Identification of the apple spoilage causative fungi and prediction of the spoilage degree using electronic nose

Apple is resistant to storage, but it is susceptible to fungal infection during transportation and storage, resulting in serious losses after harvest. A convenient and nondestructive monitoring method for fungi-inoculated apples was proposed in this research. Four dominant spoilage fungi, including Aspergillus niger, Penicillium expansum, Penicillium chrysogenum, and Alternaria alternata, were inoculated on apple samples. The volatile information of samples with different degrees of spoilage was obtained by gas sensors. The pattern recognition methods were compared to classify the fungi and degrees of spoilage. Back propagation-artificial neural networks (BP-ANN) had the best identification model result with the highest recognition rates of 95.62 and 99.58% for fungi and spoilage degrees, respectively. The variable selection methods were employed, and variables of the gas sensors data for the prediction of apple spoilage area were optimized. The best prediction models of Aspergillus niger, Penicillium expansum, Penicillium chrysogenum, and Alternaria alternata were 0.854, 0.939, 0.909, and 0.918, respectively. The results show that the gas sensors can be used as a nondestructive technique in apple fungi infection evaluation. This proposed fruit spoilage detection technology is expected to provide a reference for the early detection of apple spoilage to promote food quality and safety inspection. Practical Applications This research used gas sensors to identify the four main spoilage fungi of apples and predicted the spoilage degree of apples using established prediction models. The apple spoilage detection method adopted in this research provides a reference for the early detection of fruit spoilage, which is helpful for apple storage and reduces the economic loss caused by corruption. It is an important measure to help ensure the economic benefits of apple and provide consumers with a large number of high-quality apple products.

Automated summary

This research utilized gas sensors to identify the four main spoilage fungi of apples and predicted the spoilage degree of apples with established prediction models. The apple spoilage detection method adopted in this research provides a reference for the early detection of fruit spoilage, facilitating apple storage and reducing economic loss caused by corruption. It is an important measure to ensure the economic benefits of apples and provide consumers with a large number of high-quality apple products.