IMPACT OF STORAGE ON NONDESTRUCTIVE DETECTABILITY OF CODLING MOTH INFESTATION IN APPLES

Different conditions during cold storage of codling moth (CM)-infested apples lead to different infestation levels, which affect overall product quality. In this study, the effects of postharvest storage duration (up to 20 weeks) and temperature (0°C, 4°C, and 10°C) on the detectability of CM-infested apples were investigated using the near-infrared (NIR) hyperspectral imaging (HSI) method (900-1700 nm). Fresh organic Gala apples were obtained directly from a commercial market and stored in a controlled environmental chamber at three temperatures for 20 weeks in two groups: control and CM-infested samples. Every four weeks, NIR hyperspectral images in reflectance mode were acquired directly for each set of samples. Machine learning models for the classification of CM-infested apples were developed based on the HSI data. The results revealed that storage duration and temperature had a significant effect on the performance of the classification models in the detection of CM-infested and control apples. Overall, the best classification rates were obtained for apples stored for 16 weeks, with accuracies of 97%, 94%, and 100% at storage temperatures of 0°C, 4°C, and 10°C, respectively. This study is critical for determining the effectiveness of HSI as a nondestructive method for sorting apples into classes based on CM infestation when stored under different conditions and duration, as in this study.