Journal
FOOD CHEMISTRY
Volume 273, Issue -, Pages 31-38Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2018.04.136
Keywords
Coffee adulteration; Discriminant analysis; Multivariate calibration; Differential pulse voltammetry; Variable selection; Genetic algorithm; Carbon paste electrode
Funding
- Brazilian agency Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) [456569/2014-6]
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This work presents a simple and low-cost analytical approach to detect adulterations in ground roasted coffee by using voltammetry and chemometrics. The voltammogram of a coffee extract (prepared as simulating a home-made coffee cup) obtained with a single working electrode is submitted to pattern recognition analysis preceded by variable selection to detect the addition of coffee husks and sticks (adulterated/unadulterated), or evaluate the shelf-life condition (expired/unexpired). Two pattern recognition methods were tested: linear discriminant analysis (LDA) with variable selection by successive projections algorithm (SPA), or genetic algorithm (GA); and partial least squares discriminant analysis (PLS-DA). Both LDA models presented satisfactory results. The voltammograms were also evaluated for the quantitative determination of the percentage of impurities in ground roasted coffees. PLS and multivariate linear regression (MLR) preceded by variable selection with SPA or GA were evaluated. An excellent predictive power (RMSEP = 0.05%) was obtained with MLR aided by GA.
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