Perfume Odor Categorization: To What Extent Trained Assessors and Consumers Agree?

PERFUME ODOR CATEGORIZATION: TO WHAT EXTENTTRAINED ASSESSORS AND CONSUMERS AGREE?MILY VERAMENDI1,3, PILAR HERENCIA1 and GASTÓN ARES2

1Laboratorio de Evaluación Sensorial, Belcorp, Av. San Genaro 150 – Urb. Molitalia Lima 39, Lima, Perú2Departamento de Ciencia y Tecnología de Alimentos, Facultad de Química, Universidad de la República, Montevideo, Uruguay

3Corresponding author.TEL: +519-98078239;FAX: +511-2113386;EMAIL: [email protected]

Accepted for Publication December 4, 2012

doi:10.1111/joss.12025

ABSTRACT

The perfume industry is increasingly interested in developing perfume classifica-tion systems based on their odor character profile to improve consumer shoppingexperience. However, concerns have been raised regarding the external validity ofthe classifications performed by experts and trained assessors. In this context, theaim of the present work was to evaluate the influence of training on perfume odorcharacterization based on a sorting task, by comparing sensory maps of perfumesand descriptions provided by trained assessors and consumers. Fifteen commer-cially fine female perfumes were evaluated by trained assessors and consumersusing a sorting task, followed by a description phase. Trained assessors and con-sumers provided similar sample configurations. However, there was no perfectagreement in their classifications and some differences in the conclusions regard-ing similarities and differences between samples were identified. Although thedescriptions provided by trained assessors and consumers to characterize the odorprofile of the perfumes were different, the most frequently mentioned terms weresimilar and were used in a similar way.

PRACTICAL APPLICATIONS

Results from the present work showed that training affected to some extent theway in which perfumes are categorized and described, which suggests that con-sumer perception could be a valuable complementary tool when developing classi-fication systems as well as perfume descriptions for marketing or communicationpurposes.

INTRODUCTION

Perfumes can be defined as substances that have a pleasantand fragrant odor (Salvador-Carreño and Chisvert 2005).They have been used since at least 10,000 years ago for reli-gious rites, cleaning and softening the skin and maskingbody odor (Api and Hakkinen 2005). Nowadays, perfumesare used to improve the quality of life and to convey infor-mation about the personality of the users, having a strongpsychological impact, being related to emotion, mood andmemory (Api and Hakkinen 2005; Salvador-Carreño andChisvert 2005).

The odor character profile of perfumes and their associ-ated psychological effects determine their suitability for aparticular consumer and strongly impacts his/her purchasedecisions (Jellinek 1997). Odor characterizations of per-fumes is of broad interest in the perfume industry to better

understand consumer preferences, describe complex mix-tures of odorants, classify perfumes and better communi-cate the sensory characteristics of the products (Jellinek1992). Considering the large number of new perfumeslaunched to the market every year, the perfume industry isincreasingly interested in developing perfume classificationsystems based on their odor profile to improve consumershopping experience (Zarzo and Stanton 2009). This classi-fication system should be easy to understand for consumersand be in agreement with their own odor perception (Jell-inek 1997) and also contain standard sensory terms fordescribing the odors most frequently used in perfumery.

Perfume odor characterization has been traditionallyperformed by experts and trained assessors who analy-tically identify the set of descriptors that best apply todescribe the smell (Jellinek 1992). Despite the fact thatvalid, reproducible and reliable information is gathered,

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Journal of Sensory Studies ISSN 0887-8250

76 Journal of Sensory Studies 28 (2013) 76–89 © 2013 Wiley Periodicals, Inc.

this approach has several disadvantages that limit theirapplication in the industry. Creating and maintaining awell-trained and calibrated panel can be expensive andtime-consuming, and many small food companies usuallycannot afford it (Varela and Ares 2012). Moreover, trainedassessors might perceive the perfumes differently from con-sumers, who have a unified and holistic impression of thistype of product (Mensing and Beck 1988). For example,according to Jellinek (1992), expert classifications of per-fumes tend to be based on the identification of basicodorants, which do not always correlate to consumerclassifications. In this context, interest in consumer-basedcharacterization of perfumes has increased in the lastdecade (Zarzo and Stanton 2009).

Sorting tasks are one of the most popular approaches forsensory characterization of food and nonfood products basedon consumer perception (Varela and Ares 2012). The aimof this technique is to measure the global degree of simila-rity among samples. Assessors are asked to evaluate a set ofsamples and to sort them into groups according to their simi-larities and differences using their own personal criteria. Inorder to gather information about the sensory characteristicsof the samples which are responsible for the similarities anddifferences among samples, once the sorting has been com-pleted, assessors are asked to provide descriptive words foreach of the groups they formed (Lawless et al. 1995; Popperand Heymann 1996). This methodology has been appliedwith trained and untrained assessors for characterizing thesensory properties of a wide range of products with differentcomplexity, including cheese (Lawless et al. 1995), beer(Chollet and Valentin 2001), yogurt (Saint-Eve et al. 2004),breakfast cereals (Cartier et al. 2006), orange-flavored pow-dered drinks (Ares et al. 2011a) and fabrics (Soufflet et al.2004). Considering its ease of use, sorting tasks can be auseful methodology to build a categorization system forperfumes based on consumer perception.

Several studies have compared trained assessors and con-sumers’ sensory characterizations using sorting tasks andhave concluded that product spaces are remarkably similar,but that their descriptions are not always comparable(Lawless et al. 1995; Chollet and Valentin 2001; Souffletet al. 2004; Saint-Eve et al. 2004; Lim and Lawless 2005;Chollet et al. 2005; Cartier et al. 2006; Lelièvre et al. 2008;Chollet et al. 2011). In the case of perfumes, it is expectedthat consumers and trained assessors would slightly differ intheir categorizations (Jellinek 1992) and that their descrip-tions would strongly differ due to the fact that the processof labeling and the ability to recognize complex odor mix-tures is improved during training (Laing et al. 1991; Lawlessand Heymann 2010). Considering that perfumes consist ofcomplex mixtures of aromatic chemicals and essential oils(Api and Hakkinen 2005), their odor characterization isquite a difficult task. According to Zarzo and Stanton

(2009), a consensual odor description of scents is difficult toachieve without training and the use of reference products.Perfumers share a common language for describing theodor character of perfumes, which are generally unfamiliarto those outside the field (Chastrette et al. 1988).

In this context, the aim of the present work was to evalu-ate the influence of training on perfume odor characteriza-tion based on a sorting task by comparing sensory mapsof perfumes and descriptions provided by trained assessorsand consumers.

MATERIALS AND METHODS

Samples

The stimuli were 15 fine female perfumes (named 1 to 15)available in the Peruvian market. Samples were selectedtaking into account brand and price. The selection wasmade to represent the fragrance notes of the perfumes com-mercialized by Belcorp (Lima, Perú), and included samplesof different brand and price range. The selected perfumescomprised floral, oriental, woody and fresh notes. Theclassification of the perfumes in families (Edwards 2004),according to the information provided by the manu-facturers, is provided in Table 1.

Six milliliters of the perfumes were placed in 8 mLdark-colored glass containers with screw caps and codedwith three-digit random numbers prior to their evaluation.

Trained Assessors

Fifteen trained assessors with a minimum of 5 years in rec-ognizing and discriminating odors participated in the study.

TABLE 1. DESCRIPTION OF THE PERFUMES BASED ON THEINFORMATION PROVIDED BY THE MANUFACTURERS

Sample code Description

1 and 16 Floral2 Soft floral, water3 Soft floral4 Mossy woods, floral oriental5 Mossy woods, fruity6 Oriental7 Floral, fruity8 Floral, woods9 Oriental

10 Soft floral, water11 Oriental12 Floral13 Floral, fruity14 Floral15 Floral, water

M. VERAMENDI, P. HERENCIA and G. ARES PERFUME ODOR CATEGORIZATION

77Journal of Sensory Studies 28 (2013) 76–89 © 2013 Wiley Periodicals, Inc.

They were all female and their ages ranged between 27 and62 years. The assessors had been trained in the identificationof citric, floral, aldehydic, fruity, green, animal, woody, spicyand herbal odor notes, as well as in odor intensity quantifi-cation using n-hexanal solutions. These notes have beenconsidered the most important in perfumes commercializedby Belcorp.

Consumers

Fifty women from Lima (Perú), ranging in age from 25 to45 years participated in the study. They all used perfumesat least three times a week and differed in their perfumeusage habits and preferences. A convenient consumersample (Kinnear and Taylor 1993) was recruited from theconsumer database of the company.

Sorting Task

Each assessor and each consumer received 16 samples,corresponding to the 15 commercial perfumes plus a blindduplicate (samples 1 and 16 were identical). They wereinstructed to agitate the glass containers, remove the cap,wet a strip of paper up to a marked level, agitate vigorouslyfor 10 s and sniff. After sniffing the samples ad libitum,assessors were asked to sort them into groups accordingto their similarities. The exact wording of the sorting taskwas the following: “Please sniff the strip of paper, trying toremember its odor characteristics. After smelling the per-fumes you have to sort them into different groups, accord-ing to the similarities and differences among samples. Pleasetake into account that similar perfumes must belong to thesame group, whereas widely different perfumes must belongto different groups. You have to sort the samples using aminimum of 2 groups.” After completing the sorting task,assessors were instructed to wait 20 min and to verify theirgrouping of the samples.

Once the sorting was performed, assessors were askedto provide descriptive words for each of the groups theyformed (Cartier et al. 2006). Assessors were instructed totake as much time as they needed to perform the task; thewhole session lasted approximately 2 h with both consum-ers and trained assessors. Preliminary studies with trainedassessors and consumers showed that olfactory fatigue didnot affect the results from sorting tasks with 16 perfumes.

The testing was carried out in a sensory laboratory thatwas designed in accordance with ISO 8589 (1988). Evalua-tions were performed under artificial daylight type illumi-nation, temperature control (between 22 and 24C) and aircirculation. Roasted coffee beans and alcohol (70%) wereavailable for smelling between samples, as normally doneby the trained panel of Belcorp. This procedure has beenrecommended by Secundo and Sobel (2006) to erase odor

sensations due to the strong binding affinity to olfactoryreceptors of some coffee odorants, which could lead to thedetachment of other odorants (Dorri et al. 2007). However,despite its widespread application among perfume sellers,Grosofsky et al. (2011) reported that coffee beans did notimprove odor identification compared to air.

Data Analysis

Descriptive information was first gathered by determiningthe number of groups per assessor and the number of per-fumes per group for trained assessors and consumers. Chi-square test was used to compare the frequency distributionsof trained assessors and consumers.

Data were analyzed using a factorial approach for sortingtasks, as suggested by Cadoret et al. (2009). This approachprovides an optimal representation of the samples based oncorrespondence analysis (CA) performed on the perfumes ¥assessor data matrix containing consumers’ grouping of theevaluated samples, as shown in Table 2. This methodologyhas been reported to provide similar results to multidimen-sional scaling (Cadoret et al. 2009; Ares et al. 2011b), withthe possibility of obtaining a representation of the consum-ers and adding confidence ellipses by partial bootstrap tosample configurations (Cadoret et al. 2009).

Hierarchical cluster analysis was performed to identifygroups of perfumes with different sensory characteristics.This analysis was performed on samples’ coordinates inthe first four dimensions of CA, considering Euclidean dis-tances and Ward’s aggregation criterion. Nonstandardizedsample coordinates were considered to avoid giving equalweight to all dimensions. Using this approach, the dimen-sions with higher explained variance have more weight indetermining similarities and differences among samplesthan those with lower explained variance.

Multiple factor analysis (MFA) was used to comparesample configurations from the sorting tasks performed bytrained assessors and consumers. MFA enables the analysisof samples described by different sets of variables, seekingthe common structures present in all or some of these sets.

TABLE 2. EXAMPLE OF THE DATA MATRIX USED TO ANALYZE DATAFROM THE SORTING TASK USING THE FACTORIAL APPROACHPROPOSED BY CADORET ET AL. (2009). EACH COLUMN INDICATESHOW EACH CONSUMER SORTED THE SAMPLES

Sample Assessor 1 Assessor 2 . . . Assessor n

1 A A . . . A2 B B . . . K3 A C . . . B

. . . . . . . . . . . . . . .15 E F . . . K16 F F . . . K

Samples with the same letter were sorted in the same group.

PERFUME ODOR CATEGORIZATION M. VERAMENDI, P. HERENCIA and G. ARES


It provides a consensus representation of the samples and aprojection of the individual data sets to analyze agreementand discrepancies among them (Pagès 2004). The regressionvector (RV) coefficient (Robert and Escoufier 1976) was cal-culated between sample configuration in the first fourdimensions of the CA performed on data from the sortingtask performed by trained assessors and consumers. The RVcoefficient is an index measuring the degree of correspon-dence between data matrices. It is interpreted similar to thePearson regression coefficient: 0 for dissimilar matrices and1 for identical matrices.

The elicited words provided by trained assessors and con-sumers to describe the groups were qualitatively analyzed.Words with similar meaning were grouped into groups ofterms. In order to identify consensual terms, the methodol-ogy proposed by Kostov et al. (2012) was used. Multiplefactor analysis for contingency tables (MFACT) was appliedon the frequency table with the descriptions of all the con-sumers and a representation of terms was obtained. Con-sensual terms were identified as those for which the P-value,computed as the proportion of random subsets, selectedfollowing a bootstrap methodology, having a within-inertiasmaller or equal to the observed inertia, was smaller than0.05. This analysis was performed for all the terms men-tioned by at least three assessors (Kostov et al. 2012).

Then, frequency of mention of the consensual terms foreach perfume was determined by counting the number ofconsumers that used that term to describe each perfume.A frequency table of perfumes ¥ categories was constructedand the terms were projected into the sample space fromCA.

Agreement between the description of trained assessorsand consumers was analyzed using MFACT on the fre-quency table of the consensual terms used by trained asses-sors and consumers to describe the perfumes (Bécue-Bertauand Pagès 2004).

All statistical analyses were carried out in XL-Stat 2009(Addinsoft, Paris, France) and R language (R DevelopmentCore Team 2007) using SensoMineR (Lê and Husson 2008)and FactoMineR (Lê et al. 2008).

RESULTS

Descriptive Statistics of the SortingPerformed by Trained Assessorsand Consumers

As shown in Fig. 1A, trained assessors sorted the 16 samplesusing four to six groups, which resulted in an average of 4.9groups. Meanwhile, the number of groups used by consum-ers ranged from 3 to 12, whereas the average number ofgroups used to sort the 16 perfumes was 5.1. According to a

chi-square test, no statistically significant differences existedin the frequency distribution of consumers and trainedassessors (c2 = 4.74, P = 0.53).

Similar results were found for the number of samples pergroup in the sorting task performed by consumers andtrained assessors. The majority of groups formed by trainedassessors were composed of two or three samples, whereasfor consumers most groups were composed of one to threesamples (Fig. 1B). The average number of samples pergroup was similar for trained assessors and consumers (3.2and 3.1, respectively). No significant differences existedbetween trained assessors and consumers in the frequencydistribution of the number of samples per group in thesorting task (c2 = 5.99, P = 0.54).

Categorization of the Perfumes byConsumers and Trained Assessors

The first and second dimensions of the CA performed ontrained assessors’ and consumers’ sorting of the perfumes

FIG. 1. (A) FREQUENCY DISTRIBUTION OF THE NUMBER OF GROUPSUSED BY TRAINED ASSESSORS AND CONSUMERS TO SORT THE 16PERFUME SAMPLES AND (B) FREQUENCY DISTRIBUTION OF THENUMBER OF SAMPLES PER GROUP OF TRAINED ASSESSORS ANDCONSUMERS



explained 34.4 and 20.7%, respectively (Table 3). This lowproportion of the variance explained by the first dimensionsis expected considering the complexity in the odor characterprofile of the perfumes. Besides, it is important to highlightthat when CA is used to analyze a sorting task with a largenumber of participants, it could potentially underestimatethe proportion of explained variance (Cadoret et al. 2009).As shown in Table 3, the percentage of the varianceexplained by the first dimensions was higher for trainedassessors than for consumers, which could be explained bytheir higher agreement due to training, as reported byLawless and Glatter (1990).

Figure 2 show the perfumes’ representation in the firstfour dimensions of the CA for trained assessors. Identicalsamples (1 and 16) were located very close to each other,

indicating that trained assessors were able to correctly sortthe samples according to their global similarity.

The first dimension opposed samples 7 and 10 to samples8 and 11, whereas the second dimension mainly opposedsamples 1, 2, 4, 15 and 16 to samples 8 and 11. Meanwhile,the third dimension mainly differentiated samples 6, 9, 12,13 and 14 from the rest, and the fourth dimension opposedsamples 13 and 14 to samples 6 and 9.

Hierarchical cluster analysis was applied on the samples’coordinates in the first four dimensions of the CA to iden-tify groups of perfumes with similar odor character profile.Five groups of perfumes were identified, as it was theaverage number of groups used by trained assessors andconsumers to sort the 16 samples. As shown in Fig. 3A,samples 5, 1, 16, 15, 2 and 4, which tended to be located at

TABLE 3. EIGENVALUES AND EXPLAINED VARIANCE OF THE FIRST 10 DIMENSIONS OF THE CORRESPONDENCE ANALYSIS PERFORMED ON DATAFROM THE SORTING TASK OF TRAINED ASSESSORS AND CONSUMERS

Dimension

Trained assessors Consumers

EigenvalueExplainedvariance (%)

Cumulative explainedvariance (%) Eigenvalue

Explainedvariance (%)

Cumulative explainedvariance (%)

1 10.61 18.1 18.1 22.70 11.1 11.12 9.29 15.8 33.9 19.67 9.6 20.73 7.30 12.6 46.5 19.51 9.5 30.24 5.73 9.9 56.4 17.86 8.7 38.95 4.08 7.0 63.4 14.40 7.0 45.96 3.91 6.7 70.1 13.79 6.7 52.67 3.26 5.6 75.7 13.71 6.7 59.38 2.89 5.0 80.7 12.89 6.3 65.69 2.72 4.7 85.4 12.60 6.1 71.7

10 2.36 4.1 89.5 12.00 5.9 77.6

FIG. 2. REPRESENTATION OF THE PERFUMES IN THE FIRST FOUR DIMENSIONS (DIM) OF THE CORRESPONDENCE ANALYSIS OF TRAINED ASSES-SORS’ SORTING DATA



negative values of the second and third dimensions (Fig. 2),were grouped. Samples 8 and 11 were perceived as differentfrom the rest, being located at positive values of the firstand second dimension. Something similar happened withsamples 7 and 10, which were sorted apart from the rest andwere located at negative values of the first dimensions andpositive values of the first dimension, as shown in Fig. 2.Samples 6 and 9 showed a distinct position in the third andfourth dimension of the CA, composing a separate group ofsamples. Finally, samples 3, 12, 13 and 14 formed anothergroup, which was located at negative values of the second

dimension and positive values of the third dimension(Fig. 2).

Figure 4 shows perfume configuration in the first fourdimensions of the CA for consumers. Samples 1 and 16(which were identical) were located very close to each otherin the first four dimensions of the CA, indicating that con-sumers were also able to correctly sort identical samplesaccording to their global similarity.

As shown in Fig. 4, the first dimension of the CA of con-sumers’ data opposed samples 8 and 11 to samples 7 and 10,as in the CA performed on trained assessors’ data (Fig. 2).

A B

FIG. 3. DENDROGRAM OBTAINED FROM HIERARCHICAL CLUSTER ANALYSIS ON SAMPLE CONFIGURATION ON THE FIRST FOR DIMENSIONS OFTHE CORRESPONDENCE ANALYSIS PERFORMED ON DATA FROM THE SORTING TASK OF (A) TRAINED ASSESSORS AND (B) CONSUMERS

FIG. 4. REPRESENTATION OF THE PERFUMES IN THE FIRST FOUR DIMENSIONS (DIM) OF THE CORRESPONDENCE ANALYSIS OF CONSUMERS’SORTING DATA



Meanwhile, the second dimension mainly opposed samples6 and 9 to samples 1, 16 and 11. Regarding the third dimen-sion, it differentiated samples 2, 4 and 5 from the rest;whereas the fourth dimension differentiated samples 5 and10.

Five groups of samples were identified using hierarchicalcluster analysis (Fig. 3B). The first group was composed ofsamples 15, 6 and 9, which were located at positive values ofthe first and the second dimension. Samples 8 and 11 wereperceived as different from the rest and were located at posi-tive values of the first dimension and negative values ofthe second dimension (Fig. 4). Samples 3, 7, 10, 12 and 13formed a third group, which was located at negative valuesof the first dimension and positive values of the second andthird dimensions. Samples 1, 14 and 16 were characterizedby their distinct position at negative values of the first andsecond dimensions; whereas samples 2, 4 and 5 were sortedapart from the rest due to their position at negative valuesof the third dimension.

The RV coefficient between sample configurations was0.63 (P < 0.0001), which suggests a moderate agreement.MFA was carried out to compare results from the sortingtask performed by trained assessors and consumers. Asshown in Fig. 5, although at first sight the partial represen-tation of the perfumes in the first two dimensions of theMFA was similar for trained assessors and consumers, it hasto be considered that MFA tends to find consensus betweenthe partial representations (Pagès 2004), which could poten-tially underestimate differences between consumers andtrained assessors. For this reason, differences between the

perceptions of both types of assessors were also studiedusing results from CA and cluster analysis.

As shown in Figs. 2 and 4, according to both types ofassessors, samples 8 and 11 were located at a distinct posi-tion, as were samples 7 and 10, suggesting that these twogroups of perfumes showed a distinct odor profile. On theother hand, differences in the classification performed bytrained assessors and consumers could also be highlighted.For example, samples 6 and 9 were perceived as havingintermediate characteristics for trained assessors (Fig. 2),whereas consumers perceived them as different from everyother sample except for sample 15 (Fig. 4). Moreover, theposition of samples 5 clearly differed between the samplerepresentation of trained assessors and consumers (Figs. 2and 4).

Moreover, hierarchical cluster analysis showed that theclassification of trained assessors and consumers showeddifferences, indicating that there was no perfect agreementbetween them in perfume categorization. For example,despite samples 8 and 11 were sorted in a separate group byboth types of assessors, samples 2, 4 and 5 were sorted apartfor consumers, whereas trained assessors considered themsimilar to samples 1 and 15.

Description of the Perfumes

Trained assessors elicited a total of 74 terms for describingthe groups of perfumes they identified in the sorting task,using an average of 3.0 terms per sample. The terms weregrouped into 71 categories, which are shown in Table 4. The

FIG. 5. PARTIAL REPRESENTATION OF THEPERFUMES IN THE FIRST AND SECONDDIMENSION (DIM) OF THE MULTIPLE FACTORANALYSIS OF TRAINED ASSESSORS (T) ANDCONSUMERS’ (C) SORTING DATA



great majority of the terms elicited by trained assessorswere specific odor terms related to their source, insteadof subjective descriptions. Assessors also mentioned someterms related to odor intensity (e.g., soft, smooth, moderateand strong), a hedonic term (disgusting) and some unspe-cific odors (e.g., chewing gum, air freshener, detergent, dis-infectant). The fact that trained assessors had been trainedin the identification of a reduced set of odors could havelimited the number of terms used to describe the perfumes.

As shown in Table 5, from the 22 terms mentioned by atleast three trained assessors, 12 were consensual. It is inter-esting to note that the assessors were trained in the identifi-cation of five of these notes: citric, floral, fruity, herbal andwoody.

Figure 6 shows the projection of the consensual terms onthe first four dimensions of the CA performed on resultsfrom the sorting task. The second dimension of the CA wasassociated with the terms floral, sweet, lemon and soft, sug-gesting that it sorted perfumes according to their floral,fresh and sweet notes. Therefore, samples 1, 2, 4, 5, 15 and16 were sorted apart from the rest due to the presence ofthese notes (cf. Figs. 2 and 6). Meanwhile, samples 7 and 10were characterized by their fruity, rose and jasmine notesand samples 8 and 11 by wood, herbal, anise and bitternotes. Samples 6 and 9 were located at a distinct position onthe third and fourth dimensions (Fig. 2) due to their sweetand fruity notes. Samples 3, 12, 13 and 14, were mainlyassociated with woody notes.

Consumers elicited more terms (217) than trained asses-sors for describing the groups of perfumes they identifiedin the sorting task, using an average of 3.3 terms persample. Although consumers elicited more terms thantrained assessors, their descriptions were much more het-erogeneous, as expected due to their lack of training inodor recognition. As shown in Table 6 consumers elicitedmany vague, ambiguous and redundant (e.g., baby, sophis-ticated, subtle, sunset, strange weak, wild, etc.), hedonicterms (e.g., pleasant, nice, and disgusting) and also usedsome brand names, which were not used by trained asses-sors (Table 4). Besides, consumers used intensity wordssuch as very, too much, intense, strong, weak and notmuch to describe the perfumes, which were not frequentlyused by trained assessors. After removing intensity termsand grouping similar words into categories (e.g., baby odorand baby), 153 terms were kept for the analysis. The mostfrequently used terms were floral, soft, sweet, strong andwood.

As shown in Table 7, from the 42 terms mentioned byat least three consumers, only 12 were consensual. Figure 7shows the projection of the consensual terms on the firstfour dimensions of the CA performed on results fromthe sorting task did. The terms lemon and fresh were nega-tively associated with the first dimension and positivelyassociated with the second dimension, whereas the termsorange, fruity refreshing and lemon were positively associ-ated with the third dimension. This suggests that, according

TABLE 4. TERMS USED BY TRAINED ASSESSORS TO DESCRIBE THE GROUPS OF PERFUMES IN THE SORTING TASK

Terms and frequency of mention (between brackets)

Floral (80), Sweet (57), Soft (45), Herbal (44), Wood (32), Mint (26), Jasmine (24), Coriander (23), Fresh (23), Rose (21), Strong (20), Talcum (19),Anise (16), Air Freshener (14), Countryside (13), Fruity (13), Lemon (13), Citric (12), Vanilla (11), Caramel (10), Syrup (10), Bitter (9), Lavender(9), Sea (9), Aldehydic (8), Detergent (7), Egg (7), Grass (7), Baby (6), Camphor (6), Lilies (6), Lime (6), Ozonic (6), Cotton (5), Lilium (5),Medicine (5), Moss (5), Banana (4), Chewing gum (4), Fig (4), Incense (4), Orange (4), Parsley (4), Passion fruit (4), Plant (4), Sawdust (4), Smoke(4), Wild mint (4), Air (3), Apple (3), Aromatic (3), Bread (3), Egg white (3), Pepper (3), Pineapple (3), Sour (3), Yeast (3), Medicinal cream (3),Chamomile (2), Cinnamon (2), Cleaning product (2), Disinfectant (2), Green (2), Honey (2), Moderate (2), Smooth (2), Alcohol (1), Disgusting (1),Shampoo (1)

TABLE 5. PROPORTION OF SUBSETS (P-VALUE) OBTAINED USINGBOOTSTRAP ON TERM CONFIGURATION ON MULTIPLE FACTORANALYSIS FOR CONTINGENCY TABLES HAVING A WITHIN-INERTIASMALLER OR EQUAL TO THE OBSERVED INERTIA, FOR THE TERMSELICITED BY MORE THAN THREE TRAINED ASSESSORS

WordNumber of trainedassessors P-value

Floral 13 0.001Bitter 3 0.001Rose 4 0.001Jasmine 5 0.002Lemon 5 0.018Soft 9 0.019Fresh 3 0.026Wood 11 0.029Anise 4 0.032Fruity 4 0.038Sweet 12 0.042Herbal 11 0.049Talcum 5 0.272Strong 8 0.45Syrup 4 0.464Caramel 4 0.51Citric 4 0.658Mint 6 0.694Coriander 8 0.822Egg 3 0.926Vanilla 5 0.936Baby 3 0.95



to consumers’ perception, samples 3, 7, 10, 11, 12 and 13were characterized by their citric, fruity and fresh notes (cf.Figs. 4 and 7). Meanwhile, the term floral was negativelyassociated with the first, second and fourth dimensionsof the CA, which indicates that samples 1, 14 and 16 werecharacterized by their floral notes, particularly violet.Samples 6, 9 and 15 were located at positive values of the

first and second dimension and at negative values of thefourth dimension (Fig. 4), suggesting that they were charac-terized by their clove and floral notes. Regarding samples 8and 11, which were located at positive values of the firstdimension and negative values of the second dimension, itcould be concluded that they were characterized by theirsweet notes. Finally, although samples 2, 4 and 5 showed a

FIG. 6. PROJECTION OF THE CONSENSUAL TERMS ON THE FIRST FOUR DIMENSIONS (DIM) OF THE CORRESPONDENCE ANALYSIS OF TRAINEDASSESSORS’ SORTING DATA

TABLE 6. TERMS USED BY CONSUMERS TO DESCRIBE THE GROUPS OF PERFUMES IN THE SORTING TASK

Terms and frequency of mention (between brackets)

Floral (273), Soft (225), Sweet (183), Wood (138), Strong (130), Citric (105), Nice (90), Jasmine (72), Rose (61), Alcohol (56), Fruity (51), Fresh (48),Vanilla (48), Lemon (45), Caramel (33), Baby (30), Lavender (30), Violet (30), Penetrating (29), Orange (26), Cinnamon (25), Very strong (21),Chewing gum (18), Countryside (18), Intense (18), Intense odor (18), Herbal (17), Mandarin (16), Refreshing (15),Syrup (15), Disgusting (14),Aromatic (12),Clove (12), Daisy (12), Too much alcohol (12), Detergent (11), Carnation (10), Headache (10), Soap (10), Chamomile (9), Remedies(9), Somewhat sweet (9), Strength (9), Strong herbal (9), Talcum (9), Chocolate (8), Mango (8), Mint (8), Not much alcohol (8), Not strong (8),Youth (8), Baby odor (7), Cherry chewing gum (7), Image (7), Not sweet (7), Persistent (7), Sober (7), Syrup odor (7), Delicate (6), Disinfectant(6), Intense alcohol (6), Milk (6), Mixture of plants (6), Not intense (6), Pear (6), Acetone (5), Baby and Woman (5), Bitter (5), Child odor (5),Deep (5), Energizing (5), Fresh air (5), Fresh cologne (5), Gardenia (5), Honey (5), Masculine (5), Medicine (5), Mixture of citric fruits (5), Notbitter (5), Not lasting (5), Not very strong odor (5), Oak (5), Past (5), Poppy (5), Relaxing (5), Rose petal (5), Shampoo (5), Sophisticated (5), Sour(5), Spices (5), Strange (5), Strong alcohol (5), Subtle (5), Toilet soap (5), Very sweet (5), Watermelon (5), Weak alcohol (5), Yummy (5), Acid (4),Acid fruit (4), Clean (4), Clothes (4), High (4), Mystic (4), Pepper (4), Poet (4), Similar (4), Spring (4), Tobacco (4), Warm (4), Adolescence (3), Airfreshener (3), Apple (3), Chili (3), Clean sensation (3), Dense (3), Do not like (3), Dry (3), Eucalyptus (3), Floral and vanilla (3), Frail (3),Gelatin (3),Kind of fabric softener (3), Laundry detergent (3), Light (3), Lilies (3), Lime (3), Mahogany (3), Melon (3), Moderate odor (3), Not soft (3), Oily(3), Olive oil (3), Rosemary (3), Shaving cream (3), Shaving lotion (3), Sky (3), Smooth (3), Soft and strong (3), Somewhat citric (3), Strawberry(3), Strong odor (3), Tenacious (3), Too strong (3), Too sweet (3), Totally sweet (3), Very persistent (3), Very soft (3), Vitamins (3), Weak (3), Wetcloth (3), A little bitter (2), Iodine (2), Baby perfume (2), Baby shampoo (2), Bread crumbs (2), Coffee (2), Disgusting odor (2), Grape (2), Icecream (2), Makeup remover (2), Merci (2), Nice odor (2), Not citric (2), Not that nice (2), Passion (2), Perfume (2), Pine (2), Sawdust (2), Sea (2),Somewhat citric (2), Toffee (2), Too strong alcohol (2), Tree (2), Woods (2), Very intense (2), Very strong odor (2), Very woody (2), Anise (1), Babycream (1), Bathroom (1), Cloying sweetness (1), Different odor (1), Disturbing (1), Fabric softener (1), Grass (1), Hair conditioner (1), Immediatealcohol (1), Insulin (1), Love (1), Low intensity (1), Men (1), Moderate (1), Nasty (1), Natural (1), Pleasant (1), Not much odor (1), Not nice (1),Not persistent (1), Parsley (1), Permanent (1), Plant extract (1), Quiet (1), Raspberry (1), Sedal (1), Sunflower (1), Sunset (1), Too intense (1),Too smooth (1), Too soft (1), Vegetables (1), Very moderate (1), Very nice (1), Very yummy (1), Weak (1), Essence (1), Whisky (1), Wild (1)



distinct position in the third and fourth dimensions, it wasnot easy to identify consensual terms to characterize them.These samples were slightly associated with the terms freshand countryside.

Figure 8 shows results from MFACT performed onthe frequency table containing consensual terms used bytrained assessors and consumers, which allows visualizingthe relationship between the terms. Identical terms are

connected with a line to indicate the size of the difference inhow the term was used. As shown, most of the terms weresimilarly used by trained assessors and consumers. All theterms related to floral notes were located in a similar placeof the MFACT, suggesting a common perception. Also, theterms fresh, soft and sweet were used similarly by both typesof assessors. The largest difference was found in the termsrelated to fruity and citric notes.

TABLE 7. PROPORTION OF SUBSETS(P-VALUE) OBTAINED USING BOOTSTRAP ONTERM CONFIGURATION ON MULTIPLEFACTOR ANALYSIS FOR CONTINGENCYTABLES HAVING A WITHIN-INERTIA SMALLEROR EQUAL TO THE OBSERVED INERTIA, FORTHE TERMS ELICITED BY MORE THAN THREECONSUMERS

WordNumber ofconsumers P-value Word

Number ofconsumers P-value

Soft 37 0.001 Pleasant 14 0.402Sweet 31 0.001 Penetrating 7 0.448Floral 46 0.002 Masculine 3 0.548Violet 6 0.002 Alcohol 20 0.582Refreshing 5 0.016 Chewing gum 9 0.612Lemon 12 0.028 Intense 6 0.614Orange 6 0.032 Sour 5 0.654Clove 3 0.036 Vanilla 14 0.66Fruity 11 0.038 Detergent 4 0.672Countryside 4 0.04 Lasting 3 0.718Fresh 10 0.048 Caramel 10 0.722Strong 34 0.05 Herbal 8 0.786Rose 14 0.218 Lavender 9 0.814Citric 28 0.274 Medicine 8 0.816Syrup 7 0.278 Cinnamon 9 0.836Mandarin 6 0.28 Disgusting 7 0.838Jasmine 17 0.282 Wood 38 0.878Clean 3 0.294 Mint 5 0.886Soap 5 0.3 Chamomile 6 0.914Daisies 3 0.306 Baby 14 0.946Chocolate 3 0.308 Shampoo 5 0.974

FIG. 7. PROJECTION OF THE CONSENSUAL TERMS ON THE FIRST FOUR DIMENSIONS (DIM) OF THE CORRESPONDENCE ANALYSIS OF CONSUM-ERS’ SORTING DATA



DISCUSSION

Training did not significantly affect the number of groupsin which the samples were sorted (Fig. 1). Sample confi-gurations obtained from the sorting task performed bytrained assessors and consumers were similar (Figs. 2 and4), suggesting agreement in their categorization of the per-fumes. The RV coefficient between sample configurationfrom trained assessors and consumers was similar to thosereported by Lelièvre et al. (2008) for the comparison oftrained assessors and consumers categorization of beers(RV = 0.65–0.71). In general, results are in agreement withother studies that have reported similar results from sortingtasks performed by trained and untrained assessors (Cholletand Valentin 2001; Soufflet et al. 2004; Chollet et al. 2005;Valentin et al. 2007; Lelièvre et al. 2008; Chollet et al. 2011).

Although sample configurations were similar, it is impor-tant to highlight that there was no perfect agreementbetween the classification performed by trained assessorsand consumers. As shown in Fig. 3, sample classification oftrained assessors and consumers was not exactly the sameand some differences in the conclusions regarding similari-ties and differences between samples were identified. Differ-ences between experts and novices in the classification ofperfumes were described by Béguin (1993), who reportedthat experts structured their perceptual space using a largernumber of dimensions than novices. However, whenworking with simple odors, Lawless and Glatter (1990)reported that training did not have a strong effect, whichwas explained, in part, by the fact that training was relatedto the identification of specific odors and not at how to sortthe perfumes.

According to Zarzo and Stanton (2009), odor descrip-tions of perfumes are based on four basic types of terms:odor descriptors referring to the odor source (e.g., floral,fruity), perfume materials according to their common name(e.g., vanilla, jasmine, lemon), sensory perceptions (e.g.,bitter, sweet, soft) and physiological and psychologicaleffects (e.g., relaxing, exciting). In the present work, bothtrained assessors and consumers mainly used the first threetypes of terms for describing the groups of terms they iden-tified in the sorting task. Some psychological terms (e.g.,tenacious, warm, sophisticated, relaxing) were elicited by aconsumers but with a very low frequency.

Consumers and trained assessors strongly differed in thenumber and type of words used for describing the groupsof perfumes identified in the sorting task. Consumers elic-ited many vague, ambiguous, redundant and hedonic termswhich were not used by trained assessors. Similar resultswere reported by Lelièvre et al. (2008) when comparingconsumers’ and trained assessors’ descriptions when sortingbeer samples.

Differences between trained assessors’ and consumers’descriptions could be attributed to the linguistic compo-nents involved in odor training (Lawless and Glatter 1990).In the present work, trained assessors were able to providemore accurate terms than consumers, which is in agreementwith several studies which report that trained assessors havea greater ability to verbalize their sensory perception andthat their descriptions tend to be more reliable, specific andconsensual than those provided by consumers (Lawlesset al. 1995; Chollet and Valentin 2001; Saint-Eve et al. 2004;Soufflet et al. 2004; Chollet et al. 2005, 2011; Lim andLawless 2005).

FIG. 8. REPRESENTATION OF THE CONSEN-SUAL TERMS USED BY TRAINED ASSESSORSAND CONSUMERS TO DESCRIBE THE PER-FUMES ON THE FIRST AND SECOND DIMEN-SION (DIM) OF THE MULTIPLE FACTORANALYSIS FOR CONTINGENCY TABLES



Although Zarzo (2008) reported that the most salientdescriptions when a wide range of odors were assessed byconsumers were related to hedonic aspects, in the presentwork, consumers elicited a large number of objective odorterms. The three most frequently used terms were the samefor trained assessors and consumers: floral, soft and sweet.The fact that the terms floral and soft were among the mostfrequently used is in agreement with Jellinek (1990, 1992),who reported that consumers distinguish perfumes alongtwo main dimensions: heavy/light and floral/nonfloral. Bylooking at the projection of the terms on the sensory space,it could be clearly seen that these were the main dimensionsused by both trained assessors and consumers to describethe perfumes. Floral notes were negatively correlated withthe second dimension of the CA of trained assessors’sorting data, whereas fresh was negatively correlated withthe first dimension (Fig. 6). Similarly, in the CA of consum-ers’ sorting data, the floral dimension was negatively corre-lated with the first and second dimension, whereas the freshdimension was independent, being negatively correlatedwith the first dimension and positively with the seconddimension (Fig. 7).

The number of consensual terms was the same fortrained assessors and consumers. It is interesting to high-light that 6 out of the 12 consensual terms used by trainedassessors and consumers to describe the groups of perfumeswere the same and most of them (floral, fresh, soft andsweet) were similarly used (Fig. 8). However, several termsthat were consensual for trained assessors (e.g., wood, anise,bitter) were not for consumers.

In general, the description of the perfumes provided bytrained assessors and consumers was not in agreement withthe main notes provided by the manufacturers. Besides,consumers and trained assessors disagreed in the main dif-ferences in the odor profile of the perfumes. For example,considering trained assessors’ perception samples 8 and 11were associated with woody, herbal and bitter notes (Fig. 6),whereas consumers described them as sweet (Fig. 7).Another clear difference was observed for samples 3, 12 and13, which were described as having woody notes for trainedassessors and as fruity, citric and fresh by consumers. Onthe other hand, samples 1, 4 and 16, were described withfloral notes by both consumers and trained assessors. Thesedifferences could be explained by the fact that trainingallowed assessors to develop a richer vocabulary, becomefamiliar with it and learn how to apply it reliably.

Results from the present work suggest that training influ-enced categorization and description of perfumes. However,further research is necessary to overcome some of the limi-tations of the present work. Firstly, a very limited sample of15 commercial perfumes was considered. Thus, this typeof research should be replicated with a wider range of pro-ducts. Besides, in the present work, only female assessors

and female perfumes were considered, which could limit thegeneralization of the results.

CONCLUSIONS

Results from the present work suggest that althoughtrained assessors’ and consumers’ perception of perfumeswas similar, some differences in their categorization anddescriptions were identified. Although development ofexpertise involves complex changes in perception, categori-zation, memory and other components of human cognition(Palmeri et al. 2004), differences in the perception of globaldifferences between consumers and trained assessors werenot large.

Differences between trained assessors and consumerswere more relevant when considering perfume descriptions.

Despite the fact that training has been extensively recom-mended for obtaining reliable product profiles (Labbe et al.2004), it is important to consider that trained assessors’ cat-egorizations and descriptions of perfumes could not exactlyreflect consumers’ perception. This stresses the importanceof relying on consumer perception as a complementary toolto trained assessor panels when developing classificationsystems as well as perfume descriptions for marketingor communication purposes. Further research should becarried out to establish consensus for the classification ofcommercial perfumes based on olfactory perception. Thedevelopment of odor maps for perfumes is of relevant inter-est because they are easy to understand for consumers andthey could potentially benefit their shopping experience.

ACKNOWLEDGMENTS

The authors would like to thank Carmen Caballero,manager of Belcorp laboratories for the continued supportreceived. The authors are also grateful to the two anony-mous reviewers who reviewed the original version of themanuscript for contributing to improve its quality.

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Perfume Odor Categorization: To What Extent Trained Assessors and Consumers Agree?

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