One-class classification of special agroforestry Brazilian coffee using NIR spectrometry and chemometric tools

被引：28

作者：

Baptista Manuel, Monis Neves ^{[1
]}

da Silva, Adenilton Camilo ^{[2
]}

Lopes, Gisele Simone ^{[2
]}

Dias Ribeiro, Livia Paulia ^{[1
]}

机构：

[1] Univ Integracao Int Lusofonia Afrobrasileira Unil, Nucleo Avancado Tecnol Analit NATA, Redencao, Ceara, Brazil

[2] Univ Fed Ceara UFC, Dept Quim Analit & Fis Quim, Lab Estudos Quim Aplicada LEQA, Fortaleza, Ceara, Brazil

来源：

FOOD CHEMISTRY | 2022年 / 366卷

关键词：

Agroforestry coffee; Classification; DD-SIMCA; NIRS; SPECTROSCOPY; ADULTERATION;

D O I：

10.1016/j.foodchem.2021.130480

中图分类号：

O69 [应用化学];

学科分类号：

081704 ;

摘要：

The near-infrared spectrometry combined with the one-class classification method was applied as quality control of the agroforestry-grown specialty coffee. A total of 34 samples were analyzed in this study. Spectral data were obtained using a NIR portable and different pre-treatment strategies for baseline correction were evaluated. Unsupervised pattern recognition (PCA and HCA) techniques were performed. The construction of the classification model was carried out using the DD-SIMCA algorithm with 19 samples acquired directly from producers that are recognized for the best quality control of the specialty type coffee. In order to test the model, 15 samples of non-specialty type, obtained in local markets, were evaluated. The classification model with the highest correct classification rate (CCR) scored 100% and 87% in the validation and test groups, respectively. The results demonstrated that the application of this strategy was successful in verifying the authenticity of specialty type agroforestry-grown coffee samples.

引用

页数：6

共 28 条

[1] Screening Malaysian edible bird's nests for structural adulterants and geographical origin using Mid-Infrared - Attenuated Total Reflectance (MIR-ATR) spectroscopy combined with chemometric analysis by Data-Driven - Soft Independent Modelling of Class Analogy (DD-SIMCA) [J].