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Identifier 000441550
Title Laser-induced breakdown spectroscopy (LIBS) combined with machine learning models and neural networks enables screening and classification of hard tissues such as bones and teeth, with potential applications in archaeological science
Alternative Title Χαρακτηρισμός και ταξινόμηση οστικών και οδοντικών ιστών μέσω στοιχειακής ανάλυσης με φασματοσκοπία πλάσματος λέιζερ, σε συνδυασμό με μεθόδους μηχανικής μάθησης και νευρωνικά δίκτυα. Πιθανές εφαρμογές στην αρχαιολογική έρευνα.
Author Σπανός, Λάμπρος Σ.
Thesis advisor Άγγλος, Δημήτριος
Reviewer Παυλίδης, Παύλος
Σπύρος, Απόστολος
Abstract The excavation of mass graves and sites of accidents or natural disasters, which reveal numbers of hard tissue remains originating from multiple individuals, is usual in archaeology and forensic science. The discrimination of the individuals and the classification of their remains is useful for revealing the identity, as well as information about life and death of those individuals. However, poor preservation makes the task of discrimination/classification extremely difficult and time consuming, using conventional methods based on morphological characteristics or DNA analysis. Thus, the need for simple, direct and cost-effective analysis of hard tissue remains, with minimal damage to the artifacts, has emerged. In the current work, Laser Induce Breakdown Spectroscopy (LIBS), combined with Machine Learning algorithms and a simple Artificial Neural Network, were employed for the discrimination and classification of hard tissue remains. Several bone fragments and teeth were studied, using a LIBS microscopy setup (micro-LIBS) for data collection, while Machine Learning algorithms and a Neural Network were used for data analysis. Micro-LIBS is a micro-destructive, fast and transferable method, with high spatial resolution (around 50μm/spot) that enables analysis of the surface or the crosssection of samples, with little or no sample preparation, providing massive amounts of data in little time. Thus, it is a suitable technique to be combined with machine learning algorithms for the analysis of the collected data. Hydroxyapatite (Ca5(PO4)3OH) is the main component of both bones and teeth, while proteinaceous materials(mainly collagen) and water, in different ratios, complete the hard tissue matrix. Magnesium (Mg), Strontium (Sr) and Barium (Ba) can replace Calcium (Ca) in metabolic processes and thus can replace it in hydroxyapatite’s crystal. Spectral emission lines from biogenic elements in the remains are observed across the spectral range used (200 - 660 nm). Hence, the data collected provide significant information to the algorithms employed. Machine Learning and Neural Networks enable computers to learn from experience following a similar process with several living organisms. This process is based on pattern recognition on given data, improving future decisions and giving computers the ability to learn without being explicitly programmed. This pattern recognition on LIBS data is the aim of this work. The comparison of four different methods (k-Nearest Neighbors, Random Forest, Support Vector Machine, Artificial Neural Network) with gradual complexity, after parameter tuning and feature selection, provided the best behaved model to achieve the requested task. Artificial Neural Network had significantly better results compared to the rest of the models used, while the selection of specific spectral areas corresponding in spectral lines from biogenic elements increased the resulting classification accuracy. The achieved classification varied from decent to excellent, giving a good classification accuracy regardless of the used data. Concluding, the present work is an attempt for development of a fast, accurate and easily accessible and applicable methodology for the discrimination and classification of hard tissue remains, based on the analysis of LIBS data using machine learning models.
Language English
Subject Archaeology
Forensic science
Hard tissue remains
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Φασματοσκοπία πλάσματος επαγόμενου από λέιζερ
Issue date 2021-07-30
Collection   School/Department--School of Sciences and Engineering--Department of Chemistry--Post-graduate theses
  Type of Work--Post-graduate theses
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