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

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.