SO-PLS as an exploratory tool for path modelling

This paper discusses the use of the new path modelling approach based on Sequential Orthogonalised PLS regression within the context of consumer science. The method is based on splitting the estimation process into a sequence of modelling steps for each dependent block versus its predictive blocks. Focus will be on how the method can be used to combine individual variables or specific groups of variables in more general blocks with a broader interpretation, such as for instance consumer habits, attitudes and demographic variables. It will be discussed how the methods can solve some of the challenges met when starting a path modelling process. It will be explored how the method handles multidimensionality of the blocks and thus how the analysis is simplified, at least for explorative purposes, as compared to other more traditional path modelling approaches. The study shows that important relations are revealed in presence of different types of information like product attributes, consumer characteristics and acceptance.     

Comparison between linear and nonlinear PLS methods to explain overall liking from sensory characteristics

The influence of sensory characteristics on overall liking can be statistically studied with Partial Least Squares (PLS) regression methods. To correctly model nonlinear dependence relationships, some nonlinear PLS extensions are useful. The purpose of the present paper is to compare performances and results of three PLS methods, using a real data set: regular PLS with sensory attributes as explanatory variables; PLS with attributes and their respective squares; and a new nonlinear PLS extension, called ASPLS. In case of a nonlinear dependence relationship between sensory characteristics and hedonic responses, this last method is shown to be worth considering.     

Predicting paired preferences from sensory data

Paired-preference studies for market research tend to focus on highly replicating the few comparisons of most interest to the client. By establishing a predictive model between sensory panel means scores for products and the relative proportions of consumers preferring them when presented in pairs, it is possible to get reliable predictions of all possible comparisons among a whole group of products. The binomial discrete choice logistic model is appropriate for building this relationship between sensory and consumer preference and can be easily fitted using the commercially available statistical package SAS. Modifications to the model are described to account for consumers who score products either as “equally liked” or “equally disliked”. Rather than treat these as ties, the argument is made that no-preference is best modelled as a function of the sensory data itself. An application of the new model is given to data from a home-test of 12 personal care products that were evaluated by more than 500 consumers, each receiving six pairs of products.     

基于消费者感知的面料材质感官意象研究

以消费者为中心的设计是一种确保设计符合目标消费者的期望的方法。设计越来越注重消费者的感官体验,通过消费者感官意象的研究,创造出一种吸引消费者感官特性的设计方式,已成为现在设计的主要核心。本研究以面料材质为研究对象,选取代表性的材质样本,调查消费者的感官意象,确立感官意象的材质特性。研究中使用多种方法整合进行面料材质的消费者感官意象研究,使用多维尺度分析法构建消费者认知图,发现五种材质族群;通过因素分析法汲取消费者材质意象主因素,获得四种主因素;通过回归分析法获得材质族群对应的意象特征。研究结果为以消费者为中心的设计方法提供有价值的设计参考。     

Product selection for liking studies: The sensory informed design

Liking studies are designed to ascertain consumers likes and dislikes on a variety of products. However, it can be undesirable to construct liking studies where each panelist evaluates every target product. In such cases, an incomplete-block design, where each panelist evaluates only a subset of the target products, can be used. These incomplete blocks are often balanced, so that all pairs occur the same number of times. While desirable in many situations, balanced incomplete blocks have the disadvantage that, by their nature, they cannot favor placing dissimilar products next to one another. A novel incomplete-block design is introduced that utilizes the target product’s sensory profile to allocate products to each panelist so that they are, in general, as dissimilar as possible while also ensuring position balance. The resulting design is called a sensory informed design (SID). Herein, details on the formulation of SIDs are given. Data arising from these SIDs are analyzed using a simultaneous clustering and imputation approach, and the results are discussed.     

Combining extrinsic and intrinsic information in consumer acceptance studies

This paper proposes a methodology for combining extrinsic and intrinsic attributes in consumer testing of food products. The paper attempts to focalize on the main sensory drivers of liking or choice probability and their interaction with additional information, and to investigate effects related to the population as well as how consumers differ in their assessments. Two different data analysis approaches are considered and compared on choice probability data from a consumer study of orange juice. One of the methods is based on mixed model ANOVA of individual differences, the other approach is based on fuzzy clustering related to regression residuals. The main results show that extrinsic consumer attributes are easily and efficiently related to the sensory properties of products, allowing for interactions. The methodology estimates population or segment means and gives an overview of individual differences in choice intent or liking.     

Alternatives to data averaging of consumer preference data

The relationship between a consumer preference data set and a corresponding sensory profile on eight cooked wheat noodles with different formulas was examined using several multivariate techniques. Individual consumer hedonic responses (100 noodle/pasta consumers) and eight appearance and texture sensory attributes were collected. The consumer preference data were treated in two different ways: mean values averaged across all consumers or principal components extracted from individual responses. The mean preference scores were submitted to both principal component stepwise regression and partial least squares regression (PLS1), whereas the summarized major preference components were subjected to canonical correlation analysis, as well as partial least squares regression (PLS2). The results suggested that in case of complex consumer data, using mean value can only capture the most manifest trends in consumer preference patterns, while studying individual responses and by further categorizing major preference patterns provide an opportunity to discover the hidden information that are masked by data averaging.     

Sensory changes in liquid milk during storage and the effect on consumer acceptance

The acceptance of dairy products by consumers is generally determined by the product's sensory characteristics. These characteristics are a result of various changes in the product and the surrounding environment. The purpose of this paper is to report on a detailed sensory and consumer investigation of the changes in liquid skimmed milk held over an eight day period at three different storage temperatures: 1°C, 5°C and 10°C. Results of an objective sensory profile and subsequent data analysis revealed that sensory changes in milk could be described and quantified, clearly indicating the product's shelf-life profile. Product acceptability as perceived by consumers was undertaken on a subset of samples, illustrating the points at which consumers noticed significant changes. Overall, as the samples went through their shelf-life at the higher temperature they became less acceptable. The final part of the experiment used statistical modelling procedures to interpret and understand the data. As expected, it was shown that products became more stale during storage, and that temperature of storage affected the rate of staling, this being most marked at the higher temperature. Staleness was related to the attributes vegetable, sour, cardboard, chalky and mouthcoating.