Portable Mass Spectrometry Approach Combined with Machine Learning for Onsite Field Detection of Huanglongbing Disease.

Abstract

Huanglongbing (HLB) is one of the most serious citrus diseases in the world. Rapid, onsite, and accurate field detection of HLB is a challenging task in analytical science for a long time. Herein, we have developed a novel HLB detection method that combines headspace solid phase microextraction with portable gas chromatography-mass spectrometry (PGC-MS) approach for onsite field detection of volatile metabolites of citrus leaves. Detectability and characteristics of HLB-affected metabolites from leaves were validated, and the important biomarkers were verified by authentic compounds. A machine learning approach based on random forest algorithm is established to model the volatile metabolites from healthy, symptomatic, and asymptomatic citrus leaves. In this work, a total of 147 citrus leaf samples were analyzed. Analytical performances of this newly developed method were investigated by in-field detection of various volatile metabolites. Results demonstrated limits of detection and quantification of 0.04-0.12 and 0.17-0.44 ng/mL for different metabolites, respectively. Linear calibration curves of various metabolites were established over a concentration dynamic range of at least three orders ( R 2 > 0.96). Good reproducibility was obtained for intraday (3.0-17.5%, n = 6) and interday precision (8.7-18.2%, n = 7). This new HLB field detection method provides a rapid detection with 6 min for each sample via a simple optimized procedure, including onsite sampling, PGC-MS analysis, and data process and provides a high accuracy (93.3%) for simultaneous identification of healthy, symptomatic, and asymptomatic trees. These data support the use of this new method for reliable field detection of HLB. Furthermore, metabolic pathways of HLB-affected metabolites were also proposed. Overall, our results not only provide a rapid and onsite field HLB detection method but also provide valuable information for understanding metabolic change of HLB infection.

Plant phenotyping relevance

携帯型GC-MSと機械学習による柑橘葉の揮発性代謝物測定・HLB感染状態判定を中心に、検出性能と再現性を検証した植物表現型取得法である。

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