Data fusion of synchronous fluorescence and surface enhanced Raman scattering spectroscopies for geographical origin traceability of Atractylodes macrocephala Koidz

Geographical origin has great influence on the quality of traditional Chinese medicine. This work reported an application of geographical origin traceability of Atractylodes macrocephala Koidz. based on chemometrics classification methods combined with data fusion of synchronous fluorescence spectroscopy and surface-enhanced Raman spectroscopy. The classification model was built by principal component analysis-linear discriminant analysis, partial least squares discriminant analysis and random forest. The cross-validation showed that the correct classification rate could achieve 91.0% for partial least squares discriminant analysis based on low-level data fusion and the classification model based on low-level data fusion could achieve accurate classification of 14 new Atractylodes macrocephala Koidz. samples. The results demonstrated that the synchronous fluorescence spectroscopy and surface-enhanced Raman spectroscopy complemented each other, and better classification results can be obtained based on data fusion strategy.