Early Diagnosis of Pine Wood Nematode Disease in Pinus massoniana Based on Benzoic Acid Detection in Needles Using the Composite Nanosensor Lumino-1

Pine wilt disease is the most dangerous forestry disease in China, presenting significant challenges for early diagnosis and severe difficulties in prevention and control. Benzoic acid is regarded as a pivotal defense biomarker in pine trees responding to nematode infection; therefore, developing high-sensitivity detection methods for this compound is crucial for the early warning of the disease. In this study, a composite nanosensor (lumino-1) based on molecularly imprinted mesoporous silica-encapsulated luminol was fabricated via a "covalent bonding-template method," and its structure was characterized using transmission electron microscopy and infrared spectroscopy. Simultaneously, the variations in benzoic acid content in the needles of Pinus massoniana were determined via high-performance liquid chromatography at various time points following inoculation with pine wood nematodes. The fabricated nanosensor exhibited exceptionally high selectivity and sensitivity toward benzoic acid, with a limit of detection as low as 1.15×10⁻¹¹ M. Plant experiments indicated that the benzoic acid content in the needles of  Pinus massoniana infected with pine wood nematodes increased significantly and continuously starting from day 9; Significantly, the sensor elicited a distinct fluorogenic signature at this discrete stage, whereas cohorts subjected to mechanical trauma or analogous pathogenic stressors exhibited no such manifestation. This investigation not only introduces a pioneering, high-sensitivity diagnostic modality for the incipient detection of pine wilt disease but also substantiates the efficacy of benzoic acid as a pathognomonic biomarker from a pathophysiological perspective. Consequently, this work establishes a robust conceptual and empirical framework for the advancement of silvicultural monitoring technologies predicated on endogenous phyto-defensive signaling.