Abstract Sensor-based detection of pests and pathogens in a high throughput and non-destructive manner is essential for mitigating crop loss. Infrared (IR) sensors in the form of vibrational spectroscopy provide both biochemical information about disease, as well as a large number of variables for chemometrics. This approach is highly adaptable to most biological systems including interactions between plants and their environments. Fast-acting necrotrophic fungal pathogens present a specific group of pests with adverse effects on food production and supply and are therefore pertinent to food security. Botrytis cinerea and Solanum lycopersicum are models for the study of fungal and crop biology respectively. Herein we use a compact mid-IR spectrometer with attenuated total reflection (ATR) attachment to measure the plant-microbe interaction between S. lycopersicum and B. cinerea on leaves, in vivo of intact plants. Chemometric models including exploratory principal component analysis (PCA) solely, and as a classifier in combination with linear discriminant analysis (PCA-LDA) are applied. Fingerprint spectra (1800-900 cm-1) were excellent discriminators of plant disease in both visually symptomatic as well pre-symptomatic plants. Major biochemical alterations in leaf tissue as a result of infection are discussed. Diagnostic potential for automatic decision-making platforms is shown by high accuracy rates of 100% for detecting plant disease at various stages of progression.

中文翻译：

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衰减全反射傅里叶变换红外光谱结合化学计量学直接检测感染灰葡萄孢的番茄植株的症状前后变化

摘要 以高通量和非破坏性方式基于传感器检测害虫和病原体对于减少作物损失至关重要。振动光谱形式的红外 (IR) 传感器既提供有关疾病的生化信息，也提供大量化学计量学变量。这种方法高度适用于大多数生物系统，包括植物与其环境之间的相互作用。速效坏死性真菌病原体是一组特定的害虫，对粮食生产和供应产生不利影响，因此与粮食安全有关。Botrytis cinerea 和 Solanum lycopersicum 分别是真菌和作物生物学研究的模型。在此，我们使用带有衰减全反射 (ATR) 附件的紧凑型中红外光谱仪来测量完整植物体内的 S. lycopersicum 和 B. cinerea 之间的植物-微生物相互作用。化学计量模型仅包括探索性主成分分析 (PCA)，并作为分类器与线性判别分析 (PCA-LDA) 相结合。指纹光谱 (1800-900 cm-1) 是有视觉症状和有症状前植物病害的极好鉴别器。讨论了由感染引起的叶组织的主要生化变化。自动决策平台的诊断潜力显示为 100% 的高准确率，用于检测不同进展阶段的植物病害。化学计量模型仅包括探索性主成分分析 (PCA)，并作为分类器与线性判别分析 (PCA-LDA) 相结合。指纹光谱 (1800-900 cm-1) 是有视觉症状和有症状前植物病害的极好鉴别器。讨论了由感染引起的叶组织的主要生化变化。自动决策平台的诊断潜力显示为 100% 的高准确率，用于检测不同进展阶段的植物病害。化学计量模型仅包括探索性主成分分析 (PCA)，并作为分类器与线性判别分析 (PCA-LDA) 相结合。指纹光谱 (1800-900 cm-1) 是有视觉症状和有症状前植物病害的极好鉴别器。讨论了由感染引起的叶组织的主要生化变化。自动决策平台的诊断潜力显示为 100% 的高准确率，用于检测不同进展阶段的植物病害。指纹光谱 (1800-900 cm-1) 是有视觉症状和有症状前植物病害的极好鉴别器。讨论了由感染引起的叶组织的主要生化变化。自动决策平台的诊断潜力显示为 100% 的高准确率，用于检测不同进展阶段的植物病害。指纹光谱 (1800-900 cm-1) 是有视觉症状和有症状前植物病害的极好鉴别器。讨论了由感染引起的叶组织的主要生化变化。自动决策平台的诊断潜力显示为 100% 的高准确率，用于检测不同进展阶段的植物病害。