The ability of an electronic nose system to differentiate alterations in the volatile organic compound (VOCs) profile caused by early spoilage of ‘Golden Delicious’ apples due to Penicillium expansum was thoroughly evaluated. Three strains of this mould species (M639, pe2280 and pe2278) were individually used to inoculate ‘Golden Delicious’ apples, resulting in four different batches: (i) M639, (ii) pe2280, (iii) pe2278 and (iv) a control group (inoculated with sterile water). Volatile compounds were identified and quantified by gas chromatography/mass spectrometry (GC/MS), and signals from an electronic nose system (E-nose) were recorded for each batch with three different growth diameters: early stage (10 mm), middle stage (20 mm) and late stage (40 mm). A strong correlation was observed between the responses from multiple E-nose biosensors and the aromatic profile associated with fungal alterations in apples. Using linear discriminant analysis (LDA) models based on E-nose sensor data from the specified batches, we successfully differentiated healthy and infected samples, specifically three distinct groups: control, M639+pe2280 and pe2278. Remarkably, the recognition rates for total external samples exceeded 87% and reached 97% for samples in the early stage of fungal infection. These results validate the online capability of E-nose technology for the non-destructive evaluation of apple storage processes.
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