Chemical Fingerprint Analysis for the Quality Evaluation of Deepure Instant Pu-erh Tea by HPLC Combined with Chemometrics

Wang, Chao; Zhang, Chenxia; Shao, Chun-Fu; Li, Changwen; Liu, Shunhang; Peng, Xiaopei; Xu, Yong-Quan

doi:10.1007/s12161-016-0524-4

Cited by 34 publications

(20 citation statements)

References 35 publications

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“…Principal component analysis (PCA) is a classification method that uses dimensionality reduction techniques to simplify numerous original variables into several representative composite indicators [32,33]. According to the contribution rate of each comprehensive indicator, the information of the original data could be reflected when using appropriate numbers of principal components (PCs) [34].…”

Section: Methodsmentioning

confidence: 99%

Protein-Based Fingerprint Analysis for the Identification of Ranae Oviductus Using RP-HPLC

et al. 2019

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This work demonstrated a method combining reversed-phase high-performance liquid chromatography (RP-HPLC) with chemometrics analysis to identify the authenticity of Ranae Oviductus. The fingerprint chromatograms of the Ranae Oviductus protein were established through an Agilent Zorbax 300SB-C8 column and diode array detection at 215 nm, using 0.085% TFA (v/v) in acetonitrile (A) and 0.1% TFA in ultrapure water (B) as mobile phase. The similarity was in the range of 0.779–0.980. The fingerprint chromatogram of Ranae Oviductus showed a significant difference with counterfeit products. Hierarchical clustering analysis (HCA) and principal component analysis (PCA) successfully identified Ranae Oviductus from the samples. These results indicated that the method established in this work was reliable.

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Section: Methodsmentioning

confidence: 99%

Protein-Based Fingerprint Analysis for the Identification of Ranae Oviductus Using RP-HPLC

et al. 2019

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“…PCA was used to analyze and describe the distribution of the studied sample as it could explain most of data variance with a greater fitting and predictive ability [19]. Besides, PLS-DA can maximize the difference among the groups and screen out key markers that could be responsible for sample variation [21,22]. The sample variation could be assessed from PCA score plot while PLS-DA was used to screen out the main markers that were responsible for discrimination.…”

Section: Chemical Pattern Recognition Analysismentioning

confidence: 99%

High‐performance liquid chromatography–based fingerprint analysis with chemical pattern recognition for evaluation of Mahonia bealei (Fort.) Carr

Huang

Wang

Yin

et al. 2020

J of Separation Science

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A simple and efficient high‐performance liquid chromatography method combined with chemical pattern recognition was established for quality evaluation of Mahonia bealei (Fort.) Carr. A common pattern of 30 characteristic peaks was applied for similarity analysis, hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis in the 37 batches of M. bealei (Fort.) Carr. to discriminate wild M. bealei (Fort.) Carr., cultivated M. bealei (Fort.) Carr., and its substitutes. The results showed that partial least squares discriminant analysis was the most effective method for discrimination. Eight characteristics peaks with higher variable importance in projection values were selected for pattern recognition model. A permutation test and 26 batches of testing set samples were performed to validate the model that was successfully established. All of the training and testing set samples were correctly classified into three clusters (wild M. bealei (Fort.) Carr., cultivated M. bealei (Fort.) Carr., and its substitutes) based on the selected chemical markers. Moreover, 26 batches of unknown samples were used to predict the accuracy of the established model with a discrimination accuracy of 100%. The obtained results indicated that the method showed great potential application for accurate evaluation and prediction of the quality of M. bealei (Fort.) Carr.

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“…The HPLC analysis was based on a slightly modified method described by Wang et al (2016a). Each sample (about 0.2 g) was placed into a 10-mL centrifuge tube, and 4 mL of preheated 70% methanol was added.…”

Section: Polyphenols and Alkaloids Analyzed By Hplcmentioning

confidence: 99%

Comparison of volatile profiles and bioactive components of sun-dried Pu-erh tea leaves from ancient tea plants on Bulang Mountain measured by GC-MS and HPLC

Zhang

Yang

et al. 2019

J. Zhejiang Univ. Sci. B

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To explore the volatile profiles and the contents of ten bioactive components (polyphenols and caffeine) of sun-dried Pu-erh tea leaves from ancient tea plants on Bulang Mountain, 17 samples of three tea varieties were analyzed by headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and high-performance liquid chromatography (HPLC). A total of 75 volatile components were tentatively identified. Laomaner (LME), Laobanzhang (LBZ), and other teas on Bulang Mountain (BL) contained 70, 53, and 71 volatile compounds, respectively. Among the volatile compounds, alcohols (30.2%-45.8%), hydrocarbons (13.7%-17.5%), and ketones (12.4%-23.4%) were qualitatively the most dominant volatile compounds in the different tea varieties. The average content of polyphenol was highest in LME (102.1 mg/g), followed by BL (98.7 mg/g) and LBZ (88.0 mg/g), while caffeine showed the opposite trend, 27.3 mg/g in LME, 33.5 mg/g in BL, and 38.1 mg/g in LBZ. Principal component analysis applied to both the volatile compounds and ten bioactive components showed a poor separation of samples according to varieties, while partial least squares-discriminant analysis (PLS-DA) showed satisfactory discrimination. Thirty-four volatile components and five bioactive compounds were selected as major discriminators (variable importance in projection (VIP) >1) among the tea varieties. These results suggest that chromatographic data combined with multivariate analysis could provide a useful technique to characterize and distinguish the sun-dried Pu-erh tea leaves from ancient tea varieties on Bulang Mountain.

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Chemical Fingerprint Analysis for the Quality Evaluation of Deepure Instant Pu-erh Tea by HPLC Combined with Chemometrics

Cited by 34 publications

References 35 publications

Protein-Based Fingerprint Analysis for the Identification of Ranae Oviductus Using RP-HPLC

Protein-Based Fingerprint Analysis for the Identification of Ranae Oviductus Using RP-HPLC

High‐performance liquid chromatography–based fingerprint analysis with chemical pattern recognition for evaluation of Mahonia bealei (Fort.) Carr

Comparison of volatile profiles and bioactive components of sun-dried Pu-erh tea leaves from ancient tea plants on Bulang Mountain measured by GC-MS and HPLC

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