表面增强拉曼光谱(SERS)是一种重要的高灵敏度分析技术。 基于SERS的技术特点, 建立了真实体系下孔雀石绿定性检测方法。 提出了一种光谱自动识别算法, 有机整合了稳健的傅里叶变换基线校正, 基于主成分分析的特征提取与人工神经网络分类器。 该方法结合基线的低频特征, 通过迭代傅里叶变换实现基线校正; 通过样本空间中类间与类内的欧氏距离判别自动获取拉曼光谱信号主成分的最优组合, 实现光谱数据的降维与特征提取; 最后构建三层反向传播神经网络分类器进行样本分类。 实验结果表明, 基线去除可排除基线变化对检测结果的影响; 光谱主成分的优化组合可减小基线校正残余及复杂体系中被测物以外的物质拉曼峰对检测结果的干扰, 同时实现了分类器最小化。 该方法用于养殖用海水中孔雀石绿的现场检测, 最低检出浓度0.1 μg·L-1。 该方法具有可拓展性, 可以直接应用于其他溶胶/凝胶体系中SERS光谱的定性分析。

Surface enhanced Raman spectroscopy (SERS) is a useful chemical analysis technique for its high sensitivity, which was used for Malachite Green qualitative analysis in real cases in the present article. Automatic recognition algorithms were put forward, which is a combination of three modules, including a robust Fourier transform for background rejection, a principal component analysis based character extraction method and artificial neural networks for classifying. Low-frequency background was rejected by iterative Fourier transform in order to eliminate the effect of variable background. The best principal component combination was obtained according to the Euclidean distances between-class and within-class in the sample space. And a three-layer back-propagating neural network was constructed for classifying. As it was shown, it would both minimize the network and reduce the classifying mistakes from variable baseline and Raman characters of other substances in seawater with best principal component combination. Malachite Green real-time detection in aquaculture used seawater was realized with a lower density limit of 0.1 μg·L-1. Moreover, the method proposed in this article could be extended for other sol analysis based on SERS technique.