Analysis of Internal Quality Changes in Apples During Storage Using Near-Infrared Spectroscopy

This study aims to comprehensively evaluate the internal quality changes in apples during storage via near-infrared spectroscopy. Specifically, we focus on the performance differences in different apple varieties under diverse storage conditions and construct predictive models to determine the optimal storage period. By using near-infrared spectroscopy technology, 384 samples of four apple varieties (Xinjiang Akesu, Wafangdian Huangyuanshuai, Shandong Fuji, and Luochuan Fuji) were analyzed to monitor the changes in their soluble solid content (SSC) and fruit firmness within 7 weeks. The results indicated that, under cold storage conditions, SSC and firmness gradually decreased after peaking between the third and fifth weeks, while the opposite trend was observed at room temperature. To enhance the predictive accuracy of the model, several pretreatment methods were employed, including standardization, multiplicative scatter correction (MSC), and standard normal variate transformation (SNV). Additionally, competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) were utilized for band selection. These pretreatment and selection processes significantly reduced noise and improved model reliability. The best results were achieved with the Normalization-CARS-PLS model for the sugar content at 1 °C, which demonstrated an optimal predictive correlation coefficient (Rp) of 0.904 and a root mean square error of prediction (RMSEP) of 0.67. For firmness at room temperature, the Normalization-CARS-PLS model also showed an excellent performance, with an Rp of 0.823 and an RMSEP of 0.809. The study of the quality of four varieties of apples under three storage conditions in this paper was able to analyze the changes in the internal quality of apples and predict the optimal storage period of different varieties of apples, which is important for guiding the optimal storage period of apples before ripening.