A Cost-Effective and Scalable Machine Learning Approach for Quality Assessment of Fresh Maize Kernel Using NIR Spectroscopy.

ABSTRACT: In fresh maize breeding, developing robust and accurate near-infrared (NIR) calibration models traditionally requires significant time, cost, and labor. To address these challenges, a novel machine learning approach is proposed using a Prediction-Correction Neural Network (PCNN) that enables effective modeling from small sample sets augmented with synthetic data based on NIR spectroscopy. For key quality traits such as amylopectin, protein, crude fiber, and total sugar, the PCNN achieved residual predictive deviation (RPD) values between 2.821 and 4.862, and coefficients of determination ( RV2$R_V^2$ ) ranging from 0.869 to 0.951, using an average of only 32 calibration samples. For sugars including fructose, glucose, and sucrose, the model yielded RPD >2 and RV2≥0.747$R_V^2 \ge 0.747$ wit

SUBMITTER: Shi J 

PROVIDER: S-EPMC12697840 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature