Fluorescence spectroscopy as tool for the geographical discrimination of coffees produced in different regions of Minas Gerais State in Brazil

被引:26
|
作者
Botelho, Bruno G. [1 ]
Oliveira, Leandro S. [1 ,2 ]
Franca, Adriana S. [1 ,2 ]
机构
[1] Univ Fed Minas Gerais, PPGCA, Av Antonio Carlos, BR-31270901 Belo Horizonte, MG, Brazil
[2] Univ Fed Minas Gerais, DEMEC, Av Antonio Carlos, BR-31270901 Belo Horizonte, MG, Brazil
关键词
Fluorescence spectroscopy; PARAFAC; NPLS-DA; UPLS-DA; Coffee; Geographical origin; ARTIFICIAL NEURAL-NETWORKS; STABLE-ISOTOPE ANALYSIS; PHENOLIC-COMPOUNDS; ORIGIN; ARABICA; CLASSIFICATION; CAFFEINE; BEANS;
D O I
10.1016/j.foodcont.2017.01.020
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
The designation of origin of high-value agricultural and food products has become increasingly relevant for the producers, since it allows the consumers to relate the singular characteristics of their preferred product to its respective provenance. Thus, coffee producers are in pursuit of ways to certify their products according to their authenticity pertaining provenance. Fluorescence spectroscopy was applied in order to develop a geographical discrimination model of coffees produced in Minas Gerais State, Brazil. PARAFAC, NPLS-DA and UPLS-DA were used in order to discriminate samples produced in four major production areas in Minas Gerais, namely Cerrado Mineiro (CM), Matas de Minas (MM), Norte de Minas (NM) e Sul de Minas (SM). The UPLS-DA presented the best results, with f-scores for CM and SM higher than 0.8, for both training and test sets, which indicates good classification. MM model presented a good f-score for the training set (1.000), but a poor result was obtained for the test set (0.250), mainly due to false positive samples. NM models presented an intermediary result, with a f-score of 0.913 for training set and 0.625 for test set. The proposed method requires a simple sample pre-treatment, it is fast and can be used for the determination of the geographical origin of coffees produced in Minas Gerais State. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:25 / 31
页数:7
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