CAJ | 학술논문

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0.1μg · 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。
표면증강랍만광보（SERS）시일충중요적고령민도분석기술。기우SERS적기술특점，건립료진실체계하공작석록정성검측방법。제출료일충광보자동식별산법，유궤정합료은건적부리협변환기선교정，기우주성분분석적특정제취여인공신경망락분류기。해방법결합기선적저빈특정，통과질대부리협변환실현기선교정；통과양본공간중류간여류내적구씨거리판별자동획취랍만광보신호주성분적최우조합，실현광보수거적강유여특정제취；최후구건삼층반향전파신경망락분류기진행양본분류。실험결과표명，기선거제가배제기선변화대검측결과적영향；광보주성분적우화조합가감소기선교정잔여급복잡체계중피측물이외적물질랍만봉대검측결과적간우，동시실현료분류기최소화。해방법용우양식용해수중공작석록적현장검측，최저검출농도0.1μg · L -1。해방법구유가탁전성，가이직접응용우기타용효/응효체계중SERS광보적정성분석。
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 tech-nique .