This study introduces a reliable, non-coding software named qREAD-Raman, written in the JavaScript® language, for analyzing and interpreting Raman spectral information. It is designed with a focus on the early detection of diseases in tomato plants (S. lycopersicum) during the asymptomatic stage. The platform integrates a set of machine learning algorithms necessary for the preprocessing consisting of outlier removal, baseline correction, fluorescence removal, smoothing, and normalization. For classification, we applied a Consensus of five different classifiers: Multilayer Perceptron (MLP), Partial Least Squares-Discriminant Analysis (PLS-DA), Linear Discriminant Analysis (LDA), Long Short-Term Memory (LSTM), and K-nearest neighbors (kNN). The experiments were conducted on two bacterial diseases: bacterial canker of tomato induced by Clavibacter michiganesis subsp. michiganensis (Cmm), and the tomato vein-greening associated with Candidatus Liberibacter solanacearum (CLso), a non-culturable bacteria transmitted by Bactericera cockerelli insect. Binary models (Cmm-Healthy and CLso-Healthy) demonstrated excellent classification ability. Asymptomatic Cmm-infected plants were distinguished with an accuracy of 88–95 %, while CLso-infected plants showed an accuracy of 68–77 %. The three-class model (CLso-Cmm-Healthy) exhibited acceptable performance in differentiating between Cmm and CLso, with accuracy rates of 71–83% and 58–67%, respectively. The model's performance highlights differences in the relevant spectral regions associated with the biochemical changes induced by each studied disease. The qREAD-Raman software, implemented for the purpose of this research, was found to be a valuable and comprehensive tool that effectively differentiate diseased tomato plants during their asymptomatic stage.

中文翻译：

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通过拉曼光谱法进行早期植物病害检测：一款开源软件，旨在实现光谱数据集预处理和分析的自动化


本研究介绍了一种名为 qREAD-Raman 的可靠非编码软件，该软件以 JavaScript® 语言编写，用于分析和解释拉曼光谱信息。它的设计重点是在无症状阶段对番茄植株 （S. lycopersicum） 的疾病进行早期检测。该平台集成了一组预处理所需的机器学习算法，包括异常值去除、基线校正、荧光去除、平滑和归一化。对于分类，我们应用了五种不同分类器的共识：多层感知器 （MLP）、偏最小二乘判别分析 （PLS-DA）、线性判别分析 （LDA）、长短期记忆 （LSTM） 和 K 最近邻 （kNN）。对 2 种细菌病害进行了试验：密歇根克维杆菌亚种 （Cmm） 诱导的番茄细菌性溃疡病，以及与 Bactericera cockerelli 昆虫传播的不可培养细菌 Candidatus Liberibacter solanacearum （CLso） 相关的番茄静脉绿化。二元模型 （Cmm-Healthy 和 CLso-Healthy） 表现出优异的分类能力。无症状的 Cmm 感染植物的准确率为 88-95%，而 CLso 感染的植物的准确率为 68-77%。三类模型 （CLso-Cmm-Healthy） 在区分 Cmm 和 CLso 方面表现出可接受的性能，准确率分别为 71-83% 和 58-67%。该模型的性能突出了与每种研究疾病诱导的生化变化相关的相关光谱区域的差异。 为本研究目的而实施的 qREAD-Raman 软件被发现是一种有价值且全面的工具，可以有效地区分无症状阶段的患病番茄植株。