Differentiation of Aurantii Fructus Immaturus and Fructus Poniciri Trifoliatae Immaturus by Flow-injection with Ultraviolet Spectroscopic Detection and Proton Nuclear Magnetic Resonance Using Partial Least-squares Discriminant Analysis.

Abstract

Two simple fingerprinting methods, flow-injection coupled to ultraviolet spectroscopy and proton nuclear magnetic resonance, were used for discriminating between Aurantii fructus immaturus and Fructus poniciri trifoliatae immaturus. Both methods were combined with partial least-squares discriminant analysis. In the flow-injection method, four data representations were evaluated: total ultraviolet absorbance chromatograms, averaged ultraviolet spectra, absorbance at 193, 205, 225, and 283 nm, and absorbance at 225 and 283 nm. Prediction rates of 100% were achieved for all data representations by partial least-squares discriminant analysis using leave-one-sample-out cross-validation. The prediction rate for the proton nuclear magnetic resonance data by partial least-squares discriminant analysis with leave-one-sample-out cross-validation was also 100%. A new validation set of data was collected by flow-injection with ultraviolet spectroscopic detection two weeks later and predicted by partial least-squares discriminant analysis models constructed by the initial data representations with no parameter changes. The classification rates were 95% with the total ultraviolet absorbance chromatograms datasets and 100% with the other three datasets. Flow-injection with ultraviolet detection and proton nuclear magnetic resonance are simple, high throughput, and low-cost methods for discrimination studies.

Cite this paper

@article{Zhang2016DifferentiationOA, title={Differentiation of Aurantii Fructus Immaturus and Fructus Poniciri Trifoliatae Immaturus by Flow-injection with Ultraviolet Spectroscopic Detection and Proton Nuclear Magnetic Resonance Using Partial Least-squares Discriminant Analysis.}, author={Mengliang Zhang and Yang Zhao and Peter de Boves Harrington and Pei Chen}, journal={Analytical letters}, year={2016}, volume={49 5}, pages={711-722} }