Biases in Sensory Evaluation

Transcription

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2 Biases in Sensory Evaluation

3 Tasters are very prone to biases. Even the most experienced, well - intentioned taster will be influenced by irrelevant or unintended stimuli unrelated to his / her sensory perceptions during a taste panel It is the panel leaders job to be aware of the different types of biases, and to minimize the possibility of biases affecting a panels results

4 Psychological Factors: Expectational Errors preconceived notions about samples being tasted example: Samples always arranged in same order leads to taster to expect certain samples to possess certain attributes solution: Panel leader must mix order to keep from being predictable

5 Psychological Factors: Habitual Errors panelist gives same responses to samples day in day out example: by tasting one brand over and over a panelist becomes familiar with the attributes and intensities that are normally present leads to insensitivity; a new attribute can become more pronounced over period of time solution: keep tasters on their toes by spiking samples, introducing competitors brands - keep tasters from falling into a rut

6 Psychological Factors: Stimulus Errors irrelevant stimulus affecting response example: clarity or foam stability may influence tasters response these are not necessarily related to beer flavor but can influence a taster s response solution: use ruby red glasses, red lighting for taste panels

7 Psychological Factors: Logical Errors associate one flavor attribute with another example: high level of hop aroma may be judged as having a high bitterness level. All dark beers are high in alcohol aroma and bitterness or color and strength are not necessarily related solution: train panelists to look at each attribute independently, and not to draw conclusions based on other attributes being either absent or present

8 Psychological Factors: Halo Effect if a product is rated as having a high quality, tasters tend to rate flavor attributes favorably as well example: can also work in reverse; sample with diacetyl is graded low however style guideline may allow to an extent this is correct solution: separate panels should be run for product acceptability and specific flavor attributes

9 Physiological Factors: Adaptation Error tasters sensitivity is changed, usually decreased, with prolonged exposure to a stimulus example: bitterness and sourness will carry over to other samples even at increasing concentrations, tasters can become desensitized to certain flavor attributes solution: allow sufficient time between samples to rest palate

10 Physiological Factors: Enhancement Error presence of one attribute will increase a tasters sensitivity to another example: carbonation, umami, salt most are related to trigeminal amplifying attribute solution: be aware of this phenomena and use training to help illustrate this concept

11 Order of Presentation: Contrast Effect when a sample of superior quality is placed just before a sample of poorer quality, the second sample will be rated lower than it would have if judged on its own example: presenting a light beer before a malt liquor solution: always present samples from lightest to more flavorful. Additionally, hold separate panels for styles that possess large differences

12 Order of Presentation: Group Effect when a sample of superior quality is placed in a group of samples of poorer quality, the sample of superior quality will be rated lower than it would have if judged on its own example: presenting a light beer with abused samples solution: try to evaluate similar samples at the same time

13 Order of Presentation: Pattern Effect panelists, especially good ones are quick to pick up on patterns example: presenting a group of samples in the same order again and again solution: mix up the presentation order and be on the look-out for patterns emerging

14 Order of Presentation: Positional Bias Effect the first sample in a set is generally rated higher than the last panelists eagerly anticipating the taste panel find their first sample inordinately pleasing solution: through training, tasters must be made aware of this phenomena and make efforts to overcome it

15 Mutual Suggestion: Influence by Others some panelists (company president or master brewer) may exert more influence than their acuity warrants example: dominating personalities, loud comments, facial expressions solution: no talking or panel interaction until all ballots are turned in

16 Lack of Motivation: persons who lack motivation or are under emotional stress should be excused from participating in a taste panel example: elevated work schedule, conflicting schedule solution: thorough screening and interview should help to identify those that are interested in participating. Panel leader is responsible for the upkeep of moral through perks, rewards, etc.

17 Gary Spedding and Tony Aiken for the American Brewers Guild Brewing School

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19 Color Biases: A case study Charles Bamforth s 2014 book, Flavor, describes a triangle test he conducted in the 1980s where a panel was given 2 beer samples that were identical except for the color. In Bamforth s account Sample A was described as a Standard Lager and Sample B was the same plus 9 units of color

20 Color Biases: A case study We conducted the Bamforth color test with the following parameters: q 16 BJCP judges (Recognized or above) q well brewed local Pilsner (Blue Stallion Brewing of Lexington, KY) q panelists told to judge beers as European Lagers q dyed beer color appeared to be black (was actually a very dark green) q one panelist remarked aloud that the beer looked green (I thought the gig was up)

21 Color Biases: A case study We conducted the Bamforth Color test... continued: q early in the training session panelists were instructed not to consider color when judging any beers during the class but not again just prior to beginning this particular assessment q assessments were done using BDAS s 40 question paper ballot q samples could be tasted in any order but the darker sample was listed first on the ballot q none of the panelists were familiar with beer or the ballot

22 Color Biases: A case study The results are exemplary of how a panel functions Most of the individual panelists varied greatly in there scores on at least a few of the attributes When the results are compared in the aggregate the findings were quite similar

23 Color Biases: A case study

24 Color Biases: A case study Frequency of single judges rating the same attribute with a difference of 40% or more. 50.0% Bitterness Impact 50.0% Overall Rating: 43.8% Harsh-Smooth 37.5% Hop Intensity 37.5% Dry 37.5% Astringent/Drying 37.5% Bitterness Lingers 31.3% Aroma 31.3% Grainy/Husk/Cereal 31.3% Spicy/Citrus/Floral 31.3% Malty 31.3% Burnt/Roasted 31.3% Phenolic/Medicinal

25 Color Biases: A case study

26 Color Biases: A case study Mean average of attributes graphed for dark sample Mean average of attributes graphed for plain sample Differences between the mean averages of attributes graphed for plain and dark beer samples

27 Gary Spedding and Tony Aiken for the American Brewers Guild Brewing School