Predictive modeling of antioxidant activity in Syzygium malaccense leaf extracts using image processing and machine learning

S. malaccense, from the Myrtaceae family, is used in traditional medicine and is rich in flavonoids and phenolic compounds. This study evaluated the antioxidant potential of S. malaccense leaf extracts and their fractions using DPPH and ABTS radical scavenging assays, Ferric Reducing Antioxidant Power (FRAP), and total phenolic content. Spectroscopic methods were used, and greyscale tones from the RGB channels of assay images were analyzed through machine learning (ML) models such as SVM, decision tree, Random Forest (RF), XGBOOST, LightGBM, and CatBoost. The performance of these models was assessed using determination coefficients (R2) and root mean square error (RMSE). XGBOOST and RF were the best performers, with R2 values ranging from 88.65 to 99.35% for training data and 60.12-95.50% for test data. GLM analysis showed that acetate solvent resulted in the highest FRAP values, while hexane had the lowest. Ethanol extraction yielded the highest ABTS values, and dichloromethane was best for DPPH. These modeling approaches using GLM, images, and ML algorithms show promise for measuring the antioxidant properties of plants. We built machine learning (ML) predictive models to determine antioxidant activity titers in plant extracts based on digital image processing (DIP) combined with reference analysis by spectroscopic method.Six ML models were generated, among which we can mention SVM, Decision Tree, Random Forest, XGBOOST, LightGBM, and CatBoost.The machine learning models showed high coefficients of determination.