Geological Forensics and Its Evaluation


Geoforensics, which is also referred to as geological forensics, is a branch of study that collects and analyzes geological evidence to solve crimes. Soil is a geological sample that is commonly applied in geoforensics. The basis for its use is the “Locard’s exchange principle,” which states that physical contact will always lead to the exchange of substances between bodies.1 Therefore, characterizing and comparing physical evidence between a crime scene and potential perpetrators can facilitate linking the suspect to the crime. Additionally, the soil has several distinct properties that promote its use in this field. For example, the composition of the soil is site-specific and varies from one place to another all over the world. The physical and chemical constituents of soil are substantially affected by the source, climate, topography, pollution, and age. Soil can easily be transferred from one source to another and is amenable to collection, separation, and characterization by chemical techniques. However, its major shortcoming is that soil from different areas tends to collect on shoes over time. Therefore, it may not be a reliable source of evidence in such a scenario.

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Initial methods of soil analysis included high-performance liquid chromatography (HPLC) and gas chromatography (GC) either alone or simultaneously. Liquid chromatography is a good analytical technique that is effective at separating substances based on their interaction and retention in mobile and stationary phases. However, accurate identification of samples requires known standards to be run alongside the analytical samples, which may not always be possible when analyzing unknowns. Consequently, mass spectrometry solves this problem by estimating the mass-to-charge ratio of ions and using it to identify unknown compounds.2 Mass spectroscopy can also ascertain the relative abundance of each ion type, thus helping to deduce its concentration. For these reasons, analytical methods have shifted towards the use of MS technologies such as inductively coupled plasma MS in geoforensics. Nonetheless, there is a need to verify the reliability of these techniques. The appraised paper examines the use of ICP-MS and multivariate statistics in the elemental fingerprinting of soil.

Physical Evidence

A mock scenario that students were expected to solve involved a murder case with one suspect. Soil samples were collected from the suspect’s car mat, crime scene, neighboring cultivated land, and other distant localities. Only the surface soil consisting of the upper 5 cm layer was sampled. This approach was appropriate because it contributed to the accuracy of the outcomes. During normal walking, only the surface soil has the probability of making contact with the suspect’s footwear.

All the samples were from Lafayette County, Mississippi, USA. Examination and classification of the soil indicated that it was sandy loam. This sampling method could be considered a weakness gave that all the soil was from one region and belonged to one class of soil. One characteristic of soil as a forensic sample is that it varies in particle size, which also influences its chemical composition. However, the general characterization of the various soil samples from each locality was Memphis silt loam, which was further stratified based on additional unique components, for example, alluvium that was classified as Vicksburg silt loam. From a forensic inquiry standpoint, these variations facilitated the determination of the local extent of variation of the soil’s elemental fingerprint. Nonetheless, the sampling was also done meticulously to include two reference materials from outside the state, which served as controls. This procedure helped to circumvent the initial limitation of soil samples from the same state. A second limitation in the sampling approach was the number of samples collected at each point. Three samples were taken at the crime scene (triplicate sampling), whereas only one specimen was taken from the surrounding regions. This aspect introduced an element of bias to the sampling procedure.

The goal of the experiment was to carry out a comparative analysis based on the soil’s geochemical signature to exclude or include the soil in the car from that found at the crime scene. The key tools used in the investigation were ICP-MS to quantify different elements in the soil and multivariate analysis to determine any patterns in the distribution of the element. Soil samples were prepared by microwave-assisted acid digestion (using hydrochloric acid), filtered, and diluted using deionized water before ICP analysis. This method of sample preparation was appropriate because it lowered the probability of contamination and loss of volatile constituents, which could happen if the alternative method of sample preparation (open-beaker digestion) was done. Furthermore, prepared samples were stored at approximately 4oC before analysis to prevent further chemical reactions that could alter the outcomes.

The prepared liquid samples were introduced into the ICP-MS instrument with the aid of a nebulizer, which created an aerosol that was swept through the spray chamber into an argon-based plasma.2 The atoms in the sample were ionized before accessing the mass spectrometer for separation based on the charge-to-mass ratio. Specimens were collected from all members of the class and analyzed at the same time. Sector field ICP-MS, which uses a double-focusing magnetic sector design, was used. The main advantage of this mode of ICP-MS is that it enables the analysis to be done at three different resolutions, which enhances the resolving power of the analysis. Other benefits include increased sensitivity and low background noise, which improves the limits of detection.3 A high detection limit is particularly beneficial for elements with a high atomic mass whose analysis does not require high resolution. Uranium, which was part of the assayed elements, is an example of such elements where resolution is not critical. Given that a wide range of elements was quantified, the chosen ICP-MS mode offered versatile resolutions for optimal analysis. The magnetic sector approach to ICP-MS also enhances the precision of measurements, which is crucial in geochemistry applications.

The resultant raw intensity and concentration data were recorded in a spreadsheet format for further analysis by the students. Two multivariate statistical methods were applied in the data analysis, including principal component analysis (PCA) and discriminant analysis (DA). These two techniques are powerful enough to handle vast data and detect similarities and dissimilarities between samples and groups of specimens.4 PCA evaluated the multiple datasets for any linear associations. Conversely, DA was used to summarize variances between groups and determine the elements that were responsible for the discrepancies. All processes, from sample preparation to data analysis, were done in eight groups, each consisting of three students.

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Overall, 22 elements were analyzed. All seven soil samples were classified appropriately based on their original locations. The precision of the method was ascertained by having three replicates of samples from the suspect’s car, which yielded values in the range of less than 10% of the relative standard deviation. The methods used were appropriate for the objective of the investigation. However, some elements such as uranium, aluminum, potassium, calcium, barium, and zinc had low recoveries. The low recovery could be attributed to the reference values for the soil material, which were based on total dissolution and not acid-leaching that was used in the study. It was also noted that there were no databases for the assessment of comparative outcomes. In cases where databases were available, they were based on the evaluation of bulk soil samples, which was not practical when testing soil samples collected by footwear. A possible pitfall of the microwave-assisted acid digestion that was used for sample preparation is wastage, which could potentially affect the final concentrations of various elements, especially those that were assumed to have low recoveries. Another second shortcoming of the method was a bias in the sampling approach.

The method could be improved by including soil samples with various soil textures such as clay or sand. Also, a uniform number of soil specimens could be collected from each point to avoid sampling biases. The overall efficiency of the preparation technique could also be improved by comparing outcomes obtained when the same samples are analyzed using laser ablation ICP-MS (that minimizes wastage). The problem regarding low recoveries of some elements could be improved by developing new reference values for elements found in soil based on the acid-leaching method of sample preparation. Another possible improvement to the method is developing a database of elemental composition based on surface soil samples for direct comparisons. This method could contribute to forensic science by facilitating the accurate and reproducible analysis of elements in forensic samples using ICP-MS.


Mistek, E.; Fikiet, M. A.; Khandasammy, S. R.; Lednev, I. K. Toward Locard’s Exchange Principle: Recent Developments in Forensic Trace Evidence Analysis. Anal. Chem. 2018, 91(1), 637-654.

Reidy, L.; Bu, K.; Godfrey, M.; Cizdziel, J. V. Elemental Fingerprinting of Soils Using ICP-MS and Multivariate Statistics: A Study for And by Forensic Chemistry Majors. Forensic Sci. Int. 2013, 233(1-3), 37-44.

Yeung, V.; Miler, D. D.; Rutzke, M. A. Atomic Absorption Spectroscopy, Atomic Emission Spectroscopy, and Inductively Coupled Plasma-Mass Spectroscopy. In Food Analysis, 5th ed.; Nielsen, S. S. Ed. Springer: Cham, Switzerland, 2017; pp. 129-150.

Habte, G.; Hwang, I. M.; Kim, J. S.; Hong, J. H.; Hong, Y. S.; Choi, J. Y.; Nho, E. Y.; Jamila, N.; Khan, N.; Kim, K. S. Elemental Profiling and Geographical Differentiation of Ethiopian Coffee Samples Through Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES), ICP-Mass Spectrometry (ICP-MS) and Direct Mercury Analyzer (DMA). Food Chem. 2016. 212, 512-520.

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