Rapid determination of acidity index of peanut during storage by a portable near-infrared spectroscopy system

Abstract

In the process of peanut storage, the acidity index can be used as one of the important evaluation criteria of peanut storage characteristics. This study proposed a rapid detection method of acidity index of peanuts during storage based on a portable near-infrared (NIR) spectroscopy system. The portable spectroscopy system was built to collect the NIR spectra of peanuts during storage. The Savitzky-Golay (SG) smoothing combined with multiple scattering correction (MSC) was used to preprocess the raw spectra and normalize it. Three model population analysis-based (MPA-based) wavelength variable selection methods (namely: variable combination population analysis (VCPA), iterative retained information variable (IRIV) and (VCPA-IRIV) were employed comparatively to optimize the characteristics of the pre-treated spectra. And support vector machine (SVM) models were established based on optimized features to achieve rapid detection of acidity index of peanuts during storage. The results obtained showed that compared with the full-spectrum SVM model, the prediction performance of the SVM model based on optimized features has been improved. In addition, the VCPA-SVM model obtained the best prediction performance. The root mean square error (RMSEP) of the model was 0.61 g·kg−1, the coefficient of determination of the prediction set (RP2) was 0.95, and the residual predictive deviation (RPD) was 4.31. The overall results demonstrate that the portable NIR spectroscopy system can realize the rapid detection of the acidity index during peanut storage, and the VCPA algorithm can efficiently obtain the characteristic wavelengths of the NIR spectra of peanuts during storage.