Biblioteca do Café
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Item Authentication of Specialty Coffees from the Fluminense Northwest and Caparaó Regions (Brazil) Using UV-Vis Spectroscopy and Synthetic Samples Partial Least Square Discriminant Analysis (SS-PLS-DA)(Sociedade Brasileira de Química, 2024-02-09) Caldeira, Gabriel R. F.; Costa, Tayná O.; Nascimento, Marcia H. C.; Corradini, Patricia G.; Filgueiras, Paulo R.; Ferreira, Daniel C.; Ferreira, Daniel C.Caparaó and the Fluminense northwest regions are nationally recognized by the important contribution on coffee production and exportation. Adulterations involving specialty coffees result in a decrease in the quality of the final product. However, obtaining many different samples from the same region is unfeasible in some cases, needing strategies to work with a limited number of samples for pattern recognition. Thus, this work is the first to use the construction of synthetic samples (SS) for analysis of coffees, and its objective is to identify adulterations in specialty coffees with bark, straw and low-quality beans, using UV-Vis spectroscopy, associated with chemometric methods. The synthetic samples partial least square discriminant analysis (SS-PLS-DA) showed better specificity, sensitivity and reliability rates than the Hard PLS-DA models. One-class methods (soft independent modeling of class analogy (SIMCA) and data driven soft independent modeling of class analogy (DD-SIMCA)) showed low specificity and reliability. The discriminant methods together with the synthetic samples proved to be adequate to identify adulterations in specialty coffees.Item Arabica coffee classification using near infrared spectroscopy and two-stage models(Embrapa Café, 2015) Marquetti, Izabele; Link, Jade Varaschim; Lemes, André Luis Guimarães; Scholz, Maria Brígida dos Santos; Valderrama, Patrícia; Bona, EvandroCoffee quality depends on the environment al conditions of the growing area. Factors such as climate, soil type and altitude, associated with agricultural practices, directly influence the chemical composition of the coffee beans. This study developed two - stage models to determine the geographic and genotypic origin of the grain. For the first stage, the partial least squares with discriminant analysis (PLS - DA) and principal component analysis (PCA) models were tested. Then, two artificial neural network (ANN) non - linear models, i.e. multilayer perceptron (MLP) and the radial - basis function (RBF), were evaluated as the second stage. Samples from four genotypes, cultivated in four different cities within Parana State in Brazil, were analyzed using near infrared spectroscopy (NIRS) in the 1100 to 2498 nm range. Three preprocessing techniques were tested on the spectra, i.e. multiplicative scatter correction (MSC); the Savitzky - Golay second - derivative and both combined. The best models were obtained with the spectra treated using MSC plus the second - derivative, with PLS - DA as first stage followed by the RBF network. For geographic and genotypic classification the sensitivity and specificity values of 100% were obtained for the training and test sets. The NIRS spectra presented better class separation when compared with the FTIR spectra used in a previous work. These results demonstrate that NIRS spectra, allied with the right pattern recognition techniques, can be used as a quick and efficient technique to distinguish green coffee samples both geographically and genotypically.