scholarly journals Study of Methanol Extracts from Different Parts of Peganum harmala L. Using 1H-NMR Plant Metabolomics

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yinping Li ◽  
Qing He ◽  
Shushan Du ◽  
Shanshan Guo ◽  
Zhufeng Geng ◽  
...  
Keyword(s):  
Univariate Analysis ◽  
Principal Component ◽  
Peganum Harmala ◽  
Multivariate Statistical ◽  
Methanol Extracts ◽  
Plant Metabolomics ◽  
Rational Development ◽  
Stem Leaf ◽  
Loading Plot ◽  
Different Parts

A nuclear magnetic resonance- (NMR-) based metabolomics method was used to identify differential metabolites of methanol extracts obtained from six parts of Peganum harmala L. (P. harmala), namely, the root, stem, leaf, flower, testa, and seed. Two multivariate statistical analysis methods, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), were combined to clearly distinguish among the P. harmala samples from the six different parts. Eleven differential components were screened by the PLS-DA loading plot, and the relative contents were calculated by univariate analysis of variance. Chemometric results showed significant differences in the metabolites of the different parts of P. harmala. The seeds contained large amounts of harmaline, harmine, and vasicine compared to other organs. The acetic acid, proline, lysine, and sucrose contents of the roots were significantly higher than those of the other parts. In the testa, the vasicine, asparagine, choline, and 4-hydroxyisoleucine contents were clearly dominant. The obtained data revealed the distribution characteristics of the metabolomes of the different P. harmala parts and provided fundamental knowledge for the rational development of its medicinal parts.

scholarly journals Chemical Differentiation and Quantitative Analysis of Different Types of Panax Genus Stem-Leaf Based on a UPLC-Q-Exactive Orbitrap/MS Combined with Multivariate Statistical Analysis Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Lele Li ◽  
Yang Wang ◽  
Yang Xiu ◽  
Shuying Liu
Keyword(s):  
Quantitative Methods ◽  
Principal Component ◽  
American Ginseng ◽  
Multivariate Statistical ◽  
Different Types ◽  
Q Exactive ◽  
Full Scan ◽  
Scan Data ◽  
Stem Leaf ◽  
Orbitrap Ms

Two quantitative methods (−ESI full scan and −ESI PRM MS) were developed to analyze ginsenosides in ginseng stem-leaf by using UPLC-Q-Exactive Orbitrap/MS. By means of −ESI PRM MS method, the contents of eighteen ginsenosides in Asian ginseng stem-leaf (ASGSL) and American ginseng stem-leaf (AMGSL) were analyzed. The principal component analysis (PCA) model was built to discriminate Asian ginseng stem-leaf (ASGSL) from American ginseng stem-leaf (AMGSL) based on −ESI PRM MS data, and six ginsenosides (F11, Rf, R2, F1, Rb1, and Rb3) were obtained as the markers. To further explore the differences between cultivated ginseng stem-leaf and forest ginseng stem-leaf, the partial least squares-discriminant analysis (PLS-DA) model was built based on −ESI full scan data. And twenty-six markers were selected to discriminate cultivated ginseng stem-leaf (CGSL) from forest ginseng stem-leaf (FGSL). This study provides reliable and effective methods to quantify and discriminate among different types of ginseng stem-leaf in the commercial market.

scholarly journals Different Metabolites in the Roots, Seeds, and Leaves of Acanthopanax senticosus and Their Role in Alleviating Oxidative Stress

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jie Su ◽  
Qi Wang ◽  
Zhifeng Li ◽  
Yan Feng ◽  
Yan Li ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Phenolic Acid ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Leaf Extracts ◽  
Multivariate Statistical ◽  
Acanthopanax Senticosus ◽  
Different Parts

In this study, we examined the metabolites from different parts of Acanthopanax senticosus and their role in alleviating damage caused by oxidative stress. We used UHPLC-QTOF-MS to analyze the chemical components in the root, seed, and leaf extracts of A. senticosus. Two multivariate statistical analysis methods—namely, principal component analysis and partial least square discriminant analysis—were used to distinguish the samples obtained from different parts of the plant. Using univariate statistics, 130 different metabolites were screened out. Among these, the relative content of flavonoids and terpenoids was found to be highest in the leaves, the lignin and phenolic acid content was highest in the roots, and the amino acid and phenolic acid levels were highest in seeds. An MTT assay was used to test the anti-H2O2 oxidative damage to PC12 cells in different parts of the sample. Lastly, using Pearson’s correlation analysis, various metabolites from different parts of A. senticosus were correlated with their antioxidant effects from the corresponding parts. Fifty-two related different metabolites were found, of which 20 metabolites that were positively correlated to oxidative stress were present at a relatively higher level in the roots, whereas 32 metabolites that were negatively correlated were present at relatively higher levels in the seeds and leaves. The results of this study reveal the distribution characteristics and the antioxidant activity of different metabolites of A. senticosus and provide a reference for the rational development of its medicinal parts.

Statistical Analsysis to Evaluate Heavy Metal Pollution in the Air Obatained by Moss Technique in Hanoi and its Surrounding Region

2019 ◽  
Vol 29 (3SI) ◽  
pp. 411
Author(s):  
N. H. Quyet ◽  
Le Hong Khiem ◽  
V. D. Quan ◽  
T. T. T. My ◽  
M. V. Frontasieva ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Five Factors ◽  
Heavy Metal Elements ◽  
Surrounding Areas ◽  
Potential Pollution

The aim of this paper was the application of statistical analysis including principal component analysis to evaluate heavy metal pollution obtained by moss technique in the air of Ha Noi and its surrounding areas and to evaluate potential pollution sources. The concentrations of 33 heavy metal elements in 27 samples of Barbula Indica moss in the investigated region collected in December of 2016 in the investigated area have been examined using multivariate statistical analysis. Five factors explaining 80% of the total variance were identified and their potential sources have been discussed.

It is better an approximate answer to the right question than the exact answer to the wrong question : the case of the psychometric analysis of the ASQ:SE

2020 ◽  
Author(s):  
Luis Anunciacao ◽  
janet squires ◽  
J. Landeira-Fernandez
Keyword(s):  
Internal Structure ◽  
Statistical Methods ◽  
Principal Component ◽  
Psychological Theory ◽  
Published Data ◽  
Multivariate Statistical ◽  
Exact Answer ◽  
Wide Range ◽  
Ages And Stages Questionnaire ◽  
The Right

One of the main activities in psychometrics is to analyze the internal structure of a test. Multivariate statistical methods, including Exploratory Factor analysis (EFA) and Principal Component Analysis (PCA) are frequently used to do this, but the growth of Network Analysis (NA) places this method as a promising candidate. The results obtained by these methods are of valuable interest, as they not only produce evidence to explore if the test is measuring its intended construct, but also to deal with the substantive theory that motivated the test development. However, these different statistical methods come up with different answers, providing the basis for different analytical and theoretical strategies when one needs to choose a solution. In this study, we took advantage of a large volume of published data (n = 22,331) obtained by the Ages and Stages Questionnaire Social-Emotional (ASQ:SE), and formed a subset of 500 children to present and discuss alternative psychometric solutions to its internal structure, and also to its subjacent theory. The analyses were based on a polychoric matrix, the number of factors to retain followed several well-known rules of thumb, and a wide range of exploratory methods was fitted to the data, including EFA, PCA, and NA. The statistical outcomes were divergent, varying from 1 to 6 domains, allowing a flexible interpretation of the results. We argue that the use of statistical methods in the absence of a well-grounded psychological theory has limited applications, despite its appeal. All data and codes are available at https://osf.io/z6gwv/.

scholarly journals Characterization of the Volatile Profile of Cultivated and Wild-Type Italian Celery (Apium graveolens L.) Varieties by HS-SPME/GC-MS

2021 ◽  
Vol 11 (13) ◽  
pp. 5855
Author(s):  
Samantha Reale ◽  
Valter Di Cecco ◽  
Francesca Di Donato ◽  
Luciano Di Martino ◽  
Aurelio Manzi ◽  
...  
Keyword(s):  
Solid Phase ◽  
Principal Component ◽  
Apium Graveolens ◽  
Gas Chromatography Mass Spectrometry ◽  
Bioactive Metabolites ◽  
Chemical Components ◽  
Volatile Profile ◽  
Wild Type ◽  
Multivariate Statistical ◽  
Clear Differentiation

Celery (Apium graveolens L.) is a vegetable belonging to the Apiaceae family that is widely used for its distinct flavor and contains a variety of bioactive metabolites with healthy properties. Some celery ecotypes cultivated in specific territories of Italy have recently attracted the attention of consumers and scientists because of their peculiar sensorial and nutritional properties. In this work, the volatile profiles of white celery “Sedano Bianco di Sperlonga” Protected Geographical Indication (PGI) ecotype, black celery “Sedano Nero di Torricella Peligna” and wild-type celery were investigated using head-space solid-phase microextraction combined with gas-chromatography/mass spectrometry (HS-SPME/GC-MS) and compared to that of the common ribbed celery. Exploratory multivariate statistical analyses were conducted using principal component analysis (PCA) on HS-SPME/GC-MS patterns, separately collected from celery leaves and petioles, to assess similarity/dissimilarity in the flavor composition of the investigated varieties. PCA revealed a clear differentiation of wild-type celery from the cultivated varieties. Among the cultivated varieties, black celery “Sedano Nero di Torricella Peligna” exhibited a significantly different composition in volatile profile in both leaves and petioles compared to the white celery and the prevalent commercial variety. The chemical components of aroma, potentially useful for the classification of celery according to the variety/origin, were identified.

scholarly journals Chemical Fingerprinting of Cryptic Species and Genetic Lineages of Aneura pinguis (L.) Dumort. (Marchantiophyta, Metzgeriidae)

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1180
Author(s):  
Rafał Wawrzyniak ◽  
Wiesław Wasiak ◽  
Beata Jasiewicz ◽  
Alina Bączkiewicz ◽  
Katarzyna Buczkowska
Keyword(s):  
Cluster Analysis ◽  
Cryptic Species ◽  
Plant Material ◽  
Chemical Constituents ◽  
Principal Component ◽  
Molecular Data ◽  
Rapid Identification ◽  
Multivariate Statistical ◽  
Genetic Lineages ◽  
Marker Compounds

Aneura pinguis (L.) Dumort. is a representative of the simple thalloid liverworts, one of the three main types of liverwort gametophytes. According to classical taxonomy, A. pinguis represents one morphologically variable species; however, genetic data reveal that this species is a complex consisting of 10 cryptic species (named by letters from A to J), of which four are further subdivided into two or three evolutionary lineages. The objective of this work was to develop an efficient method for the characterisation of plant material using marker compounds. The volatile chemical constituents of cryptic species within the liverwort A. pinguis were analysed by GC-MS. The compounds were isolated from plant material using the HS-SPME technique. Of the 66 compounds examined, 40 were identified. Of these 40 compounds, nine were selected for use as marker compounds of individual cryptic species of A. pinguis. A guide was then developed that clarified how these markers could be used for the rapid identification of the genetic lineages of A. pinguis. Multivariate statistical analyses (principal component and cluster analysis) revealed that the chemical compounds in A. pinguis made it possible to distinguish individual cryptic species (including genetic lineages), with the exception of cryptic species G and H. The classification of samples based on the volatile compounds by cluster analysis reflected phylogenetic relationships between cryptic species and genetic lineages of A. pinguis revealed based on molecular data.

scholarly journals Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films

2021 ◽  
Vol 11 (13) ◽  
pp. 5895
Author(s):  
Kristina Serec ◽  
Sanja Dolanski Babić
Keyword(s):  
Thin Films ◽  
Learning Algorithm ◽  
Principal Component Regression ◽  
Principal Component ◽  
Conformational Transitions ◽  
Cancer Diagnostics ◽  
Dna Conformation ◽  
Support Vector ◽  
Multivariate Statistical ◽  
The Impact

The double-stranded B-form and A-form have long been considered the two most important native forms of DNA, each with its own distinct biological roles and hence the focus of many areas of study, from cellular functions to cancer diagnostics and drug treatment. Due to the heterogeneity and sensitivity of the secondary structure of DNA, there is a need for tools capable of a rapid and reliable quantification of DNA conformation in diverse environments. In this work, the second paper in the series that addresses conformational transitions in DNA thin films utilizing FTIR spectroscopy, we exploit popular chemometric methods: the principal component analysis (PCA), support vector machine (SVM) learning algorithm, and principal component regression (PCR), in order to quantify and categorize DNA conformation in thin films of different hydrated states. By complementing FTIR technique with multivariate statistical methods, we demonstrate the ability of our sample preparation and automated spectral analysis protocol to rapidly and efficiently determine conformation in DNA thin films based on the vibrational signatures in the 1800–935 cm−1 range. Furthermore, we assess the impact of small hydration-related changes in FTIR spectra on automated DNA conformation detection and how to avoid discrepancies by careful sampling.

scholarly journals STATISTICAL MULTIVARIATE ANALYSIS APPLIED TO ENVIRONMENTAL CHARACTERIZATION OF SOIL IN SEMIARID REGION1

2019 ◽  
Vol 32 (1) ◽  
pp. 200-210
Author(s):  
Antônio Italcy de Oliveira Júnior ◽  
Luiz Alberto Ribeiro Mendonça ◽  
Sávio de Brito Fontenele ◽  
Adriana Oliveira Araújo ◽  
Maria Gorethe de Sousa Lima Brito
Keyword(s):  
Organic Matter Content ◽  
Principal Component ◽  
Matter Content ◽  
Multivariate Statistical ◽  
Statistical Tool ◽  
Environmental Characterization ◽  
Hierarchical Grouping ◽  
Two Factors ◽  
Total Data

ABSTRACT Soil is a dynamic and complex system that requires a considerable number of samples for analysis and research purposes. Using multivariate statistical methods, favorable conditions can be created by analyzing the samples, i.e., structural reduction and simplification of the data. The objective of this study was to use multivariate statistical analysis, including factorial analysis (FA) and hierarchical groupings, for the environmental characterization of soils in semiarid regions, considering anthropic (land use and occupation) and topographic aspects (altitude, moisture, granulometry, PR, and organic-matter content). As a case study, the São José Hydrographic Microbasin, which is located in the Cariri region of Ceará, was considered. An FA was performed using the principal component method, with normalized varimax rotation. In hierarchical grouping analysis, the “farthest neighbor” method was used as the hierarchical criterion for grouping, with the measure of dissimilarity given by the “square Euclidean distance.” The FA indicated that two factors explain 75.76% of the total data variance. In the analysis of hierarchical groupings, the samples were agglomerated in three groups with similar characteristics: one with samples collected in an area of the preserved forest and two with samples collected in areas with more anthropized soils. This indicates that the statistical tool used showed sensitivity to distinguish the most conserved soils and soils with different levels of anthropization.

scholarly journals Continuum Power CCA: A Unified Approach for Isolating Coupled Modes

2015 ◽  
Vol 28 (3) ◽  
pp. 1016-1030 ◽  
Author(s):  
Erik Swenson
Keyword(s):  
Signal To Noise Ratio ◽  
Full Range ◽  
Synthetic Data ◽  
Principal Component Regression ◽  
Principal Component ◽  
Accurate Estimate ◽  
Unified Approach ◽  
Coupled Modes ◽  
Multivariate Statistical ◽  
Sample Covariance

Abstract Various multivariate statistical methods exist for analyzing covariance and isolating linear relationships between datasets. The most popular linear methods are based on singular value decomposition (SVD) and include canonical correlation analysis (CCA), maximum covariance analysis (MCA), and redundancy analysis (RDA). In this study, continuum power CCA (CPCCA) is introduced as one extension of continuum power regression for isolating pairs of coupled patterns whose temporal variation maximizes the squared covariance between partially whitened variables. Similar to the whitening transformation, the partial whitening transformation acts to decorrelate individual variables but only to a partial degree with the added benefit of preconditioning sample covariance matrices prior to inversion, providing a more accurate estimate of the population covariance. CPCCA is a unified approach in the sense that the full range of solutions bridges CCA, MCA, RDA, and principal component regression (PCR). Recommended CPCCA solutions include a regularization for CCA, a variance bias correction for MCA, and a regularization for RDA. Applied to synthetic data samples, such solutions yield relatively higher skill in isolating known coupled modes embedded in noise. Provided with some crude prior expectation of the signal-to-noise ratio, the use of asymmetric CPCCA solutions may be justifiable and beneficial. An objective parameter choice is offered for regularization with CPCCA based on the covariance estimate of O. Ledoit and M. Wolf, and the results are quite robust. CPCCA is encouraged for a range of applications.

Sign in / Sign up

Export Citation Format

Share Document