Unleashing the power of visible‐ near infrared spectroscopy: predicting Golden Delicious apple enzyme activity

BACKGROUND: Enzymatic browning is a significant reaction in fruits that affects their color, appearance, and quality. The quality of apples, as a perishable product, is mainly influenced by the activity of two browning-related enzymes, polyphenol oxidase (PPO) and peroxidase (POD), during storage. Assessment of these enzymes using conventional methods is often destructive and time-consuming, preventing rapid and non-invasive monitoring of fruit quality. In this study, a visible-near infrared (visible-NIR) spectroscopy approach was developed to predict the enzymatic activity of PPO and POD in intact Golden Delicious apples, aiming to enable rapid, non-destructive evaluation and to identify the most informative spectral regions for industrial applications. RESULTS: Both support vector regression (SVR) and decision tree (DT) algorithms achieved high performance when combined with non-linear feature selection algorithms. The best performance, in terms of elapsed time and figure of merits, was achieved by combining particle swarm optimization (PSO) with SVR and DT. However, partial least squares (PLS) models outperformed both SVR-PSO and DT-PSO. CONCLUSIONS: This study is an advanced proof of concept of the use of visible-NIR spectroscopy - combined with variable selection and machine learning algorithms - for predicting browning-related enzyme activity in apples. The SVR and DT algorithms, coupled with metaheuristic strategies, reached lower performances than PLS, but the success of the variable selection strategy lays the groundwork for developing a miniaturized sensor for assessing apple quality during storage and controlling browning.