Discrimination and classification of true or mock blood based on photoacoustic spectroscopy combined with support vector machine

Zhong Ren; Junli Wu; Chengxin Xiong; Tao Liu

doi:10.1117/12.2602296

24 November 2021 Discrimination and classification of true or mock blood based on photoacoustic spectroscopy combined with support vector machine

Zhong Ren, Junli Wu, Chengxin Xiong, Tao Liu

Author Affiliations +

Proceedings Volume 12067, AOPC 2021: Biomedical Optics; 1206702 (2021) https://doi.org/10.1117/12.2602296
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

The blood is an important tissue in the human and animal body. It has significant role in the fields of bio-medical diagnosis, animal quarantine, criminal investigation, food safety, etc. However, there are some illegal cases reported about the real blood abused by fake blood recently, which seriously impact the human health and society stability. The rapid and accuracy detection of blood is very important and urgent. To achieve this aim, the photoacoustic spectroscopy was used to detect the real blood and fake one. A set of photoacoustic detection system was established based on OPO pulsed laser and focused ultrasonic detector. In experiments, 150 groups of real and fake blood samples was test, where 120 groups were used as the training samples, 30 groups were used as the test samples. The time-resolved photoacoustic signal and peak-to-peak values of all samples were captured in the wavelengths from 700-1064nm. To classify and distinguish the real and fake blood, the support vector machine (SVM) algorithm was used to train the training blood samples and test the correct rate of classification and distinction of the real and fake blood. The results show that the correct rate is 83.3% by using the SVM algorithm. To further improve the correct rate, the principal components analysis (PCA) algorithm was used to extract the characteristic information from the photoacoustic peak-to-peak values of blood samples in full wavelengths. The correct rates of real and fake blood based on PCA-SVM algorithm under the different principal components were obtained and compared. The results show that the correct rate can be improved to 90% for the PCA-SVM algorithm with 21 principal components.

Citation Download Citation

Zhong Ren, Junli Wu, Chengxin Xiong, and Tao Liu "Discrimination and classification of true or mock blood based on photoacoustic spectroscopy combined with support vector machine", Proc. SPIE 12067, AOPC 2021: Biomedical Optics, 1206702 (24 November 2021); https://doi.org/10.1117/12.2602296

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Blood

Photoacoustic spectroscopy

Principal component analysis

Pulsed laser operation

Ultrasonics

Tissue optics

Transducers

Show All Keywords

Keywords/Phrases

Search In:

Publication Years