A rapid method based on Fourier transform infrared (FTIR) spectroscopy with multivariate data analysis was developed to identify and quantify aflatoxin contamination in peanuts. This technique was proposed over the chromatographic method due to faster, practical, and less reagent consumption as it is considered a nondestructive method. The spectra of samples were scanned using an FTIR spectrophotometer with attenuated total reflectance in the mid-infrared region (4,000−475 cm−1). Chemometric techniques for classification purposes using principal component analysis (PCA) and multivariate calibrations of partial least square (PLS) regression were used to establish the quantitative model and predict the levels of aflatoxin contamination in peanuts. PCA was successfully applied to classify the whole peanuts, those without shells, and those without shells and testa, based on PC1 and PC2 score plots. The determination of aflatoxin contamination levels using high-performance liquid chromatography coupled with a fluorescence detector (HPLC-FD) showed that 4 out of 20 samples (20%) resulted in 1.272–9.585 μg kg−1 for AFB1 and 0.448–2.943 μg kg−1 for AFB2. The method performance of aflatoxin analysis by HPLC-FD was validated by high coefficients of determination (R2 > 0.999) and low coefficients of variation (CV = 1.68−2.94%). Furthermore, PLS regression based on FTIR spectra at a selected wavenumber region (1,800−800 cm−1) provided satisfactory prediction of aflatoxin contamination in peanut samples, including aflatoxin B1 [R2 = 0.9995; root mean square error of calibration (RMSEC) = 0.1180; root mean square error of prediction (RMSEP) = 0.3000)] and aflatoxin B2 (R2 = 0.9999; RMSEC = 0.0119; RMSEP = 0.0595). Therefore, the developed method can be an analytical tool to identify the occurrence of aflatoxins in peanuts in the market.
Key words: Groundnut, FTIR-ATR, Mycotoxin, Principal Component Analysis, Partial Least Square.
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