2020
DOI: 10.1002/fsn3.1717
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Differentiation between wild and artificial cultivated Stephaniae tetrandrae radix using chromatographic and flow‐injection mass spectrometric fingerprints with the aid of principal component analysis

Abstract: High‐performance liquid chromatographic (HPLC) and flow‐injection mass spectrometric (FIMS) fingerprinting profiles were used to differentiate between wild and artificial cultivated Stephaniae tetrandrae Radix samples. HPLC and FIMS fingerprints of 15 wild S. tetrandrae Radix samples and 12 artificial cultivated S. tetrandrae Radix samples were obtained and analyzed with the aid of principal component analysis (PCA). PCA of the fingerprints showed that the chemical differences between wild and artificial culti… Show more

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Cited by 4 publications
(2 citation statements)
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“…Pharmacological studies have shown that the fungus contains biologically active chemical substances including nucleosides, cordycepic acid, cordycepin, beauvericin, and myriocin ( Sun et al, 2017 ). C. cicadae has been historically utilized for liver and kidney protection, analgesia–antipyresis, blood fat reduction, and its antitumor activities ( Qin et al, 2020 ). It is well known that the active ingredients extracted from C. cicadae are effective in ameliorating CKD induced by diabetes or hypertension.…”
Section: Introductionmentioning
confidence: 99%
“…Pharmacological studies have shown that the fungus contains biologically active chemical substances including nucleosides, cordycepic acid, cordycepin, beauvericin, and myriocin ( Sun et al, 2017 ). C. cicadae has been historically utilized for liver and kidney protection, analgesia–antipyresis, blood fat reduction, and its antitumor activities ( Qin et al, 2020 ). It is well known that the active ingredients extracted from C. cicadae are effective in ameliorating CKD induced by diabetes or hypertension.…”
Section: Introductionmentioning
confidence: 99%
“…The PCA method is used for feature extraction and dimension reduction; it converts numerous variables into a small number of relevant variables, allowing a clearer understanding of categories, clusters, and outliers [27]. In PCA, the model is visualized, allowing it to be understandable and avoiding subjective judgment [28].…”
Section: Discussionmentioning
confidence: 99%