Chemical fingerprint analysis for quality control of Fructus Aurantii Immaturus based on HPLC-DAD combined with chemometric methods

Xu, Xianlin; Jun-hui, Jiang; Liang, Yi‐Zeng; Yi, Lunzhao; Cheng, Jun

doi:10.1039/c0ay00455c

Cited by 33 publications

(17 citation statements)

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“…The HPLC fingerprint of Baizhi consisted mainly of 22 peaks, and the attribution of 10 peaks in the profile was confirmed by comparison of the retention time, UV spectrum of reference substances profile, spiking some of the chemical reference substances, as well as re-ferring to the data published in the literature [21]. The attribution of the ten peaks was: adenosine (1), xanthotoxol (6), xanthotoxin (10), oxypeucedanin hydrate (12), isopimpinellin (14), bergapten (16), oxypeucedanin (18), imporatorin (19), cnidilin (21), and isoimporatorin (22). The peaks 10,12,16,17,18,19,21, and 22 dominated the profile of the sun-dried authenticated Baizhi; among them, peak 19 (imporatorin) was the uppermost obvious one (l " Fig.…”

Section: Examples Of Case Studies By Chromatographic Fingerprintingsupporting

confidence: 58%

“…Convergence or divergence obviously depends on the final purposes [15]. Another example for phytotaxonomy and quality analysis can be shown by the work on high-performance liquid chromatographic (HPLC) fingerprints of Fructus Aurantii immaturus (FAI) from different sources of China [16]. The fingerprints were generated in the deliberately collected 12 authentic samples and 26 commercial samples.…”

Section: Examples Of Case Studies By Chromatographic Fingerprintingmentioning

confidence: 99%

“…The attribution of the ten peaks was: adenosine (1), xanthotoxol (6), xanthotoxin (10), oxypeucedanin hydrate (12), isopimpinellin (14), bergapten (16), oxypeucedanin (18), imporatorin (19), cnidilin (21), and isoimporatorin (22). The peaks 10,12,16,17,18,19,21, and 22 dominated the profile of the sun-dried authenticated Baizhi; among them, peak 19 (imporatorin) was the uppermost obvious one (l " Fig. 5).…”

Section: Examples Of Case Studies By Chromatographic Fingerprintingmentioning

confidence: 99%

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Perspective of Chemical Fingerprinting of Chinese Herbs

2010

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Section: Examples Of Case Studies By Chromatographic Fingerprintingsupporting

confidence: 58%

Section: Examples Of Case Studies By Chromatographic Fingerprintingmentioning

confidence: 99%

Section: Examples Of Case Studies By Chromatographic Fingerprintingmentioning

confidence: 99%

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Perspective of Chemical Fingerprinting of Chinese Herbs

2010

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“…In previous reports, components in AF were authenticated and quantified using HPLC-diode array detection (DAD) [14], LC-MS [15], GC-MS [16], and NMR [17]. Recently, highresolution mass spectrometry, including TOF-MS [13] and ion trap MS [18], has been applied to identify chemical compositions in AF.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of multicomponents by single marker combined with HPLC fingerprint qualitative analyses for comprehensive evaluation of Aurantii Fructus

Lei

Wang

Sun³

et al. 2020

J of Separation Science

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The present study aimed to develop a strategy involving quantitative analysis of multicomponents by single marker in combination with high-performance liquid chromatography fingerprint qualitative analysis for performing the quality control of Aurantii Fructus. The content of 12 components (eriocitrin, neoeriocitrin, narirutin, naringin, hesperidin, neohesperidin, meranzin, poncirin, naringenin, nobiletin, tangeretin, and auraptene) in samples was determined using reliable relative correction factors that were obtained using naringin as an internal reference standard. The new method demonstrated good applicability, and no significant differences were observed between the external standard method and the new method as determined by calculating standard method difference. Qualitative evaluation of samples was conducted using similarity analysis, hierarchical cluster analysis, and quality fluctuation analysis. Chromatographic fingerprint data were divided into three groups by similarity and hierarchical cluster analyses, and seven components may have a more significant impact on the quality of Aurantii Fructus in quality fluctuation analysis. Overall, the study suggests that the qualitative and quantitative analyses of multicomponents using quantitative analysis of multicomponents by single marker combined with chromatographic fingerprinting can be considered good quality criteria for performing quality control and providing technical support for the further pharmacological and pharmaceutical research of Aurantii Fructus. K E Y W O R D Schromatographic fingerprints, quality control, quantitative analysis, traditional Chinese medicine Article Related Abbreviations: AF, Aurantii Fructus; DAD, diode array detection; ESM, external standard method; HCA, hierarchical cluster analysis; QAMS, quantitative analysis of multicomponents by single marker; RCF, relative correction factor; SA, similarity analysis; SMD, standard method difference; TCM, traditional Chinese medicine. 1382

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“…Also, if the botanical material is available as a powder, then morphological identification is impossible. Chromatographic fingerprint methods based on high-performance liquid chromatography (HPLC) techniques with ultraviolet spectroscopic detectors (Ding et al, 2007, Chen et al, 2012, Xu et al, 2010, Chuang, et al, 2007b, a) have been reported, which require long separation times in excess of sixty minutes and the methods are instrumentation-dependent with varying profiles for the same samples. HPLC coupled with mass spectrometry was reported for the determination of flavonoids, alkaloid compounds (Ding et al, 2007), and coumarins (Chen et al, 2012) in citrus materials, but are time-consuming, expensive, and require expertise to operate and maintain.…”

Section: Introductionmentioning

confidence: 99%

Differentiation ofAurantii fructus immaturusandFructus poniciri trifoliatae immaturusby Flow-Injection with Ultraviolet Spectroscopic Detection and Proton Nuclear Magnetic Resonance Using Partial Least-Squares Discriminant Analysis

et al. 2015

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Two simple fingerprinting methods, flow-injection coupled to ultraviolet spectroscopy and proton nuclear magnetic resonance, were used for discriminating between Aurantii fructus immaturus and Fructus poniciri trifoliatae immaturus. Both methods were combined with partial least-squares discriminant analysis. In the flow-injection method, four data representations were evaluated: total ultraviolet absorbance chromatograms, averaged ultraviolet spectra, absorbance at 193, 205, 225, and 283 nm, and absorbance at 225 and 283 nm. Prediction rates of 100% were achieved for all data representations by partial least-squares discriminant analysis using leave-one-sample-out cross-validation. The prediction rate for the proton nuclear magnetic resonance data by partial least-squares discriminant analysis with leave-one-sample-out cross-validation was also 100%. A new validation set of data was collected by flow-injection with ultraviolet spectroscopic detection two weeks later and predicted by partial least-squares discriminant analysis models constructed by the initial data representations with no parameter changes. The classification rates were 95% with the total ultraviolet absorbance chromatograms datasets and 100% with the other three datasets. Flow-injection with ultraviolet detection and proton nuclear magnetic resonance are simple, high throughput, and low-cost methods for discrimination studies.

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Chemical fingerprint analysis for quality control of Fructus Aurantii Immaturus based on HPLC-DAD combined with chemometric methods

Cited by 33 publications

References 31 publications

Perspective of Chemical Fingerprinting of Chinese Herbs

Perspective of Chemical Fingerprinting of Chinese Herbs

Quantitative analysis of multicomponents by single marker combined with HPLC fingerprint qualitative analyses for comprehensive evaluation of Aurantii Fructus

Differentiation ofAurantii fructus immaturusandFructus poniciri trifoliatae immaturusby Flow-Injection with Ultraviolet Spectroscopic Detection and Proton Nuclear Magnetic Resonance Using Partial Least-Squares Discriminant Analysis

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