支持向量机(SVM)在傅里叶变换近红外光谱分析中的应用研究

Study on Application of Fourier Transformation Near-Infrared Spectroscopy Analysis with Support Vector Machine (SVM)

支持向量机(SVM)用于两类问题的识别研究,它是统计学习理论中最年轻的分支,所建分析模型有严格的数学基础。同时介绍了SVM学习的基本原理和方法,并将该方法引入化学计量学,以103个中药大黄样品为实验材料,通过SVM近红外光谱法建立了大黄样品真伪识别模型。对学习集中33个样品模型识别准确率为100%;对70个预测样品的识别准确率为9677%,为中药大黄的快速识别提供了参考。研究结果表明了SVM近红外光谱法建立生物样品识别模型的可行性。通过旨在介绍SVM学习方法的基本思想,以引起化学计量学工作者的进一步关注。

Support Vector Machine (SVM) is a method for the research on identifying two types of problem. It is the latest branch in the statistics study theories, and the identification model has a strict mathematics foundation. In this paper, the basic principle and method of SVM are not only introduced, but also applied to chemometrics. One hundred and three rhubarb samples were used as experimental materials. The identification models were established with near-infrared spectroscopy and SVM training method with the intention of identifying whether the rhubarb samples are true or false. The thirty-three samples in training set were identified by the identifying models with the accurate rate of 100 %, while seventy estimate samples had an accurate rate of 96.77 %. The research result provided the method of identifying the traditional Chinese medicine rhubarb quickly. So, it shows the feasibility of establishing the models with near-infrared spectroscopy and SVM method to identify biological samples. This paper introduced the theme of SVM training method in order to beget the attention of the research members who deal with chemometrics.