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

Abstract

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 ApplicationsThis 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 highquality apple products.

Spoilage Monitoring and Early Warning for Apples in Storage Using Gas Sensors and Chemometrics

“…The responses of the C 2 H 4 sensor were 7 > 6 > 8 > 5 > 4 > 3 > 2 > 1 in sequence, with the highest content on the seventh day and the lowest content on the first day (Figure 4d). During the decaying process of apples from fresh to severe spoilage, the production of C 2 H 4 increased gradually, except that the content decreased slightly on the eighth day of treatment [24].…”

“…To simulate the conditions of apple storage in warehouses, nine simulated warehouses were set up in the laboratory [24]. Each simulated warehouse contained 30 fresh apple samples, and the gas sensing data and temperature and humidity data of the apple samples were collected for two days by the acquisition terminal prototype.…”

“…Based on the results obtained [24,40], it was found that with the intensification of the degree of spoilage, the release of VOC and C 2 H 4 gradually increased, while the release of CO 2 and the consumption of O 2 were generally reduced. This was because the metabolic capacity of apples decreases as they spoil, leading to a lower consumption of O 2 and a lower release of CO 2 .…”

Detection of Soybean-Derived Components in Dairy Products Using Proofreading Enzyme-Mediated Probe Cleavage Coupled with Ladder-Shape Melting Temperature Isothermal Amplification (Proofman–LMTIA)

“…Due to the continuous improvement in adulteration technology, it is difficult to identify the authenticities in dairy products using ordinary sensory methods or simple chemical methods [ 10 , 11 , 12 , 13 ]. DNA-based detection methods are highly sensitive and repeatable, and have been widely used in the adulteration of dairy products [ 14 ].…”

An Apple Fungal Infection Detection Model Based on BPNN Optimized by Sparrow Search Algorithm

“…The convenient and fast operation of the electronic nose means it plays an important role in the quality inspection of fruits. Guo et al collected the characteristic information of apples infected by different fungi through an electronic nose and used the BPNN pattern recognition model to classify and identify the information of apples; the results achieved were good [ 8 ]. Nouri et al used an electronic nose combined with the BPNN pattern recognition model to detect the fungal infection of pomegranates, and the recognition rate of pomegranate samples infected with mycoplasma was as high as 100% [ 9 ].…”