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Qualitative and quantitative analysis of ochratoxin A contamination in green coffee beans using Fourier transform near infrared spectroscopy
Author(s) -
Taradolsirithitikul Panchita,
Sirisomboon Panmanas,
Dachoupakan Sirisomboon Cheewanun
Publication year - 2016
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.7859
Subject(s) - contamination , ochratoxin a , correlation coefficient , food science , mathematics , food contaminant , environmental science , chemistry , mycotoxin , statistics , biology , ecology
BACKGROUND Ochratoxin A ( OTA ) contamination is highly prevalent in a variety of agricultural products including the commercially important coffee bean. As such, rapid and accurate detection methods are considered necessary for the identification of OTA in green coffee beans. The goal of this research was to apply Fourier transform near infrared spectroscopy to detect and classify OTA contamination in green coffee beans in both a quantitative and qualitative manner. RESULTS PLSR models were generated using pretreated spectroscopic data to predict the OTA concentration. The best model displayed a correlation coefficient ( r ) of 0.814, a standard error of prediction ( SEP and bias of 1.965 µg kg −1 and 0.358 µg kg −1 , respectively. Additionally, a PLS‐DA model was also generated, displaying a classification accuracy of 96.83% for a non‐ OTA contaminated model and 80.95% for an OTA contaminated model, with an overall classification accuracy of 88.89%. CONCLUSION The results demonstrate that the developed model could be used for detecting OTA contamination in green coffee beans in either a quantitative or qualitative manner. © 2016 Society of Chemical Industry