基于MATLAB的振动光谱数据预处理与多元分析自动化

T. Bhattacharjee
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引用次数: 0

摘要

本教程是根据预处理和多变量分析的步骤组织的。因此,我们将首先讨论如何将频谱导入MATLAB,然后在第4节中讨论如何对该频谱进行导数化,从导数频谱中选择特定的光谱范围,并对所选范围的导数频谱进行归一化。第5节将演示如何通过脚本导入多个光谱并对其应用预处理命令来自动化预处理。第6节将扩展这个概念,从多个子文件夹导入多个光谱,并对它们进行预处理。第7节将详细介绍在预处理光谱上应用PCA、PC-LDA和留一交叉验证(LOOCV),并对支持向量机(svm)进行说明。第8节教授编写测试预测代码的脚本。第9节和第10节将讨论自动化PCA绘图和脚本微调,以构建打开和关闭特性的选项。在执行代码时遇到的常见错误,以及如何调整代码以自动化其他过程(如平均值和标准偏差计算)的示例包含在最后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automating Vibrational Spectroscopy Data Preprocessing and Multivariate Analysis with MATLAB
The tutorial is structured according to the steps of preprocessing and multivariate analysis. We will, therefore, start with a discussion on how to import a spectrum into MATLAB, followed by how to derivatize this spectrum, select a specific spectral range from the derivative spectrum, and normalize the derivative spectrum of the selected range in Section 4. Section 5 will demonstrate how to automate the preprocessing by scripting to import multiple spectra and applying preprocessing commands to them. Section 6 will extend this concept to import multiple spectra from multiple subfolders and preprocess them all. Section 7 will provide details on applying PCA, PC-LDA, and leave-one-out cross validation (LOOCV) on the preprocessed spectra with a note on support vector machines (SVMs). Section 8 teaches scripting of test prediction code. Sections 9 and 10 will discuss automating PCA plotting and script fine-tuning to build in options to turn features on and off. Common errors encountered while executing the code and an example of how to adapt the code to automate other processes such as mean and standard deviation calculations are included at the end.
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