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Qualitative and quantitative analysis of ochratoxin A contamination in green coffee beans using Fourier transform near infrared spectroscopy
Author(s)
Date Issued
March 15, 2017
Type
Article
Abstract
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.
Citation
Journal of the Science of Food and Agriculture, 97(4), 1260-1266, 2017