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

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

The early identification of rotten potatoes is one of the most important challenges in a storage facility because of the inconspicuous symptoms of rot, the high density of storage, and environmental factors (such as temperature, humidity, and ambient gases). An electronic nose system based on an ensemble convolutional neural network (ECNN, a powerful feature extraction method) was developed to detect potatoes with different degrees of rot. Three types of potatoes were detected: normal samples, slightly rotten samples, and totally rotten samples. A feature discretization method was proposed to optimize the impact of ambient gases on electronic nose signals by eliminating redundant information from the features. The ECNN based on original features presented good results for the prediction of rotten potatoes in both laboratory and storage environments, and the accuracy of the prediction results was 94.70% and 90.76%, respectively. Moreover, the application of the feature discretization method significantly improved the prediction results, and the accuracy of prediction results improved by 1.59% and 3.73%, respectively. Above all, the electronic nose system performed well in the identification of three types of potatoes by using the ECNN, and the proposed feature discretization method was helpful in reducing the interference of ambient gases.

Biosynthesis of Scopoletin in Sweet Potato Confers Resistance against Fusarium oxysporum

Fusarium wilt is a severe fungal disease caused by Fusarium oxysporum in sweet potato. We conducted transcriptome analysis to explore the resistance mechanism of sweet potato against F. oxysporum. Our findings highlighted the role of scopoletin, a hydroxycoumarin, in enhancing resistance. In vitro experiments confirmed that scopoletin and umbelliferone had inhibitory effects on the F. oxysporum growth. We identified hydroxycoumarin synthase genes IbF6'H2 and IbCOSY that are responsible for scopoletin production in sweet potatoes. The co-overexpression of IbF6'H2 and IbCOSY in tobacco plants produced the highest scopoletin levels and disease resistance. This study provides insights into the molecular basis of sweet potato defense against Fusarium wilt and identifies valuable genes for breeding wilt-resistant cultivars.

IbINV Positively Regulates Resistance to Black Rot Disease Caused by Ceratocystis fimbriata in Sweet Potato

Black rot disease, caused by Ceratocystis fimbriata Ellis & Halsted, severely affects both plant growth and post-harvest storage of sweet potatoes. Invertase (INV) enzymes play essential roles in hydrolyzing sucrose into glucose and fructose and participate in the regulation of plant defense responses. However, little is known about the functions of INV in the growth and responses to black rot disease in sweet potato. In this study, we identified and characterized an INV-like gene, named IbINV, from sweet potato. IbINV contained a pectin methylesterase-conserved domain. IbINV transcripts were most abundant in the stem and were significantly induced in response to C. fimbriata, salicylic acid, and jasmonic acid treatments. Overexpressing IbINV in sweet potato (OEV plants) led to vigorous growth and high resistance to black rot disease, while the down-regulation of IbINV by RNA interference (RiV plants) resulted in reduced plant growth and high sensitivity to black rot disease. Furthermore, OEV plants contained a decreased sucrose content and increased hexoses content, which might be responsible for the increased INV activities; not surprisingly, RiV plants showed the opposite effects. Taken together, these results indicate that IbINV positively regulates plant growth and black rot disease resistance in sweet potato, mainly by modulating sugar metabolism.

Challenges and applications of volatile organic compounds monitoring technology in plant disease diagnosis

Biotic and abiotic stresses are well known to increase the emission of volatile organic compounds (VOCs) from plants. The analysis of VOCs emissions from plants enables timely diagnostic of plant diseases, which is critical for prompting sustainable agriculture. Previous studies have predominantly focused on the utilization of commercially available devices, such as electronic noses, for diagnosing plant diseases. However, recent advancements in nanomaterials research have significantly contributed to the development of novel VOCs sensors featuring exceptional sensitivity and selectivity. This comprehensive review presents a systematic analysis of VOCs monitoring technologies for plant diseases diagnosis, providing insights into their distinct advantages and limitations. Special emphasis is placed on custom-made VOCs sensors, with detailed discussions on their design, working principles, and detection performance. It is noteworthy that the application of VOCs monitoring technologies in the diagnostic process of plant diseases is still in its emerging stage, and several critical challenges demand attention and improvement. Specifically, the identification of specific stress factors using a single VOC sensor remains a formidable task, while environmental factors like humidity can potentially interfere with sensor readings, leading to inaccuracies. Future advancements should primarily focus on addressing these challenges to enhance the overall efficacy and reliability of VOCs monitoring technologies in the field of plant disease diagnosis.

Identifying Early-Stage Changes in Volatile Organic Compounds of Ceratocystis fimbriata Ellis & Halsted-Infected Sweet Potatoes (Ipomoea batatas L. Lam) Using Headspace Gas Chromatography-Ion Mobility Spectrometry

Ceratocystis fimbriata Ellis & Halsted is the pathogen causing black rot in sweet potatoes that can lead to flavor change and toxin release. This study detected the volatile organic compounds (VOCs) of C. fimbriata-infected sweet potatoes in the early stages using headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS). A total of 55 VOCs were identified, including aldehydes, alcohols, esters, ketones, and others. The content of aldehydes and ketones showed a decreasing trend, while alcohols and esters showed an increasing trend. An increase in infection time elevated the content of malondialdehyde (MDA) and pyruvate, while the starch content decreased, the content of soluble protein initially increased, then decreased, and the activities of lipoxygenase (LOX), pyruvate decarboxylase (PDC), alcohol dehydrogenase (ADH), and phenylalanine ammonia-lyase (PAL) increased. The changes in VOCs were closely related to the content of MDA, starch, pyruvate, and the activities of LOX, PDC, ADH, and PAL. Sweet potatoes showed a good discrimination effect by principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) from 0 to 72 h. Twenty-five differential VOCs could be used as early-stage characteristic compounds of C. fimbriata-infected sweet potatoes for early disease monitoring.

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

Potato is an important agricultural product, ranked as the fourth most common product in the human diet. Potato can be consumed in various forms. As customers expect safe and high-quality products, precise and rapid determination of the quality and composition of potatoes is of crucial significance. The quality of potatoes may alter during the storage period due to various phenomena. Soluble solids content (SSC) and pH are among the quality parameters experiencing alteration during the storage process. This study is thus aimed to assess the variations in SSC and pH during the storage of potatoes using an electronic nose and Vis/NIR spectroscopic techniques with the help of prediction models including partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), support vector regression (SVR) and an artificial neural network (ANN). The variations in the SSC and pH are ascending and significant. The results also indicate that the SVR model in the electronic nose has the highest prediction accuracy for the SSC and pH (81, and 92%, respectively). The artificial neural network also managed to predict the SSC and pH at accuracies of 83 and 94%, respectively. SVR method shows the lowest accuracy in Vis/NIR spectroscopy while the PLS model exhibits the best performance in the prediction of the SSC and pH with respective precision of 89 and 93% through the median filter method. The accuracy of the ANN was 85 and 90% in the prediction of the SSC and pH, respectively.