Chemistry of Taste - American Chemical Society

Chapter 17

Sensory Drivers of Liking and Sensory Preference Segmentation Howard R . Moskowitz

Downloaded by UNIV LAVAL on April 15, 2016 | http://pubs.acs.org Publication Date: July 19, 2002 | doi: 10.1021/bk-2002-0825.ch017

Moskowitz Jacobs Inc., 1025 Westchester Avenue, White Plains, N Y 10604

Abstract People differ in what they like and dislike. Nowhere is this inter-individual variability so evident as in the chemical senses, taste and smell. This paper presents an analysis of the individual differences, focusing on food products (specifically coffee and pasta sauce). The paper shows how to measure 'drivers of liking' (sensory attributes that drive product acceptance). The relation between sensory intensity and liking is typically an inverted U shaped curve. On a person by person basis, however, this relation takes on different shapes. One can segment consumers by the specific shape of their sensory-liking function. This segmentation generates different groups of people, distributed throughout the population, with the segments showing radically different patterns relating sensory magnitude to liking. The paper discusses the use of this analytic approach both for science (viz., to understand how people transform the sensory information into liking ratings), and for practical product development (viz., how to create highly acceptable products, targeted to a specific sensory segment).

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© 2002 American Chemical Society Given and Paredes; Chemistry of Taste ACS Symposium Series; American Chemical Society: Washington, DC, 2002.

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Introduction


Quite often the first response to a food, perfume or to a simple chemosensory stimulus is Ί like it' or Ί dislike it'. Hedonics, likes and dislikes, constitute an exceptionally important aspect of taste and smell. The hedonic tone of a chemo-sensory stimulus may drive consumption i f the stimulus is a food, or purchase and use if the stimulus is used for health and beauty aids. A recurrent research problem concerns the 'drivers' of liking - or, more specifically, the variables of a chemo-sensory stimulus that change the degree of liking. One key driver is the sensory quality. We like the taste of sweet, we dislike the taste of bitter, and we may like or dislike the sour and salt tastes. Whether working in taste or in smell, researchers do not know why some sensory qualities are liked, whereas others are disliked, and thus this issue is not one that is empirically addressed, except perhaps by large scale polling questions (viz., where the issue is to measure the proportion of people who like or dislike a particular stimulus). One way to look at the drivers of liking uses an equation relating overall liking to each of the separate liking attributes (e.g., liking of appearance, liking of aroma, liking of taste, liking of texture, etc.). The equation describes a straight line, since in most cases there is a positive relation between attribute liking and overall liking. A simple correlation coefficient will not do, however, because correlation analysis often shows that all attribute liking ratings correlate with overall liking. A more productive analysis fits the linear equation to the results (Overall Liking = k + ^(Attribute Liking)), and then looks at the slope (kj). The higher the value of k the more important is the particular sensory input to the overall liking rating (1). 0

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A more tractable aspect of drivers of liking deals with the change in degree of liking as the physical stimulus magnitude changes. A typical sensoryliking curve appears in Figure 1. As the physical magnitude of the stimulus changes (producing a perceived change in the sensory magnitude), the degree of liking also changes. The particular pattern is a function of the stimulus being tested, whether the stimulus is a model system or a food/perfume, and whether there is an accompanying cognitive input, such as a description of what a stimulus is supposed to be. [For instance, we may hate the bitter taste of a liquid, until we find out that the liquid is a bitter aperitif, meant to be consumed before a meal]. The key metric here will be the area under the sensory-liking curve.

Given and Paredes; Chemistry of Taste ACS Symposium Series; American Chemical Society: Washington, DC, 2002.

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Figure 1: The relation between sensory level and liking. The area under the curve is a measure of relative importance. What Determines The Sensory Liking Curve? Figure 1 shows a quadratic function fitted to the empirical point. The specific nature of the curve will vary as a function of the products evaluated, the sensory range considered (larger sensory ranges give more information about the real relation), the sensory attribute used as the independent variable (taste / smell / flavor attributes generate steeper curves than do texture / appearance attributes), and the range of products evaluated.

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The approach to develop the sensory-liking curves follows these steps: Lay out the data in the form of a scatter-gram, with sensory attribute as the independent variable, and liking (or other evaluative attribute) as the dependent variable. Use average data only, rather than individual data. Fit a quadratic function to the data, using this expression: Liking = k +ki (Sensory Level) + k (Sensory Level) Estimate the goodness of fit (e.g., the multiple R ) Show the estimated quadratic function It is worth noting that the quadratic function is the most parsimonious expression of the relation between sensory attribute and liking. The quadratic function allows for a non-linear relation (but does not force that non-linear relation). 0

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Individual Differences In The Drivers O f Liking

Individual differences have long been recognized as pervasive in the chemical senses (2,3,4,5). For the most part these differences have been relegated to the realm of unexplained or unexplainable variability. Occasionally the differences might be traced to body state and ingestion (6,7), but for the most part the variability is so pronounced and the organizing principle underlying the variability so absent that the inter-individual differences have been ignored. Typically, they are averaged out. Consumer researchers recognize this variability, leading them to tabulate the data from many people by different key subgroups that can be easily determined in the population (e.g., age, income, gender, product usage, etc.). The tabulations do not shed much light on the relation between the variability and membership in a key subgroup. In the mid 1980's the author suggested an alternative way to look at inter-individual variability (8). The key was the sensory-liking function. A given individual might show a peak on the function at some specific sensory level. Rather than looking at variability due to ratings of liking, the suggestion was made to look at the variability of the optimum level of an individual. Each individual generated a specific optimum level somewhere on the curve. The height of the curve (corresponding to the highest degree of liking) was not relevant, since this could be due to scaling biases. [Viz., some individuals might naturally assign higher numbers, whereas other individuals might assign lower numbers. Yet, the individuals might agree on the sensory level at which the liking ratings would reach its optimum].

Using The Sensory-Liking Optimum To Create Operationally Defined Segments The sensory-liking curve generates a single point for each individual, for each attribute. For a complex product that excites several sensory systems, and has multiple sensory attributes (even within the same sensory system) one can develop a number of different sensory-liking curves. Each sensory attribute will generate one specific sensory-liking curve for a particular panelist. With M sensory attributes (distributed across appearance, aroma, taste, and texture) and with Ρ people, there will be M x P different curves, and consequently M x P different sensory optima. Many of these sensory optima will correlate with each other, simply because many of the sensory attributes correlate with each other. Consequently, one must factor analyze the matrix of the M different optima, and reduce this matrix to a smaller size, comprising uncorrected variables. [The specific methods have been previously presented(8)].


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The analysis generates a set of variables, and the factor scores of the panelists. One then clusters the panelists together, based upon their factor scores. Panelists falling into the same cluster typically have similar profiles of factor scores, and thus show similar profiles of sensory optima. Although this approach is empirical, it has worked for 20 years (9) and appears to generate segments of panelists with radically different sensory-liking curves. Furthermore, the sensory-liking curves for panelists in different sensory segments diverge from each other, whereas the sensory-liking curves for panelists divided by more traditional means (e.g., age, brand used most often) converge, and are almost identical to each other.

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Figure 2: Relation between the bitter taste of coffeefromthe expert panel (xaxis), and overall liking by the consumer panel (y-axis). The graph on the left is from five countries on a country-by-country basis, and the graph on the right represents segment analysis results. Figure 2 shows the results of a large-scale study by the European Sensory Network, as reanalyzed by the author. The independent variable is the bitter taste, as profiled by expert panels. The dependent variable is liking, assigned by the consumer panelist. The left panel shows the results for the five European countries. The right panel shows the results after the data were subjected to sensory preference modeling and segmentation. The five countries show similar results, whereas the sensory preference results show three segments, distributed in different proportions (see Table I).


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Table I: Distribution of three sensory segments for coffee as a function of the country. Note the differences in proportions for each country.


Segment 1

Segment 2

Segment 3

Denmark France Germany Poland United K . Total

10% 8% 12% 38% 29% 20%

51% 40% 45% 29% 46% 41%

39% 53% 42% 34% 25% 38%

Male Female

25% 19%

41% 42%

35% 40%

Age Age Age Age Age Age

17% 19% 27% 14% 22% 27%

46% 40% 36% 45% 50% 27%

37% 41% 37% 41% 28% 47%

16-24 25-34 35-44 45-54 55-64 65+

Results - Looking A t A Complex Product That Excites Different Senses

One way to understand these drivers of liking and sensory segments is through an analysis of a single product. The optimal conditions for that product are that the product represent either many different in-market products with varying sensory characteristics, or that the product be systematically varied on a set of ingredients, which variation will generate a wide variation in sensory attribute levels. Since sensory preference segmentation is a post-hoc analysis, it is critical that the researcher starts off with a wide range of sensory levels. The specific product is a pasta sauce. During the past decade and a half the pasta sauce market has expanded with many new products exciting different appearance attributes, aromas, tastes and textures. As a consequence, any evaluation of the product category will generate a plethora of different sensory impressions. Thus pasta sauce makes a perfect stimulus with which to investigate the drivers of liking and the sensory segments.


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Research Protocol


Tests with multiple products are often run in a central location, well supervised, in an extended test session (9). The extended session allows the panelists to evaluate a number of different products (here 18 of 50), over several days (here two sessions, each lasting four hours, on two consecutive days). The protocol has been used in other studies for a variety of different products (10). Each panelist from a group of 225 individuals (75 in each of three geographically dispersed markets) evaluated a randomized 9 products per day from the total set of 50. The randomization was not complete, since it was impossible to prepare 50 pasta sauces and keep these sauces fresh (no older than 10 minutes). Consequently, the set of samples was broken into blocks, the blocks rotated, and the panelists evaluated on product per block. Panelists rated each of the sauces on a set of sensory and liking scales, anchored at both ends. Liking ratings were anchored at 0 by the phrase 0=hate, and the phrase 100=love. This is a simple scale to use. Panelists could use any number within the range. Sensory ratings were anchored by the appropriate terms. For instance, spiciness was anchored by the phrase 0=not spicy, and by the phrase 100=extremely spicy. Results - What Sensory Inputs A r e Most Important

The analysis of attribute liking versus overall liking (by a linear equation) suggests that liking of aroma and texture are extremely important, with slopes around 1.4-1.5, that liking of appearance and liking of flavor are important, but less so (see Table II). However, these results do not give any idea of what specific attributes drive liking. They show that some sensory inputs are more important than others. Table II: Slope of the relation between attribute liking and overall liking. The higher the slope the more important the sensory input.

Attribute Aroma Texture Appearance Flavor

Slope 1.47 1.43 1.00 0.96

Goodness Of Fit - R 0.78 0.89 0.77 0.98


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Results - Sensory Liking Curves For The Full Panel A n d Usage Subgroups


Figure 3 (left) shows how a visual attribute (size of vegetable pieces), and how a flavor attribute (strength of onion flavor) drives overall liking for two groups of users - those who use Product A and those who use Product B. These groups have some overlap in the population, but not much. The curves are fitted. Note the similarity of the sensory-liking relation for the two user groups.

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Figure 4: How sensory attributes (size of vegetable pieces, onionflavor)drive overall liking for three sensory segments A quite different story emerges when we look at the sensory-liking curves for the three segments that emerged from this data set. The three


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segments were high flavor impact, low flavor impact, and high texture seekers, respectively. These three groups differed in the products that they liked, and in the nature of the sensory-liking curve. Figure 4 shows the sensory-liking curves for size of vegetable pieces (left panel), and the sensory-liking curves for onion flavor (right panel). Whereas the user groups show similar patterns, the sensory segments show different patterns. Furthermore, these patterns evidence themselves on a variety of sensory dimensions, not just chemo-sensory ones.


Quantifying The Magnitude O f The " D r i v e r " Thus far the analysis has focused on the segments. What is missing, however, is a measure of how important is a specific attribute as a driver of liking. That is, we see the different sensory-liking curves, and we can recognize their difference. A n important additional analysis is a single index of relative importance, much as we used the slope of the linear function as a measure of importance. One plausible index is the area under the sensory liking curve. This is defined as the definite integral of the quadratic function minus the actual rectangular area not involved in the function. Figure 1 shows this area, as that subtended by the sensory-liking curve. The definite integral takes into account two things - the actual sensoryliking curve (a quadratic function), and the sensory range tested. Table 3 presents these areas under the curve for the different sensory attributes and the different key subgroups. We see from Table 3 that the area under the curve varies by attribute, with appearance (e.g., brown color of sauce, amount of tomato pieces) being very important, and other attributes (e.g., those involving mushrooms) being least important. Furthermore, we can compute the highest area under the curve for the user groups (use product A , use product B), and for the sensory preference segments (1,2,3 respectively). The ratio (maximum area for the segments divided by maximum area for user groups) is always greater than 1.0, meaning that for each attribute one of the sensory preference segments always shows the greatest area. It is also worth noting that the chemical senses are not always the sensory inputs that are the key drivers of liking. Appearance is as important as taste/flavor in driving liking. Texture, however, is not particularly important as a driver of liking. It is also worth nothing that the importance of an attribute is a function of the specific attribute, the product being tested, the range of sensory levels encountered in that attribute, and the specific sensory segment under consideration. Depending upon the particular sensory segment an attribute can have a lot of area, or only a little area.


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Table III. Relative Importance O f Attributes As Drivers O f Liking, Based Upon Area Under The Sensory-Liking Curve (Partial List) Attribute


Larger Area Appearance - Brown Appearance - Tomato Pieces Appearance - Chunky Flavor - Herb Flavor - Pepper Flavor - Tomato Appearance - Flecks Flavor - Strength Smaller Areas Flavor - Garlic Appearance - Oily Flavor - Sour Appearance - Size of Vegetable Pieces Flavor - Mushroom Appearance - Size of Mushroom Pieces Flavor - Salty Flavor - Vegetable Appearance - Amount Mushrooms

Total

Use A Use Β

Segl

Seg2 Seg3 Ratio

2452 1856 1824 1758 1689 1585 1574 1513

2599 1971 2161 2012 1892 1680 1737 1684

2368 2047 1855 1709 1742 1600 1547 1509

1988 2195 2571 2118 1736 1331 927 1721

2844 2106 2244 1902 1428 1848 1854 1537

2089 1310 1312 1290 1906 1325 1985 1303

1.09 1.07 1.19 1.05 1.01 1.10 1.14 1.02

882 809 806 506

1055 746 866 670

777 777 757 540

989 713 936 1292

1123 990 942 761

531 725 614 772

1.06 1.27 1.09 1.93

405 374

488 463

400 365

401 592

444 567

526 526

1.08 1.28

267 248 212

314 298 287

268 194 204

45 702 404

304 412 350

390 800 353

1.24 2.68 1.41

[Note: Ratio = the ratio of the largest area for an attribute among the three sensory segments to the largest area for the same area amount the two user subgroups.]

Discussion The Link Between Model Systems A n d Real Products The non-linear relation between sensory magnitude and liking was noticed more than a century ago by Wilhelm Wundt, the founder of experimental psychology (11). Wundt speculated that as a sensory impression increased in strength, the corresponding liking of that impression increased, peaked, and then dropped beyond an optimal level. It fell to later researchers to show that this type of sensory-liking relation pervaded the chemical senses (2, 12, 13). Most of that early work to establish the sensory-liking relation came from studies that dealt with model systems, such as sugar or salt dissolved in water, at different concentrations. Although it might seem that a consumer would have a problem rating i i k i n g ' of a sugar solution, nonetheless from study to study it appeared that the sensory-liking relation existed, and that the optimum sensory level for a caloric sweetener lay at the sweetness


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corresponding to 9% sucrose. It is no surprise that many carbonated sweetened beverages possess this level of sweetness.


Importance O f The Analysis F o r A n Understanding O f How We React To Chemo-Sensory Stimuli

At a basic science level the understanding of 'drivers of liking' tell the researcher a lot about the behaving organism. Although a great deal of work has been done on the perception of sensory magnitude (e.g., thresholds, suprathreshold scaling), to a great degree it is the hedonics of the stimulus that motivate behavior, such as ingestion. Indeed, it is the pleasantness of a stimulus, rather than its sensory quality or magnitude that is often the determining factor for the usefulness of that stimulus. [This is especially true in taste and smell]. One of the interesting results of this study is that appearance attributes, along with texture attributes, can be drivers of liking. For model systems it is rare that appearance or texture (kinaesthesis) drive liking, except in the most artificial of circumstances, when the panelist is required to scale perceived liking (13, 14, 15). For model systems, which have no context of food, liking ratings can be assigned, but the task may require a stretch of the panelist's imagination. In contrast, when the stimulus is food panelists have no trouble assigning ratings of liking. Furthermore, other attributes besides those resulting from taste and smell can emerge and become important. O n Sensory Segmentation As A n Organizing Principle F o r Future Research

Researchers in the chemical senses and in food science often seek organizing principles by which to understand human perception. Sensory segmentation provides a useful organizing principle. It divides people by the sensory attribute levels that they find most acceptable. Furthermore, the sensory segmentation approach presented here does not just cluster people on the basis of the magnitude of liking (which could generate an artifact, since it could be influenced both by the actual sensory preferences and by the numbers used by a specific panelist). Rather, the sensory segmentation approach uses a wellestablished relation between liking and sensory magnitude (the quadratic function). Sensory segmentation can be used to explore a number of different aspects of sensory perception. 1)

How do sensory segments, as shown here, distribute themselves in different countries? The European Sensory Network project on coffee (16) showed the existence of the same sensory segments in five different countries. The


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2) 3) 4)


5)

juice study (17) showed the existence of the same sensory segments in three European countries. Is membership in a sensory segment constant across a person's life, or does a person change? This requires longitudinal studies. Is membership in a sensory segment familial (inherited) or can it be influenced by advertising and experience? Are there more overarching sensory segments, applying to a variety of stimuli, or is membership in a sensory segment for one product category (e.g., high impact for sauces) independent of member in a sensory segment for another product (e.g., coffee)? Do people in different sensory segments differ also in their responsiveness to communications (words, phrases), colors (e.g., package designs), etc? Up to now the sensory segmentation procedure has operated only within the realm of products, for sensory research, and only in the realm of concepts and communications for concept research (18). Is there a connection between membership in a sensory segment for a product category (e.g., coffee) and membership in a segment for communication (e.g., concepts about coffee)? In other words, does the 'mind know what the tongue likes'?

Importance O f The Analysis For The Development O f Consumer Products

A great deal of research in industry deals with the creation of products for consumers. Traditionally, sensory researchers as well as market researchers, divided the population in ways that reflected easy to measure variables, such as age, gender, product used more often, etc. The data shown here, as well as extensive data in other product categories, shows that the traditional method for dividing consumers generates products that are similar. That is, a product created for users of Product A will be very similar to a product created for users of Product B . [Similar outcomes occur when the development is focused on products created for different geographical markets, or even countries]. Sensory segmentation provides a way to divide the consumer population into truly different groups, and generate products for each group that are maximally acceptable. In a sense, the product development is guided by the preference patterns of the sensory segment, so that the product developer truly creates meaningfully different products to segments who likes and dislikes differ and are established.

References 1. Moskowitz, H.R., & Krieger, B. Food Quality and Preference, (1995), 6, 83. 2. Ekman, G., & Akesson, Report 177, (c.a. 1964), Psychological Laboratories, University O f Stockholm, Sweden. 3. Moskowitz, H.R. Journal O f The Society O f Cosmetic Chemistry, (1986), 37, 233.



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4. Pangborn, R . M . Psychonomic Science, (1970), 21, 125. 5. Pangborn, R. M. Criteria O f Food Acceptance: How Man Chooses What He Eats, (ed. J. Solms & R.L. Hall), (1981), Forster Verlag, Zurich, 177. 6. Moskowitz, H.R., Kumaraiah, V . , Sharma, K . N . , Jacobs, H.I., & Sharma, S.D. Physiology & Behavior, (1976) 16, 471. 7. Rodin, J., Moskowitz, H.R., & Bray, G.A. Physiology & Behavior, (1976), 17, 591. 8. Moskowitz, H.R., Jacobs, B.E., & Lazar, N. Journal O f Food Quality, (1985) 8, 168. 9. Moskowitz, H.R. New Directions In Product Testing And Sensory Evaluation O f Foods, (1985), Food And Nutrition Press Inc. Trumbull,CT. 10. Moskowitz, H.R.Food Quality and Preference, (2000), In Press. 11. Boring, E . G . A History O f Experimental Psychology, (1929), Appleton Century, New York. 12. Engel, R. Pfluegers Archiv fur die Gesamte Physiologie, (1928), 64, 1. 13. Moskowitz, H.R. Journal O f Food Quality, (1981), 109-138. 14. Beebe-Center,J.G. The Psychology O f Pleasantness And_Unpleasantness, (1932), Van Nostrand Reinhold, New York. 15. Moskowitz, H.R. American Journal O f Psychology, (1971), 84, 387. 16. European Sensory Network, A European Sensory and Consumer Study: A Case Study on Coffee. Published by the European Sensory Network, (1996), Available from Campden & Chorleywood Food Research Association, Chipping Campden, Gloucestershire, GL55 6LD, UK 17. Moskowitz, H.R. & Krieger, B . Food Quality and Preference, (1998), 9, 443. 18. Moskowitz, H.R. Proceedings O f The 49th E S O M A R Congress, Istanbul, (1996), 35.


Chemistry of Taste - American Chemical Society

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