光谱学与光谱分析, 2015, 35 (2): 390, 网络出版: 2015-02-15   

特征提取算法在福美双表面增强拉曼光谱定量分析中的应用

Quantitative Analysis of Thiram by Surface-Enhanced Raman Spectroscopy Combined with Feature Extraction Algorithms
作者单位
1 安徽大学电子信息工程学院, 安徽 合肥 230601
2 安徽师范大学原子与分子物理研究所, 安徽 芜湖 241000
摘要
表面增强拉曼散射(SERS)技术具有快速、指纹效应与极低的检测限等优点, 被越来越多地应用到有害污染物、有毒物质、危险物质的检测与分析中。在SERS光谱的测量过程中, 易受基底、仪器、宇宙射线与测量环境等因素影响, 出现波动现象, 对后续的分析与检测造成较大的干扰。基于农药福美双SERS光谱数据, 尝试利用多种特征提取算法, 如主成分分析(PCA)、离散余弦变换(DCT)、非负因式分解(NMF) , 对光谱的主分量进行提取, 以减弱光谱数据波动对其后续的定量分析结果的影响。然后将提取后的分量分别结合线性回归算法——偏最小二乘法回归(PLSR), 非线性回归算法——支持向量机回归(SVR)建立定量模型。最后, 利用5-折交叉验证方法对比不同特征提取算法在不同类型的回归算法下的效果。通过实验验证可知, SVR对福美双溶液的分析精度要明显高于PLSR, 这主要是由于SERS光谱强度与被分析物浓度之间为非线性关系。同时针对两种类型回归算法, 特征提取算法都能明显地提升了分析结果, 主要是由于其提取了源数据的主体信息, 去除干扰信息。其中在线性回归中使用PCA效果最佳, 在非线性拟合中使用NMF结果最佳, 分析误差最好时可降低近3倍。最优回归模型(NMF+SVR)的交叉验证均方误差(RMSECV)为0.0455 μmol·L-1(10-6 mol·L-1), 达到国家对福美双的检测标准, 为农药快速检测提供一种新的方法。
Abstract
Three feature extraction algorithms, such as the principal component analysis (PCA), the discrete cosine transform (DCT) and the non-negative factorization (NMF), were used to extract the main information of the spectral data in order to weaken the influence of the spectral fluctuation on the subsequent quantitative analysis results based on the SERS spectra of the pesticide thiram. Then the extracted components were respectively combined with the linear regression algorithm—the partial least square regression (PLSR) and the non-linear regression algorithm—the support vector machine regression (SVR) to develop the quantitative analysis models. Finally, the effect of the different feature extraction algorithms on the different kinds of the regression algorithms was evaluated by using 5-fold cross-validation method. The experiments demonstrate that the analysis results of SVR are better than PLSR for the non-linear relationship between the intensity of the SERS spectrum and the concentration of the analyte. Further, the feature extraction algorithms can significantly improve the analysis results regardless of the regression algorithms which mainly due to extracting the main information of the source spectral data and eliminating the fluctuation. Additionally, PCA performs best on the linear regression model and NMF is best on the non-linear model, and the predictive error can be reduced nearly three times in the best case. The root mean square error of cross-validation of the best regression model (NMF+SVR) is 0.045 5 μmol·L-1 (10-6 mol·L-1), and it attains the national detection limit of thiram, so the method in this study provides a novel method for the fast detection of thiram. In conclusion, the study provides the experimental references the selecting the feature extraction algorithms on the analysis of the SERS spectrum, and some common findings of feature extraction can also help processing of other kinds of spectroscopy.

张保华, 江永成, 沙文, 张先燚, 崔执凤. 特征提取算法在福美双表面增强拉曼光谱定量分析中的应用[J]. 光谱学与光谱分析, 2015, 35(2): 390. ZHANG Bao-hua, JIANG Yong-cheng, SHA Wen, ZHANG Xian-yi, CUI Zhi-feng. Quantitative Analysis of Thiram by Surface-Enhanced Raman Spectroscopy Combined with Feature Extraction Algorithms[J]. Spectroscopy and Spectral Analysis, 2015, 35(2): 390.

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