DETERMINING THE CONCENTRATION OF POLYCYCLIC AROMATIC HYDROCARBONS IN WATER USING SURFACE ENHANCED RAMAN SPECTROSCOPY AND KERNEL PRINCIPAL COMPONENTS ANALYSIS COMBINED WITH SUPPORT VECTOR REGRESSION
Abstract
Determining the concentration of polycyclic aromatic hydrocarbons (PAHs) in water is vital for reducing negative effects on human health, such as cancer and malformation. This study proposed an alternative analytical method based on surface enhanced Raman spectroscopy and kernel principal components analysis combined with support vector regression (SVR) for the determination of PAH concentration in water. For this, a dataset containing 300 Raman spectra of polycyclic aromatic hydrocarbon mixtures was made using naphthalene (NAP), pyrene (PYR), and phenanthrene (PHE) with concentrations ranging from 0 to 1000 ppb. In order to improve the effect of the model detection, different pre-processed methods were applied: normalization, multiplicative scatter correction, detrending, standart normal variate transformation, and Savitzky–Golay smoothing. For comparison, partial least squares (PLS) and SVR with the polynomialkernel were also used. The pre-processing method with the best prediction effect was SNV for all the three substances. For NAP, the optimal correlation coefficient of cross-validation (Rcv), correlation coefficient of prediction (Rpred), RMSECV, RMSEP, and RPD are 0.90, 0.937, 138.9, 117.4, and 2.9 ppb, respectively, while for PYR the optimal Rcv, Rpred, RMSECV, RMSEP, and RPD are respectively 0.881, 0.897, 152.3, 142.8, and 2.3 ppb. For PHE, the optimal Rcv, Rpred, RMSECV, RMSEP, and RPD are 0.980, 0.982, 64.5, 62.9, and 5.3 ppb, respectively. This study provides a new method with a better prediction effect for quantitative analysis of low concentrations of polycyclic aromatic hydrocarbons in water by using surface enhanced Raman spectroscopy.
Keywords
polycyclic aromatic hydrocarbons,
surface enhanced Raman spectroscopy,
kernel principal components analysis,
kernel function,
support vector regression
Supporting agencies: The research was funded by the Chinese Academy of Sciences Scientific Equipment Research Project (Grant No. YJKYYQ20170003).
About the Authors
C. Jian
School of Software and Microelectronics at Peking University
China
Beijing, 102600
China
Beijing, 102600
J. Boyan
School of Software and Microelectronics at Peking University
China
Beijing, 102600
China
Beijing, 102600
Zh. Ying
School of Software and Microelectronics at Peking University
China
Beijing, 102600
China
Beijing, 102600
W. Zhenyu
School of Software and Microelectronics at Peking University
China
Beijing, 102600
China
Beijing, 102600
References
1. I. Tongo, L. Ezemonye, K. A. Akpeh, Environ. Monit., 189, No. 6, 247 (2017).
2. N. Xiang, C. Jiang, T. Yang, P. Li, H. Wang, Y. Xie, et al. Chin. Ecotoxicol. Environ. Saf., 152, No. 8, 15 (2018).
3. M. L. Vestal, Science, 226, No. 4672, 275–281 (1984).
4. B. Sharma, R. R. Frontiera, A.-I. Henry, E. Ringe, R. P. V. Duyne, SERS: Materials, Applications, and the Future. Mater. Today, 15, No. 1-2, 16–25 (2012).
5. J. Kneipp, H. Kneipp, K. Kneipp, Chem. Soc. Rev., 37, No. 5, 1052 (2008).
6. H.-X. Gu, K. Hu, D.-W. Li, Y.-T. Long, Analyst, 141, No. 14, 4359 (2016).
7. X. Gu, S. Tian, Q. Zhou, J. Adkins, Z. Gu, X Li, et al. RSC Adv., 3, No. 48, 25989–25996 (2013).
8. D. W. Li, W. L. Zhai, Y. T. Li, Y. T. Long, Microchim. Acta, 181, No. 1-2, 23–43 (2014).
9. R. Gautam, S. Vanga, F. Ariese, S. Umapathy, EPJ Tech. Instrum., 2, No. 1, 8 (2015).
10. S. Wold, Principal Component Anal., 2, No. 1, 37–52 (1987).
11. A. C. Kak, A. M. Martínez, PCA versus LDA, 23, No. 3-4, 228–233 (2001).
12. P. Geladi, B. R. Kowalski, J. Anal. Chim. Acta, 185, No. 1, 1–17 (1986).
13. A. J. Smola, B. Schölkopf, Stat. Comput., 14, No. 3, 199–222 (2004).
14. L. A. Reisner, A. Cao, A. K. Pandya, Chemometr. Intell. Lab. Syst., 105, No. 1, 83–90 (2011).
15. T. Bocklitz, A. Walter, K. Hartmann, P. Rusch, J. Popp, Anal. Chim. Acta, 704, No. 1, 47–56 (2011).
16. A. Rinnan, F. V. D. Berg, S. B. Engelsen, Trends Anal. Chem., 28, No. 10 1201–1222 (2009).
17. H. Chen, C. Tan, Z. Lin, T. Wu, Spectrochim. Acta A: Mol. Biomol. Spectrosc., 189, 183 (2017).
18. H. S. Tapp, M. Defernez, E. K. Kemsley, J. Agric. Food Chem., 51, No. 21, 6110–6115 (2003).
19. J. K. Holland, E. K. Kemsley, R. H. Wilson, J. Sci. Food Agric., 76, No. 2, 263–269 (2015).
20. X. L. Yang, Y. F. Li, X. W. Zhang, S. Q. Hu, Appl. Mech. Mater., 494-495, 964–967 (2014).
21. F. Kuang, W. Xu, S. Zhang, Appl. Soft Comput. J., 18C, 178–184 (2014).
22. P. C. Lee, D. J. J. Meisel, J. Phys. Chem., 86, No. 17, 3391–3395 (1982).
23. P. S. Sampaio, A. Soares, A. Castanho, A. S. Almeida, J. Oliveira, C. Brites, Food Chem., 242, 196–204 (2018).
24. G. Krepper, F. Romeo, D. D. S. Fernandes, P. H. G. Diniz, M. C. U. de Araújo, M. S. Di Nezio, et al. Spectrochim. Acta A: Mol. Biomol. Spectrosc., 189, 300–306 (2017).
25. O. Frank, J. Jehlika, H. G. M. Edwards, Spectrochim. Acta A: Mol. Biomol. Spectrosc., 68, No. 4, 1065–1069 (2007).
26. J. Neugebauer, E. J. Baerends, E. V. Efremov, F. Ariese, C. Gooijer, J. Phys. Chem. A, 109, No. 10, 2100–2106 (2005).
27. I. Bandyopadhyay, S. Manogaran, J. Mol. Struct. THEOCHEM, 496, No. 1, 107–119 (2000).
28. A. Bree, R. A. Kydd, T. N. Misra, V. V. B. Vilkos, Spectrochim. Acta A: Mol. Spectrosc., 27, No. 11, 2315–2332 (1971).
Review
For citations:
Jian C., Boyan J., Ying Zh., Zhenyu W. DETERMINING THE CONCENTRATION OF POLYCYCLIC AROMATIC HYDROCARBONS IN WATER USING SURFACE ENHANCED RAMAN SPECTROSCOPY AND KERNEL PRINCIPAL COMPONENTS ANALYSIS COMBINED WITH SUPPORT VECTOR REGRESSION. Zhurnal Prikladnoii Spektroskopii. 2021;88(1):173(1)-173(8).
Views: 268
Email this article 