scholarly journals Multivariate statistical analysis of tropane alkaloids in Anisodus tanguticus (Maxim.) Pascher from different regions to trace geographical origins

2021 ◽  
Author(s):  
Chen Chen ◽  
Jingjing Li ◽  
Feng Xiong ◽  
Bo Wang ◽  
Yuanming Xiao ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Quantitative Analysis ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Geographical Origin ◽  
Principal Component ◽  
Tropane Alkaloids ◽  
Component Analysis ◽  
Tibet Plateau

Abstract Anisodus tanguticus (Maxim.) Pascher is an important Tibetan folk medicine and the source of tropane alkaloids (TAs) grown in Qinghai-Tibet Plateau. There are marked differences in quality of A. tanguticus from geographic areas. The aim of present research was to establish a method for the quantitative analysis of TAs coupled with chemometrics analysis to trace geographical origins. Qualitative analysis of TAs in A. tanguticus was carried out using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry and quantitative analysis of TAs in different plant organs from different geographical origin was achieved. Contents of TAs were subjected to the principal component analysis, and orthogonal partial least-squares discriminant analysis. The contents of the three marker compounds (anisodamine, anisodine and atropine) in the roots and acrial parts of A. tanguticus were positive correlated and varied significantly from different geographical origins. Principal component analysis, and orthogonal partial least-squares discriminant analysis results showed excellent discrimination between different geographical origin of A. tanguticus. This study could provide comprehensive evaluation and further utilization of A. tanguticus resources.

scholarly journals Distinguishing Smilax glabra and Smilax china rhizomes by flow-injection mass spectrometry combined with principal component analysis

2018 ◽  
Vol 68 (1) ◽  
pp. 87-96
Author(s):  
Jian Liang ◽  
Meng Zhou ◽  
Lin-Yu Li ◽  
Ji-Cheng Shu ◽  
Yong-Hong Liang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Flow Injection ◽  
Principal Component ◽  
Component Analysis ◽  
Chemometric Method ◽  
Predictive Study

Abstract Flow-injection mass spectrometry (FIMS) coupled with a chemometric method is proposed in this study to profile and distinguish between rhizomes of Smilax glabra (S. glabra) and Smilax china (S. china). The proposed method employed an electrospray-time-of-flight MS. The MS fingerprints were analyzed using principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) with the aid of SIMCA software. Findings showed that the two kinds of samples perfectly fell into their own classes. Further predictive study showed desirable predictability and the tested samples were successfully and reliably identified. The study demonstrated that the proposed method could serve as a powerful tool for distinguishing between S. glabra and S. china.

scholarly journals Exosome-Enriched Plasma Analysis as a Tool for the Early Detection of Hypertensive Gestations

2021 ◽  
Vol 12 ◽  
Author(s):  
Rodrigo Barbano Weingrill ◽  
Sandra Luft Paladino ◽  
Matheus Leite Ramos Souza ◽  
Eduardo Manoel Pereira ◽  
Aldilane Lays Xavier Marques ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component ◽  
Component Analysis ◽  
Proton Nuclear Magnetic Resonance ◽  
Hypertensive Disorders Of Pregnancy ◽  
H Nmr ◽  
Hypertensive Disorders

Hypertensive disorders of pregnancy are closely associated with prematurity, stillbirth, and maternal morbidity and mortality. The onset of hypertensive disorders of pregnancy (HDP) is generally noticed after the 20th week of gestation, limiting earlier intervention. The placenta is directly responsible for modulating local and systemic physiology by communicating using mechanisms such as the release of extracellular vesicles, especially exosomes. In this study, we postulated that an analysis of exosome-enriched maternal plasma could provide a more focused and applicable approach for diagnosing HDP earlier in pregnancy. Therefore, the peripheral blood plasma of 24 pregnant women (11 controls, 13 HDP) was collected between 20th and 24th gestational weeks and centrifuged for exosome enrichment. Exosome-enriched plasma samples were analyzed by Raman spectroscopy and by proton nuclear magnetic resonance metabolomics (1H NMR). Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to analyze the Raman data, from the spectral region of 600–1,800 cm–1, to determine its potential to discriminate between groups. Using principal component analysis, we were able to differentiate the two groups, with 89% of all variances found in the first three principal components. In patients with HDP, most significant differences in Raman bands intensity were found for sphingomyelin, acetyl CoA, methionine, DNA, RNA, phenylalanine, tryptophan, carotenoids, tyrosine, arginine, leucine, amide I and III, and phospholipids. The 1H NMR analysis showed reduced levels of D-glucose, L-proline, L-tyrosine, glycine, and anserine in HDP, while levels of 2-hydroxyvalerate, polyunsaturated fatty acids, and very-low-density lipoprotein (VLDL) were increased. 1H NMR results were able to assign an unknown sample to either the control or HDP groups at a precision of 88.3% using orthogonal partial least squares discriminant analysis and 87% using logistic regression analysis. Our results suggested that an analysis of exosome-enriched plasma could provide an initial assessment of placental function at the maternal-fetal interface and aid HDP diagnosis, prognosis, and treatment, as well as to detect novel, early biomarkers for HDP.

scholarly journals Modelos PCA a partir de conjuntos de datos con información faltante. ¿Se afectan sus propiedades?

SaberEs ◽  
2010 ◽  
Author(s):  
María Susana Vitelleschi ◽  
Directora: Marta Beatriz Quaglino
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Expectation Maximization ◽  
Principal Component ◽  
Component Analysis

En este trabajo se aborda la problemática de la construcción de modelos PCA (Principal Component Analysis) a partir de conjuntos de datos con información faltante. Se trabaja sobre tres situaciones diferentes con relación a la matriz de datos originales. En cada situación se generaron pérdidas a través de mecanismos aleatorios y no aleatorios, en diferentes porcentajes en una sola variable por vez, seleccionada mediante dos criterios: la que más contribuye y menos contribuye en la formación de la primera componente principal. A partir de cada conjunto de datos incompletos se construye el modelo PCA utilizando: Casos Completos, Nonlinear Iterative Partial Least Squares (NIPALS) y Expectation Maximization (EM). Se comparan los resultados con los obtenidos a través del conjunto de datos originales. Se definen una serie de medidas para estudiar cómo se afectan los resultados según la dimensión de la matriz de datos, el porcentaje y el mecanismo de pérdida, con relación a: bondad del ajuste, bondad de predicción, vectores cargas, ortonormalidad de la matriz de cargas y ortogonalidad de la matriz de “scores”.

Comparison and Discrimination of Artemisia argyi and Artemisia lavandulifolia by Gas Chromatography-Mass Spectrometry-Based Metabolomic Approach

2019 ◽  
Vol 102 (6) ◽  
pp. 1814-1821 ◽  
Author(s):  
Long Guo ◽  
Dan Zhang ◽  
Lei Wang ◽  
Zijing Xue ◽  
Mei Guo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Volatile Compounds ◽  
Partial Least Squares ◽  
Herbal Medicines ◽  
Principal Component ◽  
Component Analysis ◽  
Chemical Markers ◽  
Artemisia Argyi ◽  
Metabolomic Approach

Abstract Background: Artemisia argyi and A. lavandulifolia are two morphologically similar herbal medicines derived from the Artemisia genus. Although the two Artemisia herbs have been used as herbal medicines for a long time, studies on their phytochemicals and bioactive compositions are still limited, and no research has been devoted to compare the volatile compounds in A. argyi and A. lavandulifolia. Objective: To compare the volatile constituents in A. argyi and A. lavandulifolia and to explore chemical markers for discrimination and quality evaluation of the two Artemisia herbal medicines. Methods: A GC-MS-based metabolomic approach was employed to compare and discriminate A. argyi and A. lavandulifolia from the aspect of volatile compounds. Multivariate statistical methods, including principal component analysis and orthogonal partial least-squares discriminate analysis, were applied to explore chemical markers for discrimination of the two Artemisia herbal medicines. Results: Thirty volatile compounds were identified, and the chemical profiles of volatile compounds in A. argyi and A. lavandulifolia were quite similar. Principal component analysis and orthogonal partial least-squares discrimination analysis results indicated that the two Artemisia herbal medicines could be distinguished effectively from each other. Ten volatile compounds were selected as potential chemical markers for discrimination of the two Artemisia herbal medicines. Conclusions: The GC-MS-based metabolomics could be an acceptable strategy for comparison and discrimination of A. argyi and A. lavandulifolia as well as authentication of herbal medicines derived from other closely related species. Highlights: GC-MS based metabolomic approach was firstly applied to compare and discriminate Artemisia argyi and Artemisia lavandulifolia.

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