Early Detection of Monilinia laxa in Yellow-Fleshed Peach Using a Non-Destructive E-Nose Approach

This study evaluated the performance of an electronic nose (E-nose) system for the early detection of fungal spoilage in yellow-fleshed peach (<i>Prunus persica</i> cv. 'Carla'). Fruits were divided into two groups: one inoculated with <i>Monilinia laxa</i> and a non-inoculated control. Volatile organic compounds (VOCs) were identified and quantified via gas chromatography-mass spectrometry (GC-MS), while E-nose sensor responses were recorded at two post-inoculation stages: early and middle decay. A strong correlation was observed between E-nose biosensor signals and VOC profiles associated with fungal development. Linear discriminant analysis (LDA) models based on E-nose data successfully classified samples into three categories: healthy, early decay, and middle decay. Recognition rates exceeded 97% across all external validations, with 100% accuracy for early-stage infections. These results demonstrate the potential of E-nose technology as a rapid, non-destructive tool for monitoring peach quality during storage.