Skip to main content
SpringerLink
Log in

Determining the Concentration of Polycyclic Aromatic Hydrocarbons in Water Using Surface Enhanced Raman Spectroscopy and Kernel Principal Components Analysis Combined with Support Vector Regression

  • Published:
Journal of Applied Spectroscopy Aims and scope

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 polynomial-kernel 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. I. Tongo, L. Ezemonye, and K. A. Akpeh, Environ. Monit., 189, No. 6, 247 (2017).

    Article  Google Scholar 

  2. N. Xiang, C. Jiang, T. Yang, P. Li, H. Wang, Y. Xie, et al., Chin. Ecotoxicol. Environ. Saf., 152, No. 8, 15 (2018).

    Google Scholar 

  3. M. L. Vestal, Science, 226, No. 4672, 275–281 (1984).

    Article  ADS  Google Scholar 

  4. B. Sharma, R. R. Frontiera, A.-I. Henry, E. Ringe, and R. P. V. Duyne, SERS: Materials, Applications, and the Future. Mater. Today, 15, Nos. 1–2, 16–25 (2012).

    Article  Google Scholar 

  5. J. Kneipp, H. Kneipp, and K. Kneipp, Chem. Soc. Rev., 37, No. 5, 1052 (2008).

    Article  Google Scholar 

  6. H.-X. Gu, K. Hu, D.-W. Li, and Y.-T. Long, Analyst, 141, No. 14, 4359 (2016).

    Article  ADS  Google Scholar 

  7. X. Gu, S. Tian, Q. Zhou, J. Adkins, Z. Gu, X Li, et al., RSC Adv., 3, No. 48, 25989–25996 (2013).

    Article  ADS  Google Scholar 

  8. D. W. Li, W. L. Zhai, Y. T. Li, and Y. T. Long, Microchim. Acta, 181, No. 1–2, 23–43 (2014).

    Article  Google Scholar 

  9. R. Gautam, S. Vanga, F. Ariese, and S. Umapathy, EPJ. Tech. Instrum., 2, No. 1, 8 (2015).

    Article  Google Scholar 

  10. S. Wold, Principal Component Anal., 2, No. 1, 37–52 (1987).

    Google Scholar 

  11. A. C. Kak and A. M. Martínez, PCA versus LDA, 23, No. 3-4, 228–233 (2001).

    Google Scholar 

  12. P. Geladi and B. R. Kowalski, J. Anal. Chim. Acta, 185, No. 1, 1–17 (1986).

    Article  Google Scholar 

  13. A. J. Smola and B. Schölkopf, Stat. Comput., 14, No. 3, 199–222 (2004).

    Article  MathSciNet  Google Scholar 

  14. L. A. Reisner, A. Cao, and A. K. Pandya, Chemometr. Intell. Lab. Syst., 105, No. 1, 83–90 (2011).

    Article  Google Scholar 

  15. T. Bocklitz, A. Walter, K. Hartmann, P. Rusch, and J. Popp, Anal. Chim. Acta, 704, No. 1, 47–56 (2011).

    Article  Google Scholar 

  16. A. Rinnan, F. V. D. Berg, and S. B. Engelsen, Trends Anal. Chem., 28, No. 10 1201–1222 (2009).

    Article  Google Scholar 

  17. H. Chen, C. Tan, Z. Lin, and T. Wu, Spectrochim. Acta A: Mol. Biomol. Spectrosc., 189, 183 (2017).

    Article  ADS  Google Scholar 

  18. H. S. Tapp, M. Defernez, and E. K. Kemsley, J. Agric. Food Chem., 51, No. 21, 6110–6115 (2003).

    Article  Google Scholar 

  19. J. K. Holland, E. K. Kemsley, and R. H. Wilson, J. Sci. Food Agric., 76, No. 2, 263–269 (2015).

    Article  Google Scholar 

  20. X. L. Yang, Y. F. Li, X. W. Zhang, and S. Q. Hu, Appl. Mech. Mater., 494-495, 964–967 (2014).

    Article  Google Scholar 

  21. F. Kuang, W. Xu, and S. Zhang, Appl. Soft Comput. J., 18C, 178–184 (2014).

    Article  Google Scholar 

  22. P. C. Lee and D. J. J. Meisel, J. Phys. Chem., 86, No. 17, 3391–3395 (1982).

    Article  Google Scholar 

  23. P. S. Sampaio, A. Soares, A. Castanho, A. S. Almeida, J. Oliveira, and C. Brites, Food Chem., 242, 196–204 (2018).

    Article  Google Scholar 

  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).

    Article  ADS  Google Scholar 

  25. O. Frank, J. Jehlika, and H. G. M. Edwards, Spectrochim. Acta A: Mol. Biomol. Spectrosc., 68, No. 4, 1065–1069 (2007).

    Article  ADS  Google Scholar 

  26. J. Neugebauer, E. J. Baerends, E. V. Efremov, F. Ariese, and C. Gooijer, J. Phys. Chem. A, 109, No. 10, 2100–2106 (2005).

    Article  Google Scholar 

  27. I. Bandyopadhyay and S. Manogaran, J. Mol. Struct. THEOCHEM, 496, No. 1, 107–119 (2000).

    Article  Google Scholar 

  28. A. Bree, R. A. Kydd, T. N. Misra, and V. V. B. Vilkos, Spectrochim. Acta A: Mol. Spectrosc., 27, No. 11, 2315–2332 (1971).

    Article  ADS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Software and Microelectronics at Peking University, Beijing, 102600, China

    C. Jian, J. Boyan, Zh. Ying & W. Zhenyu

Authors
  1. C. Jian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Boyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zh. Ying
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. W. Zhenyu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. Jian.

Additional information

Abstract of article is published in Zhurnal Prikladnoi Spektroskopii, Vol. 88, No. 1, p. 173, January–February, 2021.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jian, C., Boyan, J., Ying, Z. et al. Determining the Concentration of Polycyclic Aromatic Hydrocarbons in Water Using Surface Enhanced Raman Spectroscopy and Kernel Principal Components Analysis Combined with Support Vector Regression. J Appl Spectrosc 88, 225–232 (2021). https://doi.org/10.1007/s10812-021-01161-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10812-021-01161-z

Keywords

Search

Navigation