Abstract This paper introduces SensoGraph, a novel approach for fast sensory evaluation using two-dimensional geometric techniques. In the tasting sessions, the assessors follow their own criteria to place samples on a tablecloth, according to the similarity between samples. In order to analyse the data collected, first a geometric clustering is performed to each tablecloth, extracting connections between the samples. Then, these connections are used to construct a global similarity matrix. Finally, a graph drawing algorithm is used to obtain a 2D consensus graphic, which reflects the global opinion of the panel by (1) positioning closer those samples that have been globally perceived as similar and (2) showing the strength of the connections between samples. The proposal is validated by performing four tasting sessions, with three types of panels tasting different wines, and by developing a new software to implement the proposed techniques. The results obtained show that the graphics provide similar positionings of the samples as the consensus maps obtained by multiple factor analysis (MFA), further providing extra information about connections between samples, not present in any previous method. The main conclusion is that the use of geometric techniques provides information complementary to MFA, and of a different type. Finally, the method proposed is computationally able to manage a significantly larger number of assessors than MFA, which can be useful for the comparison of pictures by a huge number of consumers, via the Internet.
Projective Mapping has been successfully used with many different kinds of products, among which the application to wine stands out . Other examples of beverages analysed by these methods are beers , citrus juices , drinking waters high alcohol products , hot beverages , lemon iced teas , powdered juices , or smoothies . The book by @cite_6 details more products to which consumer based descriptive methodologies have been applied.
@cite_6
1979883873 2065540379 2766298634 2506076252
Abstract This paper deals with the method of projective mapping and its use in sensory analysis. Projective mapping is a method which allows naive consumers to map products on a two dimension space, based on similarities and differences in the products. An experiment was carried out on seven blueberry soups, whereby sensory profiling was undertaken using a trained panel and projective mapping. Preference assessment was undertaken using a small group of consumers. Results indicated that the three replicates of the mapping exercise produced visually very similar maps, at least on the first two dimensions. However, it was found that consumers perceived the samples in somewhat different ways as high-lighted by RV coefficients. The consensus mapping dimensions were compared to those from the profile data, and it was apparent that the best similarity was found when comparing the first dimension, thus suggesting good agreement on the obvious aspects of the product. The internal preference map also revealed major product contrasts along this dimension with some weak evidence of segmentation. The overall conclusion of this paper raises further fundamental questions about consensus spaces with consumers, and the question of dimensionality of consumer perception as compared to trained panels. Abstract Studies have indicated that profiling and (dis)similarity scaling yield different perceptual product maps. Conceptually, these two procedures are different. This paper looks at a third and alternative method of producing a two-dimensional, perceptual map utilizing a projective-type method whereby individual assessors themselves are required to place products on the space according to the similarities and differences they perceive. However, visual comparison of the final results provided by each assessor is difficult and, hence, generalized Procrustes analysis is applied to compare each assessor's map for similarity with the others. In this study it was found that the perceptual map derived from projective mapping was as similar to the map derived from profiling as from dissimilarity scaling. However, consistency over repeated trials was greater for projective mapping than for the other two methods. It is suggested that projective mapping could be a potentially useful technique for linking sensory analysis and consumer research data. Human beings have developed a diverse food culture. Many factors like ingredients, visual appearance, courses (e.g., breakfast and lunch), flavor and geographical regions affect our food perception and choice. In this work, we focus on multi-dimensional food analysis based on these food factors to benefit various applications like summary and recommendation. For that solution, we propose a delicious recipe analysis framework to incorporate various types of continuous and discrete attribute features and multi-modal information from recipes. First, we develop a Multi-Attribute Theme Modeling (MATM) method, which can incorporate arbitrary types of attribute features to jointly model them and the textual content. We then utilize a multi-modal embedding method to build the correlation between the learned textual theme features from MATM and visual features from the deep learning network. By learning attribute-theme relations and multi-modal correlation, we are able to fulfill different applications, including (1) flavor analysis and comparison for better understanding the flavor patterns from different dimensions, such as the region and course, (2) region-oriented multi-dimensional food summary with both multi-modal and multi-attribute information and (3) multi-attribute oriented recipe recommendation. Furthermore, our proposed framework is flexible and enables easy incorporation of arbitrary types of attributes and modalities. Qualitative and quantitative evaluation results have validated the effectiveness of the proposed method and framework on the collected Yummly dataset. "Sensory characterization is one of the most powerful, sophisticated and extensively applied tools in sensory science. This book focuses on sensory characterization of food and non-food products, providing an overview of classical and novel alternative methodologies. A complete description of the methodologies would be provided. Each description would be accompanied by detailed information for their implementation, discussion of examples of applications and case-studies. The implementation of the majority of the methodologies would be performed in the statistical free software R, which would make the book very useful for people non-familiar with complex statistical software"--