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                            Funding & Awards
 Data fusion from infrared elemental, MSP and Raman analysis techniques to the maximization of the efficiency of the analytical sequence for the forensic examination of paint evidence
Award Information
Awardee
              SAM HOUSTON STATE UNIVERSITY
Award #
              15PNIJ-21-GG-04163-RESS
Funding Category
              Competitive Discretionary
Location
                    HUNTSVILLE, TX
Awardee County
              Walker
Congressional District
              17
Status
              Open
Funding First Awarded
              2021
Total funding (to date)
              $338,823
Original Solicitation
              Research  and Development in Forensic Science for Criminal Justice Purposes, Fiscal Year 2021
Description of original award (Fiscal Year 2021, $338,823)
PROJECT ABSTRACT

Data fusion from infrared, elemental, MSP and Raman analysis techniques to the maximization of the efficiency of the analytical sequence for the forensic examination of paint evidence

Following urgent research needs identified by the OSAC (trace) Materials subcommittee and the American Society of Trace Evidence Examiners (ASTEE), this research aims maximizing the efficiency of analytical schemes for forensic paint examinations by implementing multi-block data fusion statistical methods applied to infrared, elemental, MSP and Raman analysis techniques.
Currently, during comparative examinations involving multiple analytical techniques, examiners evaluate their results sequentially by technique.  That is, when they cannot differentiate the compared sets with a method, a subsequent one is applied until all available methods are applied.  In such events, an overall evaluation of the collected data is necessary to address questions of a common source which require estimating the chance to randomly observe another coated object in a relevant population that exhibits the observed properties as a function of the adopted analytical sequence.  Currently, this process is carried out subjectively, with interference of potential redundant data collected with the different methods, and without consideration of the combined contributions of the features detected using the adopted analytical scheme.  This study addresses this critical barrier by investigating the implementation of a holistic, objective, and verifiable comparison process based on data fusion methods, that efficiently combines analytical data from different instruments.  Methods of data fusion involving multi-block exploratory data analysis, feature selection, and predictive modeling methods are implemented and evaluated to identify the most efficient approach to capture the most informative chemical data from the different analytical techniques of microscopical examinations, Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS), UV-Vis microspectrophotometry (MSP) and Raman spectroscopy.  Four sample sets of architectural and automotive paint samples are used to elucidate different aspects such as the ability of the selected technique to reliably detect minor components resulting from heterogenous paint formulations, the redundancies of analytical information collected from the different analytical techniques, and the potential interferences of the adjacent layers within multilayered automotive paint systems in the effort to detect features of interest.
This study is expected to enhance the objectivity and reliability of trace evidence examinations and the consecutive interpretations and conclusions presented in Court through the adoption of a verifiable holistic and objective comparison process that can be carried out in one step instead of sequentially.  The proposed tool will assist trace evidence examiners in managing a large quantity of data gathered during examinations and properly using them during comparative examinations in a more time-effective way.

  
Date Created: December 9, 2021
    
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