Three-dimensional fluorescence combined with alternating trilinear decomposition and random forest algorithm for the rapid prediction of species, geographical origin and main components of Glycyrrhizae Radix et Rhizoma (Gancao).

[Display omitted] • A method for predicting species, geographical origin and main compounds of Gancao. • The method was based on 7 components resolved from three-dimensional fluorescence. • The fluorescence resolved and prediction algorithm were ATLD and random forest. • The accuracy of this geographical origin discriminant model can reach 94.4%. • The accuracy of main compounds prediction model of Gancao could reach 95.6%. Glycyrrhizae Radix et Rhizoma (Gancao) is a functional food whose quality varies significantly between distinct geographical sources owing to the influence of genetics and the geographical environment. This study employed three-dimensional fluorescence coupled with alternating trilinear decomposition (ATLD) and random forest (RF) algorithms to rapidly predict Gancao species, geographical origins, and primary constituents. Seven fluorescent components were resolved from the three-dimensional fluorescence of the ATLD for subsequent analysis. Results indicated that the RF model distinguished Gancao from various species and origins better than other algorithms, achieving an accuracy of 94.4 % and 88.9 %, respectively. Furthermore, the RF regressor algorithm was used to predict the concentrations of liquiritin and glycyrrhizic acid in Gancao, with 96.4 % and 95.6 % prediction accuracies compared to HPLC, respectively. This approach offers a novel means of objectively evaluating the origin of food and holds substantial promise for food quality assessment. [ABSTRACT FROM AUTHOR]