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

Area of Science:

• Agricultural Science
• Food Science
• Biosensor Technology

Background:

• Fungal spoilage significantly impacts peach quality and shelf-life.
• Early detection of spoilage is crucial for reducing food waste and ensuring consumer safety.
• Traditional methods for detecting spoilage are often destructive or time-consuming.

Purpose of the Study:

• To evaluate the efficacy of an electronic nose (E-nose) system for the early detection of fungal spoilage in yellow-fleshed peaches.
• To correlate E-nose sensor responses with volatile organic compound (VOC) profiles indicative of fungal decay.
• To assess the classification accuracy of E-nose data in distinguishing between healthy and decayed peaches at different spoilage stages.

Main Methods:

• Yellow-fleshed peaches (cv. 'Carla') were inoculated with *Monilinia laxa* or left as a control.
• Volatile organic compounds (VOCs) were analyzed using gas chromatography-mass spectrometry (GC-MS).
• E-nose sensor responses were recorded at early and middle decay stages post-inoculation.
• Linear discriminant analysis (LDA) was employed to build classification models based on E-nose data.

Main Results:

• A strong correlation was found between E-nose signals and VOCs associated with fungal growth.
• LDA models achieved over 97% recognition rates for classifying peach samples.
• The E-nose system demonstrated 100% accuracy in identifying early-stage fungal infections.
• Successful classification into healthy, early decay, and middle decay categories was achieved.

Conclusions:

• E-nose technology is a viable tool for the rapid, non-destructive detection of fungal spoilage in peaches.
• The system shows high potential for real-time quality monitoring of peaches during storage.
• This technology can aid in minimizing post-harvest losses and improving food safety standards.