Early Discrimination and Prediction of C. fimbriata-Infected Sweetpotatoes during the Asymptomatic Period Using Electronic Nose

Abstract

Sweetpotato is prone to disease caused by C. fimbriata without obvious lesions on the surface in the early period of infection. Therefore, it is necessary to explore the possibility of developing an efficient early disease detection method for sweetpotatoes that can be used before symptoms are observed. In this study, sweetpotatoes were inoculated with C. fimbriata and stored for different lengths of time. The total colony count was detected every 8 h; HS-SPME/GC–MS and E-nose were used simultaneously to detect volatile compounds. The results indicated that the growth of C. fimbriata entered the exponential phase at 48 h, resulting in significant differences in concentrations of volatile compounds in infected sweetpotatoes at different times, especially toxic ipomeamarone in ketones. The contents of volatile compounds were related to the responses of the sensors. E-nose was combined with multiple chemometrics methods to discriminate and predict infected sweetpotatoes at 0 h, 48 h, 64 h, and 72 h. Among the methods used, linear discriminant analysis (LDA) had the best discriminant effect, with sensitivity, specificity, precision, and accuracy scores of 100%. E-nose combined with K-nearest neighbours (KNN) achieved the best predictions for ipomeamarone contents and total colony counts. This study illustrates that E-nose is a feasible and promising technology for the early detection of C. fimbriata infection in sweetpotatoes during the asymptomatic period.

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

“…The results of this research (about acidity) were consistent with the reports by Huang and Gu [ 60 ], who used a sensor array and machine learning (SVR) to distinguish pork-fake beef with an accuracy of 92%. In another research, Wu et al [ 61 ] utilized an E-nose to detect and predict the contamination of sweet potato with C. Fimbriata with a respective accuracy of 65 and 66% for PLS and PCR, which is very close to the results of this research on the SSC.…”

Biosynthesis of Scopoletin in Sweet Potato Confers Resistance against Fusarium oxysporum

“…Healthy sweet potatoes accumulate negligible amounts of ipomeamarone but upon infection by Coltricia fimbriata, Rhizopus stolonifer, or F. solani, the storage roots of sweet potatoes can accumulate substantial levels of ipomeamarone. − Ipomeamarone possesses high toxicity, and consumption of affected sweet potatoes can lead to fatalities in humans and livestock . Therefore, considering the food nature of sweet potatoes, ipomeamarone is unsuitable as a phytoprotectant.…”

Early Identification of Rotten Potatoes Using an Electronic Nose Based on Feature Discretization and Ensemble Convolutional Neural Network

“…In the past, the traditional method for detecting rotten potatoes was manual inspection, which is labor-intensive and inefficient. Recently, there has been a lot of research focusing on identifying rotten potatoes automatically, including using computer vision [10][11][12], spectral technology [13,14], GC-MS [15,16], and GC-IMS [17,18]. However, rotten potatoes are always hidden in piles when potatoes are stacked together.…”