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一种机器学习辅助的方法用激光诱导分解光谱来分类的种类和品种

Maryam Manzoor¹, Muhammad Faheem¹, Muhammad Nadeem¹

¹Laser Spectroscopy Lab, Department of Physics, University of Agriculture Faisalabad, 38090, Pakistan.

Analytica chimica acta

|September 7, 2025

概括

激光诱导分解光谱 (LIBS) 与机器学习相结合,为分类种和品种提供了一种新的方法. 这种方法可以达到很高的准确性,特别是叶的线性差异分析 (LDA).

科学领域:

分析化学分析化学
频谱学是一种光谱学.
机器学习机器学习

背景情况:

的种类和品种分类是复杂的.
在全球范围内在化品,医药和香水中具有多样化的应用.
传统的分类方法可能是耗时和劳动密集的.

研究的目的:

探索激光诱导分解光谱 (LIBS) 快速分类.
评估机器学习算法在提高LIBS准确性的有效性.
建立一种新的,高效的植物识别方法.

主要方法:

使用Q开关Nd:YAG激光器对样品进行LIBS分析.
应用主要组件分析 (PCA) 用于初始数据的维度缩小.
雇佣监督机器学习模型,包括二次支持矢量机 (SVM) 和线性差异分析 (LDA).

主要成果:

LIBS提供了元素分析,但单独进行准确分类是不够的.
方位SVM在花上获得了95%的准确性 (101.59s).
线性差异分析 (LDA) 实现了叶的100%准确性,计算时间显著减少 (50.59秒).

结论:

机器学习辅助的LIBS显著提高了物种和品种分类的准确性.
在分类叶方面,LDA表现出卓越的性能.
这种综合方法为植物学和农业应用提供了强大的工具.

关键词:

的分类的分类花朵这些花朵在LIBS中,LIBS是指LIBS.机器学习是机器学习.在PCA中,PCA是PCA.

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