| position effect, n —bias occurring as a function of the evaluation of stimuli in the first, second, or third, etc. place or position. (2008)

Reprinted, with permission, from ASTM E-253-Standard Terminology Relating to Sensory Evaluation of Materials and Products, copyright ASTM International, 100 Barr Harbor Drive, West Conshohocken, PA 19428. A copy of the ASTM standard may be obtained from ASTM International. http://www.astm.org/Standards/E253.htm

Spectrum Descriptive analysis overview

Descriptive analysis methods began with the Flavor Profile Method (Caul, 1957)¹ developed by the Arthur D Little Company in the early 1950s. Several food and pharmaceutical companies developed Flavor Profile panels over the next several decades. General Foods [now a part of Kraft] maintained several Flavor Profile panels for research and development and for quality purposes and began to change the Flavor Profile Method to adapt it to the various products and applications that needed detailed flavor descriptions. These adaptations included modifications such as: expanding the Flavor Profile 7 point scale to 14 points; using a control sample as a calibration sample in each tasting session; using intensity references as well as references for the terms or attributes used in the description; training panelists extensively and validating them for use on specific product testing; providing rigorous treatment of product procurement and preparation to insure product sampling integrity.

In addition, the Texture Center at General Foods was developing the Texture Profile Method, based on the Flavor Profile method and underpinned by the texture technology developed by Alina Szcsesniak and her team (Szczesniak, 1963)²; (Szczesniak, Brandt and Friedman, 1963)³; (Civille and Szczesniak 1973)⁴. With this foundation in Flavor and Texture Profile understanding and application to documenting products in development and operations, Gail Vance Civille developed the Spectrum™ Descriptive Analysis method during the 1970s and presented the method at the 3 IFT Sensory Evaluation Courses in 1979. The Spectrum Method incorporates the rigor of the training and structure of the Flavor and Texture Profile Methods and then adds a more refined scale [over 150 points of discrimination]; application of statistical methods to the descriptive data; and expansion to products outside of food and beverage, such as personal care products [both skinfeel and fragrance] and paper and fabrics [both fragrance and tactile feel]. (Meilgaard, Civille, Carr, 2007)⁵

In the last 20 years the Spectrum™ Descriptive Analysis Method has been used in government labs to document the sensory properties of commodities and products that use commodities. Academia uses the Spectrum™ Method to monitor and track basic and applied research in foods, personal care and paper products. However, the bulk of the application of the method is in R&D departments of consumer product companies globally to document the sensory properties for product development and quality control with accuracy and reliability.

The Spectrum™ Method supports the interest of product developers who need a clear documentation of a product attributes and their intensities that allows product profiles to be compared across products and across time with the same level of confidence the researcher expects from instrumental data. Descriptive analysis data should provide a product profile that allows the product developer to see the direction that process, ingredients or time have had on the product’s sensory characteristics.

References

¹ Caul JF (1957). The Profile Method of Flavor Analysis. Adv Food Res, 7: 1-40

² Szczesniak AS (1963). Classification of Textural Characteristics. J Food Sci, 28:4, 397

³ Szczesniak AS, Brandt MA and Friedman HH (1963). Development of Standard Rating Scales for Mechanical Parameters of Texture and Correlation between the Objective and the Sensory Methods of Texture Evaluation. J Food Sci, 28:4, 397-403

⁴ Civille GV and Szczesniak AS (1973). Guidelines to Training a Texture Profile Panel. J Texture Stud, 4: 204–223

⁵ Meilgaard MC, Civille GV, and Carr BT (2007). Sensory Evaluations Techniques, CRC Press, Boca Raton, FL

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. The skinfeel descriptive methodology was further advanced in the early nineties with two research papers published by Civille and Dus on skinfeel methodology for cosmetic toiletries and paper/fabric³⁴

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)⁵.

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 oil⁶⁷⁸⁹.

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 type¹⁰¹¹.

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 Civille GV and Dus CA (1991). Evaluating Tactile Properties of Skincare products: A descriptive analysis technique. Cosmet Toiletries, 106:5, 83-88

⁴ Civille GV and Dus CA (1990). Development of Terminology to Describe the Handfeel Properties of Paper and Fabrics. J Sens Stud, 5:19-32

⁵ ASTM, International, ASTM Standard E1490 – 03: Standard Practice fo Descriptive Skinfeel Analysis of Creams and Lotions. ASTM International: West Conshohocken, PA, 2003; p 16.

⁶ Parente, M. E.; Gambaro, A.; Ares, G., Sensory Characterization Of Emollients. Journal of Sensory Studies (2008) 23, 149–161.

⁷ Lee, I.; et al, Terminology Development And Panel Training For Sensory Evaluation Of Skin Care Products. Journal of Sensory Studies (2005) 20, 421–433.

⁸ 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.

⁹ 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.

¹⁰ 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.

¹¹ Meilgaard MC, Civille GV, and Carr BT (2007). Sensory Evaluations Techniques, CRC Press, Boca Raton, FL 

Labeled Magnitude Scale

The labeled magnitude scale (LMS) is a hybrid scaling technique using a verbally labeled line with quasi-logarithmic spacing between each label. The scale consists of a vertical line, which is marked with verbal anchors describing different intensities (e.g. “weak,” “strong”). Typically, subjects are instructed to place a mark on the line where their perceived intensity of sensation lies.

The LMS was developed as a way to directly study the perceptual differences between subjects¹. With the upper limit of the scale being the strongest imaginable sensation, the data obtained relies on the absolute strength of sensations. Studies have shown that the data lie on a non-linear scale¹. The verbal anchors are therefore spaced based on calibration using ratio-scaling, and the data obtained is very similar to that from magnitude estimation². The LMS is also useful in quantifying and comparing various forms and intensities of oral sensations, providing magnitude estimates for taste, touch, temperature, chemesthesis, and pain. Another advantage of the scale is that the semantic descriptors associated with the scale provide more detailed information, indicating for example whether a sensation lies between “weak” and “moderate” or “strong” and “very strong.”

After the development of the category-ratio (CR) scale by Borg during his studies on physical exertion³, Green created the LMS in his examination of gustatory perceptions, modeling it after the CR scale¹. The CR scale ranges from “no sensation” to “maximal” sensation, and this idea of absolute sensations is carried over to the LMS. In his study, Green had his subjects rate the magnitudes of semantic intensity descriptors (e.g. “weak,” “strong”) as well as rate the magnitudes of various oral sensations (e.g. the bitterness of celery, the burn of cinnamon gum). The intensity descriptors were then arranged based on their average magnitudes to create the LMS.

A disadvantage of the scale is that the instructions and frame of reference used for scaling with the LMS may have a large impact on the results. For example, studies have shown that wide ranges of concentration versus narrow ranges vary in the way in which they are scored⁴. In addition, it is questionable whether or not the upper limit of the “strongest imaginable” sensation provides a common frame of reference among subjects. Subjects may have different perceptual ranges, and therefore, the scale tends to show relative comparisons rather than absolute comparisons². If subjects differ greatly in their sensation perception (e.g. varying PTC taster groups), it is not clear whether this will affect the validity of across group comparisons with the LMS²,⁵. Studies comparing the LMS to magnitude estimation (ME) show that the LMS yields comparable results to ME if painful sensations (e.g. the burn of hot peppers) are present. When painful sensations are omitted, however, the LMS produces steeper and less linear functions than ME⁶. Therefore, it is not conclusive whether the LMS can be used for absolute comparisons; the validity of the scale may change depending on the context of the study.

⁷

References

¹ Green BG, Shaffer GS and Gilmore MM 1993. Derivation and evaluation of a semantic scale of oral sensation magnitude with apparent ratio properties. Chemical Senses. 18(6):683-702.

² Lawless HT, Heymann H 1998. Sensory evaluation of food: principles and practices. Aspen Publishers, Inc. Gaithersburg, Maryland.

³ Borg G 1982. A category scale with ratio properties for intermodal and interindividual comparisons. In Geissler, H-G. and Petxold, P. (eds), Psychophysical Judgment and the Process of Perception. VEB Deutxcher Verlag der Wissenschaften, Berlin, pp. 25-34.

⁴ Lawless HT, Horne J and Spiers W 2000. Contrast and range effects for category, magnitude and labeled magnitude scales in judgements of sweetness intensity. Chemical Senses. 25:85-92.

⁵ Bartoshuk LM, Duffy VB, Green BG, Hoffman HJ, Ko CW, Lucchina LA, Marks LE, Snyder DJ and Weiffenbach JM 2004. Valid across-group comparisons with labeled scales: the gLMS versus magnitude matching. Physiology and Behavior. 82:109-114.

⁶ Green BG, Dalton P, Cowart B, Shaffer G, Rankin K and Higgins J 1996. Evaluating the “Labeled Magnitude Scale” for measuring sensations of taste and smell. Chemical Senses. 21(3):323-334.

⁷ Guimaraes A, Peres M, Vieira R, Ferreira R, Ramos-Jorge M, Apolinario S and Debom A 2006. Self-perception of side effects by adolescents in a chlorhexidine-fluoride-based preventive oral health program. Journal of Applied Oral Science. 14(4):291-296.

Kano Modeling in Product Development

Kano modeling describes the relationships between fulfillment of a consumer requirement and satisfaction/dissatisfaction experienced by an individual consumer. This theory was proposed by Professor Noriaki Kano of Tokyo Rika University, Japan¹. In sensory evaluation, an attribute that is fully implemented would be an optimal intensity. This is an important difference with attributes where for example more is better.

Kano modeling states that the relationship between the performance of a product attribute and satisfaction/dissatisfaction level is not necessarily linear. Some attributes can be asymmetrically related with satisfaction/dissatisfaction levels. According to this principle, product attributes can be classified into one of five categories: Attractive, One-dimensional, Must-be, Indifferent and Reversal. The relationship between attribute fulfillment and satisfaction/dissatisfaction are visually presented in Figure 1.

Figure 1. Kano’s model customer satisfaction¹

- Attractive attribute: consumer will be satisfied when this attribute is fully provided; however, the non-fulfillment of this attributes does not cause dissatisfaction;

- One-dimensional attribute: consumer will be satisfied if this attribute is implemented; dissatisfaction will result if the attribute is non-fulfilled;

- Must-be attribute: Dissatisfaction results from the attribute not being fulfilled. However, fulfillment of the attribute does not increase satisfaction for the product;

- Indifferent attribute: consumer satisfaction/dissatisfaction will not be affected by this attribute’ performance;

- Reversal attribute: consumers will be dissatisfied when this attribute is fulfilled and satisfied when it is not;

The classification of each attribute can be identified through Kano’s paired functional/ dysfunctional questionnaire and its evaluation table (Figure 2 and Table 1)^{1 ,2}. Each attribute’s category can be determined using the paired functional/dysfunctional questionnaire evaluation table (Table 1).

Figure 2. Kano paired functional/dysfunctional questionnaire

There are several advantages to classify consumer requirements into Kano modeling attributes³:

- Classification of attributes can be employed to optimize products and improve innovation;

- Discovering and providing attractive attributes create more opportunities for product differentiation in the target market;

- Attribute classification provides valuable help in trade-off situation in the stage of product development. Sometimes two products requirement cannot be met simultaneously due to technical or financial reason. Based on Kano modeling results, priority can be given to that attribute which has greatest influence on consumer dissatisfaction.

Reference

¹ Berger C., et al.1993. Kano’s methods for understanding customer-defined quality. Center for Quality Management Journal (Fall), 3-35.

² Kano N., Seraku Nk, Takahashi F., Tsuji S.1984. Attractive quality and must be quality. Quality,14:39-48.

³ Matzler K., Hinterhuber, HH., Bailom F., Sauerwein E.1996. How to delight your customers. Journal of Product and Brand Management. 5: 6-18.

Taste-modifying compounds.

Taste-modifying compounds, or taste modifiers, are chemical substances that alter one or more of the Basic Tastes. Many studies have discovered numerous types of taste-modifying proteins found in nature that are categorized as having sweet, antisweet, and sweetness-inducing abilities. It is beneficial to understand the mechanisms of these substances and their potential uses in foods to enhance flavor or serve as a potential treatment of diabetes, obesity, and other metabolic disorders.¹

Antisweet substances

Antisweet substances inhibit the sweet taste in food products with little to no effect on salty, sour, or bitter tastes². Some of these substances include include gymnemic acid, ziziphin, hodulcin, and gumarin. The leaves of Gymnema sylvestre contain gymnemic acid which suppresses all sweet tastes in humans³. In India, it is used as a natural remedy for diabetes mellitus. Gumarin is also found in Gymnema sylvestre although it does not have an effect on humans, but rather suppressing the sweetness in rats². Ziziphin, which is highly specific for sweetness, is found in the leaves of the northern China Ziziphus jujube which also inhibits the sweetness in numerous different sweeteners². Found in the leaves of the Chinese and Japanese Hovenia dulcis, Hodulcin is also known to suppress sweetness².

Sweet proteins

When present in foods, sweet proteins enhance the intensity of sweet taste present in a food, without the addition of sugars. The lack of sugar leads to the possible development of low calorie sweeteners which can contain one or more of these proteins. Some of the known sweet proteins include thaumatin, monellin, mabinlin, and pentadin¹. Thaumatin is 3,000 times sweeter than sucrose and is currently commercially available as a sweetener and flavor enhancer. Monellin is 2,800 times sweeter than sucrose. Mabinlin is found in the tennis-ball sized fruit of the Chinese Capparis masaikai and is approximately 350 -400 times sweeter than sucrose. Pentadin, found in Pentadiplandra brazzeana, is approximately 500 times sweeter than sucrose. When compared to thaumatin and monellin, it has both a quicker onset and decline of sweetness.

Sweetness-inducing proteins

Sweetness-inducing proteins alter the taste of one or more of the Basic Tastes, especially sour into a sweet taste. The mechanism involved with the sweet receptors on the tongue is due to the conformation of the protein. The two major proteins include miraculin and curculin, which are used to control the palability of foods or in new food product development¹. Miraculin, which is tasteless by itself, is the active compound found in the berries of the western African miracle fruit which converts sour into sweet taste. For example, when miraculin is consumed and lemons are eaten, they become excessively sweet with a taste comparable to lemonade³. Curculin is a naturally sweet compound by itself found in the fruit of the western Malaysian Curculigo latifolia². It also transforms a sour taste into a sweet taste. Both miraculin and curculin have potential uses to remove the sourness and enhance the sweetness of food products.

References

¹ Gibbs BF, Alli, I, Mulligan C. 1996. Sweet and taste-modifying proteins: a review. Nutrition Research 16: 1619-1630.

² Kurihara Y, Nirasaw S. 1994. Sweet, antisweet and sweetness-inducing substances. Trends in Food Science and Technology 5: 37-41.

³ Bartoshuk LM, Dateo GP, Vandenbelt DJ, Buttrick RL, Long L. 1969. Effects of Gymnema sylvestre and Synsepalum Dulcificum on taste in man. Olfaction and Taste III. University Press, NY.

Synesthesia, a surprisingly common sensory phenomenon.

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.

Sensory Shelf-life Test

The sensory shelf-life test is designed to validate the length of time that a product will remain the same “acceptable quality⁸” level or have “no change in desired sensory characteristics⁴” over that entire life of a product. Changes in sensory properties are very sensitive for consumers rejecting products. Some product properties are too complicate to measure objectively. Moreover, instrumental measurement alone cannot indicate consumer acceptability or rejection. It is very important to ensure no change in sensory properties since consumers want to pay for desired sensory characteristics at their acceptable level. Researchers, therefore, conducts a sensory shelf-life test by deciding on which sensory test to perform, how to organize a test, i.e. length of study, product storage condition and period, evaluated schedule^7,8, and analysis method¹².

Sensory Test Selection and Applications

A descriptive test can be done to measure quality changes¹¹, while, at critical time point, performing hedonic testing can evaluate the impact of quality changes on consumers’ acceptability or preference5. A difference test may be used¹² when the shelf-life criterion is the first detectable change in specific attribute (e.g. an occurrence of stale/off-flavor) or overall difference. Moreover, acceptance test or preference test alone has also been used⁶ to estimate shelf-life.

Examples of Test Organization and Analysis

To determine which approach to conduct a sensory shelf-life test depends on company resource flexibility, time line, product deterioration mechanism, factors affecting shelf-life, attributes to be measured, and action standards^7,8.

For example, a study on shelf-life extension of salmon slices¹⁴, the samples were purchased from different markets, assigned to 5 treatments with one being a control, and stored at 1°C. At 0, 3, 6, 9, 12, and 15 days of storage, samples were cooked and examined by 15–semi-trained panelists for overall acceptability on appearance, odor intensity, salmon flavor, aftertaste, tenderness, juiciness, off-odor, and off-flavor using hedonic scales. Scores of greater than 4 were considered acceptable, while 3 or 4 was a borderline acceptability. Based on analysis of variance , no difference was found among the treatments. Although overall acceptability significantly decreased with storage time, the scores for each attribute rated over storage periods were in acceptable range. In conclusion, minor changes over storage period were observed and all treatments could extend untreated shelf-life of 8 days to 12-15 days.

Another study was conducted to evaluate consumer acceptability of minced meat stored at various temperatures and times³. Enough minced beef was divided into glass bottles, 30-grams each and frozen at -18°C. Bottles were randomly removed, thawed to 4°C at different time interval, and assigned to the following storage temperatures and times.
• 2°C: 0, 24, 48, 96, 144, 192, 240 hours
• 9°C: 0, 24, 48, 72, 96, 120, 144 hours
• 19°C: 0, 6, 12, 18, 24, 36, 48 hours

Increased temperature treatments were used to accelerate storage condition. With this test organization, 21 samples from all storage temperatures and times could be evaluated at the same time. Sixty consumers considered minced beef appearance and answered (yes/no) whether they would normally consume each sample. Predictive equations for estimating minced beef shelf-life were constructed based on survival analysis and Arrhenius model. With 50% consumer rejection and 95% confidence intervals, the predicted shelf-life at temperatures of 2, 9, and 19°C were 88, 44, and 17 hours, respectively. Additionally, predicted shelf-lives of minced beef stored at different temperatures with different consumer rejection percentages were successfully calculated.

Various applications^2,10,13 and analyses^1,6,9 of sensory shelf-life test have been published.

Since there are many ways to conduct sensory shelf-life testing, researchers should carefully and thoroughly design the study and consider its impacts⁸ before conducting each study.

References

¹ Calle, M.L., Hough, G., Curia, A., and Gómez, G. 2006. Bayesian survival analysis modeling applied to sensory shelf life of foods. Food Quality and Preference, 17: 307-312.

² Diez, A.M., Santos E.M., Jaime, I., and Rovira, J. 2009. Effectiveness of combined preservation methods to extend the shelf life of Morcilla de Burgos. Meat Science, 81: 171-177.

³ Hough, G., Garitta, L., and Gómez, G. 2006. Sensory shelf-life predictions by survival analysis accelerated storage models. Food Quality and Preference, 17: 468-473.

⁴ IFTS. 1993. Shelf life of Foods-Guideline for its Determination and Prediction. IFTS, London.

⁵ Garitta, L., Hough, G., and Sánchez, R. 2004. Sensory shelf life od Dulce de Leche. American Dairy Science Association, 87: 1601-1607

⁶ Giménez, A., Ares, G., and Gámbaro A. 2008. Survival analysis to estimate sensory shelf life using acceptability scores. Sensory studies, 23: 571-582.

⁷ Kilcast, D. 2006. What approaches does a leading consultancy firm use to estimate shelf-life when time schedules are short? In Workshop summary: Sensory shelf-life testing. Food Quality and Preference, 17: 640-645.

⁸ Kilcast, D., Subramaniam, P. 2000. Stability and Shelf-life of Foods. pp.1-22, 79-105. New York: CRC Press.

⁹ Ledauphin, S., Pommeret, D., and Qannari, El M. 2008. Application of hidden Markov model to products shelf lives. Food Quality and Preference, 19:156-161.

¹⁰ Manzocco, M. and Lagazio, C. 2009. Coffee brew shelf life modeling by integration of acceptability and quality data. Food quality and Preference, 20: 24-29.

¹¹ Martinez, C., Mucci, A., Santa Cruz, M.J., Hough, G., and Sanchez, R. 1998. Influenze of temperature, fat content and package material on the sensory shelf-life of a commercial mayonnaise. Sensory Studies, 13: 331-346.

¹² Munoz, A.M., Carr, B.T., and Civille, G.V. 1992. Sensory Evaluation in Quality Control. New York: Van Nostrand Reinhold.

¹³ Patsias, A., Chouliara, I., Badeka, A., Savvaidis I.N., and Kontominas, M.G. 2006. Shelf-life of a chilled precooked chicken product stored in air and under modified atmospheres: microbiological, chemical, sensory attributes. Food Microbiology, 23: 423-429.

¹⁴ Sallam, K.I. 2007 Chemical, sensory and shelf life evaluation of sliced salmon treated with salts of organic acids. Food Chemistry, 101: 592-600.

Psychological Errors in Sensory testing and Ways to Reduce Them

Central Tendency: Subjects rate samples using the middle point of scale and avoid using the extreme ends.
Reasons: Subjects are afraid to use the ends because these may be a sample that has higher /lower intensity than the sample that was just tested.
Antidotes: Use scales that have less defined endpoints. Use subjects that are familiar with the tested samples.

Expectation Error: Subjects rate samples according to their expectation based on previous knowledge of the product information.
Reasons: Subjects try to make the right answer from the knowledge about product or the variables that direct interest in the study.
Antidotes: Not giving any information concerning with the test and samples. Order of presentation should be randomized for each subject and assigned unbiased codes for each sample.

Halo effect: Subjects rate the same attributes when they appear in a series of questions differently from when they were asked separately.
Reasons: This error occurs when there are more than one attributes including in the test, especially with the study that have both acceptance and intensity questions. Subjects try to rate intensity attributes to match with their liking.
Antidotes: Randomize the order of attributes, separate intensity and acceptance attributes, provide training if appropriate.

Error of Habituation: Subjects provide the same response to a series of products that might be slightly different from time to time. As a result, the test might not be able to capture any different or trend.
Reasons: Subjects become complacent and start evaluating using a routine rather than concentrating on each sample.
Antidotes: Varying the types of samples, make sure panelists know you are tracking performance; add a modified product into the test.

Stimulus Error: Subjects rate samples according to the other stimulus and not on their perception from the samples.
Reasons: Subjects use prior knowledge about products to “get it right” or because they could expect a particular characteristic or intensity based on a psychological or physical stimulus such as package (color, style, type of package) or defects or characteristic from a different preparation method.
Antidotes: Provide as little information to panelists as possible. Remove packaging or other “clues” whenever possible and appropriate. Never include the person who directly prepares or formulates the test as a subject in the study. Avoid leaving any indication that will lead to product identification or information.

Logical Error: Subjects rate attributes logically on how the attributes are associated.
Reasons: Subjects relate two or more attributes to each other. For example in case of chocolate cake, subject relates the dark color with chocolate flavor and rate the darker color with the higher intensity of chocolate flavor.
Antidotes: Keeping the sample uniform by masking the samples. Using doctored samples to exercise trained panels.

Contrast Error: Subjects rate the differences between samples greater (or lesser) than they actually are.
Reasons: Occurs when panelists “compare” (even without realizing they are comparing) samples, which can tend to exaggerate the magnitude of difference in subject mind in the mind of the subject. For example, a poorer quality sample was followed a higher quality sample the score for the higher quality sample may be artificially high.
Antidotes: Error can be minimized by using balanced and randomized sample presentation order and may be reduced to some extent by training.

References:

¹ Chambers, E. IV. and Wolf, M.B. 1996. (Eds.) Sensory Testing Methods, 2nd Edition. ASTM, West Conshohocken. PA.

² Meilgaard, D., Civille, G.V. and Carr, B. 2007 Sensory Evaluation Techniques, 4th edition. CRC Press, Boca Raton, FL.

³ Stone, H., Sidel, J. L. 2004. Sensory Evaluation Practices. 3rd edition. Academic Press, Inc., San Diego, CA.

Moderating: 10 key skills and qualities (SAQs) of an effective focus group moderator

Focus Groups are qualitative research tools used to explore/understand consumers’ perceptions, opinions, beliefs, and attitudes (POBAs)¹ regarding a specific topic. A fundamental element of a focus group is the moderator. The skills and qualities (SAQs) of a moderator are keys to success for an effective group. Below are 10 key SAQs every moderator should practice^2,3,4

1. Build Rapport- smile, make eye contact, and allow introductions; create a warm, supportive, and comfortable environment.
2. Be an Active listener- focus on what is being said; use respondent comments as you paraphrase/summarize; nod your head; lean forward as you listen.
3. Remain Neutral yet Involved- maintain objectivity both verbally and non-verbally; remember 80:20 rule- the participants talk 80% of the time and the moderator 20%.
4. Be Flexible- adapt to the flow of the discussion; remain open to changes in the moderator’s guide; adjust to client’s requests during the group; change your physical behavior-sit, stand, or walk around the room.
5. Use the “5-second Pause” and “Probe” techniques- ask clear questions and pause for consumers’ responses; probe for more information/ clarity of comments- avoid asking why.
6. Acknowledge and Respect- recognize each participant throughout the focus group session; respect various points of view, and emphasize respect among the group.
7. Practice good Organization/Management Skills- practice the guide, prepare for the unexpected; keep the discussion moving, focused, and within the established timeframe.
8. Have Knowledge of the topic- basic information on the subject helps in probing areas for more in-depth discussion; demonstrate a degree of naïveté.
9. Be Enthusiastic and Attentive- have a high energy level; pay attention to participants- recognize group dynamics.
10. Have a Sense of Humor- laughter keeps the group relaxed, encourages sharing of information, and helps the moderator maintain a human connection.

References:

¹ Puchta, C. and Potter, J. (2004). Focus group practice. California: Sage Publications.

² Krueger, R.A. and Casey, M. (2000). Focus groups, 3rd Edition. California: Sage Publications.

³ Krueger, R.A. and Casey, M. (2000). Focus groups, 3rd Edition. California: Sage Publications.

⁴ Henderson, N.R. Fundamentals of moderating. Maryland: RIVA Training Institute.

⁵ Edmunds, H. (1999). The focus group research handbook. Illinois: NTC Business Books.