KCQI: Novel Index for Assessment of Comprehensive Quality of Kiwifruit During Shelf Life Using Hyperspectral Imaging and One-Dimensional Convolutional Neural Networks

Non-destructive assessment of kiwifruit quality is critical for postharvest preservation and grading. This paper proposes a novel quantitative evaluation method for the kiwifruit comprehensive quality index (KCQI) during shelf life, based on hyperspectral imaging (HSI) combined with a one-dimensional convolutional neural network (1D-CNN). Hyperspectral images of two kiwifruit cultivars were acquired at four shelf-life stages using an HSI system, and six quality parameters were measured as reference standards. Based on correlation and factor analyses, five key parameters-soluble solids content, firmness, L*, b*, and chroma-were selected to construct the KCQI. Three spectral band selection methods and three modeling algorithms were compared, with the competitive adaptive reweighted sampling (CARS)-1D-CNN model yielding the highest prediction accuracy (RP2 = 0.82, RMSEP = 0.26, RPD_P = 2.39). Subsequently, a spatial distribution map was generated to visualize the KCQI. These results demonstrate the potential of the HSI-1D-CNN approach for accurate postharvest quality monitoring and intelligent grading.