Accurate discrimination of Gastrodia elata from different geographical origins using high-performance liquid chromatography fingerprint combined with boosting partial least-squares discriminant analysis.

Abstract

Gastrodia elata from different geographical origins varies in quality and pharmacological activity. This study focused on the classification and identification of Gastrodia elata from six producing areas using high-performance liquid chromatography fingerprint combined with boosting partial least-squares discriminant analysis. Before recognition analysis, a principal component analysis was applied to ascertain the discrimination possibility with high-performance liquid chromatography fingerprints. And then, boosting partial least-squares discriminant analysis and conventional partial least-squares discriminant analysis were applied in this study. Experimental results indicated that the adaptive iteratively reweighted penalized least-squares algorithm could eliminate the baseline drift of high-performance liquid chromatography chromatograms effectively. And compared with partial least-squares discriminant analysis, the total recognition rates using high-performance liquid chromatography fingerprint combined with boosting partial least-squares discriminant analysis for the calibration sets and prediction sets were improved from 94 to 100% and 86 to 97%, respectively. In conclusion, high-performance liquid chromatography combined with boosting partial least-squares discriminant analysis, which has such advantages as effective, specific, accurate, non-polluting, has an edge for discrimination of traditional Chinese medicine from different geographical origins. And the proposed methodology is a useful tool to classify and identify Gastrodia elata from different geographical origins.