Abstract
Terahertz spectroscopy has been widely applied in the quantitative analysis of pesticides, however, it still encounters challenge pursuing high prediction accuracy in multi-component mixtures analysis with ultra-low concentration. Here, back propagation neural network (BPNN) was applied on the determination of ternary pesticide mixtures in wheat flour. By spectral pre-processing and model parameter optimization, high-quality spectra and complete network frame was achieved. On this basis, a novel wavelength selection method was presented and the most efficient peak width was given. Our method here achieved the optimal results, the correlation coefficient of prediction sets (RP) were 0.9913, 0.9948, 0.9923, and corresponding root mean square error (RMSE) were 0.0211%, 0.0176%, 0.0191%. More importantly, the concentration of pesticides in this study was extremely low compared with similar quantitative analysis based on terahertz spectroscopy, which can promote the application of this technology into actual production.
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