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利用已知混合物拉曼光谱改善混合物成分识别精度的方法

Method for Improving Identification Accuracy of Components in Mixtures Using Raman Spectra of Known Mixtures

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摘要

构建已知纯净物光谱数据库,计算待识别混合物光谱与数据库中各纯净物光谱的相似度,是利用拉曼光谱技术进行混合物成分分析的常用策略。受测量仪器性能及待测混合物所含成分相互干扰的影响,待识别混合物中所含物质成分的光谱与数据库中对应的纯净物光谱相比会有不同程度的失真,从而给基于纯净物光谱数据库的组分鉴别带来极大困难。针对这一问题,提出了一种使用已知混合物光谱数据来改善混合物成分识别精度的方法。首先利用纯净物拉曼光谱谱峰的位移和半峰全宽信息,将已知混合物的光谱谱峰与其所含有的具体物质对应;然后基于谱峰拉曼位移、半峰全宽和谱峰强度分别构建纯净物、已知混合物和待识别混合物的特征参数,并利用模糊隶属度函数计算待识别混合物光谱与纯净物光谱、已知混合物所含物质光谱的相似度;最终根据光谱相似度确定待识别混合物中含有的疑似组分。基于204种纯净物和8种已知混合物光谱数据库,对81种混合物进行了识别,结果表明:所提方法可降低由光谱失真导致的相似度计算误差,提高识别准确率;相比于纯净物数据库搜索策略,本文方法的识别精度由76.34%提高到了92.83%。

Abstract

A common strategy for mixture composition analysis based on Raman spectroscopy is to construct the spectral database of pure substances and calculate the spectral similarity of the mixture to be identified and the pure substances in the database. However, influenced of the performance of the measuring instrument and the mutual interference of the components of the mixture, the spectrum of the substance contained in the mixture to be identified will have different degrees of distortion compared with the corresponding pure substance spectrum in the database, bringing great difficulties in component identification. To address this problem, a method to improve the identification accuracy of components in mixtures using the spectral data of known mixtures is proposed herein. The spectral peak information, including Raman shift and full width at half maximum, of the pure substance in the database is used to study the correspondence of the spectral peaks of the known mixture to the specific substances of the mixture. The spectral feature parameters of the pure substance, the known mixture, and the mixture to be identified are constructed using the Raman shift of the spectral peak, the full width at half maximum, and the peak intensity, respectively and the fuzzy membership function is used to calculate the spectral similarity between the mixture to be identified, the pure substance, and the substances contained in known mixture based on calculated feature parameters. Furthermore, suspected components contained in mixture to be identified are determined based on the spectral similarity. Based on the spectral database of 204 pure substances and 8 known mixtures, the experimental results for 81 unknown mixtures reveal that the proposed method can reduce the calculation error of similarity caused by spectral distortion and can improve the identification accuracy. Compared with search strategy based on the database of pure substance, the identification accuracy obtained using the proposed method increases from 76.34% to 92.83%.

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中图分类号:O433.4

DOI:10.3788/CJL202047.1111001

所属栏目:光谱学

基金项目:国家自然科学基金;

收稿日期:2020-05-11

修改稿日期:2020-06-15

网络出版日期:2020-11-01

作者单位    点击查看

季明强:江南大学轻工过程先进控制教育部重点实验室, 江苏 无锡 214122
朱启兵:江南大学轻工过程先进控制教育部重点实验室, 江苏 无锡 214122
黄敏:江南大学轻工过程先进控制教育部重点实验室, 江苏 无锡 214122
张丽文:北京卓立汉光仪器有限公司, 北京 101102
雷泽民:北京卓立汉光仪器有限公司, 北京 101102
张恒:北京卓立汉光仪器有限公司, 北京 101102

联系人作者:朱启兵(zhuqib@163.com)

备注:国家自然科学基金;

【1】Huang S G, Hu J P, Liu M H, et al. Density functional theory calculation and Raman spectroscopy studies of carbamate pesticides [J]. Spectroscopy and Spectral Analysis. 2017, 37(3): 766-771.
黄双根, 胡建平, 刘木华, 等. 氨基甲酸酯类农药的密度泛函理论计算及拉曼光谱研究 [J]. 光谱学与光谱分析. 2017, 37(3): 766-771.

【2】Chen S, Guo P, Wan J C, et al. Rapid detecting study of sodium saccharin additive in spirit with SERS [J]. Spectroscopy and Spectral Analysis. 2017, 37(5): 1412-1417.
陈思, 郭平, 万建春, 等. 白酒中糖精钠添加剂表面增强拉曼光谱快速检测研究 [J]. 光谱学与光谱分析. 2017, 37(5): 1412-1417.

【3】Xu H D, Lin L L, Li Z, et al. Nephrite origin identification based on Raman spectroscopy and pattern recognition algorithms [J]. Acta Optica Sinica. 2019, 39(3): 0330001.
徐荟迪, 林露璐, 李征, 等. 基于拉曼光谱和模式识别算法的软玉产地鉴别 [J]. 光学学报. 2019, 39(3): 0330001.

【4】Liu C, Zang Y C, Zeng H T, et al. Rapid detection of methotrexate and voriconazole in mixtures using surface-enhanced Raman spectroscopy with features matching in wavelet space [J]. Journal of Instrumental Analysis. 2019, 38(6): 668-674.
刘察, 臧颖超, 曾惠桃, 等. 基于小波空间特征匹配及表面增强拉曼光谱技术快速检测混合物中的甲氨蝶呤和伏立康唑 [J]. 分析测试学报. 2019, 38(6): 668-674.

【5】Li X L, Zhou R Q, Xu Y F, et al. Spectral unmixing combined with Raman imaging, a preferable analytic technique for molecule visualization [J]. Applied Spectroscopy Reviews. 2017, 52(5): 417-438.Li X L, Zhou R Q, Xu Y F, et al. Spectral unmixing combined with Raman imaging, a preferable analytic technique for molecule visualization [J]. Applied Spectroscopy Reviews. 2017, 52(5): 417-438.

【6】Zhuang X M, Li S Y, Li F, et al. Excess Raman spectroscopy of ammonium sulfate aqueous solution [J]. Acta Optica Sinica. 2018, 38(6): 0630002.
庄欣明, 李申予, 李非, 等. 硫酸铵水溶液的超额拉曼光谱研究 [J]. 光学学报. 2018, 38(6): 0630002.

【7】Liu C Z, Zhu Q B, Huang M, et al. Identification of components in mixtures based on Raman spectroscopy [J]. Laser & Optoelectronics Progress. 2019, 56(8): 083004.
刘财政, 朱启兵, 黄敏, 等. 基于拉曼光谱的混合物组分识别方法 [J]. 激光与光电子学进展. 2019, 56(8): 083004.

【8】Zhang Z M, Chen X Q, Lu H M, et al. Mixture analysis using reverse searching and non-negative least squares [J]. Chemometrics and Intelligent Laboratory Systems. 2014, 137: 10-20.

【9】Liu M H, Dong Z R, Xin G F, et al. Raman spectrum library matching method based on integrated features [J]. Chinese Journal of Lasers. 2019, 46(1): 0111002.
刘铭晖, 董作人, 辛国锋, 等. 基于集成特征的拉曼光谱谱库匹配方法 [J]. 中国激光. 2019, 46(1): 0111002.

【10】He Y, Wang J F. Rapid nondestructive identification of wood lacquer using Raman spectroscopy based on characteristic-band-Fisher-K nearest neighbor [J]. Laser & Optoelectronics Progress. 2020, 57(1): 013001.
何亚, 王继芬. 基于特征波段-Fisher-K近邻的木器漆拉曼光谱的快速无损鉴别 [J]. 激光与光电子学进展. 2020, 57(1): 013001.

【11】Liu Y D, Cheng M J, Hao Y, et al. Quantitative analysis of chlorophyll content in citrus leaves by Raman spectroscopy [J]. Spectroscopy and Spectral Analysis. 2019, 39(6): 1768-1772.
刘燕德, 程梦杰, 郝勇, 等. 柑橘叶片叶绿素含量拉曼光谱定量分析方法研究 [J]. 光谱学与光谱分析. 2019, 39(6): 1768-1772.

【12】Zhang Z M, Chen S, Liang Y Z, et al. An intelligent background-correction algorithm for highly fluorescent samples in Raman spectroscopy [J]. Journal of Raman Spectroscopy. 2010, 41(6): 659-669.

【13】Eilers P H C. A perfect smoother [J]. Analytical Chemistry. 2003, 75(14): 3631-3636.

【14】Levenberg K. A method for the solution of certain non-linear problems in least squares [J]. Quarterly of Applied Mathematics. 1944, 2(2): 164-168.

【15】Huang P X, Yao Z X, Su H, et al. Spectral pattern recognition of mixed alcohols by means of the method based on judging the subspace coincidence [J]. Journal of Instrumental Analysis. 2013, 32(3): 281-286.
黄培贤, 姚志湘, 粟晖, 等. 基于子空间重合判断的混合醇组分光谱识别方法 [J]. 分析测试学报. 2013, 32(3): 281-286.

引用该论文

Ji Mingqiang,Zhu Qibing,Huang Min,Zhang Liwen,Lei Zemin,Zhang Heng. Method for Improving Identification Accuracy of Components in Mixtures Using Raman Spectra of Known Mixtures[J]. Chinese Journal of Lasers, 2020, 47(11): 1111001

季明强,朱启兵,黄敏,张丽文,雷泽民,张恒. 利用已知混合物拉曼光谱改善混合物成分识别精度的方法[J]. 中国激光, 2020, 47(11): 1111001

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