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 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 Rapid Discrimination and Authentication of Korean Farmstead Mozzarella Cheese through MALDI-TOF and Multivariate Statistical Analysis

Metabolites ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 333
Author(s):  
Sujatha Kandasamy ◽  
Jayeon Yoo ◽  
Jeonghee Yun ◽  
Han-Byul Kang ◽  
Kuk-Hwan Seol ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Biomarker Discovery ◽  
Geographical Location ◽  
Accurate Method ◽  
Multivariate Statistical ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Flight Mass Spectrometry ◽  
Tof Ms

Geographical origin and authenticity are the two crucial factors that propel overall cheese perception in terms of quality and price; therefore, they are of great importance to consumers and commercial cheese producers. Herein, we demonstrate a rapid, accurate method for discrimination of domestic and import mozzarella cheeses in the Republic of Korea by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The protein profiles’ data aided by multivariate statistical analysis successfully differentiated farmstead and import mozzarella cheeses according to their geographical location of origin. A similar investigation within domestic samples (farmsteads/companies) also showed clear discrimination regarding the producer. Using the biomarker discovery tool, we identified seven distinct proteins, of which two (m/z 7407.8 and 11,416.6) were specific in farmstead cheeses, acting as potential markers to ensure authentication and traceability. The outcome of this study can be a good resource in building a database for Korean domestic cheeses. This study also emphasizes the combined utility of MALDI-TOF MS and multivariate analysis in preventing fraudulent practices, thereby ensuring market protection for Korean farmstead cheeses.

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.

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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