Skip to main content
Log in

Comparison of Gap-Segment and Savitzky-Golay Algorithms in Forensic Discrimination of Soils Based on Ultra-Performance Liquid Chromatography Data

  • ARTICLES
  • Published:
Journal of Analytical Chemistry Aims and scope Submit manuscript

Abstract

Soil discrimination is one of the most critical forensic analyses aiming to establish a link between a suspect and a crime scene or victim. Preliminary studies have indicated that ultra-performance liquid chromatography (UPLC) is useful in acquiring organic profiles of soils. However, the resulting chromatograms are often imperfect but consist of fluctuated baseline and overlapped peaks. Hence, this work aims to compare the performances of two derivative algorithms, i.e., Gap-Segment (GS) and Savitzky–Golay (SG) algorithms, in improving the UPLC chromatograms of soils for forensic investigation purposes. A set of 45 chromatograms was prepared by analyzing 15 Malaysian soil samples in triplicate by using a UPLC-photodiode array detector system. The capability of the two algorithms in discriminating the chromatograms from five geographical origins was studied based on predictive capability of K-nearest neighbor algorithm. Results showed that the GS algorithm slightly outperformed the SG algorithm. In conclusion, lower polynomial and differentiation order are preferred for improving the quality of the UPLC chromatograms of soils. The study provides an understanding of the relative merits between Gap-Segment and Savitzky–Golay algorithms in preprocessing UPLC data.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 2.
Fig. 3.

Similar content being viewed by others

REFERENCES

  1. Ruffell, A., Forensic Sci. Int., 2010, vol. 202, p. 9.

    Article  PubMed  Google Scholar 

  2. Fitzpatrick, R.W., Raven, M.D., and Forrester, S.T., in Criminal and Environmental Soil Forensics, Ritz, K., Dawson, L., and Miller, D., Eds., Dordrecht: Springer, 2009, p. 105.

    Google Scholar 

  3. Pye, K., Geological and Soil Evidence: Forensic Applications, Boca Raton: CRC, 2007.

    Book  Google Scholar 

  4. Dawson, L.A. and Hillier, S., Surf. Interface Anal., 2010, vol. 42, no. 5, p. 363.

    Article  CAS  Google Scholar 

  5. Dawson, L.A. and Mayes, R.W., in Introduction to Environmental Forensics, New York: Academic, 2015, p. 457.

    Google Scholar 

  6. McCulloch, G., Morgan, R.M., and Bull, P.A., Aust. J. Forensic Sci., 2017, vol. 49, no. 4, p. 421.

    Article  Google Scholar 

  7. Sangwan, P., Nain, T., Singal, K., Hooda, N., and Sharma, N., Anal. Methods, 2020, vol. 43, p. 5150.

    Article  Google Scholar 

  8. Kammrath, B.W., Koutrakos, A., Castillo, J., Langley, C., and Huck-Jones, D., Forensic Sci. Int., 2018, vol. 285, p. e25.

    Article  CAS  PubMed  Google Scholar 

  9. Zeng, R., Rossiter, D.G., Zhao, Y.G., Li, D.C., and Zhang, G.L., Forensic Sci. Int., 2020, vol 317, Article 110544.

    Article  CAS  PubMed  Google Scholar 

  10. Rinnan, Å., Norgaard, L., van den Berg, F., Thygesen, J., Bro, R., and Engelsen, S.B., in Infrared Spectroscopy for Food Quality Analysis and Control, Sun, D.-W., Ed., New York: Academic, 2009, p. 29.

    Google Scholar 

  11. Rinnan, Å., Anal. Methods, 2014, vol. 6, no. 18, p. 7124.

    Article  Google Scholar 

  12. Cieszczyk, S., IAPGOS, 2020, vol. 4, p. 25.

    Article  Google Scholar 

  13. Press, W.H. and Teukolsky, S.A., Comput. Phys., 1990, vol. 4, p. 669.

    Article  Google Scholar 

  14. Lee, L.C., Liong, C.Y., Osman, K., and Jemain, A.A., AIP Conf. Proc., 2016, vol. 1750, p. 60013.

    Article  Google Scholar 

  15. Ameeta, N.E., BSc Thesis, Selangor: Univ. Kebangsaan Malaysia, 2020.

  16. Anas, F.Z., BSc Thesis, Selangor: Univ. Kebangsaan Malaysia, 2020.

  17. Syahiera, K., BSc Thesis, Selangor: Univ. Kebangsaan Malaysia, 2020.

  18. Ali, N., Lee, L.C., and Ishak, A.A., J. Anal. Chem., 2022, vol. 77, p. 347.

    Article  Google Scholar 

  19. Lee, L.C., Hamid, N.A., Rosdi, N.A.N.M., and Sino, H., EDUCATUM: J. Sci., Math., Technol., 2022, vol. 9, p. 99.

    Google Scholar 

  20. Lee, L.C., Ishak, A.A., Eyan, A.N., Zakaria, A.F., Kharudin, N.S., and Noor, N.A.M., Forensic Sci. Res., 2022, vol. 7, no. 4, p. 761.

    Article  PubMed  Google Scholar 

  21. Stevens, A., Raminirez-Lopez, L., and Hans, G., ‘prospectr’: Miscellaneous Functions for Processing and Sample Selection of Spectroscopic data, version 0.2.4, R package, 2022. http://r.meteo.uni.wroc.pl/web/packages/prospectr/prospectr.pdf. Accessed December 15, 2022.

  22. Lee, L.C. and Jemain, A.A., Microchem. J., 2021, vol. 169, p. 106608.

    Article  CAS  Google Scholar 

  23. Ripley, B., and Venables, W., ‘class’: Various functions for classification, including k-nearest neighbor, learning vector quantization and self-organizing maps, version 7.3-21, R package, 2023. https://cran.r-project.org/web/packages/class/class.pdf. Accessed February 7, 2023.

  24. Bos, T.S., Knol, W.C., Molenaar, S.R., Niezen, L.E., Schoenmakers, P.J., Somsen, G.W., and Pirok, B.W., J. Sep. Sci., 2020, vol. 43, nos. 9–10, p. 1678.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

ACKNOWLEDGMENTS

The authors are indebted to Ameeta, Anas and Syahiera for preparing the UPLC-PDA data of soils. Mr. Abdul Aziz Ishak was thanked for assisting in running the UPLC-PDA system.

Funding

This study was funded by the CRIM, Universiti Kebangsaan Malaysia (UKM) via the GUP-2020-085.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Loong Chuen Lee.

Ethics declarations

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ravi, Y., Rosdi, N., Hamid, N.A. et al. Comparison of Gap-Segment and Savitzky-Golay Algorithms in Forensic Discrimination of Soils Based on Ultra-Performance Liquid Chromatography Data. J Anal Chem 78, 1398–1405 (2023). https://doi.org/10.1134/S1061934823100143

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1061934823100143

Keywords:

Navigation