Non-Destructive Hyperspectral Imaging and Machine Learning-Based Predictive Models for Physicochemical Quality Attributes of Apples during Storage as Affected by Codling Moth Infestation

Abstract

The demand for high-quality apples remains strong throughout the year, as they are one of the top three most popular fruits globally. However, the apple industry faces challenges in monitoring and managing postharvest losses due to invasive pests during long-term storage. In this study, the effect of codling moth (CM) (Cydia pomonella [Linnaeus, 1758]), one of the most detrimental pests of apples, on the quality of the fruit was investigated under different storage conditions. Specifically, Gala apples were evaluated for their qualities such as firmness, pH, moisture content (MC), and soluble solids content (SSC). Near-infrared hyperspectral imaging (HSI) was implemented to build machine learning models for predicting the quality attributes of this apple during a 20-week storage using partial least squares regression (PLSR) and support vector regression (SVR) methods. Data were pre-processed using Savitzky–Golay smoothing filter and standard normal variate (SNV) followed by removing outliers by Monte Carlo sampling method. Functional analysis of variance (FANOVA) was used to interpret the variance in the spectra with respect to the infestation effect. FANOVA results showed that the effects of infestation on the near infrared (NIR) spectra were significant at p < 0.05. Initial results showed that the quality prediction models for the apples during cold storage at three different temperatures (0 °C, 4 °C, and 10 °C) were very high with a maximum correlation coefficient of prediction (Rp) of 0.92 for SSC, 0.95 for firmness, 0.97 for pH, and 0.91 for MC. Furthermore, the competitive adaptive reweighted sampling (CARS) method was employed to extract effective wavelengths to develop multispectral models for fast real-time prediction of the quality characteristics of apples. Model analysis showed that the multispectral models had better performance than the corresponding full wavelengths HSI models. The results of this study can help in developing non-destructive monitoring and evaluation systems for apple quality under different storage conditions.