Читать книгу Multiblock Data Fusion in Statistics and Machine Learning - Tormod Næs - Страница 37

A typical multiblock data structure that occurs in consumer science is depicted in Figure 1.7. The context is typically product development where interest is in understanding the relations between descriptive information of a number of prototype samples and the consumer liking of the same samples. In addition, interest is in interpreting the liking patterns in terms of consumer characteristics for better understanding of which consumer groups prefer which products (see e.g., Næs et al. (2018)). Based on this type of information, the product developer can more easily design products that better fit the consumer needs and liking patterns. As can be seen from the figure, both chemical attributes as well as sensory properties/attributes, obtained by a trained sensory panel, can be of interest for describing the products. A number of different liking scores can also be of interest, for instance related to taste and texture (Menichelli et al., 2013), as depicted by the stack of data blocks for liking. Analysing this so-called L-shape data structure sheds light on, for instance, which are the sensory drivers of liking, which samples are the most liked, what characterises these samples, and what characterises the different consumer groups with different preference patterns.