Chemical references in sensory analysis of smoke flavourings

Modern data mining tools in descriptive sensory analysis: A case study with a Random forest approach

“…This implies the development of predictive discriminant models on the sensory analysis dataset previously obtained. For example, linear discriminant analysis (LDA) was used to recognize smoked food products (Ojeda, Barcenas, Perez-Elortondo, Albisu, & Guillen, 2002) and artificial neural networks (ANN) have been trained to discriminate between original Scotch whisky and similar products (Jack & Steele, 2002). Over the last decade, the machine learning and statistical communities have developed new modeling tools based on ensembles (or groups) of predictors (see Breiman, 1996;Breiman, 2001;Freund & Schapire, 1996, or reviews from Ho, 2002Tresp, 2001) that are consistently more accurate than single predictors (Bauer & Kohavi, 1999).…”

Sensory Comparison of Several Cheese Varieties Manufactured From Different Milk Sources

“…This fact indicates the descriptive lexicon selected is adequate to discriminate among cheese varieties under investigation. However, as suggested by Ojeda et al. (2002), specifically adapted lexicons are necessary to accurately describe any specific food product, more general ones serving as a baseline to start with.…”

An international ring trial for the sensory evaluation of raw ewes’ milk cheese texture

“…Zannoni (1997) identified the correct translation of the sensory vocabulary as a key step for this kind of international interlaboratory studies to succeed. Moreover, several authors have underlined the importance of using adequate references to reduce differences among panels as much as possible (Ojeda, Ba´rcenas, Pe´rez Elortondo, Albisu, & Guille´n, 2002). In this study, in order to minimize the cultural differences, standard references were used as a way of illustrating each sensory concept.…”