Determination of aflatoxin B1 value in corn based on Fourier transform near-infrared spectroscopy: Comparison of optimization effect of characteristic wavelengths
2022
Liu, Tao | He, Jiarong | Yao, Wenzhe | Jiang, Hui | Chen, Quansheng
Aflatoxin B₁ (AFB₁) is an important cause of human liver cancer. This study proposes a quantitative detection method for the AFB₁ in corn based on Fourier transform near-infrared (FT-NIR) spectroscopy technology. First, we acquired spectral data of corn samples with different degrees of mildew using FT-NIR spectrometer; then, we used the ant colony optimization (ACO) and the NSGA-Ⅱ algorithms to optimize the characteristic wavelengths of the spectra after SNV treatment respectively; Finally, the back propagation neural network (BPNN) models were established using the optimized characteristic wavelengths to realize the accurate detection of the AFB₁ in corn. The results obtained showed that the prediction performance of the BPNN model established using the four characteristic wavelength variables optimized by NSGA-Ⅱ algorithm is the best, and the best NSGA-Ⅱ-BPNN model's correlation coefficient of prediction (RP) is 0.9951, the root mean square error of prediction (RMSEP) is 1.5606 μg ⋅ kg⁻¹. The overall results demonstrate that the quantitative detection of AFB₁ in corn by FT-NIR technique is feasible; in addition, the NSGA-Ⅱ algorithm has its unique advantages in the optimization of spectral characteristics, and it can obtain characteristic wavelength variables with strong pertinence and a small number.
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