Find in Library

Abstract:: This study was undertaken to investigate the feasibility of near-infrared (NIR) hyperspectral imaging (1 000–2 500 nm) for non-destructive and quantitative prediction of protein content in peanut kernels. Partial least squares regression (PLSR) calibration model was established between the spectral data extracted from the hyperspectral images and the reference measured protein content values, with the coefficient of determination of prediction (RP2) of 0.885 and root mean square error of prediction (RMSEP) of 0.465%. Regression coefficients (RC) from PLSR analysis were used to identify the most essential wavelengths that had the greatest influence on changes in the protein content. Eight optimal wavelengths were selected by RC and its corresponding simplified RC-PLSR prediction model was also obtained, showing better performance with a higher RP2 of 0.870 and a lower RMSEP of 0.494%. The results indicate that hyperspectral imaging with PLSR analysis can be used as a rapid and non-destructive method for predicting protein content in peanut.Supported by the Natural Science Foundation of Guangdong Province (2017A030310558), China Postdoctoral Science Foundation (2017M612672), and Fundamental Research Funds for the Central Universities (2017MS067) Keywords:: Hyperspectral imaging, Peanut, Non-destructive, Protein content, Wavelength selection