Laboratory of Biological Signal Processing, Institute of Research and Development-IP&D, Universidade do Vale do Paraíba-UNIVAP, Av. Shishima Hifumi 2911, 12244-00, São José dos Campos, SP, Brazil.
Analyst. 2009 Jun;134(6):1203-7. doi: 10.1039/b821248a. Epub 2009 Apr 9.
The program ProRaman, developed for the Matlab platform, provides an interactive and flexible graphic interface to develop efficient algorithms to classify Raman spectra into two or three different classes. A set of preprocessing algorithms to decrease the variable dimensionality and to extract the main features which improve the correct classification ratio was implemented. The implemented classification algorithms were based on the Mahalanobis distance and neural network. To verify the functionality of the developed program, 72 spectra from human artery samples, 36 of which had been histopathologically diagnosed as non-diseased and 36 as having an atherosclerotic lesion, were processed using a combination of different preprocessing and classification techniques. The best result was accomplished when the variables were selected from the Raman spectrum shift range from 1200 to 1700 cm(-1), then preprocessed using wavelets for compression and principal component analysis for feature extraction and, finally, classified by a multilayer perceptron with one hidden layer with eight neurons.
该程序 ProRaman 是为 Matlab 平台开发的，它提供了一个交互式和灵活的图形界面，用于开发将拉曼光谱分类为两个或三个不同类别的有效算法。实现了一组预处理算法，以降低变量的维度并提取主要特征，从而提高正确分类的比例。实现的分类算法基于马氏距离和神经网络。为了验证所开发程序的功能，对 72 个人体动脉样本的光谱进行了处理，其中 36 个样本经过组织病理学诊断为无病变，36 个样本为动脉粥样硬化病变。使用不同的预处理和分类技术的组合来处理这些光谱。当从拉曼光谱的位移范围 1200 到 1700cm-1 中选择变量，然后使用小波进行压缩、主成分分析进行特征提取，并最终使用具有 8 个神经元的一个隐藏层的多层感知器进行分类时，获得了最佳的结果。
