Detection of Early Damage in Kiwifruit Based on Near‐Infrared Technology

作者：Po Ma, Jun Sun, Sunli Cong, Chunxia Dai, Zhentao Cai, Kunshan Yao, Xin Zhou, Xiaohong Wu, Jingyi Liu · 发表于：Journal of Food Process Engineering · 年份：2025 · DOI：10.1111/jfpe.70130 · 被引用次数：5 · 研究领域：Spectroscopy and Chemometric Analyses、Spectroscopy Techniques in Biomedical and Chemical Research、Meat and Animal Product Quality

ABSTRACT The internal quality of kiwifruit directly affects its taste. During harvesting or transportation, kiwifruit sustained surface invisible damage due to collisions or pressure. To conduct non‐destructive detection of minor mechanical damage in kiwifruit, this study investigated two widely cultivated varieties in China. Near‐infrared spectroscopy was employed to collect spectral data from both intact samples and early‐damaged samples. These datasets were utilized to develop classification models aimed at assessing the extent of damage in kiwifruit. Initially, the first derivative method was applied as a spectral preprocessing technique. Three feature selection methods—Competitive Adaptive Reweighted Sampling (CARS), Genetic Algorithm (GA), and Bootstrap Soft Shrinkage (BOSS)—were implemented to extract characteristic wavelengths from the preprocessed spectra. Subsequently, classification models were constructed based on both the selected feature spectra and the original spectra. A novel Stacking ensemble model was developed using Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Extreme Gradient Boosting (XGBoost) as first‐level classifiers, with Logistic Regression serving as the second‐level classifier. By establishing training and testing datasets while comparing performance metrics against those of individual first‐level classifiers, the study evaluated the model's efficacy. The results indicated that the Stacking model consistently demonstrated high...