Rapid identification of traditional Chinese medicines (Lonicerae japonicae flos and Lonicerae flos) and their origins using excitation-emission matrix fluorescence spectroscopy coupled with chemometrics

Lonicerae japonicae flos (LJF) and Lonicerae flos (LF) are important traditional Chinese medicine with various effects and prescription compatibility. The accurate identification of LJF and LF and their geographical origin are of great significance to the quality control and correct medication. In this work, a simple, rapid and efficient strategy for identification of Lonicerae japonicae flos and Lonicerae flos and their geographical origin was proposed by combining excitation-emission matrix fluorescence (EEMF) and chemometrics. Excitation-emission matrix fluorescence (EEMF) spectra of LJF and LF samples were characterized by parallel factor analysis (PARAFAC) to acquire chemically meaningful information. Classification models were built using three chemometric methods, including partial least squares-discrimination analysis (PLS-DA), principal component analysis-linear discriminant analysis (PCA-LDA) and random forest (RF). These models were utilized to identify LJF and LF and their geographical origin. Results revealed that PCA-LDA model gained the optimal performance with 100% classification accuracy for distinguishing between LJF and LJF from different geographical origin. Therefore, the proposed strategy could be a competitive alternative for fast and accurate differentiation of LJF and LF and their geographical origin.