Distinction of bloods based on photoacoustic spectroscopy combined with PCA-LDA algorithm

Zhong Ren; Tao Liu; Guodong Liu

doi:10.1117/12.2584032

8 November 2020 Distinction of bloods based on photoacoustic spectroscopy combined with PCA-LDA algorithm

Zhong Ren, Tao Liu, Guodong Liu

Proceedings Volume 11525, SPIE Future Sensing Technologies; 115252L (2020) https://doi.org/10.1117/12.2584032
Event: SPIE Future Sensing Technologies, 2020, Online Only

Conference Poster

Abstract

To rapidly and accurately distinguish the real and fake bloods, a photoacoustic detection system was established based on an optical parameters oscillator (OPO) pulsed laser and a focused ultrasonic transducer in this paper. The time-resolved photoacoustic signals of ten cow bloods and ten props bloods were captured. The amplitudes and profiles of photoacoustic signals for real and fake bloods were different. In addition, the photoacoustic peak-to-peak values of all blood samples were obtained from 700nm to 1064nm, and used as the train samples. The photoacoustic peak-to-peak values were firstly standardized and the data dimensions were reduced by using principle component analysis (PCA) method. Different principle components were chosen according to the cumulative contribution rate of characteristic values for the standardized photoacoustic peak-to-peak data matrix. Then, the scores of different principle components for ten cow bloods and ten props bloods were obtained and used as the train data. Five cow bloods and five props bloods were test. The linear discrimination analysis (LDA) algorithm was used to distinguish the real and fake bloods. The test results show that, based on the score data for two principle components by using PCA, the correct rate (CR) of real and fake bloods was improved from 80% to 100%. Results show that the LDA coupled with PCA, i.e.,PCA-LDA, has good distinction effect for the real and fake bloods.

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Zhong Ren, Tao Liu, and Guodong Liu "Distinction of bloods based on photoacoustic spectroscopy combined with PCA-LDA algorithm", Proc. SPIE 11525, SPIE Future Sensing Technologies, 115252L (8 November 2020); https://doi.org/10.1117/12.2584032

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