OP and P Product Research

Utrecht, Netherlands

OP and P Product Research

Utrecht, Netherlands
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Worch T.,OP and P Product Research | Dooley L.,University of Arkansas | Meullenet J.-F.,University of Arkansas | Punter P.H.,OP and P Product Research
Food Quality and Preference | Year: 2010

Sensory professionals mostly used trained or expert panels for diagnostic purposes and only use consumers for hedonic assessments. In market research, consumers are not only used for hedonic assessments, but also for product diagnostic purposes (as is the case with Just About Right procedure). In the Ideal Profile method, consumers are also used for both tasks. They rate the perceived and ideal intensities and the acceptance of a series of products. The two sets of information (product description and hedonic data) are then used for product improvement. In this paper, the results of two methods of analysis - PLS on dummy variables and Fishbone method - will be compared. The objectives of this study were (1) to compare PLS and Fishbone method to determine their similarity in predicting the impact of the attributes on overall liking, and (2) to determine if the methods would return similar or contrasting conclusions. These methodologies have been applied to a study concerning 12 commercially-available women's perfumes. Though the differences in model derivations caused some small dissimilarities, similar trends were found between products across methods for those perfumes far from the ideal. There was agreement regarding which attributes are too strong or too weak, and the order of importance of these attributes for liking. The two methods showed greater dissimilarity for perfumes that were already near the consumer's ideal. © 2010 Elsevier Ltd.


Worch T.,OP and P Product Research | Worch T.,Agrocampus Ouest | Le S.,Agrocampus Ouest | Punter P.,OP and P Product Research
Food Quality and Preference | Year: 2010

This study compares expert and consumer sensory profiles for the same 12 perfumes in different ways: the discriminatory ability and reproducibility are analyzed through ANOVA and the panelists' consensus through the correlation coefficients. Next, the two product spaces are first analyzed separately for each panel, and then compared through multiple factor analysis. Finally, the two panels are compared using the confidence ellipses methodology. These analyses show that the two panels give similar results with respect to the important criteria for panels (discrimination, consensus, reproducibility). The comparison of the two products spaces shows high similarity. From the confidence ellipses, it can be concluded that no significant differences exist for a given product between the two panels. Hence, in this particular case, the use of consumers appears to be a good alternative to the classical sensory profile provided by a trained panel. © 2009 Elsevier Ltd. All rights reserved.


Worch T.,OP and P Product Research | Worch T.,Agrocampus Ouest | Le S.,Agrocampus Ouest | Punter P.,OP and P Product Research | Pages J.,Agrocampus Ouest
Food Quality and Preference | Year: 2012

The Ideal Profile Method is a sensory method in which, for each product tested, consumers are asked to rate both the perceived and ideal intensities of a list of attributes. In addition, they are also required to indicate how much they like each product. At the end of the task, three blocks of data are collected from each consumer: the product profiles, their ideal profile and the liking ratings.The ideal profiles can be used to help improving the existing products. However, this information should be carefully managed since (1) it is obtained from consumers, and (2) it describes a virtual product. In order to use the full potential of the ideal profiles, and to avoid a possible misinterpretation of the data, one has to ensure that the information collected is consistent.The process checking for the consistency of the ideal profiles proposed here is based on the liking ratings: an ideal product should achieve higher hedonic ratings than the tested products, if it would be tested. But since the liking scores of the ideal products are unknown, they are estimated first. However, the comparison between liking scores (estimated for the ideals, measured for the tested products) would only make sense if the ideal descriptions have not been randomly given. For that matter, a hypothesis test checking for the significance of the ideal profiles is defined.In the perfume example provided, it appears that most of the consumers did not describe their ideals randomly. In addition, the estimations of the ideals liking scores are high compared to those given to the tested products. Hence, for most of the consumers, the ideal profiles are considered as consistent according to the potential liking of their ideal profiles. © 2012 Elsevier Ltd.


Worch T.,OP and P Product Research | Worch T.,Agrocampus Ouest | Le S.,Agrocampus Ouest | Punter P.,OP and P Product Research | Pages J.,Agrocampus Ouest
Food Quality and Preference | Year: 2012

In this paper, the . Ideal Mapping technique is presented. It is similar to the . preference mapping technique using the quadratic model proposed by Danzart. Indeed, both methods start from the sensory product space (. i.e. they are both called " external" maps) and aim at defining areas within the product space that would satisfy a maximum number of consumers.However many differences are observed between the maps. Among them, there is (1) the nature of the maps (based on hedonic ratings . vs. ideal profiles), (2) the way they are constructed (individual models . vs. variability of the ideal profiles), (3) their meanings (. liking zones . vs. . ideal zones) and (4) the proportion of consumers they would satisfy (high . vs. low).The application of both methodologies on the two examples shows that the . IdMap is rather a complement to the . PrefMap than a substitute. When the final ideal product (. i.e. satisfying a maximum number of consumers) belongs to the product space (. e.g. perfume dataset), the . IdMap confirms the . PrefMap solution. When the final ideal product is located outside the product space (. e.g. croissant dataset), the . IdMap can be seen as an extension of the . PrefMap. © 2012 Elsevier Ltd.


Worch T.,OP and P Product Research | Worch T.,Agrocampus Ouest | Le S.,Agrocampus Ouest | Punter P.,OP and P Product Research | Pages J.,Agrocampus Ouest
Food Quality and Preference | Year: 2012

The Ideal Profile Method is a sensory method, in which consumers are asked to describe both the perceived and the ideal intensities on a list of attributes, as well as answering acceptance questions for each product tested. At the end of test, one gets from each consumer three blocks of data: the product profiles, their ideal profile and the acceptance scores of the products. The ideal profiles can be used to help improving the existing products. But before using the ideals, one has to assess the consistency of such data by comparing them to the other descriptions provided (sensory and hedonic). This consistency is defined by answering two major questions: (1) Are the ideal descriptions in agreement with the other descriptions? (2) Would the ideal products obtained be more appreciated than the products tested? The assessment for consistency of the ideals is performed at both the panel and the consumer level. In the perfume example used for illustration, it appears that the panel is globally consistent, although individual consumers show more variability. © 2011 Elsevier Ltd.


Worch T.,OP and P Product Research | Worch T.,Agrocampus Ouest | Le S.,Agrocampus Ouest | Punter P.,OP and P Product Research | Pages J.,Agrocampus Ouest
Food Quality and Preference | Year: 2013

The Ideal Profile Method is a sensory methodology mixing classical profiling (such as QDA®) and JAR scale. It is performed by consumers who are asked to rate each product on both their perceived and ideal intensities for a list of attributes. In the same test, consumers also rate the products on liking.The strength of such methodology is that it brings a lot of information about the products and the consumers. Indeed each consumer provides the sensory profile of the products (i.e. how do they perceive the products), their liking ratings (i.e. how do they appreciate the products) as well as their ideal profiles (i.e. what are their expectations).The ideal profiles are directly actionable to guide for products' improvement. However, this particular information should be carefully managed since it is obtained from consumers and it describes virtual products. It relies on three main assumptions: (1) consumers should rate a unique and stable ideal product, (2) consumers can describe different ideals and (3) the ideal profiles provided by consumers should be consistent with the other descriptions (sensory and hedonic).The study of these assumptions on 24 projects help understanding the consumers and how they define their ideals. It comes out that, although some consumers' ideal ratings are slightly influenced positively by the products, most of the consumers are reliable. Indeed, the consumers rate unique ideal products which are consistent according to the sensory and hedonic descriptions also provided. It also appears that it needs all to make a world, as consumers show differences in their ideal products. © 2012 Elsevier Ltd.


Worch T.,OP and P Product Research | Ennis J.M.,The Institute for Perception
Food Quality and Preference | Year: 2013

Ideal point modeling is a type of multivariate mapping in which consumers are assumed to use internal ideals in their hedonic evaluation of products. In their calculation processes, these techniques typically assume that consumers have a unique ideal for the product set tested. But this assumption is difficult to verify from the liking data alone, and may be violated if different subcategories of products, such as light and dark chocolate, are included in the same experiment. In this paper we propose the use of the Ideal Profile Method (IPM) to test this assumption. In IPM, consumers are asked to rate explicitly their ideals for each product tested. The variability between products of the averaged ideal ratings can then be used to check for the assumption of a single ideal. The procedure we describe involves ANOVA and confidence ellipses associated to the Hotelling T2 test. We then consider three cases to illustrate the use of this methodology in practice. © 2013 Elsevier Ltd.

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