Classification of Aurantii Fructus samples by multivariate analysis

• Published in: Journal of separation science Vol. 30; no. 12; pp. 1827 - 1832
• Main Authors: Chuang, Ching-Ching, Wen, Wu-Che, Sheu, Shuenn-Jyi
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY-VCH Verlag 01.08.2007; WILEY‐VCH Verlag; Wiley
• Subjects: Aurantii Fructus; Biological and medical sciences; Chemistry Techniques, Analytical - methods; China; Chromatography, High Pressure Liquid - methods; Cluster Analysis; Drugs, Chinese Herbal - chemistry; Drugs, Chinese Herbal - classification; Food industries; Fruit and vegetable industries; Fundamental and applied biological sciences. Psychology; General pharmacology; High-performance liquid chromatography; Medical sciences; Multivariate Analysis; Pharmacognosy. Homeopathy. Health food; Pharmacology. Drug treatments; Phylogeny; Plants, Medicinal - classification; Plants, Medicinal - metabolism; Principal Component Analysis; Species classification; Species Specificity; Taiwan; Asia; China; Taiwan; Discriminant analysis; Chemical analysis; Aurantii Fructus; Fruit; Pharmacognosy; Essential oil plant; Citrus fruit; Interspecific comparison; Rutaceae; HPLC chromatography; Multivariate analysis; High-performance liquid chromatography; Medicinal plant; Poncirus trifoliata; Ultraviolet detector; Dicotyledones; Species classification; Angiospermae; Classification; Plant origin; Spermatophyta; Citrus aurantium; Chemometrics; Principal component analysis
• ISSN: 1615-9306; 1615-9314
• DOI: 10.1002/jssc.200700016

Twenty commercial samples of Aurantii Fructus Immaturus (Poncitrus trifoliata) and 30 of Aurantii Fructus Maturus (Citrus aurantium and C. wilsonii) were collected from the Taiwan and China herbal markets. The contents of 12 constituents in these samples were determined by HPLC and were used to assess the potential relationships with their plant origins. Multivariate analysis including principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA) were used as classification procedures. Natural groupings of the samples divided into three sets successfully, 20 P. trifoliata, 15 C. aurantium, and 15 C. wilsonii, were observed by using PCA and CA. The application of LDA gave correct assignation percentages of 100.0% for all three groups.