was proposed by Cooper (1976) and provided a table of critical values for small sample size. In the case of a large sample size normal approximation can be used but the analyst has to be careful with issues such as: the points of the Likert scale are equally spaced, consumers respond independently from each other, each category of scale has an equal opportunity to get the response from consumer (Cooper, 1976).

- Using the application of ordered probit model to treat with Likert ordinal data (Daykin & Moffatt, 2002).

- Use of cumulative logits for modeling ordinal response variables and cumulative link models for binary data (Verbych, 2007).

Reference List
Bell, R., & Marshall, D. W. (2003) The construct of food involvement in behavioral research: scale development and validation⋆. Appetite, 40 (3), 235-244.
Chryssohoidis, G. M., & Krystallis, A. (2005) Organic consumers’ personal values research: Testing and validating the list of values (LOV) scale and implementing a value-based segmentation task. Food Quality and Preference, 16 (7), 585-599.
Cooper, M. (1976). An Exact Probability Test For Use With Likert-Type Scales. Educational and Psychological Measurement, 36, 647-655.
Daykin, A. R., & Moffatt, P. G. (2002). Analyzing ordered Responses: Areview of the ordered Probit Model. Understanding Statistic, 3, 157-166.
Edmonson D.R. (2005). Likert Scale: A History. Retrieved from CHARM database.
Lawless H.T., & Haymann H. (1998) Sensory Evaluation of Food: Principle and Practices. MA: Kluwer Academic Publishers.
O’Sullivan, C., Scholderer, J., and Cowan, C. (2005) Measurement equivalence of the food related lifestyle instrument (FRL) in Ireland and Great Britain. Food Quality and Preference, 16 (1), 1-12.
Ritchey, P. N., Frank, R. A., Hursti, U., and Tuorila, H. (2003) Validation and cross-national comparison of the food neophobia scale (FNS) using confirmatory factor analysis. Appetite, 40 (2), 163-173.
Roininen, K., Tuorila, H., Zandstra, E. H., de Graaf, C., Vehkalahti, K., Stubenitsky, K., and Mela, D. J. (2001a) Differences in health and taste attitudes and reported behaviour among Finnish, Dutch and British consumers: a cross-national validation of the Health and Taste Attitude Scales (HTAS), Appetite, 37 (1), 33-45.
Verbych, E., (2007). A Comparison of Methods to Analyze Likert Scale Data. Unpublished master thesis, Kansas State University, Manhattan, KS.

Second-price Experimental Auction

Second-price experimental auctions (SPEA (s)) elicit subjects’ true willingness to pay (WTP) values for products or product attributes¹ . Researchers have utilized SPEAs to determine WTP for improved food safety, value-added production properties (e.g. improved animal welfare), and sensory quality^2,3,4,5. In typical central location tests, consumers rate their acceptance of product(s) based on predominantly sensory attributes; however, in SPEAs, consumers can determine the contribution of sensory and non-sensory attributes to their WTP values. Thus, perhaps the SPEA can better capture the total satisfaction consumers derive from product(s).

The SPEA is structured so that the highest bidder wins the auction but pays the second-highest bid, which insures incentive compatibility (i.e. consumers are compelled to bid their true WTP) (see⁶). For example, if a consumer’s true WTP were $5 and he bid $6 (an overbid), he may win the auction and have to pay $5.50, more than his true WTP. If he bid $4 (an underbid), someone else may win the auction and pay $4.50, which is less than his true WTP. In this way, underbidding and overbidding offer no strategic advantage.

To explain what drives WTP, after the bids are collected and before subjects leave, subjects fill out a survey with demographic and attitudinal questions. The nature of these questions depends on the context of the study, though the objective is to explain what drives WTP. For example, if the product were a nutraceutical, some questions should address the health consciousness of the consumer.

After the data is collected, regression analysis is used to predict willingness to pay. Willingness to pay is the y-variable, and the x-variables are chosen for their ability to explain WTP. The best models explain the most variance. If zeros bids are prevalent in the data, than tobit regression is appropriate to correct potential bias in the model. If zero bids are not prevalent, then random effects regression may be appropriate.

The SPEA is advantageous over other methods. For one, the laboratory setting in which SPEAs are executed reduces external distractions⁷. Secondly, the auction is non-hypothetical, which reminds consumers that real money is involved and thus eliminates hypothetical bias that may lead to inflated WTP values^8,9. The winner (i.e. highest bidder) of the auction actually pays the second highest bid and consumes the product. This structure compels subjects to bid their true WTP.

Generally, the SPEA costs more than other methods that measure WTP (e.g. conjoint analysis). Additionally, compensation payments can potentially bias consumers’ bids⁷. The SPEA does not mimic the grocery store setting because only one subject receives the product in the end. Potentially, this leads to competitive subjects inflating their bids because they receive additional utility from winning. Ideally, subjects should only bid on the utility they receive from the test product or product attribute. Alternative auction systems, such as the Becker, Degroot and Marschak (BDM) mechanism¹⁰ reduce the competitive nature of the auction through the establishment of a market price, any bidders above which are winners. Recently, the BDM mechanism elicited WTP for health benefits associated with added fiber¹¹, which demonstrates the BDM mechanism’s ability to determine the value of non-sensory product attributes.

SPEAs elicit WTP through a non-hypothetical mechanism. Measuring and explaining WTP can promote understanding of the consumer and potentially predict new product success¹². The SPEA is potentially a valuable asset to the sensory scientist.

References

¹ Vickrey, W. (1961). Counterspeculation, Auctions, and Competitive Sealed Tenders. Journal of Finance, 16, 8-37.

² Napolitano, F., Pacelli, C., Girolami, A. & Braghieri, A. (2008). Effect of information about animal welfare on consumer willingness to pay for yogurt. Journal of dairy science, 91, 910-917.

³ Lund, C.M., Jaeger, S.R., Amos, R.L., Brookfield, P. & Harker, F.R. (2006). Tradeoffs between emotional and sensory perceptions of freshness influence the price consumers will pay for apples: Results from an experimental market. Postharvest Biology and Technology, 41, 172-180.

⁴ Brown, J., Cranfield, J.A.L. & Henson, S. (2005). Relating consumer willingness-to-pay for food safety to risk tolerance: An experimental approach. Canadian Journal of Agricultural Economics-Revue Canadienne D Agroeconomie, 53, 249-263.

⁵ Napolitano, F. (2009). Meat liking, animal welfare and consumer willingness to pay. Italian Journal of Animal Science, 8, 469-476.

⁶ Lusk, J.L., Shogren, J.F. (2007). Experimental Auctions. Cambridge: Cambridge University Press.

⁷ Lee, K.H. & Hatcher, C.B. (2001). Willingness to pay for information: An analyst’s guide. Journal of Consumer Affairs, 35, 120-140.

⁸ Aadland D, Caplan A.J. (2006). Cheap talk reconsidered: New evidence from CVM. Journal of Economic Behavior & Organization 60, 562-578.

⁹ Harrison G.W. (2006). Experimental evidence on alternative environmental valuation methods. Environmental & Resource Economics, 34, 125-162.

¹⁰ G.M. Becker, G.M., Degroot, M.H., & Marschak, J. (1964). Measuring utility by a single-response sequential method. Behavioral Science, 9, 226-232.

¹¹ Ginon, E., Lohéac, Y., Martin, C., Combris, P., & Issanchou, S. (2009).
Effect of fibre information on consumer willingness to pay for French baguettes. Food Quality and Preference, 20, 343-352.

¹² Wertenbroch, K., Skiera, B. (2002). Measuring Consumers’ Willingness to Pay at the Point of Purchase. Journal of Marketing Research, 39, 228-241.

Antagonism

Antagonism is defined by ASTM International¹ as:

“antagonism, n—joint action of two or more stimuli whose combination elicits a level of sensation lower than that expected from combining the effects of each stimulus taken separately. (1996)”

References

¹ ASTM International. 2009. Standard Terminology Relating to Sensory Evaluations of Materials and Products, E253-09a. ASTM International, West Conshohocken, PA. E253-09a