Identification of multiple symptoms of huanglongbing (HLB) by electronic nose (E-nose) based on the variability of volatile organic compounds (VOCs). (1) Headspace solid-phase microextraction gas chromatography–mass spectrometry-based analysis demonstrates the potential of E-nose for HLB detection. (2) The best feature extraction method and pattern recognition method were confirmed. (3) Optimal VOCs pick-up conditions are temp. 40°C, time 20 min, W 0.2 g, and vol 200 mL. (4) HLB can be detected based on E-nose (98.75% for HLB+, 97.50% for Zn deficiency and HLB+).

Abstract

Huanglongbing (HLB) is highly contagious and cannot be cured, resulting in a decrease in the commercial value of citrus. Timely detection and removal of diseased trees is an effective way to reduce losses. Complex symptoms of HLB, such as nutrient deficiencies often accompany HLB; as a result effective and accurate identification of HLB remains a challenge. In this study, 175 volatile organic compounds (VOCs) were detected in three categories (healthy, HLB, and Zn-deficiency) of samples using headspace solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME-GC/MS), highlighting the variability of VOCs present in different categories of samples. In order to simplify the testing steps and reduce the cost in practical agricultural production, a method based on electronic nose technology to collect VOCs from citrus leaves for HLB detection was proposed. Among them, limiting value features and linear discriminant analysis were identified as the best combination of feature extraction and pattern recognition methods. Multiple sets of comparison experiments were set up and the collection conditions of VOCs were optimized. The results showed that the best classification performance was achieved for a 0.2 g sample at a collection time of 20 min when the collection temperature was 40°C and the headspace volume was 200 mL. Four types of samples (healthy, HLB-positive, Zn-deficiency, Zn-deficiency and HLB-positive) were used for model reliability validation, with an accuracy of 97.79% for HLB samples for multiple symptoms (including HLB-positive and Zn-deficiency and HLB-positive) identification. In addition, the accuracy of samples with a combined effect of Zn-deficiency and HLB was 96.43%. The results show that the E-nose-based HLB detection method is conducive to suppressing the spread of HLB, which can ensure the quality of citrus products and reduce the economic loss to horticulturists, and has good practical value.