使用近红外光谱学与基于子空间的集体分类器相结合,确定人参的地理来源
Hui Chen1, Chao Tan2, Zan Lin3
1Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China; Hospital, Yibin University, Yibin, Sichuan 644000, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|September 6, 2023
概括
近红外 (NIR) 光谱与随机子空间组合 (RSE) 结合,有效地识别了人参.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 机器学习 机器学习
背景情况:
- 人参是一种有价值的草药,具有不同的地理来源.
- 目前用于确定人参的来源的方法往往是主观的,缓慢的或破坏性的.
- 需要有效和准确的方法来验证人参的地理来源.
研究的目的:
- 探索使用近红外 (NIR) 光谱和集体学习来区分人参的地理来源的可行性.
- 开发和评估人参原产地认证的预测模型.
主要方法:
- 收集了270个人参样本,分为训练和测试组.
- 采用随机子空间组合与线性判别分析 (RSE-LDA) 用于模型构建.
- 优化了RSE-LDA参数 (子空间大小,学习者数量) 并与部分最小平方 (PLS) 进行了比较.
主要成果:
- 该RSE-LDA模型实现了高精度:97.8%的灵敏度,100%的特异性和99.3%的总精度.
- 参考PLS模型的性能略低:灵敏度为93.3%,特异性为96.7%,总精度为95.6%.
- 通过重复的随机抽样和对训练集大小影响的分析证实了稳定性.
结论:
- 将NIR光谱与RSE算法相结合,提供了一个有希望的,非破坏性的工具,用于验证人参的地理来源.
- 这种方法为传统的识别方法提供了有效和准确的替代方案.
- 开发的模型显示出高可靠性和在人参行业实际应用的潜力.
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