超越传统的airPLS:在SERS中改进了基线删除,并以参数为中心的优化和预测
Jiaheng Cui1, Xianyan Chen2, Yiping Zhao3
1School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, Georgia 30602, United States.
我们使用优化的airPLS算法和机器学习改进了拉曼光谱基线校正. 这提高了光谱分析的准确性和速度,克服了原来的airPLS方法的局限性.
科学领域:
- 频谱学是一种光谱学.
- 分析化学 分析化学
- 计算化学的计算化学
背景情况:
- 基线校正对于准确的拉曼和表面增强拉曼光谱 (SERS) 是至关重要的.
- 适应性代重量化惩罚最小平方 (airPLS) 方法面临的挑战是参数灵敏度和复杂的光谱.
研究的目的:
- 开发一个优化的airPLS (OP-airPLS) 算法,以改善基线校正.
- 实现一个机器学习模型来预测最佳的airPLS参数.
- 提高拉曼光谱中的光谱预处理的准确性和效率.
主要方法:
- 使用自适应网格搜索系统微调 airPLS 参数.
- 通过光谱形状识别开发用于参数预测的机器学习模型.
- 使用6000个模拟光谱对12种光谱形状进行评估.
- 实施主要组件分析和随机森林 (PCA-RF) 模型,用于直接参数预测.
主要成果:
- 在基线校正准确度方面,OP-airPLS实现了平均百分比改善 (PI) 96 ± 2%.
- 最大的MAE减少为99.46±0.06%,最小的MAE减少为91±7%.
- PCA-RF模型表现出强大的性能,具有90 ± 10%的PI,在0.038秒内处理光谱.
- 对光谱形状的最佳参数是在一个定义良好的线性区域中发现的.
结论:
- OP-airPLS显著提高了基线校正的准确性,但需要计算资源和已知的基线.
- PCA-RF模型提供了一个计算效率高的替代方案,可以直接从光谱中预测最佳参数.
- 在真实频谱上PCA-RF模型的性能取决于信号噪声比和与训练数据的频谱相似性.
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