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Browsing by Subject "IC"

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  • Rantanen, Noora-Kaisa (2022)
    In chemical forensics inorganic analysis is for example used to detect traces of explosives and drugs, to find residues of firearms, and as aid when searching for hidden burial sites. Forensic investigators also utilise inorganic information in chemical source attribution or fingerprinting, which seeks to identify chemical profiles of inorganic and/or organic compounds and elements that can provide information on the origin of the sample, how it has been produced and using which raw-materials. As the chemical profiles typically contain information for several compounds, comparison of profiles have to be analysed by multivariate statistical tools such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). In this work the applicability of inorganic source fingerprinting on soil samples was investigated. For this an extraction procedure and an ion chromatographic (IC) method for the determination of F–, Cl–, Br–, NO3–, PO43–, SO42– and AsO43– in soil were developed and validated. Extraction of anions was done by microwave assisted solvent extraction, with good recoveries (86.15 % – 115.23 %) for nearly all recovery samples. The recovery of F– from soil was 174.77 %, due to enhanced extraction efficiency caused by the high extraction temperature. AsO43– could not be quantified due to low extractability and interfering matrix components. Development of a method for elemental analysis of soil samples by inductively coupled plasma mass spectrometry (ICP-MS) was also attempted. Complete dissolution was not achieved with microwave assisted acid digestion mainly because of the large particle size of the soils analysed. Samples were analysed for As, Co, Cr, Cu, Fe, Mn, Mo, Ni, V and Zn, but Ni could not be quantified from any of the samples because of the high detection limit caused by contamination of samples. Due to contamination and incomplete dissolution the variation in the results were large, leading to a large uncertainty for the results. Analysis of variance (ANOVA) revealed that there is a significant (α = 0.05) difference in the concentrations of all analytes but Mo and V between the samples. Two step PCA and LDA were performed on tha anion and elemental results separately. Better clustering of sample results were typically got with LDA than with PCA. LDA on the anion results was able to discriminate all samples while only four out of seven samples were identified by PCA. The large variation in the data meant that only the reference soil could be identified when all elemental concentrations were included. Removal of outliers from the data lead to identification of all samples by both PCA and LDA. This work showed that samples can be identified by their inorganic profiles, but large variations in the measured concentrations will make the discrimination by multivariate statistics difficult. Further work should focus on improving the separation of the IC method and on decreasing the variation in the data by decreasing sample heterogeneity and contamination during the sample preparation.