Application of near-infrared hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of protein content in single chickpea seed

Abstract

Evaluating the protein content of a single chickpea seed in a rapid, non-destructive, and precise manner is crucial for facilitating the breeding of high-protein chickpeas. This study explored the potential of near-infrared (NIR) hyperspectral imaging (HSI) to predict the protein content in a single chickpea seed. Eight varieties of chickpeas with different protein contents were subjected to NIR reflectance hyperspectral imaging in the spectral range of 900–2500 nm at two different positions of chickpea seed (micropyle down and micropyle up). The spectral data was correlated with the measured reference protein content of chickpea seed for building the partial least square regression (PLSR) and support vector machine regression (SVMR) models based on different spectral preprocessing techniques, with full spectrum and effective wavelengths selected using competitive adaptive reweighted sampling (CARS) and iteratively retaining informative variables (IRIV) algorithms. When using the full spectrum, the optimal protein prediction model was obtained using PLSR, which yielded correlation coefficient of prediction (R²ₚ) and root mean square error of prediction (RMSEP) values of 0.935 and 0.987, respectively, with external parameter orthogonalization (EPO)+standard normal variate (SNV) preprocessing for micropyle down position of chickpea seed. The IRIV selected wavelength with PLSR yielded the best model with R²ₚ and RMSEP of 0.947 and 0.861, respectively, at the micropyle down position of chickpea seed. Hence, the optimal prediction models were obtained using PLSR with EPO+SNV at the micropyle down position of chickpea seed.

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