Description of original award (Fiscal Year 2019, $516,836)
Fingerprints have been used for over one hundred years and are still one of the most powerful means of biometric identification. In spite of the wide use and success, there are numerous cases where biometric identification cannot produce fruitful outcomes. There have been many demonstrations for the potential of chemical analysis of fingerprints to overcome this limitation; however, it has not yet been successfully applied to a criminal investigation. Triacylglycerols are body fat secreted from pores in the finger along with other sebaceous materials that are easily detected in mass spectrometry imaging analysis. Based on the preliminary data, it is hypothesized triacylglycerol patterns can be used to determine the fingerprint age and the health information of individuals, especially those with diabetes.
This project will validate our hypothesis in a systematic manner by testing under varying environmental conditions and in a large population with statistical evaluation. After studying the degradation mechanism of triacylglycerols from standard compounds under the various controlled environmental conditions, the aging of fingerprints from over one hundred healthy individuals with diverse age, gender, and ethnicity will be tested to verify the robustness of the methodology. To study the triacylglycerol patterns of diabetes patients, blood and fingerprint samples will be collected at University of Iowa Medical School. The correlation between triacylglycerols in blood and fingerprints will be made, and classification of diabetes status will be determined using a machine learning algorithm. Also studied is the fundamental understanding of the effect of diabetes on triacylglycerol patterns. Additionally, the aging of fingerprint triacylglycerols from diabetes patients will be made to delineate the effect of aging and diabetes.
Currently, there is no reliable method to determine the time since deposition of latent fingerprints, which is a crucial bottleneck for convictions using fingerprints as evidence. The proposed approach of using degradation of unsaturated triacylglycerols is a very promising innovative approach that can provide reliable quantitative information. Furthermore, diabetes or other health information will be simultaneously revealed from the triacylglycerol profiles. The use of machine learning algorithms is relatively novel to forensic research and will enable us to reach the next level in forensic science. The present project will be groundbreaking work that can reveal the hidden potential of mass spectrometry imaging in forensic investigation. Once the proposed project is successfully accomplished, it will attract tremendous interest from forensic community leading to the use of mass spectrometry imaging in forensic applications.
Note: This project contains a research and/or development component, as defined in applicable law, and complies with Part 200 Uniform Requirements - 2 CFR 200.210(a)(14).