频谱编码器提取拉曼光谱的高效特征,以提供可靠和精确的定量分析
1Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics of the Chinese Academy of Sciences, Shaanxi, 710076, China; The Key Laboratory of Biomedical Spectroscopy of Xi'an, Shaanxi, 710076, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|February 17, 2024
概括
本研究引入了一种用于拉曼光谱学定量分析的新型光谱编码方法. 它增强了特征提取,提高了复杂样本的回归精度,而没有用户设置的参数.
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:
- 拉曼光谱对于分析微生物,食品,环境和药品至关重要.
- 拉曼光谱学的定量分析在很大程度上依赖于有效的光谱特征提取.
- 复杂的样本和信号质量差对传统方法构成挑战.
研究的目的:
- 开发一种使用光谱编码器提取有效光谱特征的新方法.
- 提高拉曼光谱法定量分析的可靠性和精度.
- 为构建和验证潜在编码特征回归模型,用于组件度预测.
主要方法:
- 使用自动编码器重建光谱仪输出并从瓶层中提取隐藏特征.
- 开发了一个深度回归模型,用这些潜在特征来预测组分度.
- 在单元和多元组件数据集上进行了废除和比较实验.
主要成果:
- 与常见方法相比,拟议的模型显著提高了回归精度.
- 该方法证明了对噪声的稳定性,并消除了不相关信息的干扰.
- 潜伏编码特征显示出强大的非线性表示,低计算成本和广泛的适应性.
结论:
- 光谱编码器为光谱分析提供了一种新,有效和无参数的特征提取方法.
- 该方法可适应各种技术,适合各种现实世界测量场景.
- 潜伏编码特征具有超越光谱回归的更广泛应用的潜力.
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