No abstract

Early accurate detection of crop disease is extremely important for timely disease management. Huanglongbing (HLB), one of the most destructive citrus diseases, has brought about severe economic losses for the global citrus industry. The direct strategies for HLB identification, such as quantitative real-time polymerase chain reaction (qPCR) and chemical staining, are robust for the symptomatic plants but powerless for the asymptomatic ones at the early stage of affection. Thus, it is very necessary to develop a practical method used for the early detection of HLB. In this study, a novel method combining ultra-high performance liquid chromatography/mass spectrometry (UHPLC/MS)-based nontargeted metabolomics and machine learning (ML) was developed for conducting the early detection of HLB for the first time. Six ML algorithms were selected to build the classifiers. Regularized logistic regression (LR-L2) and gradient-boosted decision tree (GBDT) outperformed with the highest average accuracy of 95.83% to not only classify healthy and infected plants but identify significant features. The proposed method proved to be practical for early detection of HLB, which tackled the shortcomings of low sensitivity in the conventional methods and avoid the problems such as lighting condition interference in spectrum/image recognition-based ML methods. Additionally, the discovered biomarkers were verified by the metabolic pathway analysis and content change analysis, which was remarkably consistent with the previous reports.

Mango is a tropical fruit with global demand as a result of its high sensory quality and nutritional attributes. Improving fruit quality at the consumer level could increase demand, but fruit quality is a complex trait requiring a deep understanding of flavor development to uncover key pathways that could become targets for improving sensory quality. Here, a pathway-based metabolomics (untargeted and targeted) approach was used to explore biosynthetic mechanisms of key flavor compounds with five core metabolic pathways (butanoate metabolism, phenylalanine biosynthesis and metabolism, terpenoid backbone biosynthesis, linoleic and linolenic acid pathway, and carbon fixation and sucrose metabolism) in three mango cultivars. The relationships between flavor precursors and flavor compounds were identified using correlation analysis. With these novel strategies, differentially regulated metabolic flux through the pathways was first elucidated, demonstrating possible mechanisms of key flavor formation and regulation in mango fruits.