基于支持向量机算法的多环芳烃表面增强拉曼光谱的定量分析
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摘要
以硫氰化钾(KSCN)为内标物,利用主成分分析(PCA)降维,利用支持向量机(SVM)算法建立定量分析模型——支持向量回归(SVR),并结合网格搜索(GS)、遗传算法(GA)和粒子群优化算法(PSO)三种参数优化方法,实现了芘、菲单一溶液和混合溶液的定量分析。研究结果表明:以KSCN为内标物,提高了定量分析结果的准确性;利用PCA降维提高了建模速度;三种优化模型对芘预测的平均相对误差(ARE)在7.6%以内,对菲预测的ARE在11.3%以内;三种参数优化方法对同一物质的预测结果相近,但GS的运算速度最快;综合考虑误差和分析速度后,采用GS-SVR模型获得了菲、芘混合溶液的最佳结果。表面增强拉曼光谱(SERS)技术结合SVM算法有望实现多环芳烃的定量分析。
Potassium thiocyanate(KSCN) is used as the internal standard. And principal component analysis(PCA) is utilized to reduce the dimension. Quantitative analysis model, that is, support vector regression(SVR), is established by support vector machine(SVM) algorithm. Meanwhile, three parameter optimization methods, that is grid search(GS), genetic algorithm(GA) and particle swarm optimization(PSO), are used to fulfill quantitative analysis of single and mixed solutions of pyrene and phenanthrene. The research results show that the use of KSCN as the internal standard improves the accuracy of the quantitative mensuration results. The modeling speed is improved by PCA dimensionality reduction. The average relative errors(AREs) of pyrene solution predicted by three optimized models are within 7.6%. The AREs of phenanthrene solution prediction are within 11.3%. The three parameter optimization methods have similar prediction results for the same sample, but the operating rate of GS is the fastest. Considering the errors and analysis speed, the best results of phenanthrene and anthracene mixed solution are obtained by GS-SVR model. Surface-enhanced Raman spectroscopy(SERS) technology combined with SVM algorithm is expected to actualize quantitative analysis of polycyclic aromatic hydrocarbons.
Potassium thiocyanate(KSCN) is used as the internal standard. And principal component analysis(PCA) is utilized to reduce the dimension. Quantitative analysis model, that is, support vector regression(SVR), is established by support vector machine(SVM) algorithm. Meanwhile, three parameter optimization methods, that is grid search(GS), genetic algorithm(GA) and particle swarm optimization(PSO), are used to fulfill quantitative analysis of single and mixed solutions of pyrene and phenanthrene. The research results show that the use of KSCN as the internal standard improves the accuracy of the quantitative mensuration results. The modeling speed is improved by PCA dimensionality reduction. The average relative errors(AREs) of pyrene solution predicted by three optimized models are within 7.6%. The AREs of phenanthrene solution prediction are within 11.3%. The three parameter optimization methods have similar prediction results for the same sample, but the operating rate of GS is the fastest. Considering the errors and analysis speed, the best results of phenanthrene and anthracene mixed solution are obtained by GS-SVR model. Surface-enhanced Raman spectroscopy(SERS) technology combined with SVM algorithm is expected to actualize quantitative analysis of polycyclic aromatic hydrocarbons.
引文
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[7] Liang Q Y, Yang X F, Song G S. Determination of graphite in water-borne organic paint employing SERS with internal standard[J]. Chinese Journal of Light Scattering, 2018, 30(1): 10-16. 梁庆优, 杨贤锋, 宋国胜. 内标表面增强拉曼光谱法测定水性有机涂料中石墨的含量[J]. 光散射学报, 2018, 30(1): 10-16.
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[15] Costa J C S, Sant′ana A C, Corio P, et al. Chemical analysis of polycyclic aromatic hydrocarbons by surface-enhanced Raman spectroscopy[J]. Talanta, 2006, 70(5): 1011-1016.
[16] Peksa V, Jahn M, ■tolcová L, et al. Quantitative SERS analysis of azorubine (E 122) in sweet drinks[J]. Analytical Chemistry, 2015, 87(5): 2840-2844.
[17] Gao J M, Zhang Z M, Li G K. Research progress of quantitative analysis techniques for surface enhanced Raman spectroscopy[J]. Journal of Instrumental Analysis, 2016, 35(12): 1647-1653. 高嘉敏, 张卓旻, 李攻科. 表面增强拉曼光谱定量分析技术研究进展[J]. 分析测试学报, 2016, 35(12): 1647-1653.
[18] Zhang Y, Li Y, Gu Y H, et al. LIBS quantitative analysis of Cr and Ni in iron alloys with support vector machine (SVM)[J]. Spectroscopy and Spectral Analysis, 2016, 36(7): 2244-2248. 张莹, 李颖, 谷艳红, 等. 基于LIBS技术的钢铁合金中Cr和Ni元素SVM定量分析方法研究[J]. 光谱学与光谱分析, 2016, 36(7): 2244-2248.
[19] Tan Z M, Yin L H, Zhang Y. Study on Co absorption of rosiglitazone maleate and phenethyl biguanide on the surface of nano silver surface by enhanced Raman spectroscopy[J]. China Pharmacist, 2013, 16(6): 817-820. 谭忠谋, 尹利辉, 张雁. 马来酸罗格列酮与盐酸苯乙双胍纳米银表面共吸附的表面增强拉曼光谱研究[J]. 中国药师, 2013, 16(6): 817-820.
[20] Zhao B Z, Han T X, Hao X R, et al. Theoretical calculation of the solubility for polycyclic aromatic hydrocarbons[J]. Journal of Molecular Science, 2004, 20(2): 1-4. 赵宝中, 韩天熙, 郝向荣, 等. 多环芳烃水中溶解度的理论计算[J]. 分子科学学报, 2004, 20(2): 1-4.
[2] Wang X G. Analysis of PAHs sources of Hun River in Fushun City based on principal component analysis[J]. Technical Supervision in Water Resources, 2018, 26(3): 24-27. 王绪刚. 基于主成分分析法的浑河抚顺段多环芳烃来源分析[J]. 水利技术监督, 2018, 26(3): 24-27.
[3] Guo X D. Research on fiber biochemical sensing technology based on surface Raman spectroscopy[D]. Taiyuan: North University of China, 2017: 11. 郭旭东. 基于表面拉曼光谱的光纤生化传感技术研究[D]. 太原: 中北大学, 2017: 11.
[4] Ma J, Liu S, Shi X F, et al. Detection and analysis of polycyclic aromatic hydrocarbons using surface-enhanced Raman spectroscopy[J]. Spectroscopy and Spectral Analysis, 2012, 32(9): 2452-2457. 马君, 刘澍, 史晓凤, 等. 多环芳烃的表面增强拉曼光谱探测与分析[J]. 光谱学与光谱分析, 2012, 32(9): 2452-2457.
[5] Ma H K, Zhang X, Zhong S L, et al. Detection of antibiotics based on hyphenated technique of electrostatic-preconcentration and surface-enhanced-Raman-spectroscopy[J]. Chinese Journal of Lasers, 2018, 45(2): 0207028. 马海宽, 张旭, 钟石磊, 等. 基于静电富集-表面增强拉曼光谱联用技术的抗生素检测[J]. 中国激光, 2018, 45(2): 0207028.
[6] Liu J M, Yan L P, Liu W H, et al. Quantitative determination of phosmet pesticide residue by surface-enhanced Raman scattering with internal standard method[J]. Journal of Instrumental Analysis, 2016, 35(5): 605-608. 刘江美, 严丽萍, 刘文涵, 等. 表面增强拉曼光谱内标法测定亚胺硫磷农残含量[J]. 分析测试学报, 2016, 35(5): 605-608.
[7] Liang Q Y, Yang X F, Song G S. Determination of graphite in water-borne organic paint employing SERS with internal standard[J]. Chinese Journal of Light Scattering, 2018, 30(1): 10-16. 梁庆优, 杨贤锋, 宋国胜. 内标表面增强拉曼光谱法测定水性有机涂料中石墨的含量[J]. 光散射学报, 2018, 30(1): 10-16.
[8] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273-297.
[9] Weng S Z. Research on rapid analysis and detection of organophosphorus pesticide residues based on spectral absorption/ surface-enhanced Raman spectroscopy[D]. Hefei: University of Science and Technology of China, 2015: 73-83. 翁士状. 基于光谱吸收/表面增强拉曼光谱的有机磷农药残留快速分析检测研究[D]. 合肥: 中国科学技术大学, 2015: 73-83.
[10] Fang X Q, Peng Y K, Li Y Y, et al. Rapid and quantitative detection method of sodium benzoate in carbonated beverage based on surface-enhanced Raman spectroscopy[J]. Acta Optica Sinica, 2017, 37(9): 0930001. 房晓倩, 彭彦昆, 李永玉, 等. 基于表面增强拉曼光谱快速定量检测碳酸饮料中苯甲酸钠的方法[J]. 光学学报, 2017, 37(9): 0930001.
[11] Dong T, Xiao S P, He Y, et al. Rapid and quantitative determination of soil water-soluble nitrogen based on surface-enhanced Raman spectroscopy analysis[J]. Applied Sciences, 2018, 8(5): 701.
[12] Frens G. Controlled nucleation for the regulation of the particle size in monodisperse gold suspensions[J]. Nature Physical Science, 1973, 241(105): 20-22.
[13] Shinohara H, Yamakita Y, Ohno K. Raman spectra of polycyclic aromatic hydrocarbons. Comparison of calculated Raman intensity distributions with observed spectra for naphthalene, anthracene, pyrene, and perylene[J]. Journal of Molecular Structure, 1998, 442(1/2/3): 221-234.
[14] Martin J M L, El-Yazal J, Fran?ois J P. Structure and vibrational spectrum of some polycyclic aromatic compounds studied by density functional theory. 1. Naphthalene, azulene, phenanthrene, and anthracene[J]. The Journal of Physical Chemistry, 1996, 100(38): 15358-15367.
[15] Costa J C S, Sant′ana A C, Corio P, et al. Chemical analysis of polycyclic aromatic hydrocarbons by surface-enhanced Raman spectroscopy[J]. Talanta, 2006, 70(5): 1011-1016.
[16] Peksa V, Jahn M, ■tolcová L, et al. Quantitative SERS analysis of azorubine (E 122) in sweet drinks[J]. Analytical Chemistry, 2015, 87(5): 2840-2844.
[17] Gao J M, Zhang Z M, Li G K. Research progress of quantitative analysis techniques for surface enhanced Raman spectroscopy[J]. Journal of Instrumental Analysis, 2016, 35(12): 1647-1653. 高嘉敏, 张卓旻, 李攻科. 表面增强拉曼光谱定量分析技术研究进展[J]. 分析测试学报, 2016, 35(12): 1647-1653.
[18] Zhang Y, Li Y, Gu Y H, et al. LIBS quantitative analysis of Cr and Ni in iron alloys with support vector machine (SVM)[J]. Spectroscopy and Spectral Analysis, 2016, 36(7): 2244-2248. 张莹, 李颖, 谷艳红, 等. 基于LIBS技术的钢铁合金中Cr和Ni元素SVM定量分析方法研究[J]. 光谱学与光谱分析, 2016, 36(7): 2244-2248.
[19] Tan Z M, Yin L H, Zhang Y. Study on Co absorption of rosiglitazone maleate and phenethyl biguanide on the surface of nano silver surface by enhanced Raman spectroscopy[J]. China Pharmacist, 2013, 16(6): 817-820. 谭忠谋, 尹利辉, 张雁. 马来酸罗格列酮与盐酸苯乙双胍纳米银表面共吸附的表面增强拉曼光谱研究[J]. 中国药师, 2013, 16(6): 817-820.
[20] Zhao B Z, Han T X, Hao X R, et al. Theoretical calculation of the solubility for polycyclic aromatic hydrocarbons[J]. Journal of Molecular Science, 2004, 20(2): 1-4. 赵宝中, 韩天熙, 郝向荣, 等. 多环芳烃水中溶解度的理论计算[J]. 分子科学学报, 2004, 20(2): 1-4.