ABSTRACT

This study was conducted to establish a rapid and accurate method for identifying aflatoxin contamination in peanut oil. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with either partial least squares discriminant analysis (PLS-DA) or a support vector machine (SVM) algorithm were used to construct discriminative models for distinguishing between uncontaminated and aflatoxin-contaminated peanut oil. Peanut oil samples containing various concentrations of aflatoxin B1 were examined with an ATR-FTIR spectrometer. Preprocessed spectral data were input to PLS-DA and SVM algorithms to construct discriminative models for aflatoxin contamination in peanut oil. SVM penalty and kernel function parameters were optimized using grid search, a genetic algorithm, and particle swarm optimization. The PLS-DA model established using spectral data had an accuracy of 94.64% and better discrimination than did models established based on preprocessed data. The SVM model established after data normalization and grid search optimization with a penalty parameter of 16 and a kernel function parameter of 0.0359 had the best discrimination, with 98.2143% accuracy. The discriminative models for aflatoxin contamination in peanut oil established by combining ATR-FTIR spectral data and nonlinear SVM algorithm were superior to the linear PLS-DA models.

HIGHLIGHTS
  • A new identification model of aflatoxin contamination in peanut oil was proposed.

  • The identification accuracy of aflatoxin B1 in peanut oil based on SVM was 98.2143%.

  • SVM is more suitable than PLS-DA for identifying aflatoxin B1 in peanut oil.

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