Segmented modelling and analysis of disperse dye concentration based on multidimensional spectrum

Since the water-insoluble dispersing dye has both absorption and scattering characteristics, a spatial resolution hyperspectral measurement approach and experimental testing was proposed in this article, which can collect spectral and spatial data from samples simultaneously. The concentration of 81 groups of three-component disperse dye samples were measured. However, the hyperspectral data of dye solutions in the 420–800 nm band is saturated, resulting in the inability for multispectral data processing. A segmented concentration quantitative analysis model was developed. For the unsaturated band (420–510 nm), the partial least squares (PLS), the N-way partial least squares (NPLS), and support vector machine (SVM) models using the data points on the X-axis of a two-dimensional light intensity distribution map were established. The predicted performance of PLS model was worse slightly than that of the other two models, The coefficient of determination (R²) values of concentrations for red, orange and blue disperse dye were 0.888, 0.796 and 0.959, respectively. For saturated band (520–670 nm), the NPLS and SVM models using the data points on the X- and Y-axis were established. Results shows that the prediction accuracy of concentrations of the three-component disperse dye was increased by adding additional data points on the Y-axis, with R² values of 0.944, 0.807, and 0.912, respectively. For the strong scattering band (680–800 nm), a SVM model was established, and R² of concentration of the three dyes reached 0.974, 0.933 and 0.995, respectively. The results showed that multidimensional spectroscopy method can improve the prediction accuracy of component concentration of disperse dye solution, by using more spectral information from X and Y directions.