基于近红外光谱学和机器学习的类的定性分析
Haiyi Bian1, Ling Huang1, Qinxin Xu1
1Jiangsu Engineering Research Center of Lake Environment Remote Sensing Technologies, Huaiyin Institute of Technology, Huaian 223003, China.
The Review of scientific instruments
|February 3, 2025
概括
这项研究表明,随机森林算法使用近红外光谱 (NIRS) 和机器学习准确地识别了类,在定性分析和特征提取方面实现了100%的预测准确度.
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:
- 类 Ester 是一种有价值的生物活性化合物,用于制药和化品.
- 近红外光谱 (NIRS) 提供快速,非破坏性分析,对于保护样品完整性至关重要.
- 精确的特类的定性分析对于质量控制和产品开发至关重要.
研究的目的:
- 用NIRS数据评估机器学习算法,对使用NIRS数据进行类的定性分析.
- 为了比较K-最近邻居 (K-NN),随机森林和反向传播神经网络 (BPNN) 的性能.
- 评估随机森林算法在特征频谱提取中用于类 Ester 识别的有效性.
主要方法:
- 使用近红外光谱学 (NIRS) 分析了类样本.
- 机器学习算法,包括K-NN,随机森林和BPNN,用于定性分析.
- 随机森林算法专门用于特征光谱特征提取.
主要成果:
- 随机森林算法在测试集中实现了100%的预测准确性.
- K-最近邻居 (K-NN) 分类器达到96.154%的准确率,BPNN达到94.231%的准确率.
- 随机森林有效地提取了特征的光谱特征,保持了100%的预测准确性,同时减少了特征数量.
结论:
- 随机森林算法对于通过NIRS进行类的定性分析非常有效.
- 结合机器学习,特别是随机森林,NIRS提供了一个强大的和高效的分析方法.
- 这种方法可以准确地识别和表征类,在工业中有潜在的应用.
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