Characteristic wavelengths optimization improved the predictive performance of near-infrared spectroscopy models for determination of aflatoxin B1 in maize

Abstract

A neoteric measure for quantitative assay of Aflatoxin B1 (AFB1) in maize based on an optimized feature model of near-infrared (NIR) spectroscopy was proposed in the work. A portable near-infrared spectroscopy system constructed by the group was employed to collect maize samples with varying degrees of mildew. The variable selection methods of interval variable iterative space shrinkage approach (IVISSA), iterative retained information variable (IRIV), and particle swarm optimization combined moving window (PSO-CMW) were introduced to perform feature selection on the pretreatment NIR spectra. The characteristic wavelength variables after screening were used to constitute support vector machine (SVM) and partial least squares (PLS) test model respectively to implement the measurement of AFB1 in maize, and the detection performance of the two types of models was compared. The results obtained showed that the overall performances of SVM models were higher than that of PLS models, and the SVM model based on the characteristic wavelength variables optimized by the PSO-CMW method had the most prominent generalization performance. The root mean square error of prediction (RMSEP) of the model was 3.5967 μg kg−1, the coefficient of determination (RP2) was 0.9707, and the relative prediction deviation (RPD) was 5.7538. The overall results demonstrate that the optimized features of NIR spectra can realize the on-site quick testing of the AFB1 in maize with high precision by constructing a nonlinear SVM detection model. This investigation provides an original approach for speedy quantitative detection of mycotoxins in cereals.