Design and verification of improved factor number selection process for parallel factor algorithm

In order to solve the problem that the selection process of the number selection of model factors in the decomposition of three-dimensional fluorescence spectrum by parallel factor algorithm is not clear, an improved factor number selection process composed of core diagonal matrix, kernel uniform function and constant wavelength residual graph was proposed. The improved parallel factor analysis algorithm was developed to verify the accuracy of factor number selection process with humic acid as detection material. The results show that, combined with the above process, when the excitation light and emission light are in 350-450 nm/350-620 nm, respectively, and the factor number is 4, the core diagonal matrix distribution meets the demand, the kernel consistent function is 52%, the residual error of the fitting diagram is the smallest, and the decomposition effect is the best in the standard region. Compared with using a single method, the above combination process is more logical and accurate, and can quickly determine the number of factors in practical application. The four factors are two humic acid factor A located at 360-370 nm/450-500 nm and 350-360 nm/450-500 nm, one humic acid factor C located at 365-375 nm/475-525 nm, and one soil fulvic acid factor located at 380-390 nm/475-525 nm. When the concentration increased from 20 mg/L to 200 mg/L, the composition and contribution rate of the factors has little difference, that is, the change of concentration did not change the properties of the solution.