- Email: [email protected]
Beer identity in Denmark
Food Quality and Preference 17 (2006) 108–115 www.elsevier.com/locate/foodqual
Beer identity in Denmark O. Mejlholm a, M. Martens a
b,c,*
Danish Institute for Fisheries Research (DIFRES), Department of Seafood Research, DTU, Building 221, 2800 Kgs. Lyngby, Denmark The Royal Veterinary and Agricultural University, Department of Food Science, Rolighedsvej 30, DK-1958 Frederiksberg C, Denmark c The Norwegian Food Research Institute, Osloveien 1, N-1430 Aas, Norway
b
Available online 7 November 2005
Abstract In this study a sensory profiling and a consumer test including 10 commercially Danish beers were conducted. The 10 beer samples covered four types of beer namely; lager, strong lager, ale and wheat beer, representing both new and more established beers on the Danish market. A trained panel consisting of nine panellists completed the sensory profiling using the following attributes; colour, body, bitter, carbonation, alcohol, fruity, floral, spicy and grainy/roasted. All attributes discriminated significantly between the beer samples. Lager beers were mainly described by the attributes grainy/roasted, bitter, alcohol and carbonation whereas the attributes fruity, spicy, body and floral primarily described the ale and wheat beers. Consumers tasted the same 10 beers in a hedonic test (affective response) and in an appropriateness test (cognitive-contextual response). Partial least squares regression (PLSR) and a new three-block L-shapedPLSR predicting consumer liking from both consumer background characteristics and product descriptors, were used to describe the relationships between the various sets of data. The results showed that the more established beers on the Danish market were given higher liking scores than the new types of beer; however, consumer segments were revealed. Relating the sensory profiling and the consumer tests made it possible to tell which sensory characteristics influenced consumer liking and the appropriateness of the beers in various situations and uses. 2005 Elsevier Ltd. All rights reserved. Keywords: Sensory profiling; Consumer test; Affective; Cognitive-contextual; L-PLSR
1. Introduction Beer identity in Denmark is strong, tracing back in history to the first evidence of beer consumption in 1370 BC. In 1687, there existed about 140 breweries in Copenhagen itself. However, during the last 100 years the number of breweries in the whole country has been reduced from approximately 400 to 26, reflecting that sale and consumption of beer in Denmark have decreased. From 1995 until today, the annual intake of beer per inhabitant has declined by approximately 22%. Increased consumption of wine and
* Corresponding author. Address: The Norwegian Food Research Institute, Osloveien 1, N-1430 Aas, Norway. Tel.: +47 64 97 01 00/48 13 48 56. E-mail address: [email protected] (M. Martens).
0950-3293/$ - see front matter 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodqual.2005.10.001
a general change in food habits might explain the falling consumption of beer. Despite the decreasing sale and consumption a growing interest in beer has been noticed in Denmark in the last couple of years. The awareness of beer quality and the applicability of beers in different contexts have grown among consumers. A good example of the growing interest is the beer organisation ‘‘Danske Ølentusiaster/Danish Beer Enthusiasts (DE)’’. In less than six years, more than 9000 members have joined DE making this organisation the second largest beer interest association in Europe only outnumbered by the British organisation ‘‘Campaign for Real Ale (CAMRA)’’. Established and newly started Danish breweries contribute to the growing interest as well by introducing new products on the market such as ales and wheat beers. As a result of this development, the diversity of Danish beers has increased. Previously different types
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of lager made up the diversity of Danish beer but now an increasing number of ales and wheat beers are being produced as well. Consequently, it is interesting to study the variety of Danish beer by performing sensory profiling and consumer tests. The sensory characteristics of beer can be described both qualitatively and quantitatively by using sensory profiling methods (Meilgaard, Civille, & Carr, 1999a). The qualitative sensory characteristics define the profile and the quantitative evaluation draws the profile. Consumer tests, both affective and cognitive, can be used for assessing consumer opinions and attitudes towards beer (Lawless & Heymann, 1998; Schutz & Martens, 2001). One of the most frequently used affective methods is a hedonic test eliciting the degree of consumer liking towards a given sample (Meilgaard, Civille, & Carr, 1999b). As described by Schutz and Martens (2001) an appropriateness test might be used as a cognitive-contextual response of food attitudes. The principle behind this test is that the consumer evaluates the appropriateness of different food items for a number of situations and uses. In order to explain consumer behaviour concerning foods Schutz and Martens (2001) emphasise the importance of including both affective and cognitive aspects. This is due to the fact that the hedonic test only accounts for about 50% of the variance connected with food behaviour as revealed in a beer study (Sidel, Stone, Woolsey, & Macredy, 1972). Despite this knowledge most recent studies use only the hedonic test. In a study by Cardello and Schutz (1996), a hedonic test and an appropriateness test were used successfully in conjunction. Cardello and Schutz (1996) concluded that the results of the appropriateness test did not influence the results of the hedonic test significantly. Consequently, an affective and a cognitive test might be performed at the same time without affecting the results of each other. Addressing both emotion and cognition, ‘‘identity’’ may be understood as having close affiliation with others that we respect or admire—identifying with that group for judgements, beliefs, attitudes, behaviour, etc. according to classic psychological literature (e.g., Atkinson, Atkinson, Smith, Bem, & Hilgard, 1990). Thus, beer identity may refer not only to product properties, but also to rather how these properties interact with human memories and cultural factors in different contexts. A strategy for increasing sale and consumption of beer may be to strengthening beer identity in Denmark through more product variation, yet keeping the image of this being ‘‘our’’ (i.e., Danish) typical beer. As described above, the many new breweries bringing beer type variation to the market have enhanced the awareness and interest towards beer. Based on this as an overall goal three objectives were investigated in the present study: (1) develop a vocabulary for describing sensory variation in ‘‘typical’’ Danish beers through sensory profiling; (2) study consumer perception of beers through affective and cognitive-contextual tests; (3) reveal relationships between consumer liking and both consumer background characteristics and product descriptors simul-
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taneously through L-shaped partial least squares regression (L-PLSR). 2. Material and methods 2.1. Samples Ten commercially available Danish beers, selected to span a relevant space with respect to new and established products on the market, were used as samples. The samples represented four types of beer namely lager, strong lager, ale and wheat beer. Sample information and sample ID are listed in Table 1. The beer samples had a serving temperature of 10 C and were served in glasses containing approximately 75 ml beer per sample. Three-digit randomised numbers were used for coding the samples. 2.2. Sensory profiling A trained panel consisting of nine members carried out the sensory profiling. The sensory profiling took place in the sensory laboratory at the Royal Veterinary and Agricultural University, Denmark, approved for sensory analysis (ISO-standard 8589, 1988). The panellists were all experienced and familiar with the sensory method used. During three training sessions, the understanding and usage of nine predefined attributes (colour, body, bitter, carbonation, alcohol, fruity, floral, spicy and grainy/ roasted) were established. In addition, the evaluation order of the nine attributes was determined throughout the training sessions. The nine attributes concerning appearance, flavour and taste were generated from the literature (e.g., Meilgaard, Dalgliesh, & Clapperton, 1979) and from preexperiments with four beer experts from DE, and finally through discussions with the panellists. The intensity of the nine attributes was scored on a 15 cm unstructured line scale anchored by verbal endpoints at both ends. A colour scale (light–dark) was used as a reference for estimating the colour of the samples. The 10 beer samples were evaluated in triplicate during three sessions. The panellists evaluated the beer samples in different serving orders and with 2– 3 min intervals between the samples. Ordinary tap water
Table 1 Sample abbreviations and sample informations Sample abbreviations
Beer type
Alcohol (vol.%)
Status on the Danish market
L1 L2 L3 SL1 SL2 SL3 A1 A2 A3 W1
Lager Lager Lager Strong lager Strong lager Strong lager Ale Ale Ale Wheat beer
4.6 5.8 5.8 7.1 7.2 7.7 7.3 5.7 6.0 4.6
Established Established Established Newly introduced Established Established Newly introduced Established Newly introduced Newly introduced
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and neutral crisp bread were served for cleansing the palates. A data system (FIZZ, Biosystems, France) for automatic acquisition of the intensity scores was used.
of usage, favourite type of beer and favourite type of alcohol) towards beer. 2.6. Data analysis
2.3. Consumer test Thirty-eight consumers (18–59 years) with 29 males and nine females participated in the consumer test. Selection of the consumers was based on age (>18 years), beer consumption (beer drinkers) and the requirement of not being affiliated with the University. The consumer test was conducted in a meeting room providing space for eight persons at a time. The consumer test consisted of an affective and a cognitive-contextual test including answering a questionnaire concerning background information after the tasting. 2.4. Affective test As an affective method a hedonic test was chosen (Meilgaard et al., 1999b). Samples were evaluated for overall liking on a seven-point hedonic scale (1 = dislike extremely, 7 = like extremely). 2.5. Cognitive-contextual test As a cognitive method an appropriateness test was selected (Schutz & Martens, 2001). The appropriateness of the samples for different statements was evaluated on a seven-point scale (1 = not appropriate, 7 = very appropriate). Twenty-three statements were included in the test (Table 2). The order of the statements on the evaluation scheme was randomised and differed between the consumers. Upon arriving at the test location the consumers were given instructions for completing the consumer test. Each consumer assessed all of the 10 beer samples for overall liking and only half of the samples for appropriateness due to the high number of statements. The numbers of completed appropriateness tests were identical for all beer samples. The consumers evaluated the beer samples in different serving orders. Ordinary tap water and neutral crisp bread were served for cleansing the palates. Following the final sample evaluation, each consumer completed a questionnaire consisting of questions concerning demographic information (i.e., gender and age) and habits/attitudes (i.e., frequency
Intensity scores (average scores of nine panellists and three replicates) for each of the nine attributes were calculated from the sensory profiling for each of the beer samples. From the consumer test average hedonic scores for each of the beer samples were calculated across all consumers (‘‘average’’) and for different consumer groups [male, female, age (18–29 years), age (30–59 years), employed, students, beer consumption (2/month, 4/month, 10/month or >16/month), favourite type of beer (lager, strong lager or ale), preferred serving temperature (cellar or fridge) and favourite type of alcohol (beer or wine)]. The different consumer groups were based on information achieved through the consumer questionnaire. From the appropriateness test, mean scores for each of the statements were calculated for each of the samples. The achieved data were analysed using univariate analysis of variance (ANOVA) and multivariate data analysis, i.e., various versions of partial least squares regression (Martens & Martens, 2001). Discriminant partial least squares regression (DPLSR) was used to find ‘‘typical’’ sensory attributes describing the variation among the 10 samples (X = 9 sensory variables, Y = 1/0 indicator variables for the 10 samples). To study the effect of this variation of relevance to consumer liking and appropriateness scores an ANOVA partial least squares regression (APLSR) was applied, respectively, for (X = 1/0 indicator variables, Y = hedonic scores) and (X = 1/0 indicator variables, Y = appropriateness scores). By using L-shaped-PLSR (L-PLSR) the three blocks of data could be related to each other in order to reveal reliable underlying phenomena: Consumer liking data (Y) could be predicted both from consumer background data (Z) and product characteristics (X). L-PLSR theory is described in Martens et al. (2005). Sensory data for the multivariate data analysis were pre-treated (level-corrected) according to the procedure described by Martens, Wedøe, Bredie, and Martens (1999). Multivariate data analyses (DPLSR and APLSR) were performed using the software Unscrambler 8.0 (Camo ASA, Trondheim, Norway) while L-PLSR was done in MatLabTM (www.mathworks.com). All analysis were performed using non-standardised variables and full
Table 2 List of 23 statements used in this study Person served
Served with foods
Where served
Physiological
Psychological
Time
Occasion served
For men For women For guests
With With With With With With With With
At At At At
When IÕm thirsty
When I need to relax
In the evening In the afternoon On a hot summer day At the weekend
At parties When I watch TV
chicken fish grill/barbecue chips soups pizza/pasta spicy foods beef
a restaurant a cafe´ home a pub
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cross validation. ANOVA was carried out using the software Statgraphics Plus 4.1 (Manugistics, Inc., Rockville, Maryland, USA). 3. Results and discussion 3.1. ANOVA Results from the ANOVA showed significant differences (p < 0.001) between the 10 beer samples for all the sensory attributes. No important interactions between panellists and samples were observed. 3.2. Sensory profiling Results from the DPLSR analysis showed which sensory attributes described and discriminated between the beer samples. The first three principal components accounted for 93% of the total variance relevant for explaining sample variation. PC1 explained 69% of the variation, whereas PC2 and PC3 described 19% and 5%, respectively. A correlation loadings plot illustrating the first two principal components is shown in Fig. 1, reflecting variation among beer types and fermentation types, respectively. Sample variations caused by the attributes grainy/roasted, bitter, alcohol, colour, body, fruity and spicy were described by the first PC. These attributes were all positively loaded on PC1, forming two strongly correlated subgroups consisting of three (grainy/roasted, bitter and alcohol) and four attributes (colour, body, fruity and spicy), respectively. Beer sample intensity regarding the formerly mentioned attributes decreased along PC1 from the right to the left. This means that beer samples A1, A2 and SL1 were given the highest intensity scores for the attributes grainy/roasted,
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bitter, alcohol, colour, body, fruity and spicy while W1 and L1 were given the lowest intensity scores. The second PC explained sample variation due to the attributes carbonation and floral. Carbonation was positively loaded on PC2, whereas floral was negatively loaded on this component. A3 was evaluated as the most floral beer sample followed by W1, L3 and SL2 in descending order. L1 was given the highest intensity score for the attribute carbonation and A3 was evaluated as the sample with the lowest carbon dioxide content. As well as explaining the variance caused by the attributes carbonation and floral the second PC separated the samples in bottom-fermented and top-fermented beers, respectively. The bottomfermented beer samples were located on the positive side of PC2 in correlation with the attributes carbonation, grainy/roasted, bitter and alcohol whereas the topfermented samples were placed on the negative side of PC2 related to the attributes floral, spicy and fruity. These correlations between beer samples and attributes are in agreement with the sensory characteristics normally associated with bottom and top-fermented beers, respectively. Generally, the sensory diversity of the top-fermented samples (A1, A2, A3 and W1) seemed to be larger than for the bottom-fermented samples (L1, L2, L3, SL1, SL2 and SL3). The larger sensory diversity of top-fermented beers are caused by a broad variety of floral, fruity and spicy characteristics that are either absent or less pronounced when it comes to bottom-fermented beers. The third principal component (not shown here) explained to a great extent the variation associated with the attribute spicy. W1, A1, SL1 and A2 were found as the spiciest samples while L1, SL2 and L3 were evaluated as the least spicy samples. Colour, carbonation and floral were found as significant attributes for differentiating the 10 beer samples in
Fig. 1. DPLSR on sensory profiling data (X-matrix) and design variables (Y-matrix). Correlation loadings plot showing the first and second principal component (PC). The inner and outer ellipses represent 50% and 100% explained variance, respectively.
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question, i.e., showing high percent explained variance in Fig. 1.
Table 3 Average hedonic scores across all, male and female consumers Samples
Average hedonic scores All (n = 38)
Male (n = 29)
Female (n = 9)
L1 L2 L3 SL1 SL2 SL3 A1 A2 A3 W1
4.6 4.8 5.2 4.2 4.3 5.2 4.4 5.8 4.5 4.3
4.4 4.7 5.1 4.2 4.4 5.3 4.9 6.0 4.6 4.6
5.0 5.0 5.2 4.2 3.7 5.0 3.0 5.1 4.4 3.1
3.3. Consumer test (hedonic test) Results from the hedonic test were studied using APLSR. The first two principal components accounted for 82% of the variance associated with the hedonic data. A correlation loadings plot illustrating PC1 and PC2 is shown in Fig. 2. The first principal component explained the variance in liking of the samples assessed by the average consumer (‘‘average’’) as well as different consumer groups. A2 was evaluated as the best-liked sample followed by SL3, L3 and L2 while SL1, W1, SL2 and A1 were given the lowest scores (Table 3). However, all beer samples were given an average hedonic score higher than four corresponding to the assessment ‘‘neither like nor dislike’’. Generally, the better-established beers (i.e., A2, SL3, L3, L2 and L1) were preferred over the latest introduced beers (i.e., SL1, W1 and A1) on the Danish market. The results of the hedonic test carried out by the male consumers were very similar to the average consumer (‘‘average’’) due to the high number of male respondents (Table 3). The female consumers (female) gave L3 and A2 the highest hedonic scores while A1 and W1 were given the lowest scores. Several noticeable differences were observed between male and female consumers (Table 3). The male consumers liked the ale and wheat beer samples better than the female consumers did. The male consumers evaluated in particular A1 and W1 higher than the females did. Consequently, the male consumers seemed to be more amenable towards the new types of beer on the Danish market whereas the females still seemed to prefer the more traditional lager beers. The second principal component explained the variance in liking
of the samples evaluated by the group of consumers having ale as their favourite type of beer. This group of consumers assessed A1, A2, L3 and A3, as the best liked samples. Looking at all consumer groups the three ale samples included in this study were given the highest average hedonic score by this group of consumers. Consequently, there seemed to be consistency between the claimed favourite type of beer and the factual evaluation of the beer samples for this group of consumers. Something similar was the case for the group of consumers having lager as their favourite type of beer. Consumers preferring a serving temperature corresponding to either cellar or fridge cold had ale and lager as their favourite type of beer, respectively. This finding is in agreement with the general recommendation for these two types of beer when it comes to serving temperature. Furthermore, Fig. 2 shows that consumers having wine (WineFav) or beer (BeerFav) as their favourite type of alcohol were correlated to the female and male group, respectively, contributing to high percent explained variance in PC2. In a separate APLSR study, age group
Fig. 2. APLSR on design variables (X-matrix) and consumer liking (Y-matrix). Correlation loadings plot showing the first and second PC. The inner and outer ellipses represent 50% and 100% explained variance, respectively.
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(18–29 years) showed higher liking for lager and wheat beers than consumers aged 30–59 years, the latter group rather liking strong beers with high alcohol percent. The other consumer groups not discussed in the results (e.g., employed, students), were found to be of minor interest. However, care should be taken for generalising the results with respect to consumer groups since relatively few consumers participated. 3.4. Consumer test (appropriateness test) Results from the appropriateness test were studied using an APLSR analysis. The first two principal components accounted for 75% of the variance associated with the appropriateness data. A correlation loadings plot illustrating PC1 and PC2 is shown in Fig. 3. The first principal component explained to a great extent the variance in sample appropriateness towards the 23 statements listed in Table 2. Beer samples positively loaded on PC1 (L1, L3, SL3, A2 and W1) were found as the most suitable samples for the main part of the statements whereas the samples negatively loaded on PC1 (SL1, A1, A3, L2 and SL2) were evaluated to be the least appropriate. L1, W1 and to some extent L3 were found as the most suitable samples for statements such as hot summer day, thirsty, women, afternoon, fish, chicken and pizza/pasta. L1, W1 and L3 had a relatively simple sensory profile in common making them particularly suitable for the formerly mentioned group of statements. L3 was assessed as the most suitable sample for the statements spicy foods, beef and grill/barbecue. Beer samples having a more complex sensory profile (SL3 and A2) were found as the most suitable samples for statements such as men, pub, cafe´, evening and soups. SL1 and A1 were found as the least suitable samples in relation to
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the food statements mentioned in Table 2. This finding is a bit surprising taking into account that these beer samples have been introduced on the market as being particularly suitable with foods. Insufficient knowledge of these newly introduced beer samples might explain the relatively low appropriateness scores. The context associated with the consumer test could be another explanation to these results. A more realistic context as for example a real dining situation might change the results in favour of both SL1 and A1. Summed across all beer samples the statements pub, men, cafe´, chips and weekend were found as the most suitable for the product category beer. The results of the hedonic and the appropriateness test did not seem to influence each other. Samples getting high hedonic scores were not always given high appropriateness scores and vice versa. This is in agreement with the findings of Cardello and Schutz (1996). 3.5. Consumer liking explained both by consumer background and product descriptor data L-PLSR was performed to study which sensory attributes as well as consumer background characteristics could explain consumer liking. A correlation loadings plot (Fig. 4) gave a visualisation of the systematic covariation between product characteristics (e.g., beer type, percentage of alcohol and sensory descriptors), consumer background (e.g., gender, age) and consumer liking. Fig. 4 shows the product variation in PC1 (in northeast vs. southwest direction) as A1, A2 and SL1 being spicy, fruity and body vs. L1 (Type Lager). In PC2 (in northwest vs. southeast direction) SL2 and SL3 (Type Strong Lager) were described opposite to W1 being more floral and with less carbonation. With respect to consumer background variation, PC1
Fig. 3. APLSR on design variables (X-matrix) and appropriateness data (Y-matrix). Correlation loadings plot showing the first and second PC. The inner and outer ellipses represent 50% and 100% explained variance, respectively.
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1
Bitter %Alcohol Alcohol
0.8
Grainy/roasted TypeStrongLager Age30-59
Body Colour Fruity
0.6 0.4
SL2 LagerFavorite b9 b8 Carbonation b4 L2
0.2 PC2
CellarTemp
SL3 SL3
0
SL2 SL1 StronglagerFavorite >16/mnth b12 A1 b29 b10 b16 A2A1 Spicy L2 4/mnth b21 b3“Average” consumer Male SL1 A2 A3
L1 L3 b22 b2 L3 2/mnth TypeLager L1 Female
-0.2 -0.4
50%
-0.8
TypeAle
b11 Floral
FridgeTemp -0.6
b1
b18 10/mnth b7 AleFavorite A3 b17 W1
W1 Age18-29
100%
-1 -1
-0.8
-0.6
-0.4
-0.2
0 PC1
0.2
0.4
0.6
0.8
1
Fig. 4. L-PLSR correlation loadings for PC1 vs. PC2: (*) Beers, by product descriptors X; (s) beers, by liking YÕs correlation to consumer background Z; (·) product descriptors X; (,) consumer background Z; (d) individual consumersÕ liking Y. b1–b29 are background variables of minor interest. The inner and outer ellipses represent 50% and 100% explained variance, respectively.
distinguished between male and female, and PC2 between young (age 18–29 years) and elderly (age 30–59 years) consumers. Male consumers and consumers having ale and strong lager as their favourite type of alcohol were all positively loaded on PC1. For these consumer groups the sensory attributes grainy/roasted, bitter and alcohol had the greatest impact on liking followed by the attributes colour, body and fruity. The second principal component explained the variation between consumers having either lager or ale as their favourite type of beer. The first group (lager) preferred samples with a relatively high content of carbon dioxide (carbonation). The second group of consumers (ale) favoured a full-bodied (body) beer with a floral, spicy and fruity flavour. Both groups preferred a beer with a balanced bitter and a moderate alcohol and grainy/roasted flavour. Consumers preferring lager beers (Type Lager) were highly correlated to the female group, Type Ale was related to the male group and Type Strong Lager (with high percent alcohol) was liked by the elderly, while wheat beer was best liked by the younger consumers in this study. The dots in Fig. 4 show liking from the individual consumers indicating that there are various segments among the consumers. The average consumer appears close to the origin giving no information about individual differences. The L-PLSR plot can be validated by examining the 10 samples being modelled from both the product matrix (X)—underlined in Fig. 4—and the consumer background matrix (Z) on the liking matrix (Y): The samples are lying
close to each other (i.e., A1 and A1). This means that from two independent sets of measurements, the same type of variation in consumer liking could be explained. In future studies it will be of interest to apply L-PLSR including data from investigations of the role of memory for food identity in the consumer background Z-matrix. Furthermore, different consumer data addressing emotional and implicit responses, such as data from authenticity tests (e.g., Mojet & Ko¨ster, 2004), could constitute the Y-matrix. 4. Conclusion Descriptive sensory profiling and consumer tests of 10 different Danish beers have been conducted successfully and validated prediction of consumer liking simultaneously from consumer background characteristics and product descriptors have been explored. The results of the sensory profiling showed that the sensory diversity of Danish beer is large and manifold. Lager beers included in the study were mainly described by the attributes grainy/roasted, bitter, alcohol and carbonation whereas the ale and wheat beers primarily were described by the attributes fruity, spicy, floral and body. A consumer hedonic test evaluated A2, SL3 and L3 as the best-liked beer samples. In general, the better-established beers on the Danish market were given higher hedonic scores than the newly introduced beers. An appropriateness test made it possible to tell for
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which situations and uses the different beer samples were most suitable. Relating the results of the sensory profiling and the consumer data (hedonic test and consumer background information) made it possible to tell which sensory attributes influenced liking of the beer samples evaluated by different consumer groups. This knowledge can be used in connection with, for example, product development and marketing, thus contributing to future beer identity in Denmark. In general, the approach and various methods demonstrated in this study may be useful with respect to similar studies on other products and in other countries. Acknowledgements Thanks to Harald Martens for substantial contribution to the L-PLSR analysis. Part of this work was supported by the Danish Føtek programme/Danish Government and by Matforsk AS, Norway. References Atkinson, R. L., Atkinson, R. C., Smith, E. E., Bem, D. J., & Hilgard, E. R. (1990). Introduction to psychology (10th ed., pp. 756–757). Harcourt Brace Jovanovich Inc. Cardello, A. V., & Schutz, H. G. (1996). Food appropriateness measures as an adjunct to consumer preference/acceptability evaluation. Food Quality and Preference, 7(3/4), 239–249.
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ISO-Standard 8589 (1988). Sensory analysis—general guidance for design of tests rooms. Lawless, H. T., & Heymann, H. (1998). Sensory evaluation of food. Principles and practices. New York: Chapmann and Hall. Martens, H., Anderssen, E., Flatberg, A., Gidskehaug, L. H., Høy, M., Westad, F., et al. (2005). Regression of a data matrix on descriptors of both its rows and of its columns via latent variables: L-PLSR. Computational Statistics & Data Analysis, 48, 103–123. Martens, H., & Martens, M. (2001). A LaymanÕs guide to multivariate data analysis. In Multivariate analysis of quality—an introduction (pp. 51–75). Chichester: John Wiley & Sons. Martens, H., Wedøe, S., Bredie, W. L. P., & Martens, M. (1999). Manual for sensory data analysis in the Unscrambler. Copenhagen, DK: Sensory Science, The Royal Veterinary and Agricultural University. Meilgaard, M., Civille, G. V., & Carr, B. T. (1999a). Descriptive analysis techniques. In Sensory evaluation techniques (3rd ed., pp. 161–172). New York: CRC. Meilgaard, M., Civille, G. V., & Carr, B. T. (1999b). Affective tests: consumer tests and in-house panel acceptance tests. In Sensory evaluation techniques (3rd ed., pp. 231–263). New York: CRC. Meilgaard, M., Dalgliesh, C. E., & Clapperton, J. F. (1979). Beer flavor terminology. American Society of Brewing Chemists, 37(1), 47–52. Mojet, J., & Ko¨ster, E. P. (2004). Authenticity judgements of low-alcohol beverages. In Abstract book from the european conference on sensory science (p. 121). Florence, Italy, 26–29 September 2004. Schutz, H. G., & Martens, M. (2001). Appropriateness as a cognitivecontextual measure of food attitudes. In L. Frewer, E. Risvik, & H. Schifferstein (Eds.), Food, People and Society (pp. 247–266). Berlin: Springer-Verlag. Sidel, J. L., Stone, H., Woolsey, A., & Macredy, J. M. (1972). Correlation between hedonic ratings and consumption of beer. Journal of Food Science, 37, 335.
Beer identity in Denmark O. Mejlholm a, M. Martens a
b,c,*
Danish Institute for Fisheries Research (DIFRES), Department of Seafood Research, DTU, Building 221, 2800 Kgs. Lyngby, Denmark The Royal Veterinary and Agricultural University, Department of Food Science, Rolighedsvej 30, DK-1958 Frederiksberg C, Denmark c The Norwegian Food Research Institute, Osloveien 1, N-1430 Aas, Norway
b
Available online 7 November 2005
Abstract In this study a sensory profiling and a consumer test including 10 commercially Danish beers were conducted. The 10 beer samples covered four types of beer namely; lager, strong lager, ale and wheat beer, representing both new and more established beers on the Danish market. A trained panel consisting of nine panellists completed the sensory profiling using the following attributes; colour, body, bitter, carbonation, alcohol, fruity, floral, spicy and grainy/roasted. All attributes discriminated significantly between the beer samples. Lager beers were mainly described by the attributes grainy/roasted, bitter, alcohol and carbonation whereas the attributes fruity, spicy, body and floral primarily described the ale and wheat beers. Consumers tasted the same 10 beers in a hedonic test (affective response) and in an appropriateness test (cognitive-contextual response). Partial least squares regression (PLSR) and a new three-block L-shapedPLSR predicting consumer liking from both consumer background characteristics and product descriptors, were used to describe the relationships between the various sets of data. The results showed that the more established beers on the Danish market were given higher liking scores than the new types of beer; however, consumer segments were revealed. Relating the sensory profiling and the consumer tests made it possible to tell which sensory characteristics influenced consumer liking and the appropriateness of the beers in various situations and uses. 2005 Elsevier Ltd. All rights reserved. Keywords: Sensory profiling; Consumer test; Affective; Cognitive-contextual; L-PLSR
1. Introduction Beer identity in Denmark is strong, tracing back in history to the first evidence of beer consumption in 1370 BC. In 1687, there existed about 140 breweries in Copenhagen itself. However, during the last 100 years the number of breweries in the whole country has been reduced from approximately 400 to 26, reflecting that sale and consumption of beer in Denmark have decreased. From 1995 until today, the annual intake of beer per inhabitant has declined by approximately 22%. Increased consumption of wine and
* Corresponding author. Address: The Norwegian Food Research Institute, Osloveien 1, N-1430 Aas, Norway. Tel.: +47 64 97 01 00/48 13 48 56. E-mail address: [email protected] (M. Martens).
0950-3293/$ - see front matter 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodqual.2005.10.001
a general change in food habits might explain the falling consumption of beer. Despite the decreasing sale and consumption a growing interest in beer has been noticed in Denmark in the last couple of years. The awareness of beer quality and the applicability of beers in different contexts have grown among consumers. A good example of the growing interest is the beer organisation ‘‘Danske Ølentusiaster/Danish Beer Enthusiasts (DE)’’. In less than six years, more than 9000 members have joined DE making this organisation the second largest beer interest association in Europe only outnumbered by the British organisation ‘‘Campaign for Real Ale (CAMRA)’’. Established and newly started Danish breweries contribute to the growing interest as well by introducing new products on the market such as ales and wheat beers. As a result of this development, the diversity of Danish beers has increased. Previously different types
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of lager made up the diversity of Danish beer but now an increasing number of ales and wheat beers are being produced as well. Consequently, it is interesting to study the variety of Danish beer by performing sensory profiling and consumer tests. The sensory characteristics of beer can be described both qualitatively and quantitatively by using sensory profiling methods (Meilgaard, Civille, & Carr, 1999a). The qualitative sensory characteristics define the profile and the quantitative evaluation draws the profile. Consumer tests, both affective and cognitive, can be used for assessing consumer opinions and attitudes towards beer (Lawless & Heymann, 1998; Schutz & Martens, 2001). One of the most frequently used affective methods is a hedonic test eliciting the degree of consumer liking towards a given sample (Meilgaard, Civille, & Carr, 1999b). As described by Schutz and Martens (2001) an appropriateness test might be used as a cognitive-contextual response of food attitudes. The principle behind this test is that the consumer evaluates the appropriateness of different food items for a number of situations and uses. In order to explain consumer behaviour concerning foods Schutz and Martens (2001) emphasise the importance of including both affective and cognitive aspects. This is due to the fact that the hedonic test only accounts for about 50% of the variance connected with food behaviour as revealed in a beer study (Sidel, Stone, Woolsey, & Macredy, 1972). Despite this knowledge most recent studies use only the hedonic test. In a study by Cardello and Schutz (1996), a hedonic test and an appropriateness test were used successfully in conjunction. Cardello and Schutz (1996) concluded that the results of the appropriateness test did not influence the results of the hedonic test significantly. Consequently, an affective and a cognitive test might be performed at the same time without affecting the results of each other. Addressing both emotion and cognition, ‘‘identity’’ may be understood as having close affiliation with others that we respect or admire—identifying with that group for judgements, beliefs, attitudes, behaviour, etc. according to classic psychological literature (e.g., Atkinson, Atkinson, Smith, Bem, & Hilgard, 1990). Thus, beer identity may refer not only to product properties, but also to rather how these properties interact with human memories and cultural factors in different contexts. A strategy for increasing sale and consumption of beer may be to strengthening beer identity in Denmark through more product variation, yet keeping the image of this being ‘‘our’’ (i.e., Danish) typical beer. As described above, the many new breweries bringing beer type variation to the market have enhanced the awareness and interest towards beer. Based on this as an overall goal three objectives were investigated in the present study: (1) develop a vocabulary for describing sensory variation in ‘‘typical’’ Danish beers through sensory profiling; (2) study consumer perception of beers through affective and cognitive-contextual tests; (3) reveal relationships between consumer liking and both consumer background characteristics and product descriptors simul-
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taneously through L-shaped partial least squares regression (L-PLSR). 2. Material and methods 2.1. Samples Ten commercially available Danish beers, selected to span a relevant space with respect to new and established products on the market, were used as samples. The samples represented four types of beer namely lager, strong lager, ale and wheat beer. Sample information and sample ID are listed in Table 1. The beer samples had a serving temperature of 10 C and were served in glasses containing approximately 75 ml beer per sample. Three-digit randomised numbers were used for coding the samples. 2.2. Sensory profiling A trained panel consisting of nine members carried out the sensory profiling. The sensory profiling took place in the sensory laboratory at the Royal Veterinary and Agricultural University, Denmark, approved for sensory analysis (ISO-standard 8589, 1988). The panellists were all experienced and familiar with the sensory method used. During three training sessions, the understanding and usage of nine predefined attributes (colour, body, bitter, carbonation, alcohol, fruity, floral, spicy and grainy/ roasted) were established. In addition, the evaluation order of the nine attributes was determined throughout the training sessions. The nine attributes concerning appearance, flavour and taste were generated from the literature (e.g., Meilgaard, Dalgliesh, & Clapperton, 1979) and from preexperiments with four beer experts from DE, and finally through discussions with the panellists. The intensity of the nine attributes was scored on a 15 cm unstructured line scale anchored by verbal endpoints at both ends. A colour scale (light–dark) was used as a reference for estimating the colour of the samples. The 10 beer samples were evaluated in triplicate during three sessions. The panellists evaluated the beer samples in different serving orders and with 2– 3 min intervals between the samples. Ordinary tap water
Table 1 Sample abbreviations and sample informations Sample abbreviations
Beer type
Alcohol (vol.%)
Status on the Danish market
L1 L2 L3 SL1 SL2 SL3 A1 A2 A3 W1
Lager Lager Lager Strong lager Strong lager Strong lager Ale Ale Ale Wheat beer
4.6 5.8 5.8 7.1 7.2 7.7 7.3 5.7 6.0 4.6
Established Established Established Newly introduced Established Established Newly introduced Established Newly introduced Newly introduced
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and neutral crisp bread were served for cleansing the palates. A data system (FIZZ, Biosystems, France) for automatic acquisition of the intensity scores was used.
of usage, favourite type of beer and favourite type of alcohol) towards beer. 2.6. Data analysis
2.3. Consumer test Thirty-eight consumers (18–59 years) with 29 males and nine females participated in the consumer test. Selection of the consumers was based on age (>18 years), beer consumption (beer drinkers) and the requirement of not being affiliated with the University. The consumer test was conducted in a meeting room providing space for eight persons at a time. The consumer test consisted of an affective and a cognitive-contextual test including answering a questionnaire concerning background information after the tasting. 2.4. Affective test As an affective method a hedonic test was chosen (Meilgaard et al., 1999b). Samples were evaluated for overall liking on a seven-point hedonic scale (1 = dislike extremely, 7 = like extremely). 2.5. Cognitive-contextual test As a cognitive method an appropriateness test was selected (Schutz & Martens, 2001). The appropriateness of the samples for different statements was evaluated on a seven-point scale (1 = not appropriate, 7 = very appropriate). Twenty-three statements were included in the test (Table 2). The order of the statements on the evaluation scheme was randomised and differed between the consumers. Upon arriving at the test location the consumers were given instructions for completing the consumer test. Each consumer assessed all of the 10 beer samples for overall liking and only half of the samples for appropriateness due to the high number of statements. The numbers of completed appropriateness tests were identical for all beer samples. The consumers evaluated the beer samples in different serving orders. Ordinary tap water and neutral crisp bread were served for cleansing the palates. Following the final sample evaluation, each consumer completed a questionnaire consisting of questions concerning demographic information (i.e., gender and age) and habits/attitudes (i.e., frequency
Intensity scores (average scores of nine panellists and three replicates) for each of the nine attributes were calculated from the sensory profiling for each of the beer samples. From the consumer test average hedonic scores for each of the beer samples were calculated across all consumers (‘‘average’’) and for different consumer groups [male, female, age (18–29 years), age (30–59 years), employed, students, beer consumption (2/month, 4/month, 10/month or >16/month), favourite type of beer (lager, strong lager or ale), preferred serving temperature (cellar or fridge) and favourite type of alcohol (beer or wine)]. The different consumer groups were based on information achieved through the consumer questionnaire. From the appropriateness test, mean scores for each of the statements were calculated for each of the samples. The achieved data were analysed using univariate analysis of variance (ANOVA) and multivariate data analysis, i.e., various versions of partial least squares regression (Martens & Martens, 2001). Discriminant partial least squares regression (DPLSR) was used to find ‘‘typical’’ sensory attributes describing the variation among the 10 samples (X = 9 sensory variables, Y = 1/0 indicator variables for the 10 samples). To study the effect of this variation of relevance to consumer liking and appropriateness scores an ANOVA partial least squares regression (APLSR) was applied, respectively, for (X = 1/0 indicator variables, Y = hedonic scores) and (X = 1/0 indicator variables, Y = appropriateness scores). By using L-shaped-PLSR (L-PLSR) the three blocks of data could be related to each other in order to reveal reliable underlying phenomena: Consumer liking data (Y) could be predicted both from consumer background data (Z) and product characteristics (X). L-PLSR theory is described in Martens et al. (2005). Sensory data for the multivariate data analysis were pre-treated (level-corrected) according to the procedure described by Martens, Wedøe, Bredie, and Martens (1999). Multivariate data analyses (DPLSR and APLSR) were performed using the software Unscrambler 8.0 (Camo ASA, Trondheim, Norway) while L-PLSR was done in MatLabTM (www.mathworks.com). All analysis were performed using non-standardised variables and full
Table 2 List of 23 statements used in this study Person served
Served with foods
Where served
Physiological
Psychological
Time
Occasion served
For men For women For guests
With With With With With With With With
At At At At
When IÕm thirsty
When I need to relax
In the evening In the afternoon On a hot summer day At the weekend
At parties When I watch TV
chicken fish grill/barbecue chips soups pizza/pasta spicy foods beef
a restaurant a cafe´ home a pub
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cross validation. ANOVA was carried out using the software Statgraphics Plus 4.1 (Manugistics, Inc., Rockville, Maryland, USA). 3. Results and discussion 3.1. ANOVA Results from the ANOVA showed significant differences (p < 0.001) between the 10 beer samples for all the sensory attributes. No important interactions between panellists and samples were observed. 3.2. Sensory profiling Results from the DPLSR analysis showed which sensory attributes described and discriminated between the beer samples. The first three principal components accounted for 93% of the total variance relevant for explaining sample variation. PC1 explained 69% of the variation, whereas PC2 and PC3 described 19% and 5%, respectively. A correlation loadings plot illustrating the first two principal components is shown in Fig. 1, reflecting variation among beer types and fermentation types, respectively. Sample variations caused by the attributes grainy/roasted, bitter, alcohol, colour, body, fruity and spicy were described by the first PC. These attributes were all positively loaded on PC1, forming two strongly correlated subgroups consisting of three (grainy/roasted, bitter and alcohol) and four attributes (colour, body, fruity and spicy), respectively. Beer sample intensity regarding the formerly mentioned attributes decreased along PC1 from the right to the left. This means that beer samples A1, A2 and SL1 were given the highest intensity scores for the attributes grainy/roasted,
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bitter, alcohol, colour, body, fruity and spicy while W1 and L1 were given the lowest intensity scores. The second PC explained sample variation due to the attributes carbonation and floral. Carbonation was positively loaded on PC2, whereas floral was negatively loaded on this component. A3 was evaluated as the most floral beer sample followed by W1, L3 and SL2 in descending order. L1 was given the highest intensity score for the attribute carbonation and A3 was evaluated as the sample with the lowest carbon dioxide content. As well as explaining the variance caused by the attributes carbonation and floral the second PC separated the samples in bottom-fermented and top-fermented beers, respectively. The bottomfermented beer samples were located on the positive side of PC2 in correlation with the attributes carbonation, grainy/roasted, bitter and alcohol whereas the topfermented samples were placed on the negative side of PC2 related to the attributes floral, spicy and fruity. These correlations between beer samples and attributes are in agreement with the sensory characteristics normally associated with bottom and top-fermented beers, respectively. Generally, the sensory diversity of the top-fermented samples (A1, A2, A3 and W1) seemed to be larger than for the bottom-fermented samples (L1, L2, L3, SL1, SL2 and SL3). The larger sensory diversity of top-fermented beers are caused by a broad variety of floral, fruity and spicy characteristics that are either absent or less pronounced when it comes to bottom-fermented beers. The third principal component (not shown here) explained to a great extent the variation associated with the attribute spicy. W1, A1, SL1 and A2 were found as the spiciest samples while L1, SL2 and L3 were evaluated as the least spicy samples. Colour, carbonation and floral were found as significant attributes for differentiating the 10 beer samples in
Fig. 1. DPLSR on sensory profiling data (X-matrix) and design variables (Y-matrix). Correlation loadings plot showing the first and second principal component (PC). The inner and outer ellipses represent 50% and 100% explained variance, respectively.
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question, i.e., showing high percent explained variance in Fig. 1.
Table 3 Average hedonic scores across all, male and female consumers Samples
Average hedonic scores All (n = 38)
Male (n = 29)
Female (n = 9)
L1 L2 L3 SL1 SL2 SL3 A1 A2 A3 W1
4.6 4.8 5.2 4.2 4.3 5.2 4.4 5.8 4.5 4.3
4.4 4.7 5.1 4.2 4.4 5.3 4.9 6.0 4.6 4.6
5.0 5.0 5.2 4.2 3.7 5.0 3.0 5.1 4.4 3.1
3.3. Consumer test (hedonic test) Results from the hedonic test were studied using APLSR. The first two principal components accounted for 82% of the variance associated with the hedonic data. A correlation loadings plot illustrating PC1 and PC2 is shown in Fig. 2. The first principal component explained the variance in liking of the samples assessed by the average consumer (‘‘average’’) as well as different consumer groups. A2 was evaluated as the best-liked sample followed by SL3, L3 and L2 while SL1, W1, SL2 and A1 were given the lowest scores (Table 3). However, all beer samples were given an average hedonic score higher than four corresponding to the assessment ‘‘neither like nor dislike’’. Generally, the better-established beers (i.e., A2, SL3, L3, L2 and L1) were preferred over the latest introduced beers (i.e., SL1, W1 and A1) on the Danish market. The results of the hedonic test carried out by the male consumers were very similar to the average consumer (‘‘average’’) due to the high number of male respondents (Table 3). The female consumers (female) gave L3 and A2 the highest hedonic scores while A1 and W1 were given the lowest scores. Several noticeable differences were observed between male and female consumers (Table 3). The male consumers liked the ale and wheat beer samples better than the female consumers did. The male consumers evaluated in particular A1 and W1 higher than the females did. Consequently, the male consumers seemed to be more amenable towards the new types of beer on the Danish market whereas the females still seemed to prefer the more traditional lager beers. The second principal component explained the variance in liking
of the samples evaluated by the group of consumers having ale as their favourite type of beer. This group of consumers assessed A1, A2, L3 and A3, as the best liked samples. Looking at all consumer groups the three ale samples included in this study were given the highest average hedonic score by this group of consumers. Consequently, there seemed to be consistency between the claimed favourite type of beer and the factual evaluation of the beer samples for this group of consumers. Something similar was the case for the group of consumers having lager as their favourite type of beer. Consumers preferring a serving temperature corresponding to either cellar or fridge cold had ale and lager as their favourite type of beer, respectively. This finding is in agreement with the general recommendation for these two types of beer when it comes to serving temperature. Furthermore, Fig. 2 shows that consumers having wine (WineFav) or beer (BeerFav) as their favourite type of alcohol were correlated to the female and male group, respectively, contributing to high percent explained variance in PC2. In a separate APLSR study, age group
Fig. 2. APLSR on design variables (X-matrix) and consumer liking (Y-matrix). Correlation loadings plot showing the first and second PC. The inner and outer ellipses represent 50% and 100% explained variance, respectively.
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(18–29 years) showed higher liking for lager and wheat beers than consumers aged 30–59 years, the latter group rather liking strong beers with high alcohol percent. The other consumer groups not discussed in the results (e.g., employed, students), were found to be of minor interest. However, care should be taken for generalising the results with respect to consumer groups since relatively few consumers participated. 3.4. Consumer test (appropriateness test) Results from the appropriateness test were studied using an APLSR analysis. The first two principal components accounted for 75% of the variance associated with the appropriateness data. A correlation loadings plot illustrating PC1 and PC2 is shown in Fig. 3. The first principal component explained to a great extent the variance in sample appropriateness towards the 23 statements listed in Table 2. Beer samples positively loaded on PC1 (L1, L3, SL3, A2 and W1) were found as the most suitable samples for the main part of the statements whereas the samples negatively loaded on PC1 (SL1, A1, A3, L2 and SL2) were evaluated to be the least appropriate. L1, W1 and to some extent L3 were found as the most suitable samples for statements such as hot summer day, thirsty, women, afternoon, fish, chicken and pizza/pasta. L1, W1 and L3 had a relatively simple sensory profile in common making them particularly suitable for the formerly mentioned group of statements. L3 was assessed as the most suitable sample for the statements spicy foods, beef and grill/barbecue. Beer samples having a more complex sensory profile (SL3 and A2) were found as the most suitable samples for statements such as men, pub, cafe´, evening and soups. SL1 and A1 were found as the least suitable samples in relation to
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the food statements mentioned in Table 2. This finding is a bit surprising taking into account that these beer samples have been introduced on the market as being particularly suitable with foods. Insufficient knowledge of these newly introduced beer samples might explain the relatively low appropriateness scores. The context associated with the consumer test could be another explanation to these results. A more realistic context as for example a real dining situation might change the results in favour of both SL1 and A1. Summed across all beer samples the statements pub, men, cafe´, chips and weekend were found as the most suitable for the product category beer. The results of the hedonic and the appropriateness test did not seem to influence each other. Samples getting high hedonic scores were not always given high appropriateness scores and vice versa. This is in agreement with the findings of Cardello and Schutz (1996). 3.5. Consumer liking explained both by consumer background and product descriptor data L-PLSR was performed to study which sensory attributes as well as consumer background characteristics could explain consumer liking. A correlation loadings plot (Fig. 4) gave a visualisation of the systematic covariation between product characteristics (e.g., beer type, percentage of alcohol and sensory descriptors), consumer background (e.g., gender, age) and consumer liking. Fig. 4 shows the product variation in PC1 (in northeast vs. southwest direction) as A1, A2 and SL1 being spicy, fruity and body vs. L1 (Type Lager). In PC2 (in northwest vs. southeast direction) SL2 and SL3 (Type Strong Lager) were described opposite to W1 being more floral and with less carbonation. With respect to consumer background variation, PC1
Fig. 3. APLSR on design variables (X-matrix) and appropriateness data (Y-matrix). Correlation loadings plot showing the first and second PC. The inner and outer ellipses represent 50% and 100% explained variance, respectively.
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1
Bitter %Alcohol Alcohol
0.8
Grainy/roasted TypeStrongLager Age30-59
Body Colour Fruity
0.6 0.4
SL2 LagerFavorite b9 b8 Carbonation b4 L2
0.2 PC2
CellarTemp
SL3 SL3
0
SL2 SL1 StronglagerFavorite >16/mnth b12 A1 b29 b10 b16 A2A1 Spicy L2 4/mnth b21 b3“Average” consumer Male SL1 A2 A3
L1 L3 b22 b2 L3 2/mnth TypeLager L1 Female
-0.2 -0.4
50%
-0.8
TypeAle
b11 Floral
FridgeTemp -0.6
b1
b18 10/mnth b7 AleFavorite A3 b17 W1
W1 Age18-29
100%
-1 -1
-0.8
-0.6
-0.4
-0.2
0 PC1
0.2
0.4
0.6
0.8
1
Fig. 4. L-PLSR correlation loadings for PC1 vs. PC2: (*) Beers, by product descriptors X; (s) beers, by liking YÕs correlation to consumer background Z; (·) product descriptors X; (,) consumer background Z; (d) individual consumersÕ liking Y. b1–b29 are background variables of minor interest. The inner and outer ellipses represent 50% and 100% explained variance, respectively.
distinguished between male and female, and PC2 between young (age 18–29 years) and elderly (age 30–59 years) consumers. Male consumers and consumers having ale and strong lager as their favourite type of alcohol were all positively loaded on PC1. For these consumer groups the sensory attributes grainy/roasted, bitter and alcohol had the greatest impact on liking followed by the attributes colour, body and fruity. The second principal component explained the variation between consumers having either lager or ale as their favourite type of beer. The first group (lager) preferred samples with a relatively high content of carbon dioxide (carbonation). The second group of consumers (ale) favoured a full-bodied (body) beer with a floral, spicy and fruity flavour. Both groups preferred a beer with a balanced bitter and a moderate alcohol and grainy/roasted flavour. Consumers preferring lager beers (Type Lager) were highly correlated to the female group, Type Ale was related to the male group and Type Strong Lager (with high percent alcohol) was liked by the elderly, while wheat beer was best liked by the younger consumers in this study. The dots in Fig. 4 show liking from the individual consumers indicating that there are various segments among the consumers. The average consumer appears close to the origin giving no information about individual differences. The L-PLSR plot can be validated by examining the 10 samples being modelled from both the product matrix (X)—underlined in Fig. 4—and the consumer background matrix (Z) on the liking matrix (Y): The samples are lying
close to each other (i.e., A1 and A1). This means that from two independent sets of measurements, the same type of variation in consumer liking could be explained. In future studies it will be of interest to apply L-PLSR including data from investigations of the role of memory for food identity in the consumer background Z-matrix. Furthermore, different consumer data addressing emotional and implicit responses, such as data from authenticity tests (e.g., Mojet & Ko¨ster, 2004), could constitute the Y-matrix. 4. Conclusion Descriptive sensory profiling and consumer tests of 10 different Danish beers have been conducted successfully and validated prediction of consumer liking simultaneously from consumer background characteristics and product descriptors have been explored. The results of the sensory profiling showed that the sensory diversity of Danish beer is large and manifold. Lager beers included in the study were mainly described by the attributes grainy/roasted, bitter, alcohol and carbonation whereas the ale and wheat beers primarily were described by the attributes fruity, spicy, floral and body. A consumer hedonic test evaluated A2, SL3 and L3 as the best-liked beer samples. In general, the better-established beers on the Danish market were given higher hedonic scores than the newly introduced beers. An appropriateness test made it possible to tell for
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which situations and uses the different beer samples were most suitable. Relating the results of the sensory profiling and the consumer data (hedonic test and consumer background information) made it possible to tell which sensory attributes influenced liking of the beer samples evaluated by different consumer groups. This knowledge can be used in connection with, for example, product development and marketing, thus contributing to future beer identity in Denmark. In general, the approach and various methods demonstrated in this study may be useful with respect to similar studies on other products and in other countries. Acknowledgements Thanks to Harald Martens for substantial contribution to the L-PLSR analysis. Part of this work was supported by the Danish Føtek programme/Danish Government and by Matforsk AS, Norway. References Atkinson, R. L., Atkinson, R. C., Smith, E. E., Bem, D. J., & Hilgard, E. R. (1990). Introduction to psychology (10th ed., pp. 756–757). Harcourt Brace Jovanovich Inc. Cardello, A. V., & Schutz, H. G. (1996). Food appropriateness measures as an adjunct to consumer preference/acceptability evaluation. Food Quality and Preference, 7(3/4), 239–249.
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