scholarly journals Chemical Differentiation of Pseudostellariae Radix from Different Cultivated Fields and Germplasms by UPLC-Triple TOF-MS/MS Coupled with Multivariate Statistical Analysis

2016 ◽  
Vol 11 (12) ◽  
pp. 1934578X1601101 ◽  
Author(s):  
Yu-Jiao Hua ◽  
Ya Hou ◽  
Sheng-Nan Wang ◽  
Yang Ma ◽  
Li-Si Zou ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Ultra Performance Liquid Chromatography ◽  
Chemical Markers ◽  
Multivariate Statistical ◽  
Value Analysis ◽  
Spectrometric Data ◽  
Tof Ms

To explore rapidly the potential chemical markers for differentiating Pseudostellariae Radix from different cultivated fields and germplasms, a method is proposed based on ultra-performance liquid chromatography-triple time-of-flight mass/mass spectrometry (UPLC-Triple TOF-MS/MS) coupled with multivariate statistical analysis. Peak matching, peak alignment, and noise filtering were used in analyzing mass spectrometric data. Accurate m/z value analysis of MS data based on software of database search and MS/MS fragment analysis were applied to identify constituents. The obtained data were statistically analyzed with hierarchical cluster analysis (HCA), principal component analysis (PCA), and partial least squared-discriminant analysis (PLS-DA) to compare the differences among these samples. The PLS-DA loading plot obtained from all mass data showed that 21 compounds were identified as the potential chemical markers to characterize the samples. The results provide experimental data to reveal the influence of ecological environments and germplasms on metabolite biosynthesis in Pseudostellariae Radix.

scholarly journals UPLC-Q-TOF/MS Coupled with Multivariate Statistical Analysis as a Powerful Technique for Rapidly Exploring Potential Chemical Markers to Differentiate between Radix Paeoniae Alba and Radix Paeoniae Rubra

2013 ◽  
Vol 8 (4) ◽  
pp. 1934578X1300800 ◽  
Author(s):  
Nian-cui Luo ◽  
Wen Ding ◽  
Jing Wu ◽  
Da-wei Qian ◽  
Zhen-hao Li ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Ultra Performance Liquid Chromatography ◽  
Uv Spectra ◽  
Chemical Markers ◽  
Multivariate Statistical ◽  
Flight Mass Spectrometry ◽  
The Difference ◽  
Radix Paeoniae Alba ◽  
Tof Ms

To explore rapidly the potential chemical markers for differentiating Radix Paeoniae Alba and Radix Paeoniae Rubra, a method is proposed based on ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) coupled with multivariate statistical analysis. Batches of commercial samples were analyzed by UPLC-Q-TOF/MS. The datasets of tR-m/z pair, ion intensities and sample codes were further processed with orthogonal partial least squared discriminant analysis (OPLS-DA) to compare holistically the difference between these two kinds of samples. Then statistics were used to generate an S-plot, in which the variables (tR-m/z pair) contributing most to the difference were clearly depicted as points at the two ends of “S”, and the components correlated to these ions should be regarded as the chemical markers. The identities of the most changed markers can be identified by comparing the mass/UV spectra and retention times with those of reference compounds and/or tentatively assigned by matching empirical molecular formulae with those of known compounds published in the literature. Using this proposed approach, albflorin, paeoniflorin, oxypaeoniflorin, benzoylpaeoniflorin, galloylalbiflorin and paeoniflorigenone were found to be the differentiating components for discrimination of Radix Paeoniae Alba and Radix Paeoniae Rubra. Moreover, paeoniflorin sulfonate and its isomer, isomaltopaeoniflorin sulfonate, were found to be the characteristic markers for all Radix Paeoniae Alba samples that were processed by sulfurdioxide gas fumigation. The results suggested that this newly established approach could be used to explore rapidly the potential chemical markers for herbs with similar chemical characteristics.

scholarly journals Comparison of Anthraquinones, Iridoid Glycosides and Triterpenoids in Morinda officinalis and Morinda citrifolia Using UPLC/Q-TOF-MS and Multivariate Statistical Analysis

Molecules ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 160 ◽  
Author(s):  
Maoyuan Wang ◽  
Qinglong Wang ◽  
Qing Yang ◽  
Xiaoxia Yan ◽  
Shixiu Feng ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Iridoid Glycosides ◽  
Principal Component ◽  
Morinda Citrifolia ◽  
Cell Counting ◽  
Root Extract ◽  
Multivariate Statistical ◽  
Root Extracts ◽  
Tof Ms

Roots of Morinda officinalis and Morinda citrifolia have been interchangeably used in traditional Chinese medicine. However, there is no experimental evidence to support this. In this study, a ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS)-based approach and a multivariate statistical analysis (MSA) were adopted to compare the difference in the chemical compounds present in the root extract of M. officinalis and M. citrifolia. There were 26 anthraquinones, 15 triterpenes, and 8 iridoid glycosides identified in the root extracts of M. officinalis, 30 anthraquinones, 1 triterpene, and 8 iridoid glycosides in the root extracts of M. citrifolia. Among these, 25 compounds presented in both plants. In addition, a principal component analysis (PCA) showed that these two herbs could be separated clearly. Furthermore, an orthogonal partial least squares-discriminant analysis (OPLS-DA) found 9 components that could be used as chemical markers to discrimination the root extracts of M. officinalis and M. citrifolia. In addition, the results of a Cell Counting Kit 8 (CCK-8) assay and cell colony formation assay indicated that methanol root extracts of M. officinalis and M. citrifolia showed no cell cytotoxicity to normal cells, even promoted the proliferation of normal liver cells. To our knowledge, this is the first time that the differences between the root extracts of M. officinalis and M. citrifolia (Hainan province) have been observed systematically at the chemistry level.

Differential Constituents in Roots, Stems and Leaves of Polygonum multiflorum Thunb. Screened by UPLC/ESI-Q-TOF-MS and Multivariate Statistical Analysis

2019 ◽  
Vol 58 (2) ◽  
pp. 136-143 ◽  
Author(s):  
Wen Rui ◽  
Wanxia Xia ◽  
Wei Zhao ◽  
Bingling Li ◽  
Jian Li ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Composition Analysis ◽  
Chemical Compositions ◽  
Therapeutic Effects ◽  
Polygonum Multiflorum ◽  
Multivariate Statistical ◽  
Analytical Strategy ◽  
Relative Standard ◽  
Tof Ms

Abstract The differential constituents in leaves, stems and roots of Polygonum multiflorum Thunb. were analyzed by ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-Q-TOF-MS) and by multivariate statistical analysis. The established extraction and analysis method showed relative standard deviations (RSDs) for intra-day precision of less than 3.40%, for repeatability of less than 4.06% and for stability of less than 5.10%. Principal component analysis and orthogonal projections to latent structures discriminant analysis of the UPLC/ESI-Q-TOF-MS data showed good ability to classify the leaves, stems and roots of P. multiflorum Thunb. The differential constituents, such as stilbenes, polygoacetophenoside, flavonoids and anthraquinones, accounting for variations between the leaves, stems and roots, were filtered through the variable importance in projection values and were further identified by elemental composition analysis, mass fragmentation data and retention times of available standards. Differences between the chemical compositions in the leaves, stems and roots of P. multiflorum Thunb. were closely related to their various therapeutic effects. This UPLC/ESI-Q-TOF-MS-based analytical strategy could be further utilized to evaluate the overall quality of traditional Chinese medicines and their differences of chemical constituents in different parts of the plant and/or in the plants of different geographical locations.

Risk estimation and multivariate statistical analysis of the heavy metal content of drinking water samples

2018 ◽  
Vol 34 (10) ◽  
pp. 714-725
Author(s):  
Rajan Jakhu ◽  
Rohit Mehra
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Drinking Water ◽  
Statistical Analysis ◽  
Water Samples ◽  
Multivariate Statistical Analysis ◽  
Inductively Coupled Plasma ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Drinking Water Samples

Drinking water samples of Jaipur and Ajmer districts of Rajasthan, India, were collected and analyzed for the measurement of concentration of heavy metals. The purpose of this study was to determine the sources of the heavy metals in the drinking water. Inductively coupled plasma mass spectrometry was used for the determination of the heavy metal concentrations, and for the statistical analysis of the data, principal component analysis and cluster analysis were performed. It was observed from the results that with respect to WHO guidelines, the water samples of some locations exceeded the contamination levels for lead (Pb), selenium (Se), and mercury (Hg), and with reference to the EPA guidelines, the samples were determined unsuitable for drinking because of high concentrations of Pb and Hg. Using multivariate statistical analysis, we determined that copper, manganese, arsenic, Se, and Hg were of anthropogenic origin, while Pb, copper, and cadmium were of geogenic origin. The present study reports the dominance of the anthropogenic contributions over geogenics in the studied area. The sources of the anthropogenic contaminants need to be investigated in a future study.

scholarly journals Seasonal distribution and population dynamics of limnic microalgae and their association with physico-chemical parameters of river Noyyal through multivariate statistical analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Pandian Suresh Kumar ◽  
Jibu Thomas
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Sampling Site ◽  
Principal Component ◽  
Environmental Parameters ◽  
Diversity Indices ◽  
Population Diversity ◽  
Chemical Parameters ◽  
Multivariate Statistical ◽  
Physico Chemical

Abstract The present investigation embarks on understanding the relationship between microalgal species assemblages and their associated physico-chemical parameter dynamics of the catchment region of river Noyyal. Totally, 142 microalgae cultures belonging to 10 different families were isolated from five different sites during four seasons and relative percentage distribution showed that Scenedesmaceae (36.6%) and site S1 (26.4%) with predominant microalgae population. Diversity indices revealed that microalgae communities were characterized by high Hʹ index, lower Simpson dominance, and Margalef index value with indefinite patterns of annual variations. Results showed that variation in the physico-chemical parameters in each sampling site has its impact on the microalgae population during each season. Multivariate statistical analysis viz., Karl Pearson’s correlation coefficient, principal component analysis, and canonical correspondence analysis were applied on microalgae species data, to evaluate the seasonal relationship between microalgae and physico-chemical parameters. The findings of our study concluded that the physico- chemical parameters influenced the dominant taxa of microalgae Chlorellaceae, Scenedesmaceae and Chlorococcaceae in river Noyyal and gives a base data for the seasonal and dynamic relationship between environmental parameters and microalgae population.

Metabolomic profiling of Saposhnikoviae Radix from Mongolia by LC–IT–TOF–MS/MS and multivariate statistical analysis

2019 ◽  
Vol 74 (1) ◽  
pp. 170-188 ◽  
Author(s):  
Zolboo Batsukh ◽  
Kazufumi Toume ◽  
Batkhuu Javzan ◽  
Kohei Kazuma ◽  
Shao-Qing Cai ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Multivariate Statistical ◽  
Metabolomic Profiling ◽  
Saposhnikoviae Radix ◽  
Tof Ms
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