Finding Hidden Huanglongbing using an Electronic Nose.

Abstract

Background and Objective: The Huanglongbing (HLB) is one of the most destructive diseases affecting citrus worldwide. A major challenge in its management is its ability to remain asymptomatic for extended periods, delaying timely detection and control. This study aims to develop and evaluate a compact electronic nose (e-nose) system equipped with metal-oxide semiconductor (MOS) sensors for the early detection of Candidatus Liberibacter asiaticus infection in citrus leaves through Volatile Organic Compound (VOC) analysis under field-like conditions. Materials and Methods: A total of 454 Purworejo Siamese citrus leaf samples were collected from two orchards. The infection status of each sample was confirmed using conventional Polymerase Chain Reaction (PCR) prior to headspace VOC extraction. The cross-sensitive MOS sensor array converted VOC interactions into electrical signals, which were subsequently preprocessed, feature-extracted and analyzed using machine learning pipelines. Model selection and optimization were performed on baseline-shifted data. Results: A stratified 5-fold cross-validation using the Extra Trees algorithm successfully discriminated between PCR-confirmed Candidatus Liberibacter asiaticus-infected leaves and healthy controls, achieving an accuracy of 84.57% (95% confidence interval: 80.98%-88.15%). These results were obtained under field-like conditions and were further validated using headspace gas chromatography-mass spectrometry (HS-GC/MS), which revealed distinct VOC profiles for each group. Conclusion: This study demonstrates the potential of the electronic nose (e-nose) as a rapid, in-field screening tool capable of prioritizing samples for laboratory confirmation, thereby supporting effective HLB management.

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