Vibrational Spectroscopy Predicts Antimicrobial Activity of Orange Peels: A Case Study on Batch-to-Batch Variation in...

Citrus peel is a major agro-industrial by-product rich in bioactive metabolites, but batch-to-batch variation in antimicrobial activity limits its consistent valorization. This study developed a rapid machine learning-assisted spectroscopic approach to predict the antimicrobial activity of orange peel by-products. Fifteen citrus cultivars, including 10 sweet oranges and 5 mandarins, were analyzed using attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) and Raman spectroscopy. Antimicrobial activity was classified into high- and low-activity groups, and five classification models were developed, including support vector machine, k-nearest neighbor, decision tree, naïve Bayes, and bagged tree algorithms. ATR-FTIR spectroscopy showed stronger predictive performance than Raman spectroscopy. The best ATR-FTIR model was SVM, achieving an accuracy of 0.91, sensitivity of 0.87, specificity of 0.95, precision of 0.93, and F1-score of 0.90. Its high specificity indicates a low risk of falsely selecting weak antimicrobial batches, which is critical for practical screening. In contrast, Raman-based models performed less effectively, with the highest accuracy of 0.59 from BT and the highest F1-score of 0.58 from SVM; the latter showed a sensitivity of 0.83 and a specificity of 0.23. Variable importance analysis identified spectral regions associated with functional groups related to phenolics, flavonoids, and carbohydrates as important contributors to antimicrobial prediction. Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) further supported the spectral interpretation by showing that flavonoids, phenolic acids, and related metabolites were enriched in high-activity samples. These findings demonstrate that vibrational spectroscopy combined with machine learning can provide a rapid and scalable screening strategy for evaluating antimicrobial potential in orange peel by-products. PRACTICAL APPLICATIONS: This study provides a rapid screening approach to evaluate the antimicrobial potential of orange peel by-products using vibrational spectroscopy and machine learning. The method could help citrus-processing and food industries identify promising batches of citrus peel for value-added applications, such as natural antimicrobial ingredients or food safety-related product development, while reducing reliance on time-consuming bioassays.