Detection of Frost‐Damaged Maize Kernels for Food Quality Screening Using Hyperspectral Imaging and 2D‐COS‐Assisted Deep Learning

Abstract

ABSTRACT Maize is a crucial cereal crop susceptible to subzero temperature stress, which causes physicochemical damage to kernels and compromises their processing quality and food value. Traditional analytical methods are destructive, labor‐intensive, and unable to meet the requirements of rapid and high‐throughput quality screening in the food supply chain. This study established a nondestructive detection method combining hyperspectral imaging (900–1700 nm) and deep learning for the identification of frost‐damaged maize kernels. Multiple scattering correction (MSC) was verified as the optimal spectral preprocessing approach for improving spectral stability and classification performance. For feature extraction, Two‐Dimensional Correlation Spectroscopy (2D‐COS) effectively enhanced frost‐induced spectral variations by resolving overlapping and weak spectral responses, outperforming CARS and VCPA‐IRIV and extracting 432 characteristic bands associated with frost damage. The constructed 2D‐COS‐CNN model achieved a prediction accuracy of 0.9700 and a recall of 0.9647, with a calibration set accuracy of 0.9967, demonstrating excellent classification capability and reliable generalization performance. The integration of 2D‐COS‐based feature enhancement and CNN‐based nonlinear discrimination provides an effective strategy for rapid and nondestructive identification of frost‐damaged maize kernels, offering a reliable analytical tool for grain quality evaluation, damage‐level sorting, and processing suitability assessment in the food supply chain.