Optimal information in authentication of food and beverages
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Publication date
2014Metadata
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Gutiérrez Inostroza, Luis
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Optimal information in authentication of food and beverages
Abstract
Food and beverage authentication is the process by which food or beverages are verified
as complying with their label descriptions (Winterhalter, 2007). A common way to deal with an
authentication process is to measure attributes, such as, groups of chemical compounds on samples
of food, and then use these as input for a classification method. In many applications there may be
several types of measurable attributes. An important problem thus consists of determining which
of these would provide the best information, in the sense of achieving the highest possible
classification accuracy at low cost. We approach the problem under a decision theoretic strategy,
by framing it as the selection of an optimal test (Geisser and Johnson, 1992) or as the optimal
dichotomization of screening tests variables (Wang and Geisser, 2005), where the ‘test’ is defined
through a classification model applied to different groups of chemical compounds. The proposed
methodology is motivated by data consisting of measurements of 19 chemical compounds (Anthocyanins,
Organic Acids and Flavonols) on samples of Chilean red wines. The main goal is to determine the
combination of chemical compounds that provides the best information for authentication of wine
varieties, considering the losses associated to wrong decisions and the cost of the chemical analysis.
The proposed methodology performs well on simulated data, where the best combination of responses
is known beforehand.
General note
Artículo de publicación ISI
Patrocinador
The first author was partially funded by Program U-INICIA
VID 2011, grant U-INICIA 02/12A; University of Chile. The second author was
partially funded by grant FONDECYT 1100010.
Quote Item
Statistical Modelling 2014; 14(2): 179–204
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