Autores
Evandro Bona, Izabele Marquetti, Jade Varaschim Link, Gustavo Yasuo Figueiredo Makimori, Vinícius da Costa Arca, André Luis Guimarães Lemes, Juliana Mendes Garcia Ferreira, Maria Brigida dos Santos Scholz, Patricia Valderrama, Ronei Jesus Poppi
Fecha de publicación
2017/3/1
Revista
LWT-Food Science and Technology
Volumen
76
Páginas
330-336
Editor
Academic Press
Descripción
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 γ 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 …
Artículos de Google Académico
E Bona, I Marquetti, JV Link, GYF Makimori… - LWT-Food Science and Technology, 2017