2014
DOI: 10.1002/jssc.201400646
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Quantitative determination of seven chemical constituents and chemo-type differentiation of chamomiles using high-performance thin-layer chromatography

Abstract: A simple and rapid high-performance thin-layer chromatographic method was developed for the separation and determination of six flavonoids (rutin, luteolin-7-O-β-glucoside, chamaemeloside, apigenin-7-O-β-glucoside, luteolin, apigenin) and one coumarin, umbelliferone from chamomile plant samples and dietary supplements. The separation was achieved on amino silica stationary phase using dichloromethane/acetonitrile/ethyl formate/glacial acetic acid/formic acid (11:2.5:3:1.25:1.25 v/v/v/v/v) as the mobile phase. … Show more

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Cited by 22 publications
(13 citation statements)
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“…Great amounts of information [variables, such as retention factor ( R f value) or features, such as colour or intensity] for a large number of samples (test objects) obtained from a thin‐layer chromatogram, require the use of statistical procedures in order to efficiently extract the maximum of useful information from the acquired data. Based on the similarity/dissimilarity analysis or correlation matrix, a number of unsupervised and supervised chemometric methods can be performed with the data, such as principal component analysis (PCA), hierarchical cluster analysis (HCA), linear discriminant analysis (LDA), partial least square discriminant analysis (PLS‐DA), k‐nearest neighbours (KNN), artificial neural networks (ANN), and partial least square (PLS) regression . This approach provides a systemic and objective way of analysing the HPTLC plate .…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Great amounts of information [variables, such as retention factor ( R f value) or features, such as colour or intensity] for a large number of samples (test objects) obtained from a thin‐layer chromatogram, require the use of statistical procedures in order to efficiently extract the maximum of useful information from the acquired data. Based on the similarity/dissimilarity analysis or correlation matrix, a number of unsupervised and supervised chemometric methods can be performed with the data, such as principal component analysis (PCA), hierarchical cluster analysis (HCA), linear discriminant analysis (LDA), partial least square discriminant analysis (PLS‐DA), k‐nearest neighbours (KNN), artificial neural networks (ANN), and partial least square (PLS) regression . This approach provides a systemic and objective way of analysing the HPTLC plate .…”
Section: Introductionmentioning
confidence: 99%
“…Based on the similarity/dissimilarity analysis or correlation matrix, a number of unsupervised and supervised chemometric methods can be performed with the data, 26 such as principal component analysis (PCA), hierarchical cluster analysis (HCA), linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k-nearest neighbours (KNN), artificial neural networks (ANN), and partial least square (PLS) regression. 13,[27][28][29][30] This approach provides a systemic and objective way of analysing the HPTLC plate. 31 One of the major advantages of adopting multivariate analysis for chromatographic fingerprints is that it scrutinises the subtle differences within the chromatogram.…”
mentioning
confidence: 99%
“…HPTLC methodology is reliable, fast and cost efficient protocol that can be used to do qualitative and quantitative analysis of secondary metabolites extracted from natural sources (Lebot et al, 2014; Sagi et al, 2014). In Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Multiple‐ingredient quantitative analysis would address this problem. Multi‐ingredients quantitative analysis integrated with fingerprint analysis method has been increasingly used for the QC of Chinese herbal medicine and their preparations in recent years.…”
Section: Introductionmentioning
confidence: 99%