LAUSR

Abstract The accuracy of component detection of turbid media can be difficult to improve due to the mutual influence of scattering and absorption in light attenuation. In this study, a heteromorphic sample pool was introduced containing turbid media with India Ink and Intralipid-20% fat emulsion, which increases the scattering information of the non-circumferential symmetric hyperspectral image of the turbid media. A gray level co-occurrence matrix (GLCM) was used to extract textural features from the hyperspectral images. Subsequently, the textural features were correlated with the concentrations of Intralipid-20% by means of partial least squares regression, and it was compared with the frequently used analysis of two-dimensional exit light intensity. Experimental results show that textural feature modeling is superior to conventional light intensity modeling with a correlation coefficient of prediction (Rp) = 0.9831 and a root-mean-square error of prediction (RMSEP) = 0.0631% in the prediction set. This study provides a potentially viable method for detecting the components of turbid media quantitatively in analytical chemistry.