Literature DB >> 21336795

Chromatographic profiles of Phyllanthus aqueous extracts samples: a proposition of classification using chemometric models.

Lucia Regina Rocha Martins1, Edenir Rodrigues Pereira-Filho, Quezia Bezerra Cass.   

Abstract

Taking in consideration the global analysis of complex samples, proposed by the metabolomic approach, the chromatographic fingerprint encompasses an attractive chemical characterization of herbal medicines. Thus, it can be used as a tool in quality control analysis of phytomedicines. The generated multivariate data are better evaluated by chemometric analyses, and they can be modeled by classification methods. "Stone breaker" is a popular Brazilian plant of Phyllanthus genus, used worldwide to treat renal calculus, hepatitis, and many other diseases. In this study, gradient elution at reversed-phase conditions with detection at ultraviolet region were used to obtain chemical profiles (fingerprints) of botanically identified samples of six Phyllanthus species. The obtained chromatograms, at 275 nm, were organized in data matrices, and the time shifts of peaks were adjusted using the Correlation Optimized Warping algorithm. Principal Component Analyses were performed to evaluate similarities among cultivated and uncultivated samples and the discrimination among the species and, after that, the samples were used to compose three classification models using Soft Independent Modeling of Class analogy, K-Nearest Neighbor, and Partial Least Squares for Discriminant Analysis. The ability of classification models were discussed after their successful application for authenticity evaluation of 25 commercial samples of "stone breaker."

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Year:  2011        PMID: 21336795     DOI: 10.1007/s00216-011-4749-1

Source DB:  PubMed          Journal:  Anal Bioanal Chem        ISSN: 1618-2642            Impact factor:   4.142


  1 in total

1.  Cysteine-Rich Peptide Fingerprinting as a General Method for Herbal Analysis to Differentiate Radix Astragali and Radix Hedysarum.

Authors:  Jiayi Huang; Ka H Wong; Stephanie V Tay; Adrian How; James P Tam
Journal:  Front Plant Sci       Date:  2019-07-31       Impact factor: 5.753

  1 in total

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