拉曼光谱
主成分分析
线性判别分析
背景(考古学)
功能数据分析
拉曼散射
谱线
模式识别(心理学)
化学
噪音(视频)
职位(财务)
分析化学(期刊)
生物系统
相干反斯托克斯拉曼光谱
数学
人工智能
计算机科学
拉曼光学活性
统计
光学
物理
色谱法
古生物学
财务
天文
经济
图像(数学)
生物
作者
Rola Houhou,Petra Rösch,Jürgen Popp,Thomas Bocklitz
标识
DOI:10.1007/s00216-021-03360-1
摘要
Raman spectral data are best described by mathematical functions; however, due to the spectroscopic measurement setup, only discrete points of these functions are measured. Therefore, we investigated the Raman spectral data for the first time in the functional framework. First, we approximated the Raman spectra by using B-spline basis functions. Afterwards, we applied the functional principal component analysis followed by the linear discriminant analysis (FPCA-LDA) and compared the results with those of the classical principal component analysis followed by the linear discriminant analysis (PCA-LDA). In this context, simulation and experimental Raman spectra were used. In the simulated Raman spectra, normal and abnormal spectra were used for a classification model, where the abnormal spectra were built by shifting one peak position. We showed that the mean sensitivities of the FPCA-LDA method were higher than the mean sensitivities of the PCA-LDA method, especially when the signal-to-noise ratio is low and the shift of the peak position is small. However, for a higher signal-to-noise ratio, both methods performed equally. Additionally, a slight improvement of the mean sensitivity could be shown if the FPCA-LDA method was applied to experimental Raman data.
科研通智能强力驱动
Strongly Powered by AbleSci AI