Overlapping Spectral Analysis Based on Genetic Algorithms and BP Neural Networks
DU Yue, MENG Xiao-chen*, ZHU Lian-qing
Beijing Laboratory for Optoelectronic Measurement Technology, Beijing Laboratory for Biomedical Testing Technology and Instruments, Beijing Information Science and Technology University, Beijing 100101, China
Abstract：With the rapid development of spectroscopy and fluorescence detection technology, monochrome fluorescence labeling is unable to analyze cell samples accurately and has been gradually replaced by two-color or multi-color fluorescence labeling. In the multicolor fluorescence analysis, since the cells were labeled with a variety of fluorescein usually, partial spectral overlap will occur in the emission spectrum, which need to be decomposed into an independent spectral peak to analyze accurately. Aiming at it, optimized BP neural network based on genetic algorithm （GA_BP） were used for overlapping spectral peak analysis. Firstly, the concrete structure of BP neural network was determined, and the overlapping peak was pre-processed by quadratic differential to find out the number and positions of single peaks as the characteristic value of overlapping peaks to be the input layer of BP neural network; in addition, weights and thresholds of BP neural network were Initialized, and optimal parameters like initial population and population size of the genetic algorithm were selected by using the advantage of global search; after a series of genetic evolution operations like selecting, crossing and mutating, the individuals containing the optimal weights and thresholds of BP neural network were obtained; and then the optimal parameters of the network were selected to carry out network training, which the width and intensity of the independent peak can be calculated from the output node of the optimized BP neural network; finally, combined with the eigenvalues of overlapping peak identified by quadratic differential, independent spectral peak can be separated. The randomly generated Gaussian overlapping peaks model was used as experimental simulation data, and the decomposition experiments showed high precision of the peak intensity and peak width. Wherein, the maximum relative error of decomposition of two overlapping peaks was 0.30% and 3.57%, and which of the three overlapping peaks was 0.64% and 3.83%. It can also be decomposed when the four overlapping peaks. Moreover, compared the GA_BP network model with the unoptimized BP neural network model, the results showed that the GA_BP network could reach the preset error value after five steps, while the unoptimized network model takes 19 steps. This further proves that the GA_BP network model converged faster with a fairly high precision that can be widely used for the decomposition of spectral and other overlapping peaks, which has a certain practical value compared with traditional methods.
Key words：Biomedicine; Overlapping peaks decomposition; Neural networks; Genetic algorithm
[1] YE Guo-yang, XU Ke-jun（叶国阳, 徐科军）. Chinese Journal of Scientific Instrument（仪器仪表学报）, 2015, 36(2): 439.
[2] FENG Fei, WANG Fu-bei, XIE Fei, et al（冯 飞, 王府北, 谢 非, 等）. Acta Photonica Sinica（光子学报）, 2015, 44(6): 113.
[3] HU Ruo-mu, WANG Zhen-zhen, LIU Ren-wei, et al（胡若木, 王珍珍, 刘人玮, 等）. Acta Photonica Sinica（光子学报）, 2018, 47(8): 39.
[4] XU Xi-rong, PAN Zi-qi, LI Xing-hua, et al（徐喜荣, 潘子琦, 李兴华, 等）. Journal of Dalian University of Technology（大连理工大学学报）, 2019, 59(1): 97.
[5] TAO Wei-liang, LIU Yan, WANG Xian-pei, et al（陶维亮, 刘 艳, 王先培, 等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2017, 37(12): 3664.
[6] Du Hua, Chen Wuhui, Zhu Qingjun, et al. Applied Radiation and Isotopes, 2018, 141: 149.
[7] Li Lili, Wu Yanling, Dong Xuehu, et al. Analysis of Overlapping Peaks of GA Algorithms with Early Experience in Integration. ICIEA, 2018.
[8] LIU Guang-da, WEI Xing, ZHANG Shang, et al（刘光达, 魏 星, 张 尚, 等）. Journal of Biomedical Engineering（生物医学工程杂志）, 2019, 36(1): 24.
[9] Gao Yuyang, Qu Chao, Zhang Kequan. Energies, 2016, 9(10): 1.
[10] HU Yao-gai, ZHANG Xiao-xing, ZHAO Zheng-yu, et al(胡耀垓，张晓星，赵正予，等). Journal of Chongqing University(重庆大学学报)，2012，35(5)：76.
