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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. • The study designed a heteromorphic sample pool whose incident plane and exit plane are not parallel. • Gray level co-occurrence matrix (GLCM) was used to extract textural features from hyperspectral images of turbid media. • Analysis of the textural features of hyperspectral images is superior to conventional light intensity analysis in component detection of turbid media. • The study can provide a potentially viable method for detecting the components of turbid media quantitatively in analytical chemistry. [ABSTRACT FROM AUTHOR]