混合NGO-PSO优化随机森林与多能LIBS相结合,以提高茶叶的准确分类
Junjie Ma1,2, Xiaojian Hao1,2, Rui Jia1,2
1State Key Laboratory of Extreme Environment Optoelectronic Dynamic Measurement Technology and Instrument, Taiyuan, Shanxi, China. haoxiaojian@nuc.edu.cn.
The Analyst
|September 26, 2025
概括
一个新的茶叶识别系统使用激光诱导分解光谱 (LIBS) 和随机森林模型 (NGO-PSO-RF) 进行精确的茶叶分类. 这种方法显著提高了准确性,防止了茶叶分析中的改和错误判断.
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:
- 精确的茶叶分析对于质量控制,防止改和确保正确的品种识别至关重要.
- 传统方法可能会在类似的茶样之间存在微妙的差异.
研究的目的:
- 开发一个高效,准确的茶叶识别和分类系统.
- 通过结合先进的光谱和机器学习技术,提高茶叶分析的精度.
主要方法:
- 利用激光诱导分解光谱 (LIBS) 来测量8个茶样在3种不同的能量下的光谱数据.
- 应用主要组件分析 (PCA) 用于数据维度缩小和特征提取.
- 开发了一个随机森林模型,使用北方和粒子优化算法 (NGO-PSO-RF) 进行优化,用于分类.
主要成果:
- 在测试套件上实现了99.22%的分类准确性,比单能分析显著改进.
- 在召回率 (+3.11%) 和F1得分 (+3.19%) 中显著改善.
- 在分类准确度方面表现优于LSTM,SVM,RF和PSO-RF等其他模型.
结论:
- 拟议的NGO-PSO-RF方法为材料分类提供了一种创新,高效和强大的方法,特别是在茶叶方面.
- 这种技术为食品生产安全和化学分析提供了强大的技术支持.
- 多能LIBS与先进的机器学习相结合,显示了精确茶叶认证的巨大潜力.
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