Abstract
The estimation of bloodstain age is an important factor in forensic analysis. Previously, we have reported a smartphone-based colorimetric system for age estimation of bloodstain, in which Whole blood and EDTA whole blood were dropped on 4 different materials (700 μL) and captured using a smartphone for 72 h. In order to enhance sensitivity and accuracy of the previous system, the current work is dedicated towards the application of pattern recognition and classification of bloodstain images based on a smartphone. Three detection methods (blood pool, crack ratio, and colorimetric analysis) in terms of 6 steps of drying process of the bloodstain (coagulation, gelation, edge desiccation, center desiccation, crack propagation, and final desiccation) were applied to estimate age of the bloodstain accurately. Three parameters from the bloodstain images were then classified as comparing to those of stored reference images with similar trends in database. The bloodstain age was successfully determined by 9 h, 18 h, and 48 h with respect to the three detection methods mentioned above, respectively. The differences in bloodstain images were clearly distinguished every hour by using smartphone-based pattern recognition analysis. Therefore, our system is expected to shed a light on the field of forensic science by estimating bloodstain age in real time.
Original language | English |
---|---|
Pages (from-to) | 414-419 |
Number of pages | 6 |
Journal | Biosensors and Bioelectronics |
Volume | 130 |
DOIs | |
Publication status | Published - 2019 Apr 1 |
Bibliographical note
Funding Information:This research was supported by iPET (Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries), Ministry for Food, Agriculture, Forestry and Fisheries, Republic of Korea (316073-03-3-HD020), the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP (2015M3A9D7067364), and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2018R1A2A2A15019814).
Funding Information:
This research was supported by iPET (Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries), Ministry for Food, Agriculture, Forestry and Fisheries , Republic of Korea ( 316073-03-3-HD020 ), the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP ( 2015M3A9D7067364 ), and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2018R1A2A2A15019814 ).
Publisher Copyright:
© 2018 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Biotechnology
- Biophysics
- Biomedical Engineering
- Electrochemistry