Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/326209
Type: Congresso
Title: Support Vector Machines In Tandem With Infrared Spectroscopy For Geographical Classification Of Green Arabica Coffee
Author: Bona
Evandro; Marquetti
Izabele; Link
Jade Varaschim; Figueiredo Makimori
Gustavo Yasuo; Arca
Vinicius da Costa; Guimardes Lemes
Andre Luis; Garcia Ferreira
Juliana Mendes; dos Santos Scholz
Maria Brigida; Valderrama
Patricia; Poppi
Ronei Jesus
Abstract: The coffee is an important commodity to Brazil. Species, climate, genotypes, cultivation practices and industrialization are critical to final quality of the beverage. Thus, the development of analytical methods for coffee authentication is important to ensure the origin of the bean. The purpose of this study was to develop a methodology for geographical classification of different genotypes of arabica coffee using infrared spectroscopy and support vector machines (SVM). The spectra were collected in the range of near infrared (NIRS) and mid infrared (FTIR). For the data analysis, a SVM was built using radial basis as kernel function and the one-versus-all multiclass approach. The C and gamma parameters of SVM were optimized using the genetic algorithm. With the application of the NIRS-SVM approach all test samples were correctly classified with a sensitivity and specificity of 100%, while FTIR-SVM had a slightly lower performance. Therefore, it was possible to confirm that infrared spectroscopy is a fast and effective method for geographic certification with little sample preparation, and without the production of chemical wastes. Furthermore, the SVM can be a chemometric alternative in tandem with infrared spectroscopy for another classification problems. (C) 2016 Elsevier Ltd. All rights reserved.
Subject: Machine Learning
Near Infrared
Mid Infrared
Genetic Algorithm
Editor: Elsevier Science BV
Amsterdam
Rights: fechado
Identifier DOI: 10.1016/j.lwt.2016.04.048
Address: https://www.sciencedirect.com/science/article/pii/S0023643816302328
Date Issue: 2017
Appears in Collections:Unicamp - Artigos e Outros Documentos

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