Dried fig is susceptible to infection by Aspergillus flavus, the major producer of the carcinogenic mycotoxins. This fruit may be contaminated by the fungus throughout the entire chain production, especially during natural sun-drying, post-harvest, industrial processing, storage, and fruit retailing. Correct management of such critical stages is necessary to prevent mould growth and mycotoxin accumulation, with temperature being one of the main factors associated with these problems. The effect of different temperatures (5, 16, 25, 30, and 37 °C) related to dried-fig processing on growth, one of the regulatory genes of aflatoxin pathway (aflR) and mycotoxin production by A. flavus, was assessed. Firstly, growth and aflatoxin production of 11 A. flavus strains were checked before selecting two strains (M30 and M144) for in-depth studies. Findings showed that there were enormous differences in aflatoxin amounts and related-gene expression between the two selected strains. Based on the results, mild temperatures, and changes in temperature during drying and storage of dried figs should be avoided. Drying should be conducted at temperatures >30 °C and close to 37 °C, while industry processing, storage, and retailing of dried figs are advisable to perform at refrigeration temperatures (<10 °C) to avoid mycotoxin production.

This study investigated the ability of an electronic nose system (E-nose) to detect early signs of fungal contamination in the red plum variety ‘Black Splendor’. We focused on identifying changes in volatile organic compounds (VOCs) that occur with decay. For this purpose, we compared two groups of plums: a control group (healthy plums) and a group inoculated with Monilinia laxa. VOCs from both groups were analyzed and quantified using gas chromatography/mass spectrometry (GC/MS). In parallel, E-nose signals were recorded at two key moments of fungal development: an early and an intermediate phase. The results revealed a strong correlation between E-nose signals and the aromatic profile characteristic of fungal contamination in plums. Linear discriminant analysis (LDA) models, developed from the E-nose data, achieved 100% differentiation between healthy and infected samples. Furthermore, these models discriminated with 100% accuracy between healthy plums and those with incipient contamination. These findings demonstrate that E-nose technology serves as a reliable, non-destructive approach for real-time assessment of plum quality throughout storage.

This study evaluates the application of an electronic nose (E-nose) system as a non-destructive tool for the early detection of Monilinia laxa infection in yellow nectarines (Prunus persica var. nectarine, cv. “Kinolea”) through the analysis of volatile organic compounds (VOCs). Two experimental groups were established: a control group of healthy fruit and a treatment group inoculated with the pathogen. The VOCs emitted by both groups were identified and quantified using gas chromatography-mass spectrometry (GC-MS). Simultaneously, the responses of the E-nose were recorded at three critical stages of fungal development: early, intermediate, and advanced. The electronic nose used consists of a set of 11 commercial metal oxide semiconductor (MOX) sensors. The signals from these sensors showed a strong correlation with the VOC profiles associated with fungal deterioration. Linear discriminant analysis (LDA) models based on E-nose data successfully distinguished between healthy and infected samples with 97% accuracy. Furthermore, the system accurately classified samples into three stages of contamination—control, early infection, and advanced infection—with 96% classification accuracy. These findings demonstrate that E-nose technology is an effective, rapid, and non-invasive method for the real-time monitoring of post-harvest fungal contamination in nectarines, offering significant potential for improving quality control during storage and distribution.

This study evaluated the performance of an electronic nose (E-nose) system for the early detection of fungal spoilage in yellow-fleshed peach (Prunus persica cv. ‘Carla’). Fruits were divided into two groups: one inoculated with Monilinia laxa 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.