Application of hyperspectral imaging to the automatic assessment of polyphenol oxidase and peroxidase enzymatic activity levels in bell pepper cultivars

Abstract

[EN]We explored the application of hyperspectral imaging (400–1100 nm) for non-destructive evaluation of peroxidase (POD) and polyphenol oxidase (PPO) enzymes responsible for browning processes in bell pepper (Capsicum annuum L.) cultivars. Several preprocessing techniques, including Standard Normal Variate (SNV), were applied to spectral data to enhance signal quality. Analysis using Partial Least Squares Regression (PLSR) showed that raw spectral data provided stronger correlations and lower prediction errors compared to processed. Discriminant spectral bands were identified using Support Vector Machine (SVM) combined with metaheuristic optimization, with SVM–Learning Automata (LA) resulting as the most effective wavelength selection strategy. Enzyme activities were then predicted using selected wavelengths with Artificial Neural Network (ANN) and PLSR models. Model performance was evaluated using the coefficient of determination (R2), Root Mean Square Error (RMSE), and Ratio of Performance to Deviation (RPD) on independent validation sets. ANN consistently outperformed PLSR, achieving high cultivar-specific R2 values for POD of 0.86, 0.93, and 0.98, for Orange, Yellow, and Red pepper varieties, respectively and PPO R2 values of 0.91, 0.97, and 0.99, for the same pepper cultivars. A combined “Total Model” integrating data from all cultivars further demonstrated robust generalization, with R2 values of 0.9082 for POD and 0.9604 for PPO. Findings confirm that hyperspectral imaging, coupled with an effective wavelength selection technique and ANN modeling provides a rapid, reliable, and robust approach for industrial evaluation of enzymatic activity in bell peppers. The proposed methodology offers significant potential for quality monitoring, process optimization, and large-scale application in industrial environments.