- Email: [email protected]
Simple improvement of consumer fit in external preference mapping
Food Quality and Preference 14 (2003) 455–461 www.elsevier.com/locate/foodqual
Simple improvement of consumer fit in external preference mapping Nicolaas (Klaas) M. Faber1, Jos Mojet*, Astrid A.M. Poelman ATO, Agrotechnological Research Institute, PO Box 17, 6700 AA Wageningen, The Netherlands Received 21 July 2002; received in revised form 13 November 2002; accepted 28 November 2002
Abstract In the common implementation of external preference mapping consumer preferences are fitted as polynomial functions of the first two principal components (PCs) of the sensory data. A major weakness of the method is the relatively small number of consumers that can be significantly fitted. Several researchers have proposed to improve the consumer fit by including higher-numbered PCs in the analysis. We have explored the possibility of including higher-numbered PCs while restricting the model choice to the simplest polynomial function, i.e. the (linear) vector model. In addition, we have developed a heuristic decision rule for determining the number of PCs to keep in the fit. A practical example is discussed where the consumer fit improved from 51% (two PCs, polynomial) to 80% (five PCs, vector). # 2003 Elsevier Science Ltd. All rights reserved. Keywords: Preference mapping; Sensory analysis; Consumers; Apples
1. Introduction Product optimisation is the goal of every food manufacturer. Several modelling procedures have been used for product optimisation and preference mapping is one of such methods. Depending on the data analysed, two modes of preference mapping exist. Internal preference mapping (IPM) models consumer preferences collected for a number of products, i.e. a single data set only, while external preference mapping (EPM) deals with two data sets simultaneously, i.e. consumer preference and sensory attribute ratings collected for the same products. This paper is concerned with EPM. The consumer preference for a product is at least to a certain extent determined by its sensory attributes. Thus, one has a typical regression set-up where the consumer data (Y-block) can be modelled as function of the sensory data (X-block). Before turning attention to the focus of this paper, i.e. the common implementation of EPM (McEwan, 1996; Schlich, 1995), we will mention two alternatives. The first one, partial least squares regression (PLSR), has been used increasingly in recent years * Corresponding author. E-mail address: [email protected] (J. Mojet). 1 Present address. Chemometry Consultancy, Rubensstraat 7, 6717 VD Ede, The Netherlands.
(De Jong, 1991; Helgesen, Solheim, & Næs, 1997; Ka¨lvia¨inen, Salovaara, & Tuorila, 2002; Lawlor & Delahunty, 2000; Meullenet et al., 2001; Murray & Delahunty, 2000a, 2000b). The second one, which is highly popular in chemometrics, couples the data sets through a so-called inverse model (Jaeckle & MacGregor, 1998). We could not find applications of this approach in the sensometrics literature. In the common implementation of EPM, the sensory data are decomposed using principal component analysis (PCA). Subsequently, the individual consumer preferences are modelled as polynomial functions of the first two principal components (PCs). The relative fitting ability of four functions, i.e. the so-called quadratic (also referred to as elliptical ideal point model with rotation, see Greenhoff & MacFie, 1994; Schiffman, Reynolds, & Young, 1981), elliptical, circular and vector models, is examined in a stepwise manner, starting at the most complex one (i.e. quadratic). The choice for a particular function is based on a goodness-of-fit test. This test will be referred to as the first F-test in the remainder of this paper. To avoid presenting overoptimistic results, the final fit of the consumer data is validated using a second F-test. It is well documented that applications of EPM often suffer from the small number of consumers that is significantly fitted. Schlich (1995) comments that in his experience with several data
0950-3293/03/$ - see front matter # 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0950-3293(03)00011-9
456
N.M. Faber et al. / Food Quality and Preference 14 (2003) 455–461
sets, about half of the consumers never fit to the sensory space and are subsequently excluded from further analysis. Our experience is similar. Helgesen et al., (1997) report an example where only 36% of the consumers are significantly fitted. Pagliarini, Monteleone, and Wakeling (1997) do not even report EPM results because of the poor consumer fit. By contrast, Pagliarini, Monteleone, and Ratti (2001) obtain a relatively good value of 61%, while Monteleone, Frewer, Wakeling, and Mela (1998) report a value of 68.5%. Guinard, Uotani, and Schlich (2001) even report an excellent value of 75%. However, they attribute this exceptional result to the large number of products (24 beers). Unfortunately, exposing untrained (‘naive’) consumers to such a large number of products is seldom practical. Greenhoff and MacFie (1994) and Heyd and Danzart (1998), among others, have attributed the poor consumer fit to the loss of information contained in higher-numbered PCs. Several investigators have reported an improved consumer fit by including these otherwise discarded PCs (Arditti, 1997; Daillant-Spinnler, MacFie, Beyts, & Hedderley, 1996; Elmore, Heymann, Johnson, & Hewett, 1999). It is important to note that including higher-numbered PCs may easily lead to overfitting the consumer preferences, since applications of EPM are often concerned with modelling a small number of products (typically around 10). In other words, there are only a few degrees of freedom in the data and these are quickly depleted. The purpose of this paper is two-fold. First, the utility of including higher-numbered PCs is further explored. Unlike previous work, we restrict ourselves entirely to vector models, since the simplest model allows one to include more PCs before overfitting sets in. Clearly, for a fixed number of PCs, the danger of overfitting increases with the complexity of the model. Besides, we have found the more complex models to be less useful for our applications (mostly fresh products such as apples and tomatoes). Our experience concurs with the observation of Schlich (1995) and McEwan (1996), that the elliptical and quadratic models are seldom used in sensory practice because they are often difficult to interpret. The second purpose is to develop a decision rule for determining the number of PCs to include in the analysis. The need for this decision rule becomes apparent when realising that the first F-test used in EPM (for choosing a particular function) is known to have problems (Draper, Guttman, & Kanemasu, 1971; Pope & Webster, 1972). Briefly, the test statistic is not distributed as Fisher’s F under the null hypothesis. The ideas will be illustrated using a practical example.
2. Theory The sensory and consumer data are expressed in matrix notation as X and Y, respectively. The matrix X
is dimensioned Np Na where Np is the number of products and Na is the number of sensory attributes. Likewise, the matrix Y is dimensioned Np Nc where Nc is the number of consumers. In other words, the data collected for a specific product occupy corresponding rows of X and Y. The first step of EPM consists of decomposing X using PCA. PCA is a multivariate technique that produces a least-squares approximation of a data matrix in a lower-dimensional space. Approximating X using the first A PCs can be expressed as X ¼ TPT þ E where the A columns of T (Np A) and P (Na A) are known as scores and loadings respectively, the superscript ‘T’ symbolises matrix transposition and E (Np Na ) contains residuals. The standard convention is that the loadings are normalised, so that the scale of the data enters the scores. With normalised loadings, the Euclidean norm of a score vector is the percentage variance of X explained by a PC. Often a good approximation of X results with a small value for A, since the PCs are calculated in order of decreasing explained variance. Plotting the elements of the first two columns of T yields a two-dimensional view (projection) of the position of the products in the multivariate space, likewise the first two columns of P for the attributes. Relationships between products and attributes can be investigated by combining both scores and loadings in a single plot—a sensory map. This usually requires rescaling one set of vectors. Visualisation is an important aspect of preference mapping (internal as well as external mode). In the next step, the individual consumer preferences are fitted as polynomial functions of the first two PCs of X. Polynomial modelling allows one to calculate an optimum (global or local) for each individual consumer. The idea is that these optima provide useful information for product design. The following four functions are considered:
Vector: yn ¼ a þ b1 t1 þ b2 t2 þ en Circular: yn ¼ a þ b1 t1 þ b2 t2 þ cðt1 t1 þ t2 t2 Þ þ en Elliptical: yn ¼ a þ b1 t1 þ b2 t2 þ c1 t1 t1 þ c2 t2 t2 þ en Quadratic:yn ¼ a þ b1 t1 þ b2 t2 þ c1 t1 t1 þ c2 t2 t2 þ dt1 t2 þ en
where yn is the n-th column of Y (n ¼ 1; . . . ; Nc ), t1 and t2 are the first two columns of T, en is a residual vector, ‘.‘ and ‘‘ symbolise the scalar and element-wise product, respectively. The parameters a, b1 , b2 , c, c1 , c2 , and d are estimated from the data at hand. For example, the intercept, a, is dealt with by mean-centring X and Y.
N.M. Faber et al. / Food Quality and Preference 14 (2003) 455–461
The vector model is the multiple linear regression model that does not have a global optimum (Fig. 1a). However, when constraining the sensory space to a circle for which the radius is determined by the scores of the most extreme product, as proposed by Carroll (1972), it is possible to optimise the function locally on the circle, henceforth denoted as the preference circle. The circular model will have a global optimum, provided that the estimate for c is negative (Fig. 1b), otherwise it has a global minimum. The elliptical model is more flexible than the circular model in the sense that the estimates for c1 and c2 can be different. It will have a global optimum, provided that both estimates for c1 and c2 are negative. Conversely, a global minimum is obtained when both estimates are positive. A special situation arises when they are of opposite sign: this leads to a saddle point (Fig. 1c). Finally, the quadratic model is the elliptical model plus a cross-product term. The optimising conditions are the same as for the elliptical model, since the cross-product term merely amounts to a rotation of the surface (Fig. 1d). In cases where the circular, elliptical or quadratic models do not yield a global optimum within the preference circle, one may choose to replace the model by the vector model. McEwan (1996), who mentions that ‘‘In cases where the ideal point lies outside the sample space, the vector model may well be more appropriate’’, supports this solution. In our implementation the more complex model is kept and optimised locally on the preference circle. Both solutions are equivalent in the current context, where improvement of consumer fit is the focus.
457
To determine the suitable model complexity, a sequential procedure is deployed. First, the quadratic and elliptical models are compared in an F-test. If deletion of the cross-term does not lead to a significantly worse fit, then one proceeds with comparing the elliptical and circular model, and so on. The size of the test is referred to as the level of choice. Schlich (1995) uses the value pchoice ¼ 0:10, because increasing it would complicate the models, whereas decreasing it would simplify them. Unfortunately, this procedure is rather problematic. It is known that stepwise procedures connected with F-tests can be highly misleading because the test statistic is not distributed as Fisher’s F under the null-hypothesis (Draper, Guttman, & Kanemasu, 1971; Pope & Webster, 1972). The reason for this problem is, that the deleted variable is not selected at random, but using a certain rule. In the current procedure, the deleted variable is fixed beforehand, so the basic assumption behind an F-test is violated. The numerical example treated below will illustrate the problems with this F-test. One of the reviewers correctly noted that the various models could be tested from simple to complex (forwards stepwise regression). However, we will not further pursue this possibility, because the problem with the first F-test remains. In the final step, the adequacy of the fit of the individual consumers is determined using a second F-test. This step is necessary because the finally selected model may not be adequate, although it is the best fitting one of the four models considered. The size of this test is referred to as the level of selection. Schlich (1995) uses the value pselect ¼ 0:25 and comments: ‘‘The
Fig. 1. Illustration of the models considered in common EPM: (a) vector with local optimum (), (b) circular with global optimum (), (c) elliptical with saddle point (), and (d) quadratic with global minimum ().
458
N.M. Faber et al. / Food Quality and Preference 14 (2003) 455–461
rationale of such a high P-value is to avoid false rejection of consumers, but we are aware that on the average one consumer out of four may just be selected by chance.’’ Determining the best values for pchoice and pselect is beyond the scope of this paper. Our goal is to improve the consumer fit by including higher-numbered PCs, so the actual P-values employed are immaterial as long as they are kept fixed in the comparison.
Table 1 Number of consumers (out of Nc ¼ 482) fitted significantly at the level pselect ¼ 0:25 for various models connected to different levels of pchoice Model
pchoice ¼ 0:20
pchoice ¼ 0:10
pchoice ¼ 0:05
Quadratic Elliptical Circular Vector Total
49 42 0 163 254 (53%)
25 26 0 196 247 (51%)
14 12 0 212 238 (49%)
3. Materials and methods 3.1. Practical example A preference mapping study was conducted with apples. The sensory data was provided by 10 Dutch descriptive panellists, trained to evaluate apples on a total of twenty-eight odour, taste, mouthfeel and aftertaste attributes. From the 20 apples that were profiled, twelve apples were chosen for the consumer test. Eleven apples covered the sensory space well, and an additional apple from the centre of the PCA-plot was chosen to serve as a dummy in the test—to minimise first-order effects. Four hundred and eighty nine consumers (and buyers) of fresh apples (18–70 years of age), participated in the consumer test. They were recruited at five different locations spread over Germany and they conducted the test on site. The consumers evaluated appearance, taste and overall liking on a nine-point hedonic scale in two blocks of six products. The order of presentation was randomised over the consumers, but the dummy product always came first. After the taste evaluations the consumers filled in a questionnaire with questions on usage, attitude and demographics. The data on the liking of taste was used to relate to the sensory data. Seven consumers were excluded from the analysis because they expressed the same preference for all eleven products. In summary, the matrices X and Y are dimensioned 11 28 and 11 482, respectively.
significantly worse and is therefore preferable (the null hypothesis). Rather surprisingly, the circular model is not chosen at all. Here the violation of the underlying assumption of an F-test manifests itself, i.e. the deleted variable should be picked at random. The circular model can only alter the fit of the consumer preferences by adding numbers to the vector model fit that are all positive (c > 0) or negative (c < 0). By contrast, the quadratic and elliptical models have coefficients c1 and c2 that can vary separately. It is the larger flexibility of the quadratic and elliptical model that causes the test to seemingly behave as a genuine F-test. Closer examination shows that there is only a single quadratic model that attains its optimum inside the preference circle. In addition, there is one quadratic model that attains its minimum inside the preference circle. The remaining elliptical and quadratic models have a saddle point, either inside or outside the preference circle. Fig. 2 is the external preference map that is obtained by plotting the (global or local) optima of the individual consumers in the space spanned by the first two PCs of the sensory data. Since these PCs explain 87% of the total variance in the sensory data, this plot should present a rather accurate overview.
3.2. Computations All calculations are performed in Matlab (The Mathworks, Natick, MA) using in-house written routines.
4. Results and discussion Table 1 summarises the consumer fits obtained when using the common implementation of EPM. It is noted that the numbers of consumers ‘significantly’ fitted using the quadratic and elliptical model are close to what might be expected when the null hypothesis is true. For example, with pchoice ¼ 0:10 and 247 consumers one expects to stop at 0:10 247 25 quadratic models when, in reality, the simpler elliptical model does not fit
Fig. 2. External preference map in the space spanned by PCs one and two of the sensory data: products (o), (scaled) key attributes (*) and global or local optima for consumers fitted according to a vector (.), elliptical (e) or quadratic (q) model, respectively.
N.M. Faber et al. / Food Quality and Preference 14 (2003) 455–461
459
Schlich (1995) and McEwan (1996) emphasise that the elliptical and quadratic models are seldom used in sensory practice because they are often difficult to interpret. We mention another difficulty, which is of statistical nature. It is revealed when inspecting the variance-covariance matrix of the regression coefficients. For the elliptical model, this matrix is proportional to 2 3 b2 c1 c2 b1 6 b1 50
8 0:3
0:1 7 6 7 6 120
0:5
0:3 7 Ve e6 b2 8 7 4 c1 0:3 0:5 0:02 0:01 5 0:05 c2 0:1 0:3 0:01 while for the quadratic model one 2 b1 b2 c1 6 b1 82
14 0:4 6 6 b2 14 121
0:6 V q e6 6 c1 0:4
0:6 0:02 6 4 c2
0:3
0:3 0:01 d
2 0:3 0:01
has c2
0:3
0:3
0:01 0:06 0:01
d
3
2 7 7 0:3 7 7
0:01 7 7 0:01 5 0:09
where ‘ ’ signifies ‘proportional to’. The elements on the principal diagonal represent the variance of the estimates, while the off-diagonal elements measure the pair-wise correlation. One observes that the coefficients c1 and c2 are anti-correlated. In other words, an increase of c1 accompanies a decrease of c2 and vice versa. (This effect is well known for the slope and intercept in leastsquares straight-line fitting.) Being anti-correlated, the coefficients tend to have opposite sign, which favours the occurrence of saddle points. Saddle points indeed abound for the current data set because the coefficients c1 and c2 are tiny compared to the coefficients b1 and b2 . This is illustrated in Fig. 3 for the elliptical models (31 are chosen by the first F-test, of which 26 yield a significant fit according to the second F-test). It is further observed that the coefficients c1 and c2 scatter rather erratic around zero, while the coefficients b1 and b2 follow smooth distributions. The coefficients b1 and b2 are anti-correlated too. However, this does not influence the occurrence of saddle-points. It merely illustrates the fact that regression coefficient estimates are difficult to interpret for a multiple linear regression model. Finally, it is noted that the consumer fit is hardly affected by the value of pchoice . This can be explained from the relatively small contribution of the coefficients c1 , c2 and d. The preceding results seem to imply that increasing the number of PCs, while restricting oneself to vector models, is a reasonable avenue for the data at hand. Starting at the third PC, the percentage consumers significantly fitted increases much faster than the explained variance of the sensory data (Fig. 4). Stated differently, this plot suggests that some of the higher-numbered PCs carry useful information. However, at the same time, it
Fig. 3. Frequency distribution of coefficient estimates for 31 elliptical models (pchoice ¼ 0:10): (a) linear terms and (b) quadratic terms. The coefficient estimates for the quadratic terms are multiplied by a factor 100 to bring them in the same range as the estimates for the linear terms.
Fig. 4. Percentage of cumulative variance of sensory data explained by adding PCs (^) and corresponding percentage of consumers significantly fitted using pselect ¼ 0:25 ().
460
N.M. Faber et al. / Food Quality and Preference 14 (2003) 455–461
illustrates the danger of over-fitting the consumer data. With 11 products, 10 PCs can exactly represent the sensory as well as the consumer data owing to the mean centring. As a result, the consumer fit must always reach the value 100% for 10 PCs. The observed percentage increases rapidly until the seventh PC is included (95%), after which it levels off. However, with 11 products, seven PCs and mean centring, there are only 11 7
1 ¼ 3 degrees of freedom. Consequently, this excellent fit (95%) should be regarded with extreme caution. Thus, a decision rule for acceptability of an additional PC is required. We have indicated that the F-test for model choice is problematic, so we prefer not to use it: selecting PCs using this F-test would lead to the same problems. As an alternative for this test, we have developed the following heuristic decision rule. The percentage consumer fit is calculated for all combinations of two out of five PCs. (None of the individual PCs fitted the consumers in the present study significantly.) With only two PCs included in the model, the risk of overfitting should be minimal. The results are displayed graphically (Fig. 5). One expects that PCs that carry negligible information will behave similarly. By contrast, information rich PCs should stand out or form a trend. It is seen that the second PC contributes most to consumer fit. There is a clear trend (decreasing consumer fit) starting at the second PC and ending at PC six, followed by a plateau. This result suggests that one should stop at five PCs, otherwise additional consumers are predominantly fitted by chance. Including five PCs leads to 80% of the consumers significantly fitted. Optimising the consumers (locally) on the preference circle leads to a plot similar to Fig. 2 (not shown). Consequently, the interpretation of e.g. consumer segmentation does not alter much. However, being based on more consumers, it may lead to more confidence in the modelling procedure. Some final remarks seem to be in order. First, including higher-numbered PCs need not work in all cases. In
other words, it is not a panacea. For example, including the third PC did not substantially increase the number of consumers fitted by the model in the study of Monteleone et al. (1998). Second, the exclusive choice for vector models is too restrictive if one expects that a global optimum is contained in the product range, see Hough and Sa´nchez (1998) for an illustrative example. Third, Jaeger, Wakeling, and MacFie (2000) improve the consumer fit by weighing the sensory data and representing the consumer data in a reduced space using PCA. Their work is important because it enables bringing in expert knowledge into an empirical modelling procedure.
5. Conclusions Common EPM considers polynomial models of the first two PCs of the sensory data. The current results suggest that the consumer fit in EPM can be substantially improved by using vector models based on more PCs (similar results have been observed for other data sets). The benefit of improved consumer fit lies in a higher stability of post-processing results such as consumer segmentation. Conversely, when this higher stability is not required, one may decide to recruit fewer consumers. This may translate into substantial cost reduction. We have argued against the first F-test in common EPM, because the test statistic is not distributed as Fisher’s F under the null-hypothesis. At this point, we have only been able to develop a heuristic decision rule for the number of PCs to keep in the fit. We believe that developing a formal decision rule is an interesting subject for future research. One of the reviewers noted that ANOVA on the sensory PCs (assuming at least duplicate measurements have been made) ensures that only discriminatory PCs are included in the modelling. This would compliment the current PC selection procedure. We recommend that practitioners of EPM consider several variations of EPM to arrive at their final results. Including the third PC has been proposed in combination with the following models: vector and elliptical (Arditti, 1997) or vector, circular and elliptical (Elmore et al., 1999). The current variation is simplest in the sense that it is restricted to (linear) vector models. This will often allow one to go higher than three PCs without exhausting the degrees of freedom and subsequently overfitting the consumer data.
Acknowledgements Fig. 5. Percentage of consumers significantly fitted (pselect ¼ 0:25) using two PCs versus the number of one of the PCs involved in the fit. The numbers identify the companion PC that has led to the best fit.
This research was carried out with financial support of Inova Fruit bv. The constructive criticism of the reviewers is appreciated by the authors.
N.M. Faber et al. / Food Quality and Preference 14 (2003) 455–461
References Arditti, S. (1997). Preference mapping: a case study. Food Quality and Preference, 8(5-6), 323–327. Carroll, J. D. (1972). Individual differences and multidimensional scaling. In R. N. Shepard, A. K. Romney, & S. B. Nerlove (Eds.), Multidimensional scaling: theory and applications in the behavioral sciences, Vol. 1 (pp. 105–155). New York: Seminar Press. Daillant-Spinnler, B., MacFie, H. J. H., Beyts, P. K., & Hedderley, D. (1996). Relationships between perceived sensory properties and major preference directions of 12 varieties of apples from the southern hemisphere. Food Quality and Preference, 7(2), 113–126. De Jong, S. (1991). Chemometrical applications in an industrial food research laboratory. Microchimica Acta, II(1-2), 93–101. Draper, N. R., Guttman, I, & Kanemasu, H. (1971). The distribution of certain regression statistics. Biometrika, 58, 295–298. Elmore, J. R., Heymann, H., Johnson, J., & Hewett, J. E. (1999). Preference mapping: relating acceptance of ‘‘creaminess’’ to a descriptive sensory map of a semi-solid. Food Quality and Preference, 10(6), 465–475. Greenhoff, K., & MacFie, H. J. H. (1994). Preference mapping in practice. In H. J. H. MacFie, & D. M. H. Thomson (Eds.), Measurement of food preferences (pp. 137–147). Glasgow: Blackie Academic and Professional. Guinard, J.-X., Uotani, B., & Schlich, P. (2001). Internal and external mapping of preferences for commercial lager beers: comparison of hedonic ratings by consumers blind versus with knowledge of brand and price. Food Quality and Preference, 12(4), 243–255. Helgesen, H., Solheim, R., & Næs, T. (1997). Consumer preference mapping of dry fermented lamb sausages. Food Quality and Preference, 8(2), 97–109. Heyd, B., & Danzart, M. (1998). Modelling consumers’ preferences of coffees: evaluation of different methods. Lebensmittel-Wissenschaft und-Technologie, 31(7-8), 607–611. Hough, G., & Sa´nchez, R. (1998). Descriptive analysis and external preference mapping of powdered chocolate milk. Food Quality and Preference, 9(4), 197–204. Jaeckle, C. M., & MacGregor, J. F. (1998). Product design through multivariate statistical analysis of process data. AIChE J., 44(5), 1105–1118.
461
Jaeger, S. R., Wakeling, I. N., & MacFie, H. J. H. (2000). Behavioural extensions to preference mapping: the role of synthesis. Food Quality and Preference, 11(4), 349–359. Ka¨lvia¨inen, N., Salovaara, H., & Tuorila, H. (2002). Sensory attributes and preference mapping of muesli oat flakes. Journal of Food Science, 67(1), 455–460. Lawlor, J. B., & Delahunty, C. M. (2000). The sensory profile and consumer preference for ten speciality cheeses. International Journal of Dairy Technology, 53(1), 28–36. McEwan, J. A. (1996). Preference mapping for product optimisation. In T. Næs, & E. Risvik (Eds.), Multivariate analysis of data in sensory science (pp. 71–101). Amsterdam: Elsevier. Meullenet, J.-F., Griffin, V. K., Carson, K., Davis, G., Davis, S., Gross, J., Hankins, J. A., Sailer, E., Sitakalin, C., Suwansri, S., & Vasquez Caicedo, A. L. (2001). Rice external preference mapping for Asian consumers living in the United States. Journal of Sensory Studies, 16(1), 73–94. Monteleone, E., Frewer, L., Wakeling, I., & Mela, D. J. (1998). Individual differences in starchy food consumption: the application of preference mapping. Food Quality and Preference, 9(4), 211–219. Murray, J. M., & Delahunty, C. M. (2000a). Consumer preference for Irish farmhouse and factory cheeses. Irish Journal of Agricultural and Food Research, 39(3), 433–449. Murray, J. M., & Delahunty, C. M. (2000b). Mapping consumer preference for the sensory and packaging attributes of Cheddar cheese. Food Quality and Preference, 11(5), 419–435. Pagliarini, E., Monteleone, E., & Wakeling, I. (1997). Sensory profile description of mozzarella cheese and its relationship with consumer preference. Journal of Sensory Studies, 12(4), 285–301. Pagliarini, E., Monteleone, E., & Ratti, S. (2001). Sensory profile of eight tomato cultivars (Lycopersicon esculentum) and its relationship to consumer preference. Italian Journal of Food Science, 13(3), 285–296. Pope, P. T., & Webster, J. T. (1972). The use of an F-statistic in stepwise regression procedures. Technometrics, 14, 327–340. Schiffman, S. S., Reynolds, M. L., & Young, F. W. (1981). Introduction to multidimensional scaling. New York: Academic Press. Schlich, P. (1995). Preference mapping: relating consumer preferences to sensory or instrumental measurements. In P. Etievant, & P. Schreier (Eds.), Bioflavour ’95: analysis/precursor studies/biotechnology (pp. 135–150). Versailles: INRA Editions.
Simple improvement of consumer fit in external preference mapping Nicolaas (Klaas) M. Faber1, Jos Mojet*, Astrid A.M. Poelman ATO, Agrotechnological Research Institute, PO Box 17, 6700 AA Wageningen, The Netherlands Received 21 July 2002; received in revised form 13 November 2002; accepted 28 November 2002
Abstract In the common implementation of external preference mapping consumer preferences are fitted as polynomial functions of the first two principal components (PCs) of the sensory data. A major weakness of the method is the relatively small number of consumers that can be significantly fitted. Several researchers have proposed to improve the consumer fit by including higher-numbered PCs in the analysis. We have explored the possibility of including higher-numbered PCs while restricting the model choice to the simplest polynomial function, i.e. the (linear) vector model. In addition, we have developed a heuristic decision rule for determining the number of PCs to keep in the fit. A practical example is discussed where the consumer fit improved from 51% (two PCs, polynomial) to 80% (five PCs, vector). # 2003 Elsevier Science Ltd. All rights reserved. Keywords: Preference mapping; Sensory analysis; Consumers; Apples
1. Introduction Product optimisation is the goal of every food manufacturer. Several modelling procedures have been used for product optimisation and preference mapping is one of such methods. Depending on the data analysed, two modes of preference mapping exist. Internal preference mapping (IPM) models consumer preferences collected for a number of products, i.e. a single data set only, while external preference mapping (EPM) deals with two data sets simultaneously, i.e. consumer preference and sensory attribute ratings collected for the same products. This paper is concerned with EPM. The consumer preference for a product is at least to a certain extent determined by its sensory attributes. Thus, one has a typical regression set-up where the consumer data (Y-block) can be modelled as function of the sensory data (X-block). Before turning attention to the focus of this paper, i.e. the common implementation of EPM (McEwan, 1996; Schlich, 1995), we will mention two alternatives. The first one, partial least squares regression (PLSR), has been used increasingly in recent years * Corresponding author. E-mail address: [email protected] (J. Mojet). 1 Present address. Chemometry Consultancy, Rubensstraat 7, 6717 VD Ede, The Netherlands.
(De Jong, 1991; Helgesen, Solheim, & Næs, 1997; Ka¨lvia¨inen, Salovaara, & Tuorila, 2002; Lawlor & Delahunty, 2000; Meullenet et al., 2001; Murray & Delahunty, 2000a, 2000b). The second one, which is highly popular in chemometrics, couples the data sets through a so-called inverse model (Jaeckle & MacGregor, 1998). We could not find applications of this approach in the sensometrics literature. In the common implementation of EPM, the sensory data are decomposed using principal component analysis (PCA). Subsequently, the individual consumer preferences are modelled as polynomial functions of the first two principal components (PCs). The relative fitting ability of four functions, i.e. the so-called quadratic (also referred to as elliptical ideal point model with rotation, see Greenhoff & MacFie, 1994; Schiffman, Reynolds, & Young, 1981), elliptical, circular and vector models, is examined in a stepwise manner, starting at the most complex one (i.e. quadratic). The choice for a particular function is based on a goodness-of-fit test. This test will be referred to as the first F-test in the remainder of this paper. To avoid presenting overoptimistic results, the final fit of the consumer data is validated using a second F-test. It is well documented that applications of EPM often suffer from the small number of consumers that is significantly fitted. Schlich (1995) comments that in his experience with several data
0950-3293/03/$ - see front matter # 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0950-3293(03)00011-9
456
N.M. Faber et al. / Food Quality and Preference 14 (2003) 455–461
sets, about half of the consumers never fit to the sensory space and are subsequently excluded from further analysis. Our experience is similar. Helgesen et al., (1997) report an example where only 36% of the consumers are significantly fitted. Pagliarini, Monteleone, and Wakeling (1997) do not even report EPM results because of the poor consumer fit. By contrast, Pagliarini, Monteleone, and Ratti (2001) obtain a relatively good value of 61%, while Monteleone, Frewer, Wakeling, and Mela (1998) report a value of 68.5%. Guinard, Uotani, and Schlich (2001) even report an excellent value of 75%. However, they attribute this exceptional result to the large number of products (24 beers). Unfortunately, exposing untrained (‘naive’) consumers to such a large number of products is seldom practical. Greenhoff and MacFie (1994) and Heyd and Danzart (1998), among others, have attributed the poor consumer fit to the loss of information contained in higher-numbered PCs. Several investigators have reported an improved consumer fit by including these otherwise discarded PCs (Arditti, 1997; Daillant-Spinnler, MacFie, Beyts, & Hedderley, 1996; Elmore, Heymann, Johnson, & Hewett, 1999). It is important to note that including higher-numbered PCs may easily lead to overfitting the consumer preferences, since applications of EPM are often concerned with modelling a small number of products (typically around 10). In other words, there are only a few degrees of freedom in the data and these are quickly depleted. The purpose of this paper is two-fold. First, the utility of including higher-numbered PCs is further explored. Unlike previous work, we restrict ourselves entirely to vector models, since the simplest model allows one to include more PCs before overfitting sets in. Clearly, for a fixed number of PCs, the danger of overfitting increases with the complexity of the model. Besides, we have found the more complex models to be less useful for our applications (mostly fresh products such as apples and tomatoes). Our experience concurs with the observation of Schlich (1995) and McEwan (1996), that the elliptical and quadratic models are seldom used in sensory practice because they are often difficult to interpret. The second purpose is to develop a decision rule for determining the number of PCs to include in the analysis. The need for this decision rule becomes apparent when realising that the first F-test used in EPM (for choosing a particular function) is known to have problems (Draper, Guttman, & Kanemasu, 1971; Pope & Webster, 1972). Briefly, the test statistic is not distributed as Fisher’s F under the null hypothesis. The ideas will be illustrated using a practical example.
2. Theory The sensory and consumer data are expressed in matrix notation as X and Y, respectively. The matrix X
is dimensioned Np Na where Np is the number of products and Na is the number of sensory attributes. Likewise, the matrix Y is dimensioned Np Nc where Nc is the number of consumers. In other words, the data collected for a specific product occupy corresponding rows of X and Y. The first step of EPM consists of decomposing X using PCA. PCA is a multivariate technique that produces a least-squares approximation of a data matrix in a lower-dimensional space. Approximating X using the first A PCs can be expressed as X ¼ TPT þ E where the A columns of T (Np A) and P (Na A) are known as scores and loadings respectively, the superscript ‘T’ symbolises matrix transposition and E (Np Na ) contains residuals. The standard convention is that the loadings are normalised, so that the scale of the data enters the scores. With normalised loadings, the Euclidean norm of a score vector is the percentage variance of X explained by a PC. Often a good approximation of X results with a small value for A, since the PCs are calculated in order of decreasing explained variance. Plotting the elements of the first two columns of T yields a two-dimensional view (projection) of the position of the products in the multivariate space, likewise the first two columns of P for the attributes. Relationships between products and attributes can be investigated by combining both scores and loadings in a single plot—a sensory map. This usually requires rescaling one set of vectors. Visualisation is an important aspect of preference mapping (internal as well as external mode). In the next step, the individual consumer preferences are fitted as polynomial functions of the first two PCs of X. Polynomial modelling allows one to calculate an optimum (global or local) for each individual consumer. The idea is that these optima provide useful information for product design. The following four functions are considered:
Vector: yn ¼ a þ b1 t1 þ b2 t2 þ en Circular: yn ¼ a þ b1 t1 þ b2 t2 þ cðt1 t1 þ t2 t2 Þ þ en Elliptical: yn ¼ a þ b1 t1 þ b2 t2 þ c1 t1 t1 þ c2 t2 t2 þ en Quadratic:yn ¼ a þ b1 t1 þ b2 t2 þ c1 t1 t1 þ c2 t2 t2 þ dt1 t2 þ en
where yn is the n-th column of Y (n ¼ 1; . . . ; Nc ), t1 and t2 are the first two columns of T, en is a residual vector, ‘.‘ and ‘‘ symbolise the scalar and element-wise product, respectively. The parameters a, b1 , b2 , c, c1 , c2 , and d are estimated from the data at hand. For example, the intercept, a, is dealt with by mean-centring X and Y.
N.M. Faber et al. / Food Quality and Preference 14 (2003) 455–461
The vector model is the multiple linear regression model that does not have a global optimum (Fig. 1a). However, when constraining the sensory space to a circle for which the radius is determined by the scores of the most extreme product, as proposed by Carroll (1972), it is possible to optimise the function locally on the circle, henceforth denoted as the preference circle. The circular model will have a global optimum, provided that the estimate for c is negative (Fig. 1b), otherwise it has a global minimum. The elliptical model is more flexible than the circular model in the sense that the estimates for c1 and c2 can be different. It will have a global optimum, provided that both estimates for c1 and c2 are negative. Conversely, a global minimum is obtained when both estimates are positive. A special situation arises when they are of opposite sign: this leads to a saddle point (Fig. 1c). Finally, the quadratic model is the elliptical model plus a cross-product term. The optimising conditions are the same as for the elliptical model, since the cross-product term merely amounts to a rotation of the surface (Fig. 1d). In cases where the circular, elliptical or quadratic models do not yield a global optimum within the preference circle, one may choose to replace the model by the vector model. McEwan (1996), who mentions that ‘‘In cases where the ideal point lies outside the sample space, the vector model may well be more appropriate’’, supports this solution. In our implementation the more complex model is kept and optimised locally on the preference circle. Both solutions are equivalent in the current context, where improvement of consumer fit is the focus.
457
To determine the suitable model complexity, a sequential procedure is deployed. First, the quadratic and elliptical models are compared in an F-test. If deletion of the cross-term does not lead to a significantly worse fit, then one proceeds with comparing the elliptical and circular model, and so on. The size of the test is referred to as the level of choice. Schlich (1995) uses the value pchoice ¼ 0:10, because increasing it would complicate the models, whereas decreasing it would simplify them. Unfortunately, this procedure is rather problematic. It is known that stepwise procedures connected with F-tests can be highly misleading because the test statistic is not distributed as Fisher’s F under the null-hypothesis (Draper, Guttman, & Kanemasu, 1971; Pope & Webster, 1972). The reason for this problem is, that the deleted variable is not selected at random, but using a certain rule. In the current procedure, the deleted variable is fixed beforehand, so the basic assumption behind an F-test is violated. The numerical example treated below will illustrate the problems with this F-test. One of the reviewers correctly noted that the various models could be tested from simple to complex (forwards stepwise regression). However, we will not further pursue this possibility, because the problem with the first F-test remains. In the final step, the adequacy of the fit of the individual consumers is determined using a second F-test. This step is necessary because the finally selected model may not be adequate, although it is the best fitting one of the four models considered. The size of this test is referred to as the level of selection. Schlich (1995) uses the value pselect ¼ 0:25 and comments: ‘‘The
Fig. 1. Illustration of the models considered in common EPM: (a) vector with local optimum (), (b) circular with global optimum (), (c) elliptical with saddle point (), and (d) quadratic with global minimum ().
458
N.M. Faber et al. / Food Quality and Preference 14 (2003) 455–461
rationale of such a high P-value is to avoid false rejection of consumers, but we are aware that on the average one consumer out of four may just be selected by chance.’’ Determining the best values for pchoice and pselect is beyond the scope of this paper. Our goal is to improve the consumer fit by including higher-numbered PCs, so the actual P-values employed are immaterial as long as they are kept fixed in the comparison.
Table 1 Number of consumers (out of Nc ¼ 482) fitted significantly at the level pselect ¼ 0:25 for various models connected to different levels of pchoice Model
pchoice ¼ 0:20
pchoice ¼ 0:10
pchoice ¼ 0:05
Quadratic Elliptical Circular Vector Total
49 42 0 163 254 (53%)
25 26 0 196 247 (51%)
14 12 0 212 238 (49%)
3. Materials and methods 3.1. Practical example A preference mapping study was conducted with apples. The sensory data was provided by 10 Dutch descriptive panellists, trained to evaluate apples on a total of twenty-eight odour, taste, mouthfeel and aftertaste attributes. From the 20 apples that were profiled, twelve apples were chosen for the consumer test. Eleven apples covered the sensory space well, and an additional apple from the centre of the PCA-plot was chosen to serve as a dummy in the test—to minimise first-order effects. Four hundred and eighty nine consumers (and buyers) of fresh apples (18–70 years of age), participated in the consumer test. They were recruited at five different locations spread over Germany and they conducted the test on site. The consumers evaluated appearance, taste and overall liking on a nine-point hedonic scale in two blocks of six products. The order of presentation was randomised over the consumers, but the dummy product always came first. After the taste evaluations the consumers filled in a questionnaire with questions on usage, attitude and demographics. The data on the liking of taste was used to relate to the sensory data. Seven consumers were excluded from the analysis because they expressed the same preference for all eleven products. In summary, the matrices X and Y are dimensioned 11 28 and 11 482, respectively.
significantly worse and is therefore preferable (the null hypothesis). Rather surprisingly, the circular model is not chosen at all. Here the violation of the underlying assumption of an F-test manifests itself, i.e. the deleted variable should be picked at random. The circular model can only alter the fit of the consumer preferences by adding numbers to the vector model fit that are all positive (c > 0) or negative (c < 0). By contrast, the quadratic and elliptical models have coefficients c1 and c2 that can vary separately. It is the larger flexibility of the quadratic and elliptical model that causes the test to seemingly behave as a genuine F-test. Closer examination shows that there is only a single quadratic model that attains its optimum inside the preference circle. In addition, there is one quadratic model that attains its minimum inside the preference circle. The remaining elliptical and quadratic models have a saddle point, either inside or outside the preference circle. Fig. 2 is the external preference map that is obtained by plotting the (global or local) optima of the individual consumers in the space spanned by the first two PCs of the sensory data. Since these PCs explain 87% of the total variance in the sensory data, this plot should present a rather accurate overview.
3.2. Computations All calculations are performed in Matlab (The Mathworks, Natick, MA) using in-house written routines.
4. Results and discussion Table 1 summarises the consumer fits obtained when using the common implementation of EPM. It is noted that the numbers of consumers ‘significantly’ fitted using the quadratic and elliptical model are close to what might be expected when the null hypothesis is true. For example, with pchoice ¼ 0:10 and 247 consumers one expects to stop at 0:10 247 25 quadratic models when, in reality, the simpler elliptical model does not fit
Fig. 2. External preference map in the space spanned by PCs one and two of the sensory data: products (o), (scaled) key attributes (*) and global or local optima for consumers fitted according to a vector (.), elliptical (e) or quadratic (q) model, respectively.
N.M. Faber et al. / Food Quality and Preference 14 (2003) 455–461
459
Schlich (1995) and McEwan (1996) emphasise that the elliptical and quadratic models are seldom used in sensory practice because they are often difficult to interpret. We mention another difficulty, which is of statistical nature. It is revealed when inspecting the variance-covariance matrix of the regression coefficients. For the elliptical model, this matrix is proportional to 2 3 b2 c1 c2 b1 6 b1 50
8 0:3
0:1 7 6 7 6 120
0:5
0:3 7 Ve e6 b2 8 7 4 c1 0:3 0:5 0:02 0:01 5 0:05 c2 0:1 0:3 0:01 while for the quadratic model one 2 b1 b2 c1 6 b1 82
14 0:4 6 6 b2 14 121
0:6 V q e6 6 c1 0:4
0:6 0:02 6 4 c2
0:3
0:3 0:01 d
2 0:3 0:01
has c2
0:3
0:3
0:01 0:06 0:01
d
3
2 7 7 0:3 7 7
0:01 7 7 0:01 5 0:09
where ‘ ’ signifies ‘proportional to’. The elements on the principal diagonal represent the variance of the estimates, while the off-diagonal elements measure the pair-wise correlation. One observes that the coefficients c1 and c2 are anti-correlated. In other words, an increase of c1 accompanies a decrease of c2 and vice versa. (This effect is well known for the slope and intercept in leastsquares straight-line fitting.) Being anti-correlated, the coefficients tend to have opposite sign, which favours the occurrence of saddle points. Saddle points indeed abound for the current data set because the coefficients c1 and c2 are tiny compared to the coefficients b1 and b2 . This is illustrated in Fig. 3 for the elliptical models (31 are chosen by the first F-test, of which 26 yield a significant fit according to the second F-test). It is further observed that the coefficients c1 and c2 scatter rather erratic around zero, while the coefficients b1 and b2 follow smooth distributions. The coefficients b1 and b2 are anti-correlated too. However, this does not influence the occurrence of saddle-points. It merely illustrates the fact that regression coefficient estimates are difficult to interpret for a multiple linear regression model. Finally, it is noted that the consumer fit is hardly affected by the value of pchoice . This can be explained from the relatively small contribution of the coefficients c1 , c2 and d. The preceding results seem to imply that increasing the number of PCs, while restricting oneself to vector models, is a reasonable avenue for the data at hand. Starting at the third PC, the percentage consumers significantly fitted increases much faster than the explained variance of the sensory data (Fig. 4). Stated differently, this plot suggests that some of the higher-numbered PCs carry useful information. However, at the same time, it
Fig. 3. Frequency distribution of coefficient estimates for 31 elliptical models (pchoice ¼ 0:10): (a) linear terms and (b) quadratic terms. The coefficient estimates for the quadratic terms are multiplied by a factor 100 to bring them in the same range as the estimates for the linear terms.
Fig. 4. Percentage of cumulative variance of sensory data explained by adding PCs (^) and corresponding percentage of consumers significantly fitted using pselect ¼ 0:25 ().
460
N.M. Faber et al. / Food Quality and Preference 14 (2003) 455–461
illustrates the danger of over-fitting the consumer data. With 11 products, 10 PCs can exactly represent the sensory as well as the consumer data owing to the mean centring. As a result, the consumer fit must always reach the value 100% for 10 PCs. The observed percentage increases rapidly until the seventh PC is included (95%), after which it levels off. However, with 11 products, seven PCs and mean centring, there are only 11 7
1 ¼ 3 degrees of freedom. Consequently, this excellent fit (95%) should be regarded with extreme caution. Thus, a decision rule for acceptability of an additional PC is required. We have indicated that the F-test for model choice is problematic, so we prefer not to use it: selecting PCs using this F-test would lead to the same problems. As an alternative for this test, we have developed the following heuristic decision rule. The percentage consumer fit is calculated for all combinations of two out of five PCs. (None of the individual PCs fitted the consumers in the present study significantly.) With only two PCs included in the model, the risk of overfitting should be minimal. The results are displayed graphically (Fig. 5). One expects that PCs that carry negligible information will behave similarly. By contrast, information rich PCs should stand out or form a trend. It is seen that the second PC contributes most to consumer fit. There is a clear trend (decreasing consumer fit) starting at the second PC and ending at PC six, followed by a plateau. This result suggests that one should stop at five PCs, otherwise additional consumers are predominantly fitted by chance. Including five PCs leads to 80% of the consumers significantly fitted. Optimising the consumers (locally) on the preference circle leads to a plot similar to Fig. 2 (not shown). Consequently, the interpretation of e.g. consumer segmentation does not alter much. However, being based on more consumers, it may lead to more confidence in the modelling procedure. Some final remarks seem to be in order. First, including higher-numbered PCs need not work in all cases. In
other words, it is not a panacea. For example, including the third PC did not substantially increase the number of consumers fitted by the model in the study of Monteleone et al. (1998). Second, the exclusive choice for vector models is too restrictive if one expects that a global optimum is contained in the product range, see Hough and Sa´nchez (1998) for an illustrative example. Third, Jaeger, Wakeling, and MacFie (2000) improve the consumer fit by weighing the sensory data and representing the consumer data in a reduced space using PCA. Their work is important because it enables bringing in expert knowledge into an empirical modelling procedure.
5. Conclusions Common EPM considers polynomial models of the first two PCs of the sensory data. The current results suggest that the consumer fit in EPM can be substantially improved by using vector models based on more PCs (similar results have been observed for other data sets). The benefit of improved consumer fit lies in a higher stability of post-processing results such as consumer segmentation. Conversely, when this higher stability is not required, one may decide to recruit fewer consumers. This may translate into substantial cost reduction. We have argued against the first F-test in common EPM, because the test statistic is not distributed as Fisher’s F under the null-hypothesis. At this point, we have only been able to develop a heuristic decision rule for the number of PCs to keep in the fit. We believe that developing a formal decision rule is an interesting subject for future research. One of the reviewers noted that ANOVA on the sensory PCs (assuming at least duplicate measurements have been made) ensures that only discriminatory PCs are included in the modelling. This would compliment the current PC selection procedure. We recommend that practitioners of EPM consider several variations of EPM to arrive at their final results. Including the third PC has been proposed in combination with the following models: vector and elliptical (Arditti, 1997) or vector, circular and elliptical (Elmore et al., 1999). The current variation is simplest in the sense that it is restricted to (linear) vector models. This will often allow one to go higher than three PCs without exhausting the degrees of freedom and subsequently overfitting the consumer data.
Acknowledgements Fig. 5. Percentage of consumers significantly fitted (pselect ¼ 0:25) using two PCs versus the number of one of the PCs involved in the fit. The numbers identify the companion PC that has led to the best fit.
This research was carried out with financial support of Inova Fruit bv. The constructive criticism of the reviewers is appreciated by the authors.
N.M. Faber et al. / Food Quality and Preference 14 (2003) 455–461
References Arditti, S. (1997). Preference mapping: a case study. Food Quality and Preference, 8(5-6), 323–327. Carroll, J. D. (1972). Individual differences and multidimensional scaling. In R. N. Shepard, A. K. Romney, & S. B. Nerlove (Eds.), Multidimensional scaling: theory and applications in the behavioral sciences, Vol. 1 (pp. 105–155). New York: Seminar Press. Daillant-Spinnler, B., MacFie, H. J. H., Beyts, P. K., & Hedderley, D. (1996). Relationships between perceived sensory properties and major preference directions of 12 varieties of apples from the southern hemisphere. Food Quality and Preference, 7(2), 113–126. De Jong, S. (1991). Chemometrical applications in an industrial food research laboratory. Microchimica Acta, II(1-2), 93–101. Draper, N. R., Guttman, I, & Kanemasu, H. (1971). The distribution of certain regression statistics. Biometrika, 58, 295–298. Elmore, J. R., Heymann, H., Johnson, J., & Hewett, J. E. (1999). Preference mapping: relating acceptance of ‘‘creaminess’’ to a descriptive sensory map of a semi-solid. Food Quality and Preference, 10(6), 465–475. Greenhoff, K., & MacFie, H. J. H. (1994). Preference mapping in practice. In H. J. H. MacFie, & D. M. H. Thomson (Eds.), Measurement of food preferences (pp. 137–147). Glasgow: Blackie Academic and Professional. Guinard, J.-X., Uotani, B., & Schlich, P. (2001). Internal and external mapping of preferences for commercial lager beers: comparison of hedonic ratings by consumers blind versus with knowledge of brand and price. Food Quality and Preference, 12(4), 243–255. Helgesen, H., Solheim, R., & Næs, T. (1997). Consumer preference mapping of dry fermented lamb sausages. Food Quality and Preference, 8(2), 97–109. Heyd, B., & Danzart, M. (1998). Modelling consumers’ preferences of coffees: evaluation of different methods. Lebensmittel-Wissenschaft und-Technologie, 31(7-8), 607–611. Hough, G., & Sa´nchez, R. (1998). Descriptive analysis and external preference mapping of powdered chocolate milk. Food Quality and Preference, 9(4), 197–204. Jaeckle, C. M., & MacGregor, J. F. (1998). Product design through multivariate statistical analysis of process data. AIChE J., 44(5), 1105–1118.
461
Jaeger, S. R., Wakeling, I. N., & MacFie, H. J. H. (2000). Behavioural extensions to preference mapping: the role of synthesis. Food Quality and Preference, 11(4), 349–359. Ka¨lvia¨inen, N., Salovaara, H., & Tuorila, H. (2002). Sensory attributes and preference mapping of muesli oat flakes. Journal of Food Science, 67(1), 455–460. Lawlor, J. B., & Delahunty, C. M. (2000). The sensory profile and consumer preference for ten speciality cheeses. International Journal of Dairy Technology, 53(1), 28–36. McEwan, J. A. (1996). Preference mapping for product optimisation. In T. Næs, & E. Risvik (Eds.), Multivariate analysis of data in sensory science (pp. 71–101). Amsterdam: Elsevier. Meullenet, J.-F., Griffin, V. K., Carson, K., Davis, G., Davis, S., Gross, J., Hankins, J. A., Sailer, E., Sitakalin, C., Suwansri, S., & Vasquez Caicedo, A. L. (2001). Rice external preference mapping for Asian consumers living in the United States. Journal of Sensory Studies, 16(1), 73–94. Monteleone, E., Frewer, L., Wakeling, I., & Mela, D. J. (1998). Individual differences in starchy food consumption: the application of preference mapping. Food Quality and Preference, 9(4), 211–219. Murray, J. M., & Delahunty, C. M. (2000a). Consumer preference for Irish farmhouse and factory cheeses. Irish Journal of Agricultural and Food Research, 39(3), 433–449. Murray, J. M., & Delahunty, C. M. (2000b). Mapping consumer preference for the sensory and packaging attributes of Cheddar cheese. Food Quality and Preference, 11(5), 419–435. Pagliarini, E., Monteleone, E., & Wakeling, I. (1997). Sensory profile description of mozzarella cheese and its relationship with consumer preference. Journal of Sensory Studies, 12(4), 285–301. Pagliarini, E., Monteleone, E., & Ratti, S. (2001). Sensory profile of eight tomato cultivars (Lycopersicon esculentum) and its relationship to consumer preference. Italian Journal of Food Science, 13(3), 285–296. Pope, P. T., & Webster, J. T. (1972). The use of an F-statistic in stepwise regression procedures. Technometrics, 14, 327–340. Schiffman, S. S., Reynolds, M. L., & Young, F. W. (1981). Introduction to multidimensional scaling. New York: Academic Press. Schlich, P. (1995). Preference mapping: relating consumer preferences to sensory or instrumental measurements. In P. Etievant, & P. Schreier (Eds.), Bioflavour ’95: analysis/precursor studies/biotechnology (pp. 135–150). Versailles: INRA Editions.