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.
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REFERENCES
Ruffell, A., Forensic Sci. Int., 2010, vol. 202, p. 9.
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.
Pye, K., Geological and Soil Evidence: Forensic Applications, Boca Raton: CRC, 2007.
Dawson, L.A. and Hillier, S., Surf. Interface Anal., 2010, vol. 42, no. 5, p. 363.
Dawson, L.A. and Mayes, R.W., in Introduction to Environmental Forensics, New York: Academic, 2015, p. 457.
McCulloch, G., Morgan, R.M., and Bull, P.A., Aust. J. Forensic Sci., 2017, vol. 49, no. 4, p. 421.
Sangwan, P., Nain, T., Singal, K., Hooda, N., and Sharma, N., Anal. Methods, 2020, vol. 43, p. 5150.
Kammrath, B.W., Koutrakos, A., Castillo, J., Langley, C., and Huck-Jones, D., Forensic Sci. Int., 2018, vol. 285, p. e25.
Zeng, R., Rossiter, D.G., Zhao, Y.G., Li, D.C., and Zhang, G.L., Forensic Sci. Int., 2020, vol 317, Article 110544.
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.
Rinnan, Å., Anal. Methods, 2014, vol. 6, no. 18, p. 7124.
Cieszczyk, S., IAPGOS, 2020, vol. 4, p. 25.
Press, W.H. and Teukolsky, S.A., Comput. Phys., 1990, vol. 4, p. 669.
Lee, L.C., Liong, C.Y., Osman, K., and Jemain, A.A., AIP Conf. Proc., 2016, vol. 1750, p. 60013.
Ameeta, N.E., BSc Thesis, Selangor: Univ. Kebangsaan Malaysia, 2020.
Anas, F.Z., BSc Thesis, Selangor: Univ. Kebangsaan Malaysia, 2020.
Syahiera, K., BSc Thesis, Selangor: Univ. Kebangsaan Malaysia, 2020.
Ali, N., Lee, L.C., and Ishak, A.A., J. Anal. Chem., 2022, vol. 77, p. 347.
Lee, L.C., Hamid, N.A., Rosdi, N.A.N.M., and Sino, H., EDUCATUM: J. Sci., Math., Technol., 2022, vol. 9, p. 99.
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.
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.
Lee, L.C. and Jemain, A.A., Microchem. J., 2021, vol. 169, p. 106608.
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.
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.
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.
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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
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DOI: https://doi.org/10.1134/S1061934823100143