Establishment of a general prediction model for protein content in various varieties and colors of peas using visible-near-infrared spectroscopy

Abstract

Non-destructive and swift detection of protein content in peas holds significant importance for screening superior germplasm resources. This study aimed to construct a general detection model of pea protein content. Spectral detection data of pea proteins were analyzed across three bands: 400 ∼ 982 nm, 982 ∼ 2520 nm, and 400 ∼ 2520 nm. The spectral data were preprocessed by 16 different methods. The successive projections algorithm (SPA) and uninformative variables elimination (UVE) algorithms were used to screen characteristic wavelengths. Pea protein content prediction models were established by using partial least squares regression (PLSR) and extreme learning machine (ELM). Following analysis, the best modeling band was 400 ∼ 982 nm. The optimal prediction model was multiplicative scatter correction (MSC)-SPA-ELM. The correlation coefficients for the calibration and validation sets were 0.8851 and 0.8595, respectively, with root mean square errors of 0.8933 g/100 g and 1.0565 g/100 g.The ratio of error range (RER) was 9.23. These results suggest that this study offers a viable solution for non-destructive and rapid pea protein detection across diverse regions, varieties, and colors.