Descriptive sensory analysis: past, present and future

Food Research International 34 (2001) 461–471 www.elsevier.com/locate/foodres

Descriptive sensory analysis: past, present and future J.M. Murray a,*, C.M. Delahunty b, I.A. Baxter a a

Consumer Science Program, Food Science Australia, 16, Julius Avenue, Delhi Road, North Ryde, NSW 2113, Sydney, Australia b Nutritional Sciences, Department of Food Science and Technology, University College, Cork, Ireland Received 5 December 2000; accepted 31 January 2001

Abstract Descriptive sensory analyses are distinguished from other sensory testing methods in that they seek to profile a product on all of its perceived sensory characteristics. In this paper, the process of implementing a descriptive sensory programme will be reviewed, with some discussion of new approaches and applications. Variations of descriptive sensory analysis will also be considered, including The Flavour Profile MethodTM, Texture Profile MethodTM, Quantitative Descriptive AnalysisTM, Quantitative Flavour Profiling, SpectrumTM method and Free-Choice Profiling. Advantages and disadvantages of these methods will be discussed in a comparative way and the future of descriptive sensory analysis is also considered. In addition, some current assumptions of sensory panel training are questioned and potential new applications of descriptive techniques are discussed. # 2001 Elsevier Science Ltd. All rights reserved.

1. Introduction Descriptive sensory tests are amongst the most sophisticated tools in the arsenal of the sensory scientist (Lawless & Heymann, 1998) and involve the detection (discrimination) and description of both the qualitative and quantitative sensory components of a consumer product by trained panels of judges (Meilgaard, Civille, & Carr, 1991). The qualitative aspects of a product include all aroma, appearance, flavour, texture, aftertaste and sound properties of a product, which distinguish it from others. Sensory judges then quantify these product aspects in order to facilitate description of the perceived product attributes. Recent surveys (e.g. Anon., 1999) suggest that the use and application of descriptive sensory testing has increased rapidly, and will continue to do so in the next 5 years. A major strength of descriptive analysis is its ability to allow relationships between descriptive sensory and instrumental or consumer preference measurements to be determined. Knowledge of ‘‘desired composition’’ allows for product optimisation and validated models between descriptive sensory and the rele* Corresponding author. Tel.: +61-2-9490-8464; fax: +61-2-94908499. E-mail address: [email protected]fisc.csiro.au (J.M. Murray).

vant instrumental and/or preference measures are highly desirable and increasingly, are being utilised within the food industry. Descriptive sensory analyses are also used for quality control, for the comparison of product prototypes to understand consumer responses in relation to products’ sensory attributes, and for sensory mapping and product matching (Gacula, 1997). It may also be used to track product changes over time with respect to understanding shelf-life and packaging effects, to investigate the effects of ingredients or processing variables on the final sensory quality of a product, and to investigate consumer perceptions of products [e.g. Free-Choice Profiling (FCP)]. There are several different methods of descriptive analysis, including the Flavour Profile Method (Cairncross & Sjo¨strom, 1950), Texture Profile Method (Brandt, Skinner, & Coleman, 1963), Quantitative Descriptive AnalysisTM (Stone, Sidel, Oliver, Woolsey, & Singleton, 1974), the SpectrumTM method (Meilgaard et al., 1991), Quantitative Flavour Profiling (Stampanoni, 1993a,b), Free-choice Profiling (Langron, 1983; Thompson & MacFie, 1983) and generic descriptive analysis. The specific methods reflect various sensory philosophies and approaches (Lawless & Heymann, 1998), however, generic descriptive analysis, which can combine different approaches from all these methods is

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frequently employed during practical applications in order to meet specific project objectives. Reviews of descriptive analysis have been published by Amerine, Pangborn, and Roessler (1965), Einstein (1991), Heymann, Holt, and Cliff (1993), Jellinek (1964), Lawless and Heymann (1998), Meilgaard et al. (1999), Moskowitz (1983), Piggott, Simpson, and Williams (1998), Powers (1988), Sjo¨strom (1954) and Stone and Sidel (1993). This review paper aims to discuss the implementation of a descriptive sensory program (namely selecting a panel to conduct the sensory evaluations, determination of a sensory language by which to describe product attributes, and finally calibration of the panel in order to quantify the product attributes) in light of recent developments and new ideas. Specific descriptive methods and their application in sensory science will also be discussed and potential new approaches to descriptive sensory analysis will be considered.

motivation and ability of panellists to understand the need for meticulous experimental design, for delays during tasting samples, for control of eating habits prior to attendance, and so on. Personality has a large impact on the success or failure of sensory panellists. Piggott and Hunter (1999) discussed the evidence that elaborate screening procedures (e.g. Lesschaeve & Issanchou, 1996) did not necessarily predict ability to perform well as a panellist and that concentration and personality tests may be the best predictor of future ability (e.g. Zuckermann sensation seeking scale, Wilson learning model), together with verbal creativity (e.g. Wechsler, 1944) and tests of discrimination ability. Comprehensive dietary questionnaires (e.g. food frequency questionnaires) can also be revealing about panellist eating habits and reluctance to eat unfamiliar (e.g. experimental) products may be measured using the Food Neophobia Scale (FNS; Pliner & Hobden, 1992).

2. The selection of a descriptive analysis panel

3. Descriptive attribute generation

All descriptive methods require a panel with some degree of training or orientation. In most cases (with the exception of FCP) panellists are also required to have a reasonable level of sensory acuity. To achieve this, 2–3 times as many panellists as required for the project are generally screened and those selected should perform well in a variety of tests, pertinent to the project objective (Table 1). Many texts and papers discuss the selection of sensory panellists, which screening tests to perform and how panellist performance may be monitored (ASTM 758, 1981; Basker, 1988; Issanchou & Lesschaeve, 1995; Lawless & Heymann, 1998; Meilgaard et al., 1999; Moskowitz, 1983; Piggott & Hunter, 1999; Powers, 1988; Stone & Sidel, 1993). Of the utmost importance to the overall success of the project is the commitment and motivation of the panellists. Regardless of how well potential panellists perform, if they are unable to attend the training or evaluation sessions they are of no value to the programme. Individual interviews can be used to assess commitment and motivation. Availability can also be determined by filling out a ‘‘timetable’’ of available hours per week, however, candidates will nearly always over-estimate their availability. Education, although not linked to ability to perceive, may play a role in the

The training phase of descriptive sensory analysis techniques begins with the development of a common language which comprehensively and accurately describes the product attributes (with the exception of FCP). Generally, a new panel will develop the sensory language themselves, however, input from an experienced panel leader or other members of the organisation can assist the learning process. An existing language may also be adopted by a new panel, although if this was developed by another laboratory, or in a different country or region, difficulties in understanding and interpreting the terms may occur. A solution to this problem could be to ensure that full definitions and standards are available to demonstrate the sensory attributes (Hunter & McEwan, 1998). During term selection, the panel is generally exposed to a wide range of products in the category under test. Sometimes it is assumed that descriptive analysis is truly descriptive, and that the scaling of products for sensory attributes is conducted independently. In practice, this is extremely difficult to achieve. In all cases, a number of products are assessed together and the descriptive profile of one product is comparative to and in the context of other products. Therefore, at this stage it is crucial that the range of products to be assessed are defined, and if the experiment is cross-laboratory or cross-cultural,

Table 1 Factors to consider when selecting analytical sensory panellists Selection factors Health status; allergies; availability personality; verbal creativity; concentration; motivation; team player; smoker; dietary habits; education; sensitivity; specific anosmias; previous experience; dentures; medication; user of products; supplements

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that the same range of products be used in each case. The task of generating initial vocabulary should focus on the differences between the products, rather than simply compiling a dictionary of adjectives. Methods such as simplified repertory grid method (e.g. Barcenas, Elortondo, Salmeron, & Albisu, 1999) or the natural grouping method (Steenkamp & Van-Trijp, 1988) can help give more structure to the vocabulary development stage. Selecting the descriptors for inclusion in the final language is generally a consensus procedure. However, this method could be subject to bias from group dynamics and in many instances panellists may not agree on which attributes to select. The panel leader may also bias the descriptor selection process by encouraging or emphasising certain attributes which have been reported in the literature (however, this is sometimes necessary). Murray (1999) suggested that a less subjective method for descriptor selection could be to quantitatively rate the appropriateness of different terms that represent similar sensory concepts. A more structured and balanced method for descriptor selection could be to use the consensus procedure (particularly during attribute generation) and an individual procedure (especially during the final selection process). The final descriptive language should be precisely defined and contain enough terms to include all attributes likely to be encountered, yet should not be so large as to be cumbersome in use (Piggott & Canaway, 1981).

4. Concept formation Once terms are selected, the panel is trained to use a common ‘‘frame of reference’’ to illustrate/define the product attributes and their intensity in the products under test. This is generally achieved by exposing the panel to the range of products in the category under test. A common ‘‘frame of reference’’ has been defined as ‘‘the background information and reference points (frame of comparison) that assessors mentally refer to when evaluating products’’ (Munoz & Civille, 1998). Prior to training, assessors use their own personal frame of reference to evaluate products, qualitatively using their own words to describe perceptions, and quantitatively, using their previous experiences to rate intensities. Trained assessors, however, through the training process acquire a common qualitative and quantitative frame of reference, allowing for the use of a standard language to describe sensory concepts and if required by the method, a common scale. It should be reinforced to panellists that they are rating products in the context of all those which they have been exposed to during term generation and concept formation sessions, not in the context of what they have personally experienced.

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The formation of sensory concepts generally involves two processes, abstraction and generalisation (Munoz & Civille, 1998). The simplest example of concept formation and definition is probably that of colour. Concepts of colour in Western societies are similar because people are taught to associate certain labels with certain stimuli, e.g. green grass. An abstract concept of colour is thus formed. The second part, generalisation refers to the fact that the sensory concept is broadened beyond the stimuli from which it was extracted, thus we are then able to generalise our concepts of green to other stimuli, such as trees. The description and understanding of other sensory attributes, for example flavour is not so easy, particularly in the case of complex attributes such as creamy or fruity which represent weakly structured concepts. Many authors have therefore recommended the use of reference standards to achieve concept alignment in sensory panels (Civille & Lawless, 1986; Murray & Delahunty, 2000a; Nielson & Zannoni, 1998; Rainey, 1986), which are both quantitative as well as qualitative (Meilgaard et al., 1991). Reference standards have been defined as ‘‘any chemical, ingredient, spice or product’’ (Rainey, 1986). This definition could be extended to include non-food related materials which demonstrate sensory stimuli, e.g. grass for ‘‘grassy’’ or ‘‘green’’, cardboard for ‘‘oxidation’’, colour charts and so on. However, there is some evidence that for complex attributes, assessors may be unable to generalise sensory concepts to products outwith the category under evaluation (e.g. Murray & Delahunty, 2000a). The superiority of product specific training has been demonstrated. Noble, Arnold, Masuda, Pecore, Schmidt, and Stern (1984) and Noble, Arnold, Buechsenstein, Leach, Schmidt, and Stern (1987) determined better agreement between assessors when standards were presented in neutral wine bases. Meilgaard, Dalgleish, and Clapperton (1979) indicated similar results when standards were presented in beer. Sulmont, Lesschaeve, Sauvageot, and Issanchou (1999) compared three methods of training a descriptive panel for odour profiling of orange juice and concluded that a panel who had learned descriptors from the product, not external standards had superior performance. Product specific training and reference standards may therefore improve the performance of descriptive panels. However, external to product standards still have an important role to play, in particular, chemical standards assist to define relationships between volatile composition and descriptive sensory flavour profiles. Murray and Delahunty (2000a) successfully used an appropriateness scale to allow sensory panellists to select reference standards for a Cheddar cheese flavour vocabulary. Thus, the sensory panel, who best understood the meaning of their selected attributes, was responsible for standard selection and all panel mem-

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bers had an equal opportunity to contribute their opinion. Sulmont et al. (1999) also reported superior performance from a panel who were trained using by doing learning, rather than by being told learning where standards were chosen by a panel leader and imposed on assessors. However, one must also bear in mind that at least one or two assessors will nearly always disagree with the consensus, thus some degree of imposition will always be required. Time constraints may also be limiting in these training methods, although they may be considered to be timesaving in the long run. Overall, training procedures to facilitate concept alignment in descriptive analysis should be as extensive as possible. However, the procedures adopted during training will depend to a large extent on the approach of the method chosen, the time available and the products under test (in terms of complexity and the range involved).

5. Descriptive methodologies The following section distinguishes between/describes the specific descriptive methodologies that can be used and discusses their advantages and disadvantages. 5.1. Flavour Profile Method The Flavour Profile Method (FPM) was the first reported descriptive method, developed in the late 1940s at Arthur D. Little and Co. (Cairncross & Sjo¨strom, 1950) to complement existing formal and informal sensory techniques for the expanding food industry (Piggott, Simpson, & Williams, 1998). FPM is a consensus technique, and vocabulary development and rating sessions are carried out during group discussions, with panel members considering aspects of the overall flavour and the detectable flavour components of foods. FPM uses a panel of four to six judges, who are then trained to precisely define the flavours of the product category in a 2–3 week period. The selection criteria for the FPM panel are particularly rigorous. The panel is then exposed to a wide range of samples in the product category and during training panellists review and refine the flavour vocabulary. Term definition and reference standard selection also occur during the training and the temporal order of attributes is recorded. The original FPM results used numbers and symbols (or were graphically represented by the ‘‘Sunburst’’ diagram). However, with the introduction of numerical scales FPM became Profile Attributes Analysis (PAA). This allowed statistical analyses of data to be conducted. Although one of the oldest techniques, FPM is still used frequently in industry particularly in flavour houses and the brewing industry. Several recent FPM

studies have been published in the literature on fish (Chambers & Robel, 1993) and beer (Spooner, 1998). An advantage of the FPM/PAA is that the panel are highly trained and therefore sensitive to even small product differences. In addition, the amount of work in running FPM panels is generally less due to the small number of assessors involved, they also are easier to coordinate and the panel is very cohesive in comparison to, for example a Quantitative Descriptive Analysis (QDA) panel. The major disadvantage of FPM is that it depends on a small number of highly trained experts and even the departure of one panel member can have a severe impact on the sensory programme. The technical language used by assessors may also be difficult to interpret by marketing personnel in terms of relating the data to consumer preferences. 5.2. Texture Profile Method The Texture Profile Method (TPM) was developed by scientists working for General Foods in the 1960s and was based on the FPM. Initially, Szczesniak (1963) developed a texture classification system which proposed to bridge the gap between expert and consumer texture terminology, classifying perceived texture into three groups, ‘‘mechanical’’, ‘‘geometric’’ and ‘‘other’’ characteristics. The classic TPM (Brandt, Skinner, & Coleman, 1963) was then based on this classification. The technique aims to allow the description of texture from first-bite through complete mastication and also accounts for the temporal aspect of attributes. Attributes in TPM are rated on scales developed by Szczesniak (1963) to cover the range of sensations in foods, and scale points are anchored with specific food products. The method was expanded over the years (Civille & Liska, 1975) which included modifications to some of the food products used to anchor the scales and adding new scales for evaluating other product aspects such as surface properties and attributes such as ‘‘cohesiveness of mass’’. Screening procedures are conducted to eliminate candidates with dentures and those who are unable to discriminate between and describe texture differences (Civille and Szczesniak, 1973). A minimum of 10 panellists are then trained, with the number of training hours for a TPM panel being as many as 130 h over a 6–7 month period (Lawless & Heymann, 1998). The original TPM used an expanded 13 point scale, however, TPM panels have recently been trained using category, line and magnitude estimation scales (Meilgaard et al., 1991). The extent of panel training in TPM may be perceived as a disadvantage, however, this reportedly leads to greater consistency and accuracy by the TPM panel (e.g. Otremba, Dikeman, Milliken, Stroda, Chambers, & Chambers, 2000). Unfortunately, many of the products used to anchor the scales have become unavailable.

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Munoz (1986) selected new products to anchor the intensity points of the scales to overcome this difficulty, however, these products may now also be unavailable or have been re-formulated. Another limitation of TPM is that the reference products may not be available to researchers outside the US. Modifications made to the TPM scales in Columbia (Bourne, Sandoval, Villalobos, & Buckle, 1975) and Argentina (Hough, Contarini, & Munoz, 1994) demonstrate how this problem may be overcome. 5.3. Quantitative Descriptive Analysis1 Quantitative Descriptive Analysis (QDA1) was developed during the 1970s to correct some of the perceived problems associated with FPM (Stone & Sidel, 1993; Stone et al., 1974). There were several distinct differences between FPM, TPM and QDA1. Subjects for QDA1 methodology were recruited from sources removed from the project and were screened with dietary questionnaires and the products under test on the understanding that individuals who were frequent consumers of the product were more sensitive to product differences and thus more discriminating (Sawyer, Stone, Abplanalp, & Stewart, 1962). The language source in QDA1 is non-technical, everyday language, to avoid biasing response behaviour that may occur by providing a language, thus implying correct/non-correct answers. Reference standards are only used in QDA1 when a problem with a particular term is identified and it is expected that subjects only need references 10% of the time (Stone & Sidel, 1993). The panel leader is not an active participant in QDA1 in order to prevent bias and unstructured line scales are used to define/score the intensity of rated attributes. This limits number biases, however, panellists require a certain level of practice before they can confidently use these in evaluation sessions. The panel is trained over a period of perhaps 10–15 h to understand the meaning of the attributes. Unlike many other methods, QDA1 assumes that judges will use different parts of the scale to evaluate product attributes, therefore it is the relative differences among products, not absolute differences that provide the information. Results of successful QDA indicate that the panellists are calibrated with respect to the relative differences between samples. The design for descriptive analysis are based on repeated measures and the statistical analyses is generally conducted using Analysis of Variance. Often the cobweb or spider diagram is used to graphically represent the data. A limitation of QDA1 is that it is difficult to compare results between panels, between laboratories, and from one time to another with this technique. For example if we consider a situation where cheese needs to be profiled at 3, 6 and 9 months maturity, one must ensure

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that changes are related specifically to the cheese and not to panel drift (a frozen reference can sometimes be used, however sensory changes, particularly textural changes can be problematic). It is possible to compare between laboratories by concentrating on relative differences between products and work currently being carried out and the Profisens (1998–2001) project should help to address this issue. QDA1 training takes less time than other methods such as FPM or Spectrum and has been applied in many diverse studies, although often the experiments may not have been carried out as detailed by Stone and Sidel (Lawless & Heymann, 1998) which effectively invalidates the QDA1 name. 5.4. The Quantitative Flavour Profiling Technique Quantitative Flavour Profiling (QFP: Stampanoni, 1993a,b) was developed by Givaudan-Roure, Switzerland as a modified version of QDA. As opposed to QDA, which profiles all sensory attributes of products, this technique concentrates on the description of flavour only. In addition, the descriptive language used in QFP is a common standardised flavour language, developed by a panel of 6–8 people, who are typically flavourists and not directly involved in the project. The language used is technical and a proposed advantage of this method is that no erroneous terms will be included in the vocabulary as the flavourists have a wide technical knowledge. This language however, may also be considered a challenge when attempting to correlate the data with consumer perceptions and preferences. QFP depends to a large degree on the use of reference standards to demonstrate concepts and estimated intensity. An exchange of results and comparison of data across time and products can thus be made and cultural differences between subjects can be counterbalanced so that sensory panels in different countries can be equally trained. QFP is therefore highly suitable for cross-cultural or cross-laboratory projects. This method has been used for the flavour profiling of dairy products, particularly cheeses, yoghurt and sweetened milks (Stampanoni, 1994) and tends to be used by flavour houses and perfumeries. 5.5. The SpectrumTM Method The SpectrumTM method was developed by Gail Vance Civille in the 1970s. The principal tool for the SpectrumTM method is the extensive use of reference lists, specialised panel training and scaling procedures (Meilgaard et al., 1991). Spectrum is based on the philosophy of the TPM, however, rather than concentrating on only the textural aspects of products, the method examines the complete ‘‘spectrum’’ of product attributes.

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Panellists for use with the SpectrumTM method may be selected and trained to evaluate only one, or a variety of products. Terminology is usually derived by the panellists, however in the case of cross-laboratory trials, one panel may adopt a language developed by another. Generally, panellists are trained with the technical principles of each modality to be described (e.g. appearance, odour, taste and flavour) and are expected to have a basic understanding of the physiology and psychology of sensory perception. For example, a panel describing colour should understand colour intensity, colour hue and chroma. Panellists develop their list of attributes by firstly evaluating a broad array of products within the category. Products may be described in terms of only one sensory modality (e.g. appearance or aroma) or, they may be trained to evaluate all modalities. Each panellist produces a list of terms to describe the products, which are then compiled and organised into a comprehensive yet not idiosyncratic list. This process includes using references to best represent the term so it is understood in a similar way by all panellists. The scales used in the SpectrumTM method are based on the extensive use of reference points along their range which correspond to food reference samples. The use of these points purportedly greatly reduces panel variability allowing for better correlations with other data, e.g. instrumental data. The Spectrum method requires an extensive training schedule and typical times required for each stage are 15–20 h for terminology development, 10–20 h for introduction to scaling, 15–40 h for initial practice, 10–15 h for small product differences and 15–40 h for final calibration. The intensity scales are said to be absolute, that is, they are created to have equi-intensity across scales, therefore 5 on a sweetness scale is of equal strength to 5 on a salty scale and so on. In addition, it is thought that absolute calibration is feasible for most attributes (Lawless & Heymann, 1998). Such assurances would make the time and financial investment required for a SpectrumTM panel worthwhile. As with the TPM, however, reference products for anchoring attribute intensities are not available to researchers outside the US. It may also be problematic to take attributes out of context, for example having to relate hardness in the product under evaluation, to hardness in nine other products. Cultural differences may also cause difficulties when identifying an attribute in an unfamiliar product. The Spectrum MethodTM has been applied successfully in several published studies (Civille & Dus, 1990; Johnsen & Civille, 1986; Johnsen & Kelly, 1990; Johnsen, Civille, & Vercellotti, 1987; Johnsen, Civille, Vercellotti, Sanders, & Dus, 1988) and many international studies use the principals of this technique in sensory research.

5.6. Generic descriptive analysis Many organisations today use generic descriptive analysis, which allows the most suitable philosophies of the various methods to be used and combined according to the needs of the project (e.g. MacDaniel, Henderson, Watson, & Heatherbell, 1987; Muir & Hunter, 1992; Murray, 2001a). Indeed, it appears that more and more, companies are to be adopting variations on particular methods according to their research requirements. For example, we may be faced with two different sensory challenges. Case 1 is a situation where neither the company, panel leader or panel have had previous experience of the product (e.g. cheese). There is a need to conduct a oneoff descriptive profile of 10 cheeses in order to conduct preference mapping and select the two cheeses that are most liked by consumers. There is a limited amount of money available and the evaluation must be completed within four weeks. Case 2, however is a quite different situation. The company produces cheese and has being doing so for 100 years, there are experienced graders on staff and the panel leader has much technical experience of cheese production. However, the company has recently employed an external panel and wishes to begin descriptive analysis of cheese for future quality control, product development and to help understand the underlying flavour structure of the cheese from information derived from gas chromatography mass spectrometry results. Management are committed to a considerable financial investment. Both of these situations require descriptive analysis, but it would not be wise to approach them in the same way as they have different objectives. It is these many varied objectives which prompted the development of descriptive methods which have different principles. In this instance, a QDA-type evaluation may be best for situation 1, but a Spectrum-type evaluation may be more suited to situation 2, but what may really be needed is a hybrid of several methods for both situations. Many studies using generic descriptive analysis have been carried out with a great deal of success for different product categories including alcoholic products, meat, dairy products and others (Lawless & Heymann, 1998). 5.7. Free-Choice Profiling Free-Choice Profiling (FCP) was developed in the UK during the 1980s (Williams & Arnold, 1985) complemented by the development of Generalised Procrustes Analysis (GPA: Gower, 1975). FCP was developed to assist the demands of marketing and product development teams who required information on target consumers’ perceptions of products rather than the more technical descriptions of the products typically produced by trained sensory panels. The method allows

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panellists (consumers) to use any number of their own attributes to describe and quantify product attributes and is based on the assumption that panellists do not differ in their perceptions but merely in the way in which they describe them. The number of attributes generated is limited only by the perceptual and descriptive skills of the panellist (Oreskovich, Klein, & Sutherland, 1991). The distinct advantage of FCP is the avoidance of panel training, participants need only to be able to use a scale and be consumers of the product under evaluation (Piggott, Sheen, & Guy, 1989). However, sometimes the handling of individual ballots for each panellist can prove time-consuming and the interpretation of the resulting individual descriptors by the sensory analyst can also be challenging. GPA reduces the information from studies to two or three dimensions, therefore, while FCP can reveal large differences between samples, it does not show the more discriminatory differences that would be revealed by conventional profiling (e.g. Cristovam, Paterson, & Piggott, 2000). However, by allowing panellists the freedom to select idiosyncratic attributes it may also be possible to identify characteristics of products, which may not have been considered using a more traditional approach giving researchers new ways to differentiate products. At present FCP is particularly useful for perceptual mapping of product spaces (Lawless & Heymann, 1998) and in situations where conventional profiling is not recommended, for example, Murray (2001b) used FCP to measure cross-cultural perceptions of snackfoods attributes in the English and Chinese languages. Delahunty, McCord, O’Neill, and Morrissey (1997) determined a better consensus of the differences between products using Soft Independent Modelling of Class Analogy (SIMCA) to classify similar terms and Baxter, Dijksterhuis, and Bower (2001) have developed a method for the conversion of FCP to consensus data. These advances may improve the interpretation of FCP data and help relate these data to that from other sources, for example instrumental measures or consumer preference. FCP has been successfully used in numerous studies with a variety of products, e.g. cheese (Jack, Piggott, & Paterson, 1993) salmon (Morzel, Sheehan, Delahunty, & Arendt, 1999) meat (Beilken, Eadie, Griffiths, Jones, & Harris, 1991) alcoholic beverages (Gains & Thompson, 1990) and coffee (Williams & Arnold, 1985).

6. Further considerations for descriptive sensory analysis Descriptive analysis is undoubtedly one of the most valuable tools in the field of sensory analysis and is extensively used by many sensory professionals. However there are still some aspects of descriptive methodology which need to be considered and potential applications to be discussed. The following section

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discusses several aspects of descriptive analysis that may be useful for the future development of this method. 6.1. Is the psychophysical model appropriate for complex attribute description? Some potential problems using descriptive analysis for complex odour characterisation were recently discussed by Lawless (1999). When conducting a descriptive analysis, panellists generally discern the attributes (e.g. of odour) and provide an intensity rating for each one. This process is based on the psychophysical model for intensity and an assumption of this model is that the odour percept can be analysed and reported using a set of independent descriptors. However, the assumption that attributes used in descriptive analysis are independent and that they are perceptually separable features that we can attend to individually within a complex stimulus may be incorrect. . . ‘‘the use of simple and apparently independent intensity scales may produce the illusion that the odour experience is a collection of independent analysable notes when it is not’’. Lawless thus suggested that for complex odours (and also for colour) the psychophysical model may be a poor choice. Supporting this viewpoint, the work of Laing and colleagues (e.g. Laing, 1991) have discussed the limited capacity humans have to distinguish components of mixtures. Indeed, it is unlikely that humans can identify any more than three or four components in odour or taste mixtures. Furthermore, Laing and Livermore (1992) found that humans identified the complex odour of chocolate as a single entity. So, perhaps there should be another model for these specific cases of descriptive analysis? And can we realistically expect an accurate odour profile of a product when humans can only distinguish at best three or four of these? Recent work has indicated that descriptive analysis may only reveal one layer of sensory character, when in fact, many layers are discovered upon further analysis. For example, McDonnell, Delahunty, and MacNamara (2001) determined that when a distilled beverage was taken apart by fractional distillation, the re-constituted fractions were very different in character from one another and that characteristics perceived in some fractions of the beverage were hardly perceived in the original beverage or total reconstitute. It may be possible, however, to get under this top layer of sensory character by using descriptive sensory analysis in conjunction with other methods such as Time-Intensity sensory analysis (Dijksterhuis, 1996). TI sensory analysis is now achieving widespread application in research as the dynamics of aroma and flavour release have attracted attention (Piggott, 2000). Assessors can be trained to score one attribute at a time whilst ignoring interference by specialised training, however training guidelines must be correctly adhered

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to (Peyvieux & Dijksterhuis, 2001). Such analysis, may also provide better discrimination between products than is available through descriptive analysis only. These observations require further investigation.

2000). These factors should be at least investigated when considering the use of sensory analysis for determining the food preferences of a specific market segment.

6.2. Use of descriptive analyses with children and the elderly?

6.3. Descriptive analysis of packaging/labelling/other consumer goods?

Children and the elderly are becoming increasingly important segments of the consuming population with many products aimed specifically at these two groups. It may therefore be desirable to train these two groups for descriptive profiling, as their perceptions may not be interchangeable with those of adults who are under 60 years. Children have been documented as having different taste thresholds compared to adults (Glanville, Kaplan, & Fischer, 1964; Hermel, Schonwetter, & Samueloff, 1970) however, other studies have found no difference in their taste sensitivity (Anliker, Bartoshuk, Ferris, & Hooks 1991; Oespian, 1958). While the evidence is conflicting, it is apparent that children have different dietary habits and preferences compared to adults. Whether this is due to differences in perception per se, familiarity and learned behaviours, or a combination of both, is not yet clear. It may therefore be desirable to train children for descriptive profiling or certainly to investigate these implications more thoroughly. The children would need to be objective and be able to use scales correctly. Other studies have indicated that this is feasible (Baxter, Jack, & Schro¨der, 1998; Chen & Resurreccion, 1996; Moskowitz, 1994). It is reported with a great deal of confidence that the ageing process is characterised by a decline in olfaction (Cain & Stevens, 1989) and taste sensitivity (Stevens, Cruz, Hoffman, & Patterson, 1995). In addition, older consumers have different texture perceptions, most likely related to physiological factors such as difficulty of chewing and swallowing, state of dentition, muscular co-ordination and soreness of the mouth cavity (Peleg, 1993). Again, it may be that they perceive different characteristics to regular adults who conduct the descriptive profile. However, these changes in elderly consumers’ perceptions may not be heterogeneous (Cain and Stevens, 1989) which would not guarantee that differences in how one elderly panel described products were interchangeable with another. The interest in older consumers’ preferences and perceptions is increasingly rapidly, with the dramatic increase in size of this population segment recently (e.g. HealthSense, 2000–2003). One must however consider, that there may be segments in the population like these with heightened or reduced sensitivity to particular characteristics. For example, PROP status is one major and validated population segmentation (e.g. Cubero-Casillo & Noble,

The visual appearance attributes of food product’s packaging are powerful influences on acceptability (Cardello, 1994). The packaging attributes of products include aspects of shape, colour, design, symbols, logos and item names (Hutchings, 1977). Moskowitz (1998) suggested that the next step in the analysis of product image/packaging could be to develop a standard lexicon with reference standards to demonstrate the attributes. Considering these factors, Murray and Delahunty (2000b) recently used descriptive analysis to objectively analyse the packaging attributes of cheese. In addition, statistical modelling of this data, with other hedonic data, allowed preference mapping of packaging to be conducted, as it has been done previously for products’ sensory attributes (Murray & Delahunty, 2000b,c). Descriptive analysis is increasingly concentrating on the visual, colour and tactile aspects of products and packaging (in addition to more traditional uses, e.g. flavour, aroma) as reported for example, in Civille and Dus (1990), Imram (1999) and Murray and Delahunty (2000b,c). There are also further considerations as to whether the psychophysical model is appropriate for these evaluations. Perception of visual/tactile/colour attributes may not necessarily represent a psychophysical continuum from a little to a lot and therefore other methods for measuring attributes could be considered.

7. Conclusion Descriptive analysis still stands as the most comprehensive, flexible and useful sensory method, providing detailed information on all of a products’ sensory properties. In the next millennium, it is expected that descriptive analysis will be used increasingly for a wider range of end uses than ever before (Anon., 1999). Considering this, it is vital that investment continues in the development of descriptive analysis that challenges traditional ideas in order to ensure optimal potential is gained from this method in the future.

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