A Measuring Method of DOM Components Based on Fiber SPR Sensor and ICPSO-BP Neural Network

As the single sensor is not competent for the variation of the total amount and components of DOM in a large range, according to the cross-sensitivity between fiber SPR sensors with different structures, a measuring method of DOM components is proposed by combining the deep learning algorithm with the fiber SPR sensors based on the regulation of metal film thickness. We exploit an improved cooperative particle swarm optimization algorithm (ICPSO) aiming at the problem of particle diversity loss caused by premature convergence of particles which not only considers the optimization information of single particles, the global particles and particles in the groups, but also considers the proportion of shared information. Then, the ICPSO is used to optimize the weights and thresholds of back propagation neural network (BPNN) to establish ICPSO-BP network, so as to construct three classifiers consists of ICPSO-BP (wave length), ICPSO-BP (spectral width), ICPSO-BP (light intensity). By comprehensive training of the resonance wavelength, spectrum width and light intensity of SPR effect for the measured water, five DOM components (tyrosine protein, tryptophan protein, fulvic acid, dissolved microbial metabolites and humic acid) and their concentrations in four water samples, namely, Inner canal (A), Hongze lake (B), Park lake (C) and Campus lake (D), have been effectively predicted. The prediction accuracy is more than 80%, among them, the highest prediction rate of tryptophan protein and its concentration in Hongze lake (B) which can reach 86%. Therefore, the dynamic range of SPR measurement is effectively expanded and better measurement accuracy and sensitivity are maintained, which verifies the feasibility of the proposed method in DOM measuring and provide a new idea for DOM component testing.