FORMULATION OF A SOY–COFFEE BEVERAGE BY

joss_278 226..242

FORMULATION OF A SOY–COFFEE BEVERAGE BYRESPONSE SURFACE METHODOLOGY AND INTERNAL

PREFERENCE MAPPING

ILANA FELBERG1,6, ROSIRES DELIZA2,3, ADRIANA FARAH4,ERÔNICA CALADO5 and CARMEN MARINO DONANGELO4

1Embrapa Agroindústria de AlimentosAv. das Américas, 29501, Guaratiba

Rio de Janeiro, RJ, CEP 23020-470, Brazil

2Embrapa Labex EuropeAvenue Agropolis, F-34394

Montpellier, Cedex 5, France

3INRA, UMR CSGA17 rue de Sully, F-21065

Dijon, France

4Instituto de QuímicaUniversidade Federal do Rio de Janeiro

Av. Athos da Silveira Ramos149, Bloco A, Ilha do Fundão

Rio de Janeiro, CEP 21941-909, Brazil

5Escola de QuímicaUniversidade Federal do Rio de Janeiro

Av. Horácio Macedo, 2030Bloco E, Ilha do Fundão

Rio de Janeiro, CEP 21941-909, Brazil

Accepted for Publication November 18, 2009

ABSTRACT

Coffee consumers (n = 60) tasted and rated samples of a new soy–coffeebeverage made from instant coffee, soymilk powder and sugar. Ingredientconcentrations (independent variables) varied according to a 23central com-posite design for overall degree of acceptance. Data were analyzed by analysisof variance (ANOVA), least square difference and response surface method-ology, followed by internal preference mapping (IPM) with cluster analysis.

6 Corresponding author. TEL: 5521-3622-9600; FAX: 5521-2410-1090; EMAIL: [email protected]

DOI: 10.1111/j.1745-459X.2010.00278.x

Journal of Sensory Studies 25 (2010) 226–242.© 2010 Wiley Periodicals, Inc.226

ANOVA from the consumers’ acceptance data revealed that samples differedsignificantly (P � 0.05). Although soymilk content did not influence signifi-cantly the consumers’ acceptance in the tested range, IPM with cluster analy-sis indicated that at least part of the acceptance differences was based on thesoy beverage consumption habit. The final beverage formulation was evalu-ated cold and hot for overall acceptability (9-point structured hedonic scale)by 112 coffee consumers and the cold beverage reached a good acceptabilitymean score (6.2) among the participants.

PRACTICAL APPLICATIONS

The consumption of soy products has been reported to reduce the risk ofseveral diseases and a number of recent studies have found beneficial healthproperties attributed to coffee. Considering the current consumer trend forhealthier alternatives in food products, we decided to combine the healthbenefits of these two important Brazilian commodities in a functional bever-age. In order to optimize the formulation and maximize sensory acceptance,we performed consumers’ tests using response surface methodology. Internalpreference mapping and cluster analyses were also applied to provide infor-mation on the variability of the consumer individual opinions and segmentthem in groups of similar preference criteria.

INTRODUCTION

The consumption of soy products has been reported to reduce the risk ofseveral diseases, including breast and prostate cancers, osteoporosis and heartdiseases (Barnes et al. 2006; Messina et al. 2006). Among soy derivatives,soymilk has been gaining an increased interest from food industries during thelast decade (Behrens et al. 2007). Soymilk is an excellent source of protein andessential fatty acids, is cholesterol-free and is relatively cheap in comparisonwith other food protein sources (Lozano et al. 2007). Although plain soymilkis not well-accepted in Western countries because of its beany flavor andastringency (Al Mahfuz et al. 2004), soy beverages blended with fruit juicesare a new generation of soy products in these countries and a convenient andhealthy way to include soy protein in the consumer regular diet. Soy beveragesales in the U.S.A. have more than doubled since 2000, and soy–fruit bever-ages have become an important market. Even though Brazil is the secondworld soybean producer, soymilk is practically unknown to most Brazilianconsumers, and most people who try the product tend to present a negative

227FORMULATION OF A SOY–COFFEE BEVERAGE

attitude toward soy milk, mainly for its undesirable sensory properties(Tashima and Cardello 2003; Behrens and Silva 2004).

On the other hand, coffee is very much appreciated among Brazilianpeople (ABIC 2009). Brazil is the first coffee bean producer and the secondlargest coffee consumer market in the world. Although coffee is usually con-sumed mainly for pleasure because of its taste and aroma (Cristovam et al.2000; Moreira et al. 2000), a number of recent studies have found beneficialhealth properties related to coffee’s antioxidant activity and the capacity tonormalize blood pressure and glicemia among others (Farah 2009). The con-sumption of small amounts of caffeine is also known to produce a well-beingand alertness sensation (Farah and Donangelo 2006; Higdon and Frei 2006;Farah 2009).

The popularity and use of instant coffee has increased significantly allover the world because of the easy and fast preparation and long shelf life(Prakash et al. 2000; Nogueira and Trugo 2003). Although instant coffeerepresents only 5.8% of the internal Brazilian coffee market, this percentage isgrowing at an annual rate of 8%, thus reflecting the increasing interest in thiscoffee product (ABICS 2009).

Understanding the consumer attitude toward food is critical to achieve aproduct success (Deshpande et al. 2008; Hein et al. 2008). Therefore, theoptimization of a formulation is very important to maximize sensory accept-ability. Response surface methodology (RSM) has been used to investigatesensory characteristics when some ingredients are tested simultaneously(Damásio et al. 1999; Deshpande et al. 2008). Usually, the acceptability dataof samples obtained from hedonic scaling are expressed as mean scores. In thiscase, it is assumed that consumers’ acceptability criteria are homogeneous(MacFie 2007). However, consumer preference responses are often quite het-erogeneous, and mean scores may not be representative of individual opinions.Internal preference mapping (IPM) is a statistical tool that allows examinationof individual rating by consumers (Greenhoff and MacFie 1994; Yackinouset al. 1999). This technique is based on principal component analysis, clusteranalysis and polynomial regression (Yackinous et al. 1999; Wajrock et al.2008). In addition to providing information on the variability of the consumerindividual opinions, IPM provides the possibility of segmenting consumers ingroups of similar preference criteria. When combining preference mappingwith samples formulated according to a factorial design, IPM shows whichformulas are liked by consumers and also which characteristics are preferredas well (Yackinous et al. 1999).

Considering the current consumer trend for healthier alternatives in foodproducts, the possibility of combining the health benefits of these two impor-tant Brazilian commodities in a functional beverage and the fact that there areno reports of a similar product in the literature, the objective of the present

228 I. FELBERG ET AL.

study is to formulate and optimize a coffee–soy-based beverage using RSMand IPM statistical tools applied to consumer acceptability.

MATERIALS AND METHODS

Soy–Coffee Formula Ingredients

Robusta instant coffee (Coffea canephora cv. Conillon) was provided byCocam Cia de Café Solúvel e Derivados, Brazil; soymilk powder was providedby Olvebra Industrial S.A., Brazil and refined sugar (from sugar cane) waspurchased from the local market.

Consumer Tests

Three consumers’ tests were carried out. The first test (1) was performedwith coffee consumers aiming at determining the optimal beverage formula-tion. The second and third tests (2 and 3) were also performed with coffeeconsumers with the objective of verifying the acceptance of the previouslyidentified optimal formulation when served either cold (2) or hot (3).

Consumers’ Acceptance Test 1

A 23 central composite design with six axial points and four centralpoints, totaling 18 formulas, was used to determine the optimum concentrationof instant coffee, soymilk powder and sugar (independent variables) in thesoy–coffee beverage. The ingredients’ concentration ranges were selectedfollowing manufacturer’s recommendations and previous experiments(Felberg et al. 2008) and varied from 1.32 to 4.68 g for instant coffee, from5.98 to 11.02 g for soymilk and from 6.64 to 13.36 g for sugar, then added to100 mL of spring water at room temperature to prepare the beverage, as shownin Table 1. The prepared beverages were kept refrigerated (8 � 2C) until beingtested by consumers on the next day.

The test was carried out in standardized sensory booths with a group of 60consumers (29 females and 31 males) aged between 18 and 66 years, habituallyconsuming one or more cups of coffee per day, recruited among staff, studentsand visitors at Embrapa Food Technology Research Center, Brazil. Eachconsumer evaluated the acceptability of all the 18 formulations using a 9-pointstructured hedonic scale where, 1 = dislike extremely and 9 = like extremely.The sample beverages were presented at 8 � 2C in 50 mL plastic cups codedwith three-digit numbers. The samples were presented in a monadic sequentialorder. A complete randomized design was used, and each assessor evaluated sixsamples per session throughout three sessions. The order of sample presentation


was balanced to prevent carryover effects (MacFie et al. 1989). Spring waterand unsalted cracker were provided for mouth rinsing between samples. Sub-jects were asked to complete a questionnaire aiming to provide general infor-mation regarding their age, gender, occupation, monthly income and habitualconsumption of coffee and soy beverages.

Consumers’ Acceptance Tests 2 and 3

The consumer acceptance tests 2 and 3 were carried out with a total of 224coffee consumers (112 for each test) randomly recruited in a supermarket in Riode Janeiro, Brazil. The formula tested was the one with the highest acceptancein the previously described consumers test. For test 2, the coffee–soy beverageformulated was served cold (8 � 2C) and for test 3, hot (60 � 2C). The sampleswere evaluated for overall acceptability using a 9-point structured hedonic scale(1 = dislike extremely and 9 = like extremely) and intention to purchase using a7-point structured hedonic scale (1 = definitely would not buy and 7 = definitelywould buy). A dummy sample was used in both tests. The samples werepresented in 50 mL plastic cups coded with three-digit numbers, and the orderof presentation was balanced. Water and crackers were available for consumersto drink and clean the palate as they pleased.

Statistical Analysis

The software STATISTICA™, version 8.0 for Windows (StatSoft, Inc.,Tulsa, OK), was used for the experimental design and for RSM analysis.Analysis of variance (ANOVA) and Fisher’s test (LSD) were used to verifysignificant differences among means (P < 0.05). The preference mapping andcluster analyses were performed using XLSTAT-MX (2005, Addinsoft, NewYork, NY) for Windows. For cluster analysis, we used Ward’s method andEuclidean distances.

TABLE 1.CODED LEVELS AND INGREDIENTS CONCENTRATIONS

FOR CENTRAL COMPOSITE DESIGN OF THE SOY–COFFEEBEVERAGE (18 SAMPLES)

Independent variable Coded levels

-1.68 -1.0 0 1 +1.68

Concentration (g)*Instant coffee 1.32 2.00 3.00 4.00 4.68Soymilk powder 5.98 7.00 8.50 10.00 11.02Sugar 6.64 8.00 10.00 12.00 13.36

* Added to 100 mL of spring water to prepare the beverage.


RESULTS AND DISCUSSION

Consumer Tests to Establish Optimum Concentration of Ingredients forthe Soy–Coffee Beverage

The acceptability scores assigned to the samples by 60 consumers wererelated to the concentration of ingredients. Fig. 1 shows a Pareto chart whereboxes represent the Student’s t-values. The linear (L) and quadratic (Q) termsof the variables sugar and instant coffee significantly influenced the acceptanceof the beverage as their boxes were located on the right side of the dashed line.The linear term of sugar was the one that most contributed to the model. Itspositive value indicates that an increase in sugar concentration contributed toa higher acceptance of the beverage. This is probably because of the culturalinheritance of the Brazilian population with regard to the Portuguese tradi-tional appeal for sweet products (Nestlé 1990). In the case of instant coffee, itslinear and quadratic terms had a significant negative effect, indicating that adecrease in the coffee content within the limits tested in this study contributedto a higher acceptance of the beverage. On the other hand, neither the soymilkpowder content nor the interaction between the variables influenced signifi-cantly the consumers’ acceptance in the studied range.

Figure 2 shows the influence of the concentration of the studied variableson the acceptance scores. As observed in Fig. 1, the highest consumer accep-tance values were obtained for the highest sugar concentration and the lowest

0.

-.255707

-.292237

-1.05492

1.695916

-2.25668

-2.48404

-6.6293

9.5515

P = 0.05

Soymilk (L)xSugar (L)

Coffee (L)xSoymilk (L)

Coffee (L)xSugar (L)

Soymilk (Q)

Soymilk (L)

Sugar(Q)

Coffee(Q)

Coffee(L)

Sugar(L)

FIG. 1. PARETO CHART SHOWING THE STATISTICAL SIGNIFICANCE OF THEINDEPENDENT VARIABLES (FORMULA INGREDIENTS)

(L) = linear term and (Q) = quadratic term numbers near the boxes mean Student’s t values.


instant coffee concentration, independently of soymilk concentration (1). Thecontour curve (2) indicates that there was a good distribution of the designexperimental points. The dark region indicates that any point within this arearepresents a combination of instant coffee, soymilk and sugar that would resultin a higher consumer acceptance.

Table 2 presents the design matrix with the acceptability scores, approvalindexes (consumer ratings >5.0) and rejection indexes (consumer ratings

> 5 < 5 < 4 < 3 < 2

> 5 < 5 < 4 < 3 < 2

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

Coffee (g)

6

7

8

9

10

11

12

13

14

Sug

ar (

g)

A

B

FIG. 2. RESPONSE SURFACE (A) AND CONTOUR CURVE (B) FOR CONSUMERACCEPTANCE (n = 60) AS A FUNCTION OF SUGAR AND INSTANT COFFEE

CONCENTRATIONS AT 10% SOYMILK CONCENTRATION(Acceptance evaluated using a 9-point hedonic scale: 1 = disliked extremely; 9 = liked extremely).


<5.0). Hedonic ratings for the samples differed drastically. The most preferredones scored (on average) from 5.0 (neither like nor dislike) to 6.0 (like slightly)on a 9-point hedonic scale. In general, soy products do not present highhedonic scores in the literature, being our results similar to those of otherauthors (Rosenthal et al. 2002; Felberg et al. 2004; Da Silva et al. 2007; Potteret al. 2007). Deshpande et al. (2008) considered 5 to be the lowest consumerrating limit of acceptability for optimizing formulations of a chocolate-flavored peanut–soy beverage, evaluated using a 9-point hedonic scale. Whenutilizing the same criteria, 39% of the formulated beverages presented con-sumer ratings �5.0 (Table 2).

ANOVA results for acceptance data from all the consumers revealed thatthe samples differed significantly in terms of acceptability, with a range ofmean scores from 3.5 to 5.8 (Table 2). No significant difference was observedamong the most preferred samples (18, 3, 11, 13, 16 and 8). The approval indexfrom those samples varied from 65% to 57%. The least accepted samples were6, 17, 1, 14, 7 and 15. Both samples 1 and 6 (68% rejection index) had high

TABLE 2.MEAN ACCEPTABILITY§ SCORES OF THE SOY–COFFEE BEVERAGES

Samplenumber

Coffee(g)*

Soymilk(g)*

Sugar(g)*

Acceptabilityscore†

Approvalindex (%)

RejectionIndex (%)

S18 3.00 8.50 13.36 5.8a 65 30S3 2.00 7.00 12.00 5.7ab 65 32S11 2.00 10.00 12.00 5.5abc 65 28S13 1.32 8.50 10.00 5.5abc 58 33S16 3.00 11.02 10.00 5.3abcd 57 32S8 3.00 8.50 10.00 5.2abcde 57 33S2 3.00 8.50 10.00 5.0bcde 53 42S4 3.00 8.50 10.00 4.9cdef 43 37S9 4.00 10.00 12.00 4.9cdef 48 45S5 4.00 7.00 12.00 4.7def 47 42S10 3.00 8.50 10.00 4.7def 45 45S12 2.00 10.00 8.00 4.7def 38 50S15 3.00 5.98 10.00 4.5efgh 38 48S7 2.00 7.00 8.00 4.1fgh 32 55S14 4.68 8.50 10.00 3.8gh 25 70S1 4.00 7.00 8.00 3.7h 22 68S17 3.00 8.50 6.64 3.7h 20 68S6 4.00 10.00 8.00 3.5h 25 68

Values with different superscript letters within the same row are significantly different by Fisher’s test(P � 0.05).* Added to 100 mL of spring water to prepare the beverage.† Evaluated using a 9-point hedonic scale varying from 1 = dislike extremely, 5 = neither like nor

dislike, to 9 = like extremely.


coffee concentration (4 g) and low levels of sugar (8 g) with 7 g and 10 g ofsoymilk content, respectively. Sample 17 (68% rejection index) had the lowestsugar content studied (6.6 g) and sample 14 – with the highest rejection indexof 70% – presented the highest level of instant coffee (4.7 g).

Based on the experimental design, the critical values of each one of theingredients for the best predicted value of acceptability (5.9) were calculatedand found to be 1.72, 9.66 and 14.07 g/100 mL of spring water for instantcoffee, soymilk and sugar content, respectively.

Taking into account that consumer preference responses are usually het-erogeneous and averaged consumer data may not be representative of anyindividual opinion, preference mapping was applied to visualize the divergingliking directions of all the consumers on a map. Preference mapping showedthat 48% of the variance could be explained by the first three principal com-ponents, of which 30% was accounted by the first preference dimension, 10%by the second and 8% by the third.

Figure 3 presents the internal preference maps derived from the accep-tance scores of the 60 consumers for the 18 soy–coffee beverage samplesdefined by the first two principal dimensions. Sample positions (S), consum-er’s positions and the identified clusters can be seen in Fig. 3.

Principal component 1 separated the beverages into three main groups.The first group (samples 3, 11, 13, 18 and 16) was located at the top rightquadrant and contained samples with low coffee and/or high sugar content.These results were similar to those reported by Geel et al. (2005) that relatedconsumer preferences to sensory attributes of instant coffee. The authorscalled the consumers who preferred the coffee flavor with lowest intensity andthe highest sweetness as “coffee blend lovers,”

Samples 15, 7, 12, 4, 2, 10, 9, 5 and 8 were included in a second group.Samples 2, 4, 8 and 10 were the center points of the design samples. Samples17, 14, 6 and 1 made up the third group and were the least accepted ones.Sample 17 had the lowest level of sugar studied, sample 14 had the highestlevel of instant coffee, and samples 6 and 1 had high level of instant coffee andlow level of sugar content.

Although these results indicated that the majority of the participants didprefer the sweetest and the least intense coffee samples (Table 2, Fig. 3), wecan see a small cluster that preferred samples with the opposite characteristics(Table 2, Fig. 3). Probably, they could be comparable to the “coffee lovers”described by Geel et al. (2005), who liked bitterer and more coffee flavorcharacter samples. Principal components 2 and 3 seemed not to separateconsumers as well as principal component 1.

Figure 3B revealed the existence of significant differences in preferenceamong consumers. Each point represents the end point of one consumeracceptance vector, and each vector can be visualized by drawing a line from


Observations (axes F1 and F2: 40.33 %)

S1

S2

S3S4

S5

S6

S7

S8

S9S10

S11

S12S13

S14

S15S16

S17

S18

-8

-6

-4

-2

0

2

4

6

-10 -8 -6 -4 -2 0 2 4 6 8

F1 (29.93 %)

F2

(10.

40 %

)

Variables (axes F1 and F2: 40.33 %)

12

3

4

5

6

7

98

1011

1213

14

15

16

17

18

1920

21

22 2324

25

26

27

28

2930

3132

3334

353637

3839

40

41

42

4344

45

46

47

48

49

50

51

5253

54

55

5657

5859

60

Cluster 2

Cluster 3

Cluster 1

-1

-0,75

-0,5

-0,25

0

0,25

0,5

0,75

1

-1 -0,75 -0,5 -0,25 0 0,25 0,5 0,75 1

F1 (29.93 %)

F2

(10.

40 %

)

B

A

FIG. 3. INTERNAL PREFERENCE MAPPING OF 18 SOY–COFFEE BEVERAGES,SHOWING: (A) BEVERAGE SAMPLES AND (B) CONSUMER POSITION (n = 60) WITH

THREE CLUSTERS


the center of the circle. The length of the vectors indicates how much vari-ance of the consumers’ acceptability scores is explained. The circle repre-sents the fit of the regression model (P < 0.05). All the 60 consumers weresignificantly fitted and included in each plot. The consumers were segmentedand distributed around the various quadrants of the preference space. Clusteranalysis was conducted and included later in the preference mapping inorder to visualize the group of consumers who liked the samples in a similarway.

Three clusters of consumers were found for overall acceptance. Thenumber of consumers per cluster and the overall acceptance mean scores foreach sample are shown in Table 3. ANOVA results for acceptance data fromthe consumers segmented in the three clusters revealed that the samples dif-fered significantly in terms of acceptability and a formulated soy–coffee bev-erage that is agreeable to one group does not satisfy others (Table 3).

TABLE 3.MEAN ACCEPTABILITY* SCORES OF THE SOY–COFFEEBEVERAGES FOR EACH OF THE THREE PREFERENCE

SUBGROUPS (CLUSTERS)

Preference subgroups (clusters)

cluster 1 cluster 2 cluster 3

Sample number (n = 31) (n = 20 ) (n = 9)S18 6.8a 5.2ab 3.8bc

S13 6.8a 4.2bcde 4.2bc

S3 6.4a 5.2ab 4.1bc

S11 6.3a 5.0abc 4.1bc

S16 6.0ab 5.0abc 3.7bc

S8 5.2bc 5.4a 5.0abc

S12 5.1bc 4.0bcdef 4.7abc

S15 5.0c 3.9cdef 4.4abc

S2 4.9c 4.8abcd 5.6abc

S4 4.9c 4.6abcde 5.6abc

S9 4.5cd 5.6a 4.6abc

S10 4.5cd 4.5abcde 5.8ab

S7 4.5cd 3.9cdef 3.3c

S5 4.4cde 4.8abcd 5.8ab

S14 3.5ef 3.5ef 5.8ab

S17 3.5def 2.9f 5.8ab

S6 2.8f 4.1bcdef 4.8abc

S1 2.8f 3.6def 6.8a

Values with different superscript letters within the same row aresignificantly different by Fisher’s test (P � 0.05).* Evaluated using a 9-point hedonic scale varying from 1 = dislike

extremely, 5 = neither like nor dislike, to 9 = like extremely.


The consumers from cluster 1 (n = 31), corresponding to 52% of all theconsumers, gave better scores and preferred the beverages with low coffeeand/or high sugar content (3, 11, 13, 18 and 16), with acceptance mean scores�6.0 (like slightly) on a 9-point hedonic scale.

The consumers in cluster 2 (n = 20) also liked some of the samples likedby the consumers in cluster 1. However, they gave lower acceptability scores.The preferred samples were 9, 8, 3, 18, 11, 16 and 5, with acceptance meanscores >5.0 (neither like nor dislike) on a 9-point hedonic scale.

cluster 3 (n = 9), with only 15% of the consumers, liked products 1, 17,14, 10, 5, 4 and 2, with acceptance mean scores >5.0. Those consumers had avery different pattern for liking and preferred the samples with low levels ofsugar and/or high levels of instant coffee.

It was not possible to characterize any of the three clusters based on theavailable demographic data recorded in the questionnaires such as income,educational level, gender, etc. However, we did find differences in soy bever-ages and coffee consumption patterns of the three clusters. Although theconsumers’ acceptability of the beverages obtained from hedonic scalingexpressed as mean scores was not significantly influenced by soymilk concen-tration, the percentage of the participants who consumed soy beverages regu-larly varied considerably in a diminishing way in clusters 1, 2 and 3 (77%,60% and 44% of the consumers, respectively). The opposite was observed withcoffee consumption. About 52%, 69% and 77% of the consumers, respectively,consumed more than two cups of coffee a day, with 33% of cluster 3 consum-ing more than five cups a day. Unfortunately, since we did not ask the con-sumers about their sweetness preferences, we were not able to investigate thisissue when dividing them into subgroups.

In terms of the methodological approach, it is noteworthy to commentthat cluster analysis could have been thought of as a preliminary to RSM as thepurpose of cluster analysis is to find possible consumer segmentation. Onceconsumer segmentations are identified, the formulation and optimization of anew product in RSM may be based on liking means of a particular consumercluster. However, such approach was not used in the present study because itwould require a much higher number of participants in order to come out witha reasonable degree of freedom.

Because our interest was to develop a beverage with a good sensoryacceptance, but also with potential functional properties and therefore with afavorable concentration of functional ingredients, we concluded that the bestformulation, combining both criteria, contained 2 g% of instant coffee, 10 g%of soymilk and 13 g% of sugar. The sugar content was established taking intoaccount that 14 g% of sugar extrapolated the experimental design and that13 g% of sugar was enough considering the total solid content of the beverageand health concerns related to sugar consumption.


Consumer Acceptance of the Selected Soy–Coffee BeverageFormulation, Served either Cold or Hot

The selected beverage formulations reached a good acceptability amongthe consumers when served cold, with a mean score of 6.2, i.e., between 6(like slightly) and 7 (like moderately). Similar results were observed forfermented soy beverages, chocolate-flavored peanut–soy and blueberry–soybeverages (Behrens et al., 2004; Deshpande et al. 2005 and Potter et al.2007, respectively).

Considering that coffee beverages are usually consumed hot by Brazil-ians, who are not familiar with cold coffee beverages, in addition to testing thecold beverage, we decided to evaluate the acceptance of the soy–coffee bev-erage also when served hot. We chose the temperature of 60 � 2C, sinceaccording to Lee and O’Mahony (2002) and Brown and Diller (2007), this isusually the most appreciated temperature by consumers when drinking coffeeand coffee beverages. However, as we can see in Fig. 4, the results for the hotbeverage were not as good as for the cold beverage, with a mean acceptancescore of 5.5 (between neither like nor dislike and like slightly on the 9-pointhedonic scale).

Despite the fact that a mixture of coffee and milk is a very popularbeverage in Brazil, and it is usually consumed hot, the lower preference scorefor the hot product was expected because some soymilk volatile compoundsare not well-accepted by Western populations (Al Mahfuz et al. 2004), andthey can be more easily perceived in a hot beverage than in a cold version.

0

5

10

15

20

25

Per

cen

tual

(%

)

1 2 3 4 5 6 7 8 9

Acceptance scores

COLD HOT

FIG. 4. OVERALL CONSUMER ACCEPTANCE SCORES OF THE SELECTED SOY–COFFEEBEVERAGE SERVED COLD OR HOT (EVALUATED USING A 9-POINT HEDONIC SCALE)


These compounds are especially aldehydes (Behrens et al. 2004). Addition-ally, all ready-to-drink soy beverages available in the Brazilian market are soldto be consumed cold. On the other hand, as it was mentioned before, consid-ering that almost all coffee beverages are consumed hot in Brazil, this coldcoffee–soy beverage may represent a new market niche for the Brazilianindustry.

CONCLUSIONS

By using the RSM tool, we were able to define a soy–coffee formula.Acceptability of samples (obtained from hedonic data expressed as meanscores) was influenced by sugar and instant coffee concentrations and not bythe soymilk concentration. However, when IPM with cluster analysis wasperformed, we were able to distinguish three segments of consumers, andconsidering the demographic characteristic of the consumers for each cluster,part of the acceptance differences seems to be explained by the soy beverageconsumption habit. The final beverage formulation, served cold, reached agood acceptability among the participants and may represent a new marketniche in Brazil.

ACKNOWLEDGMENTS

We thank the financial support from Consórcio Brazileiro de Pesquisa eDesenvolvimento do Café and Olvebra Industrial S.A (Eldorado do Sul,Brazil) and Cocam Cia de Café Solúvel e Derivados (SP, Brazil) for kindlyoffering the samples.

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FORMULATION OF A SOY–COFFEE BEVERAGE BY