Identification of individual red blood cells by Raman microspectroscopy for forensic purposes: in search of a limit of detection
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- 作者：Claire K. Muro ; Igor K. Lednev
- 关键词：Forensics ; Raman spectroscopy ; Peripheral blood ; Limit of detection
- 刊名：Analytical and Bioanalytical Chemistry
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：409
- 期：1
- 页码：287-293
- 全文大小：
- 刊物类别：Chemistry and Materials Science
- 刊物主题：Analytical Chemistry; Biochemistry, general; Laboratory Medicine; Characterization and Evaluation of Materials; Food Science; Monitoring/Environmental Analysis;
- 出版者：Springer Berlin Heidelberg
- ISSN：1618-2650
- 卷排序：409
文摘
Traces of body fluids can be present at a variety of crime scenes. It is important that forensic investigators have a reliable and nondestructive method of identifying these traces. Of equal importance is establishing the limitations of any method in use, including its detection limit. We have previously reported on the use of Raman microspectroscopy and multivariate data analysis to identify and differentiate body fluids. While many studies use serial dilutions to establish limits of detection, we utilized a different approach and demonstrated that a single red blood cell is sufficient to be correctly identified as blood. The experimental Raman spectra of individual red blood cells were loaded into the previously reported models for body fluid identification, and all were correctly classified as peripheral blood. These results demonstrate that our model can be used to identify peripheral blood, even if there is only a single red blood cell present. Furthermore, a single red blood cell is 5000× smaller than the amount of peripheral blood required to perform DNA analysis in a modern crime laboratory. This means that if a bloodstain is large enough for DNA analysis, Raman microspectroscopy should be able to make a positive identification. Considering that the sample analysis reported here was carried out with a different instrument, not the one used for the previously reported method development, these results also represent a form of method validation. The model’s ability to correctly classify spectra acquired on a different instrumental platform is crucial in preparing it for practical application.