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A comparison of sensory attribute use by children and experts to evaluate chocolate
Food Quality and Preference 13 (2002) 545–553 www.elsevier.com/locate/foodqual
A comparison of sensory attribute use by children and experts to evaluate chocolate§ Francis Sunea,*, Pascale Lacroixb, Franc¸oise Huon de Kermadecc a
Institut Universitaire de Formation des Maıˆtres, Universite´ de Nice Sophia Antipolis, 33Bis, boulevard Carnot, 83300 Draguignan, France b Nestle´ France, Evaluation Sensorielle, 7, boulevard Pierre Carle, BP 900 Noisiel, 77446 Marne la Valle´e Cedex 02, France c ENSAR, Laboratoire de Mathe´matiques Applique´es, 65, rue de Saint Brieuc, CS–35804, 35042 Rennes Cedex, France Received 23 July 2001; received in revised form 29 April 2002; accepted 29 April 2002
Abstract Children constitute a complex but interesting market for the food industry. The objective was to compare the sensory attributes generated and rated by a panel of 261 children from 9 to 11 years old with those of a trained panel of 10 adult experts in the food industry, using a range of eight chocolate products belonging to the child segment. In a first phase, a subgroup of 27 children went through attribute generation according to the Kelly-grid method to establish a questionnaire of 13 attributes. The experts used the QDA method to set up a questionnaire of 27 attributes. Data were analysed to find out relationships between attributes, using Partial Least Square regression with experts’ attributes as explicative variables and children’s attributes as variables to be explained. Surprisingly, some of the attributes most cited by children are not those better explained by experts’ attributes. # 2002 Elsevier Science Ltd. All rights reserved. Keywords: Sensory attribute; Child; Expert; PLS regression
1. Introduction Many industries in the food sector would like to be able to take into account the opinions of children, in order to test products intended for them as consumers. The fact is that the world which has been created for children continues to grow and in most cases it is the experts (ISO, 1992) who evaluate products for them. Today firms need to know if the sensory attributes perceived by children are also detected by experts. Moreover the young people’s market is a market in its own right. It is estimated that the number of adolescents in the United States will by 2010 reach a level of 5.6 million. The purchasing power of children is still increasing and children are attracted more and more to places where money can be spent (Tootelian & Gaedeke, 1992). Children have financial autonomy, special tastes and money to spend. They are more informed than in the past § Article based on presentation to be given to The 4th Pangborn Sensory Science Symposium 2001: A Sense Odyssey. Addresser: Dr. H.L. Meiselman, US Army Natick Soldier Center, Natick, MA 017605020, USA (www.elsevier.nl/locate/foodqual; e-mail: meilselmanh@ natick.army.mil). * Corresponding author. E-mail addresses: [email protected] (F. Sune), pascale. lacroix@nestle´.com (P. Lacroix), [email protected] (F. Huon de Kermadec).
and styles of consumerism are now evolving (McNeal, 1992). Various forms of research concerning child consumerism reveal a similarity between developed countries. Children from different cultures (Hong Kong, Taiwan and New Zealand) differ little from American children in the way they spend their pocket money (Gunter & Furnham, 1998). It would appear that one of the things which children between 7 and 11 spend their money on is sweets, chocolates and ‘‘cereal bars’’. The manufacturers believe that this market can be mastered by transforming and reducing the size of products, by intensifying the colours and by giving them more attractive shapes. This product strategy does not always bring results. It is therefore useful to study the taste perception of children. It is well known that children between 7 and 11 develop considerably both socially and cognitively. A child begins to discriminate between products through applying a set of attributes that he can define. He can reason about what he is tasting, and recognise that some kinds of food are sweeter than others. He can ask himself questions in order to analyse the qualities of a product (Roedder, 1999). A programme of primary school activities concerning taste was established in France in 1980. Some schools supported it and some primary classes of 9- and 10year-old pupils were trained in it. Today, over 100,000 children have taken part in these early-learning classes.
0950-3293/02/$ - see front matter # 2002 Elsevier Science Ltd. All rights reserved. PII: S0950-3293(02)00057-5
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The discovery of food tastes at school has become a means of communicating. Moreover, by sampling different tastes, children have become more aware of the development of their senses. They have learned to talk, describe and even to quantify the intensity of what they experience. These so called ‘‘initiated to taste’’ children are even more at ease when they have to talk about their tastes (Puisais & Pierre, 1987). But what can be said about the interpretation of those children when their judgement is compared to the judgement of non-initiated children and then with the judgement of industrial experts? Some experiments were conducted with children because they alone are representative of their targets (Kimmel, Sigman-Grant, & Guinard, 1994; Kroll, 1990; Moskowitz, 1985; Worsley, Baghurst, Worsley, Coonan, & Peters, 1984). However, it can be said that, in the case of a food item like the chocolate bar, a child is able from the age of seven to describe product attributes other than its funny look. Our research leads us to understand that food products for children possess a range of attributes which must be defined by those who consume them. The child perceives those sensory attributes which allow him to categorize the products (Viswanathan & Childers, 1999). The primary aim of our research is to investigate and compare food quality defined by the youngest consumer to those produced by industrial experts from the industrial world. Subsequently our approach is more quantitative: we asked children to describe food attributes, and adult experts also described their attributes. Using their comments, we tried to establish parallel thinking so that we can better understand the differences as well as the similarities. For example, whether the crispy aspect which is mentioned by the children is similar to attributes valued by the experts. At this stage, one can easily think that the experts’ words mean the same as the children’s. Yet, in today’s world, firms want to know if children’s tastes are the same as adults. There are some articles which compare the eating habits of children and adults (Fischler & Chiva, 1986; Head, Gresbrecht, & Johnson, 1977; Oram, 1998). A good understanding of the vocabulary used by the youngest consumers can lead to products being adapted to children’s needs in a more efficient way. Our research is based on two experiments: Generation and selection of child and expert attributes=Experiment 1 Evaluation of sensory attributes by the child and the expert=Experiment 2
2. Methodology for experiment 1 The objective of the experiment was to produce with a group of 27 children and 10 experts the sensory attributes used to describe a food product such as a chocolate bar.
This first experiment was above all an exploratory stage which was to allow us to answer the following question: ‘‘Does the sensory description of the product by children, from the production of attributes, seem richer, less rich or equal to the description given by experts?’’ Additionally the aim of this first experiment was to allow us to establish a restrictive list of sensory attributes useful for the second experiment. The same products were used in two experiments conducted with children and experts. The products are chocolate bars taken from the children’s market. Most of them are made from different ingredients but all have chocolate as the main ingredient. These bars are meant to be eaten as snacks or, for example, after sporting activities; they can also be used as light meals. They are sold individually in order to have wider appeal to their intended market. During our experiments these products were always presented in portions, the original packaging being removed in order not to influence the children. The experiment was adapted slightly when conducted with the experts. 2.1. For children 27 fifth grade children, 15 girls and 12 boys, from the 9 to 11 year-old age group participated. They spent two hours becoming familiar with the process of sensory analysis. The objective of this was to enable them to distinguish between texture, flavour and visual appeal. They sampled two kinds of chocolate bars different from the eight studied before. This was done with groups of four pupils overseen by the teacher so that each pupil could properly categorize the attributes. At the end of the exercise, the child had to be able to say that ‘‘crispy’’ was an attribute linked to texture whereas ‘‘sweet’’ was linked to flavour. The aim was not to improve their vocabulary but to categorize it so as the pupil would not feel lost during the experiment. The study involving different vocabulary generation took place in the children’s school a few days after they were trained to categorize. Two exercises were necessary for each session; the presence of the children was required for 1 h. They were placed in an authentic consumer situation (Koster, 1981). They tested the products in the school canteen, where they usually had their morning snack or meal, between 10 and 11. In this way the generation of attributes occurred in a natural and unconstrained way. For the generation of vocabulary, we used the Kellygrid method, inspired by the research of Kelly (1955). This method seemed the most suited for its rigour and objectivity according to McEwan and Thomson (1989). The approach consists of generating attributes from a range of products presented in triads. We used an incomplete balanced block design in order to avoid order effects. Each child tested only the three products
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of the triad for 1 h. The children were asked to say in which way the piece of chocolate placed opposite a black circle was different from the two other pieces first visually and them in terms of texture and flavour (Fig. 1). The child was asked to rotate the plate three times—a rotation of 120 —in order to place each piece of chocolate opposite the two others. This manipulation was done first for the visual aspect and then for texture and flavour. For each product, the child wrote the attributes that seemed significant to him on a sheet of paper divided into several parts. 2.2. For experts 10 adults aged from 28 to 48 years were selected who were very trained in sensory testing of chocolates. Their expertise was the result of a year’s training using two tasting sessions per week. The experts studied in the sensory analysis laboratory of the industrial site. They were not involved with the firm and their results were regularly checked. The experts used the Q.D.A. method (quantitative descriptive analysis) (Stone, Sidel, Oliviers, Woolsey, & Singleton, 1974). It is often completed by phases of consensus in order to define in more detail the list of attributes. Two 1-h sessions were organised. Each time several triads of products, common to all the experts, were studied. They first considered the visual aspect, the texture and then the flavour of products. The progres-
Fig. 1. Products triads.
sion of the vocabulary production was the following for an ABC triad: ‘‘If you consider the AB pair in relation to C, which sensory characteristics differ?’’ ‘‘If you consider the AC pair in relation to B, . . .’’ ‘‘If you consider the BC pair in relation to A, . . .’’ The focus was on the sensory differences of products in order to collect a descriptive vocabulary as exhaustive as possible. A consensus phase allowed experts to extract a restrictive list from their exhaustive list, the first fruit of their generation of vocabulary.
3. Results to experiment 1 3.1. Children’s results We noted all the terms used by the children for describing the product in its visual aspect, its texture and flavour. We obtained a first list of 110 items, called the ‘‘exhaustive list’’ (Table 1). The 110 sensory attributes produced by children form an interesting corpus. However it was not possible to keep all the attributes for the second experiment, in which the children had to evaluate each attribute on a measuring scale. In order to avoid a cognitive overload, this exhaustive list was reduced using the frequency of occurrence. Consequently we distributed the frequency of occurrence of sensory attributes into different categories according to the different meanings they could have from an analysis grid. Indeed some attributes do not always have the same meaning and we had to divide them into different categories. This grid provided an initial sifting, and was of use to us when we hesitated over which category to choose. After that, we selected the 28 most frequent attributes and used this to form the restrictive list (Table 2). These attributes are all of particular interest. We reduced the list to 10–15 terms as recommend by
Table 1 An excerpt of the exhaustive list of sensory attributes generated for child Attribute
Freq.
Attribute
Freq.
Attribute
Freq.
Attribute
Freq.
Chocolate colour Black Taste Rice Nutty Melting Not smooth
135 56 37 31 21 15 12
White Smooth Flakes Souffle´ Melt Softa Bumps
87 54 37 29 18 14 10
Milk Hard Cereals Sweet Good Coated Waffle
86 53 35 27 17 10 8
Crispy It smells Brown Inside Mouth Snappy Softa
60 39 31 25 16 12 10
freq.=frequency. Attributes about visual aspect, texture, flavor and smell can be found in each column. a Same English words may have different equivalent in French. Soft is ‘‘moelleux’’ and ‘‘mou’’ in French.
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Table 2 Attributes with more than 10 points in the line total of analysis grid White Corn Good Bumpy Bumps Coffeeb Cerealsb
Creamy Crispyb Hardb Melt Meltingb Waffle Flavour
Smooth Brownb Softa,b Softa,b Black Nuts Not smoothb
Chocolateb Smellb Sweetb Milkb Souffle´ Flakes Rice
a Same English words may have different equivalent in French. Soft is ‘‘moelleux’’ and ‘‘mou’’ in French. b The attributes will be selected to compose the children’s restrictive list.
Barthe´le´my (1998). This seemed to be the best number to allow us to define the sensory dimensions of a product. To establish the restrictive list necessary for the second experiment, we had to group the 28 attributes of (Table 3). We kept the sensory analysis classification: visual and olfactory aspects, texture and flavour. Grouping of visual attributes: chocolate with white, brown and black smooth with not-smooth, dented and bumpy cereals with nuggets, rice, souffle´, wheat and nuts Grouping of olfactory attributes: smells (smell) Grouping of texture attributes: hard, crunchy melting with melts creamy with soft Grouping of flavour attributes: chocolate taste milk taste coffee taste sweet taste We kept the cereal attribute, in spite of its generic sense, as it is perfect for the eight products. We chose to give it two dimensions to better translate the quantity and size of the cereals: Cereals Cereals
absent to much excess small to big
Thus, we can say that we have a good balance between visual aspect (four attributes), texture (four attributes), and flavour (four attributes). Note that we have only one attribute for smell. 3.2. Experts results The experts generated 94 attributes using the QDA method. Through phases of consensus, they reduced
Table 3 Comparison between both restrictive lists (children and experts) Restrictive list of children (total=13 attributes)
Restrictive list of experts (total=27 attributes)
Visual attributes Brown Smooth Quantity of cereals Size of cereals
! ! ! !
Total=4
Total=9
Texture attributes Crispy Melting
! !
Hard
!
Soft
!
Total=4 Flavour attributes Milky taste
Shiny Colour intensity Evenness of covering Quantity of chips Size of chips Evenness of colour Quantity of filling Height of chocolate Colour intensity of filling
Crispy Melting Smooth Crunchy Sticky Strength of filling Strength of chips Greasy Total=8
!
Coffee taste
Chocolate flavour
!
Sweet
!
Total=4
Sweet condensed milk Nut Praline Caramel Vanilla Intensity of chocolate flavour Malt Cereal Sweet Wafer Total=10
Olfactory attribute Smell Total=1
Total=0
Supposed equivalence between children and experts attributes are indicated by an arrow.
this list to 27 attributes, some of which do not appear in this exhaustive list. Indeed, they only kept onedimensional sensory attributes. The other attributes were grouped in order to obtain the one-dimensional characteristic.
4. Discussion for experiment 1 The 27 children generated 94 sensory attributes and 16 hedonic attributes. The 10 experts also generated 94 sensory attributes. It is pure coincidence that we obtained the same number of attributes from the children and the experts. Even if both groups had worked
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on triads of products we have specified that the methods were different. Knowing that the class of children were homogeneous, we can assert that each child has generated between three and four attributes. Concerning the experts, we have obtained between nine and 10 attributes for each of them. For both children and experts, the visual attributes represent almost 45% of their answers. From the exhaustive list we obtained: a restrictive list voluntarily limited to 13 sensory attributes for the children. A restrictive list of 27 sensory attributes for the experts. The semantic equivalence of the 13 sensory attributes of the children can be found in the restrictive list of the experts. Only the attribute coffee taste for the young children has no equivalent in the expert’s list. However the experts have no attribute related to olfaction, whereas the attribute smell (weak or strong) was chosen by the children. The experts reduced their exhaustive list, composing new attributes that are not necessarily shown in the initial list. For example, the attribute ‘‘filling colour’’ that we had in the restrictive list does not appear in the exhaustive list. Moreover, the experts seem very analytical and thus have a restrictive list composed of attributes that are completed by a quantitative indicator. For example, the expert kept the attribute ‘‘chocolate height’’. The consensual approach and the sensory experiment of the experts can explain their language. When choosing attributes, they are evaluating and thus quantifying. For example, for the attribute ‘‘filling quantity’’, the norm is the following: The attribute ‘‘filling quantity’’ estimates the proportion of filling compared to the quantity of chocolate coating (1=no filling; 7=a lot of chocolate filling). The experts have given norms to the attributes in their restrictive list. They have given an indicator to the attributes which allows them to quantify. The 27 children generated a vocabulary (exhaustive list) which is as rich as that of the experts: 94 sensory attributes, the same as for the experts. In the exhaustive lists, visual attributes are as important as the attributes, texture and flavour together. We can presume that the vocabulary that allows a visual description is richer than the one that is used to describe flavour or texture. The child, from early childhood, learns visual description. It is not the same for other sensory aspects. Now that we have both restrictive lists of selected attributes we can begin the experiment 2 ‘‘Evaluation of sensory attributes’’. The objective was to study how the children rate these sensory attributes and to see what kind of relationships exist between these attributes and those of the experts.
549
5. Methodology for experiment 2 5.1. For the children 261 fourth and fifth grade children aged from 9 to 11 years-old participated. They were separated into two groups: 139 children had 101 h lectures on the sensory aspects as described in the course of ‘‘Classes du Gouˆt’’ (taste classes) (Puisais & Pierre, 1987). We shall call them initiated children. Here is a presentation of the progression of the 10 lectures proposed by Puisais and Pierre and followed by the 139 children: study of vocabulary related to the five senses, perception of the four basic flavours, olfaction and the memory of smells, sight in the choice or refusal of the product, sense of touch and the tactile importance of sensations, elaboration of meal, role of noise in tasting, notion of a ‘‘terroir’’ product, generation of sensory attributes, presentation of a product. 122 children had a 2-h lecture to distinguish the visual aspect, texture and flavour. These are the non-initiated children.
The sessions of sensory evaluation based on the principle of the sensory profile took place in the school the children attended. They evaluated the products at 10 o’clock in the morning, which is when they usually have their morning snack. The time was chosen on purpose because time plays a part in consumption. The children remained in the classroom. The test was monadic, so the child tasted one product per day; that is, it took 8 days to test the eight products. Each session lasted between 20 and 40 min. The children knew they would have a reward after the tests, which seemed to motivate them very much (Kimmel et al., 1994). The children used a questionnaire on which they had to evaluate the 13 selected sensory attributes. They answered 13 questions, selecting and completing a scale from 0 to 10 (Moskowitz, 1983). At each end of the scale was an indicator. For example, for ‘‘crunchy’’, 0 indicated ‘‘not crunchy at all’’ whereas 10 meant ‘‘very crunchy’’. To avoid pupils comparing their responses, we made sure that they were not all tasting the same products at the same time. Every child tested all eight products by the end of the eight sessions. That is what we call an equilibrated scheme in order (Walkeling & MacFie, 1995).
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5.2. For the experts The same group of 10 experts that generated the list of sensory attributes was selected. They used the sensory profile method. This group underwent three-weeks training to evaluate the attributes on a scale from 1 to 7. The evaluation took place in a sensory analysis laboratory on an industrial site. The experts were alone in small rooms so as to guarantee the independence of the results. The experts, just like the children, used the monadic test but they had to evaluate 27 attributes selected for them during the experiment 1. 5.3. Treatment of data The data regarding the evaluation of the sensory attributes by the initiated children, non-initiated children and the experts was treated on the basis of the Partial Least Squares (PLS) regression using (Simca-P 8.0 by Unimetrics). PLS regression (Wold, 1975) gives a linear model for the relations between a group of predictive variables (x1,. . .,xp) and a group of variables of answers (y1,. . .,yq), all calculated according to the same ‘‘n’’ observations with, as a table of calculation X(np) and Y(nq). This method is particularly efficient when ‘‘n’’ observations are small in front of ‘‘p’’, number of predictor. In our study the explicative variables (X: 27 attributes of experts) and the variables to explain (Y: 13 attributes of children) were measured on the eight same products, i.e. eight observations. Two analyses were conducted: one for the initiated children and one for the non-initiated children. We chose as indicators the index R2y and Q2 for the two regressions P.L.S. The R2y is
said to be the goodness of fit of the model that represents the percentage of information Y explained by the model. The Q2 is said to be the index of the predictive ability.
6. Results for experiment 2 Both models (initiated and non-initiated children) are of similar quality because the R2y for initiated is 0.865 and the R2y for non initiated is 0.869. Q2 are the same=0.47. There are many similarities, the children’s attributes are well explained by the experts’ attributes (R2y=87%). In order to obtain a finer model, we decided to set up two separate models a specific model for the visual and texture attributes: X=17 attributes for the experts and Y=8 attributes for the children. a specific model for the flavour attributes: X=10 attributes for the experts and Y=5 attributes for the children. Once again, we can see that the coefficients of the initiated children’s model are the same as those of the non-initiated children’s model. We can see it through Tables 4 and 5. Let us now consider the results concerning the flavour attributes. The ‘‘milky taste’’ attribute of the children is positively linked to ‘‘sweet condensed milk’’ of the experts. On the other hand the ‘‘sweet’’ of the children has
Table 4 Regression coefficients for the texture and visual attributes, between children and experts Experts attributes
Children attributes Brown
Colour
Size–cereals
0.651 0.664 0.599 0.614
0.458 0.451 0.426 0.451
Size-chips
Melting
0.624 0.499
Crispy
0.945 1.116
Melting Strength of -chips
Crispy
0.518 0.360
Quantity of -chips
Quantity of -filling
Quantity–cereals
0.021 0.022 0.455 0.473
0.321 0.345
Only the more representative coefficients are written in the table. Children attributes (in column); experts attributes (in line). The values indicated in italic characters, for example 0.360, correspond to the results of the initiated children. The values written in roman characters correspond to the results of the non-initiated children.
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F. Sune et al. / Food Quality and Preference 13 (2002) 545–553 Table 5 Regression coefficients for the flavour attributes, between children and experts Experts attributes
Children attributes Chocolate
Sweet condensed milk Chocolate
Milky taste
sweet
0.343 0.322
0.078 0.153 0.048 0.102 0.019 0.069
0.021 0.003
Sweet
0.118 0.116
Only the more representative coefficients are written in the table. Children attributes (in column); experts attributes (in line). The values indicated in italic characters, for example 0.003, correspond to the results of the initiated children. The values written in roman characters correspond to the results of the non-initiated children.
nothing to do with the ‘‘sweet’’ of the experts. The coefficients are too low to express any kind of link. Once more, the child distinguishes himself from the expert. The results about the chocolate attribute can be discussed in the same way. The initiated and non-initiated children have nearly the same opinions especially about the ‘‘milky taste’’ attribute if we compare with the ‘‘sweet condensed milk’’ and ‘‘sweet’’ attributes of the experts. The coefficients concerning the other attributes are less representative, being too low.
7. Discussion for experiment 2 The PLS regression allows us to see that there is a relationship link between the children’s sensory attributes (variables to be explained) and the experts’ attributes (explaining variables). The best models are those linked to the visual and texture attributes. Table 6 presents the highest coefficients. The coefficients of the children’s sensory attributes as presented in the table are those which allow us to establish the intensity of the relation between the evaluation of the attributes made by the experts, and the one made by the children.
For the visual attributes, the ‘‘colour’’ attribute of the experts is linked to the ‘‘brown’’ attribute of the children, whose coefficients are 0.518 for the noninitiated and 0.360 for the initiated children. In the same way, the ‘‘quantity of chips’’, ‘‘size of chips’’ and ‘‘strength of chips’’ attributes of the experts are very close to the ‘‘quantity of cereals’’ and ‘‘size of cereals’’ attributes of the children. For the attributes related to texture, the ‘‘crispy’’ attribute of the children is very close to that of the experts, because the coefficients are 0.945 for the non-initiated children and 1.116 for the initiated children. In the same way there are links between ‘‘brown’’ and ‘‘quantity of filling’’. On the other hand, it appears that the ‘‘melting’’ of the children has nothing to do with the ‘‘melting’’ of the experts. The coefficients of these attributes are quite low as they are around 0.021. Children used this term with a definition or perception which was different from that of the experts. This result is interesting as it answers the question posed in this research: certain attributes do not have the same meaning for children and experts even if the words are homonymous. These results also show that initiated and non-initiated children are very close. It can be inferred that there are not significant differences between these two groups concerning the visual attributes and the attributes of texture. These two groups can be considered infinitely linked. For the flavour attributes, there are some links, but not strong enough to obtain a model with strong predictive ability. Only the ‘‘milk taste’’ attribute of the children seems to be linked to the ‘‘sweet condensed milk’’ of the experts (0.34–0.32). The children are relevant as far as the visual aspect attributes and the ‘‘crispy’’ texture attribute are concerned (0.9–1.1). The results of the initiated and non-initiated children are very closely related because the pupils initiated to tasting had followed a progression which had nothing to do with the QDA used by the professionals in order to make a jury more efficient. The children’s training had been conducted in a different perspective, with a more cultural and qualitative
Table 6 The most representative coefficients for the visual aspect, texture attributes and flavour Children attributes
Experts attributes
Coefficients for initiated children
Coefficients for non initiated children
Brown
Colour intensity Quantity of filling Quantity of chips Strength grains Size of chips Crispy Sweet condensed milk
0.36 0.499 0.664 0.473 0.451 1.116 0.322
0.518 0.624 0.651 0.455 0.426 0.945 0.343
Quantity of cereals Size of cereals Crispy Milky taste
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objective rather than a quantitative target (see x 5.1). What we see as a result of this study does not put in doubt the progression of taste education put forward by Puisais and Pierre (1987), which is very well adapted to primary school, but not to train children to sensory evaluation. However, it can be said that with more finely-targeted training, the correlation would be a lot higher. This initiation training should mainly concern the flavour and texture attributes. As for the visual aspect, the child, from infancy, is accustomed to describing what he can see, with the result that this vocabulary remains accurate, relevant and better defined. It is a completely different matter as far as the description of flavour is concerned. Moreover, the hedonistic opinion ‘‘I like it’’ or ‘‘I don’t’’ takes precedence over the justification of the preference or dislike. The child does not describe his tastes very accurately, and as a consequence, his vocabulary remains quite poor or approximate. The child will not become an expert if he has not followed the appropriate training. On the other hand, he is not as naive as one might think Let us not forget that the 27 children generated as many attributes as the experts. According to Jack and Piggot (1993) consumer derived vocabulary is often better because it is easily interpreted and facilitate communication between marketing and scientific departments. Their vocabulary is interesting for us, because sensory analysis improves knowledge of the differences between the child’s and the experts opinion. The fact that we know that the ‘‘crispy’’ attribute has the same meaning for the child and for the expert allows us to determine a new product concept. In the same way, knowing that ‘‘melting’’ does not have the same meaning, we will have to be careful when considering its commercial and industrial use, otherwise a new study will be needed to explain these differences.
8. Conclusion This study allowed us to determine the semantic gaps and differences between children and adult experts in rating sensory attributes. According to Oram (1998) it is essential to understand these differences which may be explained by physiological differences due to age or environment (parents, school, TV . . .). These differences should be used to inform and guide product development. This study made it possible to sort out the sensory dimensions on which children can be questioned in a consumer product testing study. This helped to get relevant and useful information to guide product development and to demonstrate the complementarity between sensory evaluation and market research in providing reliable sensory input. Nowadays, market
research about children’s behaviour must take more account of these sensory variables especially as far as farm-produced goods are concerned. Until now, only the hedonistic variable linked to the consumption of these goods seemed to be taken into account. This study, though exploratory, makes us realize that it is possible to find methods of research which allow us to understand more fully the kinds of attributes that matter to a child. It is not possible to generalize too broadly from our observations; however, we can say that there are real semantic gaps between experts and children. The expert is exhaustive when he tries to define the sensory attributes of a type of food. The child remains more global and thus less accurate in his gustatory description whereas his visual description seems more analytical. It is nevertheless a fact that the industry and marketing must better understand the child and his sensory development in order to satisfy his needs.
Acknowledgement We wish to thank Nestle France for their assistance in the development of this project.
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F. Sune et al. / Food Quality and Preference 13 (2002) 545–553 Moskowitz, H. R. (1983). Product testing and sensory evaluation of food marketing and R&D approaches. Westport, CT: Food and Nutrition Press. Oram, N. (1998). Texture and chemical feeling descriptors that 6–11 years old and adults associate with food in the mouth. Journal of Texture Studies, 29, 185–197. Puisais, J., & Pierre, C. (1987). Le gouˆt de l’enfant. Paris: Flammarion. Roedder, D. J. (1999). Consumer socialisation of children: a retrospective look at twenty five years of research. Journal of Consumer Research, 26, 193–213. Stone, H., Sidel, J., Oliviers, S., Woolsey, A., & Singleton, R. C. (1974, November). Sensory evaluation by quantitative descriptive analysis. Food Technology, 24–28. Tootelian, D. H., & Gaedeke, R. M. (1992). The ten market: an exploratory analysis of income, spending and shopping patterns. Journal of Consumer Marketing, 9, 35–45.
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A comparison of sensory attribute use by children and experts to evaluate chocolate§ Francis Sunea,*, Pascale Lacroixb, Franc¸oise Huon de Kermadecc a
Institut Universitaire de Formation des Maıˆtres, Universite´ de Nice Sophia Antipolis, 33Bis, boulevard Carnot, 83300 Draguignan, France b Nestle´ France, Evaluation Sensorielle, 7, boulevard Pierre Carle, BP 900 Noisiel, 77446 Marne la Valle´e Cedex 02, France c ENSAR, Laboratoire de Mathe´matiques Applique´es, 65, rue de Saint Brieuc, CS–35804, 35042 Rennes Cedex, France Received 23 July 2001; received in revised form 29 April 2002; accepted 29 April 2002
Abstract Children constitute a complex but interesting market for the food industry. The objective was to compare the sensory attributes generated and rated by a panel of 261 children from 9 to 11 years old with those of a trained panel of 10 adult experts in the food industry, using a range of eight chocolate products belonging to the child segment. In a first phase, a subgroup of 27 children went through attribute generation according to the Kelly-grid method to establish a questionnaire of 13 attributes. The experts used the QDA method to set up a questionnaire of 27 attributes. Data were analysed to find out relationships between attributes, using Partial Least Square regression with experts’ attributes as explicative variables and children’s attributes as variables to be explained. Surprisingly, some of the attributes most cited by children are not those better explained by experts’ attributes. # 2002 Elsevier Science Ltd. All rights reserved. Keywords: Sensory attribute; Child; Expert; PLS regression
1. Introduction Many industries in the food sector would like to be able to take into account the opinions of children, in order to test products intended for them as consumers. The fact is that the world which has been created for children continues to grow and in most cases it is the experts (ISO, 1992) who evaluate products for them. Today firms need to know if the sensory attributes perceived by children are also detected by experts. Moreover the young people’s market is a market in its own right. It is estimated that the number of adolescents in the United States will by 2010 reach a level of 5.6 million. The purchasing power of children is still increasing and children are attracted more and more to places where money can be spent (Tootelian & Gaedeke, 1992). Children have financial autonomy, special tastes and money to spend. They are more informed than in the past § Article based on presentation to be given to The 4th Pangborn Sensory Science Symposium 2001: A Sense Odyssey. Addresser: Dr. H.L. Meiselman, US Army Natick Soldier Center, Natick, MA 017605020, USA (www.elsevier.nl/locate/foodqual; e-mail: meilselmanh@ natick.army.mil). * Corresponding author. E-mail addresses: [email protected] (F. Sune), pascale. lacroix@nestle´.com (P. Lacroix), [email protected] (F. Huon de Kermadec).
and styles of consumerism are now evolving (McNeal, 1992). Various forms of research concerning child consumerism reveal a similarity between developed countries. Children from different cultures (Hong Kong, Taiwan and New Zealand) differ little from American children in the way they spend their pocket money (Gunter & Furnham, 1998). It would appear that one of the things which children between 7 and 11 spend their money on is sweets, chocolates and ‘‘cereal bars’’. The manufacturers believe that this market can be mastered by transforming and reducing the size of products, by intensifying the colours and by giving them more attractive shapes. This product strategy does not always bring results. It is therefore useful to study the taste perception of children. It is well known that children between 7 and 11 develop considerably both socially and cognitively. A child begins to discriminate between products through applying a set of attributes that he can define. He can reason about what he is tasting, and recognise that some kinds of food are sweeter than others. He can ask himself questions in order to analyse the qualities of a product (Roedder, 1999). A programme of primary school activities concerning taste was established in France in 1980. Some schools supported it and some primary classes of 9- and 10year-old pupils were trained in it. Today, over 100,000 children have taken part in these early-learning classes.
0950-3293/02/$ - see front matter # 2002 Elsevier Science Ltd. All rights reserved. PII: S0950-3293(02)00057-5
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The discovery of food tastes at school has become a means of communicating. Moreover, by sampling different tastes, children have become more aware of the development of their senses. They have learned to talk, describe and even to quantify the intensity of what they experience. These so called ‘‘initiated to taste’’ children are even more at ease when they have to talk about their tastes (Puisais & Pierre, 1987). But what can be said about the interpretation of those children when their judgement is compared to the judgement of non-initiated children and then with the judgement of industrial experts? Some experiments were conducted with children because they alone are representative of their targets (Kimmel, Sigman-Grant, & Guinard, 1994; Kroll, 1990; Moskowitz, 1985; Worsley, Baghurst, Worsley, Coonan, & Peters, 1984). However, it can be said that, in the case of a food item like the chocolate bar, a child is able from the age of seven to describe product attributes other than its funny look. Our research leads us to understand that food products for children possess a range of attributes which must be defined by those who consume them. The child perceives those sensory attributes which allow him to categorize the products (Viswanathan & Childers, 1999). The primary aim of our research is to investigate and compare food quality defined by the youngest consumer to those produced by industrial experts from the industrial world. Subsequently our approach is more quantitative: we asked children to describe food attributes, and adult experts also described their attributes. Using their comments, we tried to establish parallel thinking so that we can better understand the differences as well as the similarities. For example, whether the crispy aspect which is mentioned by the children is similar to attributes valued by the experts. At this stage, one can easily think that the experts’ words mean the same as the children’s. Yet, in today’s world, firms want to know if children’s tastes are the same as adults. There are some articles which compare the eating habits of children and adults (Fischler & Chiva, 1986; Head, Gresbrecht, & Johnson, 1977; Oram, 1998). A good understanding of the vocabulary used by the youngest consumers can lead to products being adapted to children’s needs in a more efficient way. Our research is based on two experiments: Generation and selection of child and expert attributes=Experiment 1 Evaluation of sensory attributes by the child and the expert=Experiment 2
2. Methodology for experiment 1 The objective of the experiment was to produce with a group of 27 children and 10 experts the sensory attributes used to describe a food product such as a chocolate bar.
This first experiment was above all an exploratory stage which was to allow us to answer the following question: ‘‘Does the sensory description of the product by children, from the production of attributes, seem richer, less rich or equal to the description given by experts?’’ Additionally the aim of this first experiment was to allow us to establish a restrictive list of sensory attributes useful for the second experiment. The same products were used in two experiments conducted with children and experts. The products are chocolate bars taken from the children’s market. Most of them are made from different ingredients but all have chocolate as the main ingredient. These bars are meant to be eaten as snacks or, for example, after sporting activities; they can also be used as light meals. They are sold individually in order to have wider appeal to their intended market. During our experiments these products were always presented in portions, the original packaging being removed in order not to influence the children. The experiment was adapted slightly when conducted with the experts. 2.1. For children 27 fifth grade children, 15 girls and 12 boys, from the 9 to 11 year-old age group participated. They spent two hours becoming familiar with the process of sensory analysis. The objective of this was to enable them to distinguish between texture, flavour and visual appeal. They sampled two kinds of chocolate bars different from the eight studied before. This was done with groups of four pupils overseen by the teacher so that each pupil could properly categorize the attributes. At the end of the exercise, the child had to be able to say that ‘‘crispy’’ was an attribute linked to texture whereas ‘‘sweet’’ was linked to flavour. The aim was not to improve their vocabulary but to categorize it so as the pupil would not feel lost during the experiment. The study involving different vocabulary generation took place in the children’s school a few days after they were trained to categorize. Two exercises were necessary for each session; the presence of the children was required for 1 h. They were placed in an authentic consumer situation (Koster, 1981). They tested the products in the school canteen, where they usually had their morning snack or meal, between 10 and 11. In this way the generation of attributes occurred in a natural and unconstrained way. For the generation of vocabulary, we used the Kellygrid method, inspired by the research of Kelly (1955). This method seemed the most suited for its rigour and objectivity according to McEwan and Thomson (1989). The approach consists of generating attributes from a range of products presented in triads. We used an incomplete balanced block design in order to avoid order effects. Each child tested only the three products
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of the triad for 1 h. The children were asked to say in which way the piece of chocolate placed opposite a black circle was different from the two other pieces first visually and them in terms of texture and flavour (Fig. 1). The child was asked to rotate the plate three times—a rotation of 120 —in order to place each piece of chocolate opposite the two others. This manipulation was done first for the visual aspect and then for texture and flavour. For each product, the child wrote the attributes that seemed significant to him on a sheet of paper divided into several parts. 2.2. For experts 10 adults aged from 28 to 48 years were selected who were very trained in sensory testing of chocolates. Their expertise was the result of a year’s training using two tasting sessions per week. The experts studied in the sensory analysis laboratory of the industrial site. They were not involved with the firm and their results were regularly checked. The experts used the Q.D.A. method (quantitative descriptive analysis) (Stone, Sidel, Oliviers, Woolsey, & Singleton, 1974). It is often completed by phases of consensus in order to define in more detail the list of attributes. Two 1-h sessions were organised. Each time several triads of products, common to all the experts, were studied. They first considered the visual aspect, the texture and then the flavour of products. The progres-
Fig. 1. Products triads.
sion of the vocabulary production was the following for an ABC triad: ‘‘If you consider the AB pair in relation to C, which sensory characteristics differ?’’ ‘‘If you consider the AC pair in relation to B, . . .’’ ‘‘If you consider the BC pair in relation to A, . . .’’ The focus was on the sensory differences of products in order to collect a descriptive vocabulary as exhaustive as possible. A consensus phase allowed experts to extract a restrictive list from their exhaustive list, the first fruit of their generation of vocabulary.
3. Results to experiment 1 3.1. Children’s results We noted all the terms used by the children for describing the product in its visual aspect, its texture and flavour. We obtained a first list of 110 items, called the ‘‘exhaustive list’’ (Table 1). The 110 sensory attributes produced by children form an interesting corpus. However it was not possible to keep all the attributes for the second experiment, in which the children had to evaluate each attribute on a measuring scale. In order to avoid a cognitive overload, this exhaustive list was reduced using the frequency of occurrence. Consequently we distributed the frequency of occurrence of sensory attributes into different categories according to the different meanings they could have from an analysis grid. Indeed some attributes do not always have the same meaning and we had to divide them into different categories. This grid provided an initial sifting, and was of use to us when we hesitated over which category to choose. After that, we selected the 28 most frequent attributes and used this to form the restrictive list (Table 2). These attributes are all of particular interest. We reduced the list to 10–15 terms as recommend by
Table 1 An excerpt of the exhaustive list of sensory attributes generated for child Attribute
Freq.
Attribute
Freq.
Attribute
Freq.
Attribute
Freq.
Chocolate colour Black Taste Rice Nutty Melting Not smooth
135 56 37 31 21 15 12
White Smooth Flakes Souffle´ Melt Softa Bumps
87 54 37 29 18 14 10
Milk Hard Cereals Sweet Good Coated Waffle
86 53 35 27 17 10 8
Crispy It smells Brown Inside Mouth Snappy Softa
60 39 31 25 16 12 10
freq.=frequency. Attributes about visual aspect, texture, flavor and smell can be found in each column. a Same English words may have different equivalent in French. Soft is ‘‘moelleux’’ and ‘‘mou’’ in French.
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Table 2 Attributes with more than 10 points in the line total of analysis grid White Corn Good Bumpy Bumps Coffeeb Cerealsb
Creamy Crispyb Hardb Melt Meltingb Waffle Flavour
Smooth Brownb Softa,b Softa,b Black Nuts Not smoothb
Chocolateb Smellb Sweetb Milkb Souffle´ Flakes Rice
a Same English words may have different equivalent in French. Soft is ‘‘moelleux’’ and ‘‘mou’’ in French. b The attributes will be selected to compose the children’s restrictive list.
Barthe´le´my (1998). This seemed to be the best number to allow us to define the sensory dimensions of a product. To establish the restrictive list necessary for the second experiment, we had to group the 28 attributes of (Table 3). We kept the sensory analysis classification: visual and olfactory aspects, texture and flavour. Grouping of visual attributes: chocolate with white, brown and black smooth with not-smooth, dented and bumpy cereals with nuggets, rice, souffle´, wheat and nuts Grouping of olfactory attributes: smells (smell) Grouping of texture attributes: hard, crunchy melting with melts creamy with soft Grouping of flavour attributes: chocolate taste milk taste coffee taste sweet taste We kept the cereal attribute, in spite of its generic sense, as it is perfect for the eight products. We chose to give it two dimensions to better translate the quantity and size of the cereals: Cereals Cereals
absent to much excess small to big
Thus, we can say that we have a good balance between visual aspect (four attributes), texture (four attributes), and flavour (four attributes). Note that we have only one attribute for smell. 3.2. Experts results The experts generated 94 attributes using the QDA method. Through phases of consensus, they reduced
Table 3 Comparison between both restrictive lists (children and experts) Restrictive list of children (total=13 attributes)
Restrictive list of experts (total=27 attributes)
Visual attributes Brown Smooth Quantity of cereals Size of cereals
! ! ! !
Total=4
Total=9
Texture attributes Crispy Melting
! !
Hard
!
Soft
!
Total=4 Flavour attributes Milky taste
Shiny Colour intensity Evenness of covering Quantity of chips Size of chips Evenness of colour Quantity of filling Height of chocolate Colour intensity of filling
Crispy Melting Smooth Crunchy Sticky Strength of filling Strength of chips Greasy Total=8
!
Coffee taste
Chocolate flavour
!
Sweet
!
Total=4
Sweet condensed milk Nut Praline Caramel Vanilla Intensity of chocolate flavour Malt Cereal Sweet Wafer Total=10
Olfactory attribute Smell Total=1
Total=0
Supposed equivalence between children and experts attributes are indicated by an arrow.
this list to 27 attributes, some of which do not appear in this exhaustive list. Indeed, they only kept onedimensional sensory attributes. The other attributes were grouped in order to obtain the one-dimensional characteristic.
4. Discussion for experiment 1 The 27 children generated 94 sensory attributes and 16 hedonic attributes. The 10 experts also generated 94 sensory attributes. It is pure coincidence that we obtained the same number of attributes from the children and the experts. Even if both groups had worked
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on triads of products we have specified that the methods were different. Knowing that the class of children were homogeneous, we can assert that each child has generated between three and four attributes. Concerning the experts, we have obtained between nine and 10 attributes for each of them. For both children and experts, the visual attributes represent almost 45% of their answers. From the exhaustive list we obtained: a restrictive list voluntarily limited to 13 sensory attributes for the children. A restrictive list of 27 sensory attributes for the experts. The semantic equivalence of the 13 sensory attributes of the children can be found in the restrictive list of the experts. Only the attribute coffee taste for the young children has no equivalent in the expert’s list. However the experts have no attribute related to olfaction, whereas the attribute smell (weak or strong) was chosen by the children. The experts reduced their exhaustive list, composing new attributes that are not necessarily shown in the initial list. For example, the attribute ‘‘filling colour’’ that we had in the restrictive list does not appear in the exhaustive list. Moreover, the experts seem very analytical and thus have a restrictive list composed of attributes that are completed by a quantitative indicator. For example, the expert kept the attribute ‘‘chocolate height’’. The consensual approach and the sensory experiment of the experts can explain their language. When choosing attributes, they are evaluating and thus quantifying. For example, for the attribute ‘‘filling quantity’’, the norm is the following: The attribute ‘‘filling quantity’’ estimates the proportion of filling compared to the quantity of chocolate coating (1=no filling; 7=a lot of chocolate filling). The experts have given norms to the attributes in their restrictive list. They have given an indicator to the attributes which allows them to quantify. The 27 children generated a vocabulary (exhaustive list) which is as rich as that of the experts: 94 sensory attributes, the same as for the experts. In the exhaustive lists, visual attributes are as important as the attributes, texture and flavour together. We can presume that the vocabulary that allows a visual description is richer than the one that is used to describe flavour or texture. The child, from early childhood, learns visual description. It is not the same for other sensory aspects. Now that we have both restrictive lists of selected attributes we can begin the experiment 2 ‘‘Evaluation of sensory attributes’’. The objective was to study how the children rate these sensory attributes and to see what kind of relationships exist between these attributes and those of the experts.
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5. Methodology for experiment 2 5.1. For the children 261 fourth and fifth grade children aged from 9 to 11 years-old participated. They were separated into two groups: 139 children had 101 h lectures on the sensory aspects as described in the course of ‘‘Classes du Gouˆt’’ (taste classes) (Puisais & Pierre, 1987). We shall call them initiated children. Here is a presentation of the progression of the 10 lectures proposed by Puisais and Pierre and followed by the 139 children: study of vocabulary related to the five senses, perception of the four basic flavours, olfaction and the memory of smells, sight in the choice or refusal of the product, sense of touch and the tactile importance of sensations, elaboration of meal, role of noise in tasting, notion of a ‘‘terroir’’ product, generation of sensory attributes, presentation of a product. 122 children had a 2-h lecture to distinguish the visual aspect, texture and flavour. These are the non-initiated children.
The sessions of sensory evaluation based on the principle of the sensory profile took place in the school the children attended. They evaluated the products at 10 o’clock in the morning, which is when they usually have their morning snack. The time was chosen on purpose because time plays a part in consumption. The children remained in the classroom. The test was monadic, so the child tasted one product per day; that is, it took 8 days to test the eight products. Each session lasted between 20 and 40 min. The children knew they would have a reward after the tests, which seemed to motivate them very much (Kimmel et al., 1994). The children used a questionnaire on which they had to evaluate the 13 selected sensory attributes. They answered 13 questions, selecting and completing a scale from 0 to 10 (Moskowitz, 1983). At each end of the scale was an indicator. For example, for ‘‘crunchy’’, 0 indicated ‘‘not crunchy at all’’ whereas 10 meant ‘‘very crunchy’’. To avoid pupils comparing their responses, we made sure that they were not all tasting the same products at the same time. Every child tested all eight products by the end of the eight sessions. That is what we call an equilibrated scheme in order (Walkeling & MacFie, 1995).
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5.2. For the experts The same group of 10 experts that generated the list of sensory attributes was selected. They used the sensory profile method. This group underwent three-weeks training to evaluate the attributes on a scale from 1 to 7. The evaluation took place in a sensory analysis laboratory on an industrial site. The experts were alone in small rooms so as to guarantee the independence of the results. The experts, just like the children, used the monadic test but they had to evaluate 27 attributes selected for them during the experiment 1. 5.3. Treatment of data The data regarding the evaluation of the sensory attributes by the initiated children, non-initiated children and the experts was treated on the basis of the Partial Least Squares (PLS) regression using (Simca-P 8.0 by Unimetrics). PLS regression (Wold, 1975) gives a linear model for the relations between a group of predictive variables (x1,. . .,xp) and a group of variables of answers (y1,. . .,yq), all calculated according to the same ‘‘n’’ observations with, as a table of calculation X(np) and Y(nq). This method is particularly efficient when ‘‘n’’ observations are small in front of ‘‘p’’, number of predictor. In our study the explicative variables (X: 27 attributes of experts) and the variables to explain (Y: 13 attributes of children) were measured on the eight same products, i.e. eight observations. Two analyses were conducted: one for the initiated children and one for the non-initiated children. We chose as indicators the index R2y and Q2 for the two regressions P.L.S. The R2y is
said to be the goodness of fit of the model that represents the percentage of information Y explained by the model. The Q2 is said to be the index of the predictive ability.
6. Results for experiment 2 Both models (initiated and non-initiated children) are of similar quality because the R2y for initiated is 0.865 and the R2y for non initiated is 0.869. Q2 are the same=0.47. There are many similarities, the children’s attributes are well explained by the experts’ attributes (R2y=87%). In order to obtain a finer model, we decided to set up two separate models a specific model for the visual and texture attributes: X=17 attributes for the experts and Y=8 attributes for the children. a specific model for the flavour attributes: X=10 attributes for the experts and Y=5 attributes for the children. Once again, we can see that the coefficients of the initiated children’s model are the same as those of the non-initiated children’s model. We can see it through Tables 4 and 5. Let us now consider the results concerning the flavour attributes. The ‘‘milky taste’’ attribute of the children is positively linked to ‘‘sweet condensed milk’’ of the experts. On the other hand the ‘‘sweet’’ of the children has
Table 4 Regression coefficients for the texture and visual attributes, between children and experts Experts attributes
Children attributes Brown
Colour
Size–cereals
0.651 0.664 0.599 0.614
0.458 0.451 0.426 0.451
Size-chips
Melting
0.624 0.499
Crispy
0.945 1.116
Melting Strength of -chips
Crispy
0.518 0.360
Quantity of -chips
Quantity of -filling
Quantity–cereals
0.021 0.022 0.455 0.473
0.321 0.345
Only the more representative coefficients are written in the table. Children attributes (in column); experts attributes (in line). The values indicated in italic characters, for example 0.360, correspond to the results of the initiated children. The values written in roman characters correspond to the results of the non-initiated children.
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F. Sune et al. / Food Quality and Preference 13 (2002) 545–553 Table 5 Regression coefficients for the flavour attributes, between children and experts Experts attributes
Children attributes Chocolate
Sweet condensed milk Chocolate
Milky taste
sweet
0.343 0.322
0.078 0.153 0.048 0.102 0.019 0.069
0.021 0.003
Sweet
0.118 0.116
Only the more representative coefficients are written in the table. Children attributes (in column); experts attributes (in line). The values indicated in italic characters, for example 0.003, correspond to the results of the initiated children. The values written in roman characters correspond to the results of the non-initiated children.
nothing to do with the ‘‘sweet’’ of the experts. The coefficients are too low to express any kind of link. Once more, the child distinguishes himself from the expert. The results about the chocolate attribute can be discussed in the same way. The initiated and non-initiated children have nearly the same opinions especially about the ‘‘milky taste’’ attribute if we compare with the ‘‘sweet condensed milk’’ and ‘‘sweet’’ attributes of the experts. The coefficients concerning the other attributes are less representative, being too low.
7. Discussion for experiment 2 The PLS regression allows us to see that there is a relationship link between the children’s sensory attributes (variables to be explained) and the experts’ attributes (explaining variables). The best models are those linked to the visual and texture attributes. Table 6 presents the highest coefficients. The coefficients of the children’s sensory attributes as presented in the table are those which allow us to establish the intensity of the relation between the evaluation of the attributes made by the experts, and the one made by the children.
For the visual attributes, the ‘‘colour’’ attribute of the experts is linked to the ‘‘brown’’ attribute of the children, whose coefficients are 0.518 for the noninitiated and 0.360 for the initiated children. In the same way, the ‘‘quantity of chips’’, ‘‘size of chips’’ and ‘‘strength of chips’’ attributes of the experts are very close to the ‘‘quantity of cereals’’ and ‘‘size of cereals’’ attributes of the children. For the attributes related to texture, the ‘‘crispy’’ attribute of the children is very close to that of the experts, because the coefficients are 0.945 for the non-initiated children and 1.116 for the initiated children. In the same way there are links between ‘‘brown’’ and ‘‘quantity of filling’’. On the other hand, it appears that the ‘‘melting’’ of the children has nothing to do with the ‘‘melting’’ of the experts. The coefficients of these attributes are quite low as they are around 0.021. Children used this term with a definition or perception which was different from that of the experts. This result is interesting as it answers the question posed in this research: certain attributes do not have the same meaning for children and experts even if the words are homonymous. These results also show that initiated and non-initiated children are very close. It can be inferred that there are not significant differences between these two groups concerning the visual attributes and the attributes of texture. These two groups can be considered infinitely linked. For the flavour attributes, there are some links, but not strong enough to obtain a model with strong predictive ability. Only the ‘‘milk taste’’ attribute of the children seems to be linked to the ‘‘sweet condensed milk’’ of the experts (0.34–0.32). The children are relevant as far as the visual aspect attributes and the ‘‘crispy’’ texture attribute are concerned (0.9–1.1). The results of the initiated and non-initiated children are very closely related because the pupils initiated to tasting had followed a progression which had nothing to do with the QDA used by the professionals in order to make a jury more efficient. The children’s training had been conducted in a different perspective, with a more cultural and qualitative
Table 6 The most representative coefficients for the visual aspect, texture attributes and flavour Children attributes
Experts attributes
Coefficients for initiated children
Coefficients for non initiated children
Brown
Colour intensity Quantity of filling Quantity of chips Strength grains Size of chips Crispy Sweet condensed milk
0.36 0.499 0.664 0.473 0.451 1.116 0.322
0.518 0.624 0.651 0.455 0.426 0.945 0.343
Quantity of cereals Size of cereals Crispy Milky taste
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objective rather than a quantitative target (see x 5.1). What we see as a result of this study does not put in doubt the progression of taste education put forward by Puisais and Pierre (1987), which is very well adapted to primary school, but not to train children to sensory evaluation. However, it can be said that with more finely-targeted training, the correlation would be a lot higher. This initiation training should mainly concern the flavour and texture attributes. As for the visual aspect, the child, from infancy, is accustomed to describing what he can see, with the result that this vocabulary remains accurate, relevant and better defined. It is a completely different matter as far as the description of flavour is concerned. Moreover, the hedonistic opinion ‘‘I like it’’ or ‘‘I don’t’’ takes precedence over the justification of the preference or dislike. The child does not describe his tastes very accurately, and as a consequence, his vocabulary remains quite poor or approximate. The child will not become an expert if he has not followed the appropriate training. On the other hand, he is not as naive as one might think Let us not forget that the 27 children generated as many attributes as the experts. According to Jack and Piggot (1993) consumer derived vocabulary is often better because it is easily interpreted and facilitate communication between marketing and scientific departments. Their vocabulary is interesting for us, because sensory analysis improves knowledge of the differences between the child’s and the experts opinion. The fact that we know that the ‘‘crispy’’ attribute has the same meaning for the child and for the expert allows us to determine a new product concept. In the same way, knowing that ‘‘melting’’ does not have the same meaning, we will have to be careful when considering its commercial and industrial use, otherwise a new study will be needed to explain these differences.
8. Conclusion This study allowed us to determine the semantic gaps and differences between children and adult experts in rating sensory attributes. According to Oram (1998) it is essential to understand these differences which may be explained by physiological differences due to age or environment (parents, school, TV . . .). These differences should be used to inform and guide product development. This study made it possible to sort out the sensory dimensions on which children can be questioned in a consumer product testing study. This helped to get relevant and useful information to guide product development and to demonstrate the complementarity between sensory evaluation and market research in providing reliable sensory input. Nowadays, market
research about children’s behaviour must take more account of these sensory variables especially as far as farm-produced goods are concerned. Until now, only the hedonistic variable linked to the consumption of these goods seemed to be taken into account. This study, though exploratory, makes us realize that it is possible to find methods of research which allow us to understand more fully the kinds of attributes that matter to a child. It is not possible to generalize too broadly from our observations; however, we can say that there are real semantic gaps between experts and children. The expert is exhaustive when he tries to define the sensory attributes of a type of food. The child remains more global and thus less accurate in his gustatory description whereas his visual description seems more analytical. It is nevertheless a fact that the industry and marketing must better understand the child and his sensory development in order to satisfy his needs.
Acknowledgement We wish to thank Nestle France for their assistance in the development of this project.
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