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使用ATR-FTIR光谱学与机器学习算法相结合,检测棉花叶中的Verticillium感染

Xianchang Li¹, Lipeng Zhang², Shiding Zhang³

¹Huzhou Key Laboratory of Green Energy Materials and Battery Cascade Utilization, School of Intelligent Manufacturing, Huzhou College, Huzhou 313000, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|September 16, 2024

概括

在棉花中早期检测Verticillium wilt (VW) 现在可以使用福里埃变换红外光谱 (FTIR) 进行. 该方法识别了基峰值,以准确诊断植物疾病,有助于作物管理.

科学领域:

植物病理学植物病理学
分析化学分析化学
生物技术是生物技术.

背景情况:

Verticillium wilt (VW) 是一种破坏性土壤传播疾病,影响高地棉花,由Verticillium dahliae引起.
对大众汽车的有效管理依赖于快速和可访问的早期诊断方法.
当前的诊断方法可能缺乏及时干预所需的速度和用户友好性.

研究的目的:

开发和验证一种新的,快速和用户友好的诊断技术,用于棉花中的Verticillium枯.
探索福里埃变换红外光谱法 (FTIR) 的应用与减弱总反射率 (ATR) 检测VW感染.
评估机器学习算法在基于FTIR光谱数据的棉叶分类中的有效性.

主要方法:

采用里叶变换红外光谱 (FTIR) 使用减弱总反射率 (ATR) 技术.
从大约1800个样本中获得了FTIR光谱,分为三个组:感染Verticillium枯,其他感染,和健康的控制棉花叶.
分析涉及识别特征性振动峰值,特别是1558厘米-1的奇,并使用机器学习算法,包括支持矢量机器 (SVM),进行分类.

主要成果:

1558厘米-1的奇明显的振动峰被确定为区分受感染的棉花叶与健康对照的可靠指标.
支持矢量机 (SVM) 分类模型实现了高预测准确度,在单个组内超过96%,在组合数据集中超过97%.
这项研究成功地证明了FTIR光谱技术在快速准确检测垂菌感染方面的潜力.

结论:

与ATR技术相结合的FTIR光谱技术,为早期和准确的诊断提供了一个有前途的新方法,用于棉花中垂直的早期和准确诊断.
已识别的基峰值作为一个关键的光谱生物标志物,用于区分受感染的植物.
这种技术对植物疾病诊断和更广泛的植物科学研究的未来应用具有重大潜力.

关键词:

在ATR-FTIR中使用.棉花的叶子棉花的叶子机器学习是机器学习.垂直的枯会导致垂直的枯

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