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Molecules 2018, 23(5), 1235; https://doi.org/10.3390/molecules23051235

Study on the Discrimination between Citri Reticulatae Pericarpium Varieties Based on HS-SPME-GC-MS Combined with Multivariate Statistical Analyses

Guangdong Engineering & Technology Research Center for Quality and Efficacy Reevaluation of Post-Market Traditional Chinese Medicine, Guangdong Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
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Received: 19 April 2018 / Revised: 15 May 2018 / Accepted: 20 May 2018 / Published: 22 May 2018
(This article belongs to the Section Analytical Chemistry)
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Abstract

Citri reticulatae pericarpium (CRP), the dried pericarps of Citrus reticulata Blanco and its cultivars, has been widely used in drugs and foods in China for centuries. In this study, an accurate and feasible analytical method based on HS-SPME-GC-MS coupled with multivariate statistical analyses was developed to comprehensively compare volatile compounds of pericarps derived from Citrus reticulata “Chachi” (“Guangchenpi” in Chinese, GCP) and other cultivars of Citrus reticulata Blanco (“Chenpi” in Chinese, CP). Principal component analysis, hierarchical cluster analysis, and orthogonal partial least-squares-discrimination analysis were performed to extract meaningful attributes from volatile profiles based on GC-MS data. Results indicated that samples from GCP and CP could easily be differentiated, and seven potential chemical markers were screened for the quality control of CRP. This study illuminated the volatile profile in CRP, and provides a practical method for the authentication of CRP varieties. View Full-Text
Keywords: Citri reticulatae pericarpium; volatile compounds; HS-SPME-GC-MS; multivariate statistical analyses Citri reticulatae pericarpium; volatile compounds; HS-SPME-GC-MS; multivariate statistical analyses
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Zheng, Y.; Zeng, X.; Peng, W.; Wu, Z.; Su, W. Study on the Discrimination between Citri Reticulatae Pericarpium Varieties Based on HS-SPME-GC-MS Combined with Multivariate Statistical Analyses. Molecules 2018, 23, 1235.

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