Advancing freshness classification of freshly squeezed fruit juice via integrated multivariate analysis and machine...

In this study, multivariate analysis (MVA) and machine learning (ML), combined with UHPLC-HRMS, were used to evaluate the freshness of apples for juice production based on the analysis of freshly squeezed apple juice. A total of eight stages of apple juice, representing varying levels of freshness, were examined. Unsupervised MVA techniques, including PCA and Spearman rank correlation heatmap, revealed biochemical changes in the apple juice composition as rotten progressed. Fresh samples (J1-J3) exhibited similar chemical profiles and clustered closely, while samples in rotten stages (J6-J8) displayed dispersion, reflecting substantial metabolic alterations. With the Linear Support Vector Machine (SVM) model achieved 100 % classification accuracy in cross-validation, and demonstrating the high classification accuracy (91.3 %) for test samples. Fifteen key biomarkers associated with rotten were identified, providing robust evidence for freshness differentiation. This study demonstrates that multivariate analysis and machine learning enhances the accuracy of apple juice freshness classification, offering a robust approach with potential applications in food quality monitoring and safety assurance.