Impact of Storage on Nondestructive Detectability of Codling Moth Infestation in Apples

Abstract

Highlights Codling moth responds differently to low temperature conditions during the supply chain. The length of storage and temperature determine the sensitivity of the codling moth to detection. Hyperspectral imaging sensor detectability of codling moth increased with temperature. Abstract. 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. Keywords: Apples, Codling moth, Detectability, Machine learning, Nondestructive method.