Advanced Techniques

There exist many different methods to collect and analyze sensory data. In some cases, the traditional methods of descriptive or consumer sensory testing are not ideal to capture meaningful information in an efficient manner. Choosing a more specialized testing regime may elicit better information in a shorter amount of time.

Preference Mapping

Preference mapping encompasses a wide variety of multivariate statistical testing techniques. These tests target understanding how consumers like certain products and how to maximize overall consumer acceptability.

The two types of preference mapping often discussed are internal preference mapping and external preference mapping. Internal preference mapping consists of collecting consumer liking data and understanding consumer segmentation. External preference mapping involves both consumer liking data as well as descriptive analysis by a trained panel. External preference mapping first begins with creating a sensory “map” of the products based on the intensity of certain flavor/texture/appearance attributes measured by a trained panel. After that, consumer liking data is then projected on the sensory “map” in order to assess which product attributes have the biggest impact on consumer acceptability. Please see the image below for an example of an external preference map.

Figure 1. External preference map for 10 cheeses. Cheeses were evaluated by consumers (n=100) for likability and by trained descriptive panelists (n=10) for flavor/texture.


Explanation
Overall liking seems to be driven by high amounts of buttery flavor and sweetness. Low amounts of acidity and bitterness also appear to be linked to overall liking. This supposition is supported by the fact that Cheese_3, Cheese_4, and Cheese_7 had the highest overall liking scores and most intense buttery flavor and sweetness (data not shown here). Cheeses in the lower left of the preference map had the lowest liking scores and were low in buttery flavor and sweetness, as well as higher in bitterness and acidity.

Using this information, buttery flavor and sweetness can assist in determining consumer acceptability for other comparable products for the tested segmentation of consumers.

Projective Mapping

Projective mapping (aka Napping®) involves participants placing products on a product space based on how similar/different they are. The participants can use any criteria they see fit to arrange samples in the 2-D space. This technique is rapid and efficient due to the fact that no training is required.

This method is best used when there are a large number of samples (> 8) that need to be differentiated. In many cases, projective mapping can be used to differentiate products as accurately as descriptive analysis. A common use for this type of testing is to better understand where a consumer might “group” a product against competitors; i.e. (“Would consumers consider this cheese to be medium or sharp?” )This type of testing can be problematic however, when the products are too similar to each other.

Raw data from projective mapping is in the form of coordinates for each product (X,Y) along with any comments from the participants. Using these product locations, statistical operations can be conducted in order to obtain a product map; Multi-Factor Analysis (MFA) and General Procrustes Analysis (GPA) are common. Below is an example of the projective mapping process.

Figure 2. An example of an electronic product space where participants place and group products according to how similar they are. The axes are arbitrary, with evaluators choosing how to define “similar”. Here, 10 cheddar samples need to be mapped.


Figure 3. Product map generated from projective mapping of 10 cheddar cheeses. Groupings are based on comments from participants. (Cheese samples shown in red)


Explanation
This product map shows how participants were able to differentiate the 10 different cheddar cheeses based on flavor. For example, TILLA MED and MED CHED were perceived to be very similar to each other and also somewhat similar to MILD CHED. DUBLINER was found to be quite different than the other samples and is further away from other samples. Groupings and group names were created based on the comments from participant feedback.

Time Intensity

Time-intensity (TI) methods are used to give information on how flavor and texture changes over time. At a basic level, TI analysis focuses on measuring various attributes over time while the sample is being chewed and after the sample is swallowed or expectorated. The assessor will rate the intensity for each attribute during that time on either a continuous or discrete basis. A sub-set of TI is known as the Temporal Dominance of Sensations (TDS) method. Here, only the most dominant or “striking” attribute is measured at a given moment. For this reason, TDS is quicker to conduct and likely mimics how an average consumer perceives sensory attributes.

This technique is usually performed by a trained descriptive panel due to complexity of capturing measurements. Output from TI and TDS testing is usually presented in graphical form.

Figure 4. An example of TDS output for an aged cheddar evaluated by a trained descriptive panel (n=12).


Explanation
This graph shows the average dominance rates for the four basic tastes of an aged cheddar over 1 minute. (Salt: pink, Bitter: green, Acid: red, Sweet: blue) Panelists were instructed to swallow the sample after 35 seconds (denoted by black bar).

It is apparent that acid, bitter, and salt tastes were dominant during chewing. After swallowing the samples, acid and bitter became the dominant sensations. This is supported by panelists commenting on a lingering bitter aftertaste.

CATA Analysis

Check-all-that-apply (CATA) analysis involves presenting a checklist-style questionnaire to consumers. Consumers are instructed to taste a sample and then “check all that apply” from a list of attributes encompassing flavor, texture, aroma, appearance, liking, etc. This is perhaps one of the simplest consumer sensory tests due to its simple setup and ease of use.

While at the surface CATA testing appears to be a qualitative evaluation, it does in fact offer some powerful quantitative options. By counting the number of times each term is selected by consumers, statistical operations can be used to find differences between products and to even create a product map (shown below).

Figure 5. Product map generated from CATA testing of seven cheddar cheeses.


Figure 6. Attribute map generated from CATA testing of seven cheddar cheeses. (Projected onto the same space as the product map, Figure 5)

Explanation
CATA testing clearly highlights consumers found differences between products (Figure 5 – product map). Attribute map (Figure 6) indicates attributes consumers associated with each grouping of products.