Quantitative detection of fatty acid value during storage of wheat flour based on a portable near-infrared (NIR) spectroscopy system

Fatty acid value is one of the important indexes to judge wheat flour quality during storage. A portable near-infrared (NIR) spectroscopy system was developed established for the quantitative detection of fatty acids in wheat flour during storage. First, the portable NIR spectroscopy system was used to obtain the spectra of wheat flour in different storage periods, and the spectra acquired were corrected by standard normal variate (SNV) method. Then, variable combination population analysis (VCPA) was used to optimize the characteristic wavelength variables of the SNV corrected spectra, and the characteristic wavelength variables highly related to the fatty acid value were determined. Finally, extreme learning machine (ELM) was employed to construct quantitative detection models based on different characteristic wavelength variables to achieve quantitative detection of fatty acid value. In the process, the effects of the Sigmoidal and Sine activation functions on the performance of the ELM model were compared. The experimental results showed that in this study, the two activation functions have little effect on the generalization performance of the ELM model. The ELM models based on different input characteristic wavelength variables all showed good prediction accuracy and stability when predicting independent samples in the validation set, and the mean of RP from the ELM model in each mode was above 0.96. The overall results demonstrate that it is feasible to use the portable NIR spectroscopy system built combined with appropriate chemometric methods to achieve quantitative determination of fatty acid values in wheat flour during storage. In addition, the VCPA algorithm has a good application prospect in the optimization of NIR spectral characteristic wavelengths.