scholarly journals Multivariate statistical treatment of plant extract compositional data: Average mass scan of the total ion chromatogram (AMS) approach

2013 ◽  
Vol 11 (1) ◽  
pp. 85-99
Author(s):  
Polina Blagojevic ◽  
Niko Radulovic
Keyword(s):  
Clustering Analysis ◽  
Compositional Data ◽  
Statistical Treatment ◽  
Principal Component ◽  
Chemical Compositions ◽  
Agglomerative Hierarchical Clustering ◽  
Multivariate Statistical ◽  
Time Saving ◽  
Average Mass ◽  
Peak Areas

It was recently confirmed that relative abundances of m/z values of the average mass scan of the total GC chromatograms (AMS) are suitable variables for multivariate statistical comparison (MVA) of essential oils. These are even more applicable, reliable and faster than the traditionally used variables-percentages (peak areas) of individual oil constituents. Herein, we have explored if AMS-derived variables are appropriate for MVA comparison of plant solvent extract compositional data. To achieve this, average mass scans of the total GC chromatograms and chemical compositions (relative percentages) of eight diethyl ether extracts (six different species; samples were analyzed using GC-FID and GC-MS; data from the literature) were separately compared using two MVA methods: agglomerative hierarchical clustering analysis and principal component analysis. The obtained results strongly suggest that MVA of complex volatile mixtures (GC-MS analyzable fractions of plant solvent extracts), using the corresponding AMS, could be considered as a promising time saving tool for easy and reliable comparison purposes. The AMS approach gives comparable or even better results than the traditional method.

scholarly journals Metabolomics Approach for Discrimination and Quality Control of Natural and Commercial Fallopia multiflora Products in Vietnam

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Nguyen Thi Thoa ◽  
Nguyen Hai Dang ◽  
Do Hoang Giang ◽  
Nguyen Thi Thu Minh ◽  
Nguyen Tien Dat
Keyword(s):  
Clustering Analysis ◽  
Principal Component ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Fda Guidance ◽  
Promising Tool ◽  
Precision And Accuracy ◽  
And Control

A precise HPLC-DAD-based quantification together with the metabolomics statistical method was developed to distinguish and control the quality of Fallopia multiflora, a popular medicinal material in Vietnam. Multivariate statistical methods such as hierarchical clustering analysis and principal component analysis were utilized to compare and discriminate six natural and twelve commercial samples. 2,3,4′,5-Tetrahydroxystilbene 2-O-β-D-glucopyranoside (THSG) (1), emodin (4), and the new compound 6-hydroxymusizin 8-O-α-D-apiofuranosyl-(1⟶6)-β-D-glucopyranoside (5) could be considered as important markers for classification of F. multiflora. Furthermore, seven phenolics were quantified that the variation in the contents of selected metabolites revealed the differences in the quality of natural and commercial samples. Recovery of the compounds from the analytes was more than 98%, while the limits of detection (LOD) and the limits of quantitation (LOQ) ranged from 0.5 to 6.6 μg/ml and 1.5 to 19.8 μg/ml, respectively. The linearity, LOD, LOQ, precision, and accuracy satisfied the criteria FDA guidance on bioanalytical methods. Overall, this method is a promising tool for discrimination and quality assurance of F. multiflora products.

scholarly journals Reconstructing Herbivore Diets: A Multivariate Statistical Approach To Interpreting Compound-Specific Isotope Values

2021 ◽  
Author(s):  
Mickey Hong Yi Chen ◽  
Iain P. Kendall ◽  
Richard P. Evershed ◽  
Amy Bogaard ◽  
Amy K. Styring
Keyword(s):  
Case Studies ◽  
Isotope Analysis ◽  
Statistical Treatment ◽  
Principal Component ◽  
Nitrogen Isotope ◽  
Multivariate Statistical ◽  
Sharp Separation ◽  
Statistical Framework ◽  
Multivariate Statistical Approach ◽  
Isotope Analyses

Abstract Stable nitrogen (N) isotope analysis of bulk tissues is a technique for reconstructing the diets of organisms. However, bulk nitrogen isotope (δ15N) values can be influenced by a variety of metabolic and environmental factors that can confound accurate dietary reconstruction. Compound-specific isotope analyses of amino acids (CSIA-AA) have demonstrated the power of the approach in understanding how the δ15N values of bulk collagen are assembled from the constituent AAs. Furthermore, by connecting these AA δ15N values within a robust biochemical framework interpretation of diet and environment are greatly enhanced. Several new proxies have emerged, built around selected AAs; however, the interconnectedness of AA biosynthetic pathways means that patterning of δ15N values across a wider suite of collagen AAs will occur under different environmental or dietary influences. This work seeks to test this idea by situating CSIA-AA within a robust statistical framework using principal component analysis (PCA) and Bayesian statistics to increase the interpretability of a wider range of AA δ15N values in terms of reconstructing herbivore diet. The model was tested using wild and domestic herbivores from the Neolithic settlements of Çatalhöyük (Turkey), Makriyalos (Greece), and Vaihingen (Germany) as case studies. It was found that at Makriyalos there was a sharp separation between domesticated and wild herbivores, which was present to a lesser extent at Çatalhöyük and not observed at Vaihingen. The case studies presented in this work demonstrate that multivariate statistical treatment of CSIA-AA data can deliver new insights into herbivore diet, exceeding those achievable with the Bayesian model.

scholarly journals Chemical Compositional Signatures of Constituent Minerals of Iron Slags and Ores from the Khmer Monuments

Heritage ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 1724-1738
Author(s):  
Etsuo Uchida ◽  
Motoki Murasugi ◽  
Ayaka Kuroda ◽  
Yusu Lu
Keyword(s):  
Radiocarbon Dating ◽  
Compositional Data ◽  
Energy Dispersive Spectrometer ◽  
Principal Component ◽  
Chemical Compositions ◽  
Iron Ores ◽  
13Th Century ◽  
Group 2 ◽  
Scanning Electron ◽  
Group 1

Iron slags and ores were collected from 22 sites (A to V) in Preah Khan of Kompong Svay, the area surrounding Phnom Daek, and the Angkor monuments. Iron ores were taken from two outcrops in Phnom Daek. The chemical compositions of fayalite and wüstite in the iron slags and magnetite in the iron ores were determined using a scanning electron microscope equipped with an energy dispersive spectrometer. Cluster analysis and principal component analysis using averaged chemical compositional data for fayalite allowed for the investigated slag dumps to be classified into two main groups: Groups 1 and 2. The slag dumps in the area surrounding Phnom Daek and those in the Angkor monuments were classified as Group 1, and those in Preah Khan of Kompong Svay were classified as Group 2, except for sites C and U, which were classified as Group 1. Radiocarbon dating was carried out on 10 charcoal fragments from slag dumps outside the Angkor area. The dating results indicate that iron making in Preah Khan of Kompong Svay was conducted in and after the 13th century except for sites C and U, where iron ores may have been supplied from Phnom Daek before the 13th century.

scholarly journals Sensory and Olfactometry Chemometrics as Valuable Tools for Assessing Hops’ Aroma Impact on Dry-Hopped Beers: A Study with Wild Portuguese Genotypes

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1397
Author(s):  
Júlio C. Machado ◽  
Florian Lehnhardt ◽  
Zita E. Martins ◽  
Miguel A. Faria ◽  
Hubert Kollmannsberger ◽  
...  
Keyword(s):  
Volatile Compounds ◽  
Hierarchical Clustering ◽  
Statistical Treatment ◽  
Sensory Characteristics ◽  
Citrus Fruits ◽  
Agglomerative Hierarchical Clustering ◽  
Multivariate Statistical ◽  
Active Compounds ◽  
Quantitative Descriptive

Sensory, olfactometry (using the sums of odour intensities for each class of compounds) and chemometric analyses were used to evaluate Portuguese wild hops’ sensory characteristics and the aroma that those hops impart to dry-hopped beer. CATA analysis and agglomerative hierarchical clustering was applied for the sensory characterization of 15 wild hops of Portuguese genotypes, clustering them in two groups: one more sulphurous, floral, and fruity, and another more earthy, resinous, floral, and non-citrus fruits. Two hops representative of each group were selected for the production of four dry-hopped beers using the same base beer style (Munich Helles). Beers were analysed by quantitative descriptive analyses and quantification of hop-derived key volatile compounds. Multivariate statistical treatment of the data was performed. Results indicate significant differences (p < 0.05) in fruity, resinous, earthy, floral, and sulphurous attributes of hops, but the dry-hopped beers only have a significant increase (p < 0.05) in fruity and spicy notes when compared with non-dry-hopped Munich-style Helles beer. Hop olfactometry explained the sensory perception that the 11 hops not used for brewing (employed as supplementary observations) are placed into the space of the odour-active compounds profile of the four hops selected for brewing. These 11 hop samples have more spiciness than fruitiness potential.

scholarly journals Exploratory Analysis of Provenance Data Using R and the Provenance Package

Minerals ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 193 ◽  
Author(s):  
Pieter Vermeesch
Keyword(s):  
Compositional Data ◽  
Statistical Treatment ◽  
Principal Component ◽  
Compositional Data Analysis ◽  
Point Counting ◽  
Data Types ◽  
Chi Square ◽  
Counting Data ◽  
Provenance Data ◽  
Two Samples

The provenance of siliclastic sediment may be traced using a wide variety of chemical, mineralogical and isotopic proxies. These define three distinct data types: (1) compositional data such as chemical concentrations; (2) point-counting data such as heavy mineral compositions; and (3) distributional data such as zircon U-Pb age spectra. Each of these three data types requires separate statistical treatment. Central to any such treatment is the ability to quantify the `dissimilarity’ between two samples. For compositional data, this is best done using a logratio distance. Point-counting data may be compared using the chi-square distance, which deals better with missing components (zero values) than the logratio distance does. Finally, distributional data can be compared using the Kolmogorov–Smirnov and related statistics. For small datasets using a single provenance proxy, data interpretation can sometimes be done by visual inspection of ternary diagrams or age spectra. However, this no longer works for larger and more complex datasets. This paper reviews a number of multivariate ordination techniques to aid the interpretation of such studies. Multidimensional Scaling (MDS) is a generally applicable method that displays the salient dissimilarities and differences between multiple samples as a configuration of points in which similar samples plot close together and dissimilar samples plot far apart. For compositional data, classical MDS analysis of logratio data is shown to be equivalent to Principal Component Analysis (PCA). The resulting MDS configurations can be augmented with compositional information as biplots. For point-counting data, classical MDS analysis of chi-square distances is shown to be equivalent to Correspondence Analysis (CA). This technique also produces biplots. Thus, MDS provides a common platform to visualise and interpret all types of provenance data. Generalising the method to three-way dissimilarity tables provides an opportunity to combine several datasets together and thereby facilitate the interpretation of `Big Data’. This paper presents a set of tutorials using the statistical programming language R. It illustrates the theoretical underpinnings of compositional data analysis, PCA, MDS and other concepts using toy examples, before applying these methods to real datasets with the provenance package.

scholarly journals ANALYSIS OF THE GENETIC DIVERSITY IN MAIZE LANDRACE CULTIVARS FROM NORTHERN RIO GRANDE DO SUL, BRAZIL

2020 ◽  
Vol 19 ◽  
pp. 12
Author(s):  
JÉSSICA ARGENTA ◽  
JEFFERSON GONÇALVES ACUNHA ◽  
BIANCA OLIVEIRA MACHADO ◽  
ARIEL RIZZARDO ◽  
NORYAM BERVIAN BISPO
Keyword(s):  
Clustering Analysis ◽  
Principal Component ◽  
Multivariate Statistical Methods ◽  
Maize Breeding ◽  
Multivariate Statistical ◽  
Maize Landrace ◽  
Maize Landraces ◽  
Graphical Integration ◽  
Northern Rio Grande ◽  
Landrace Accessions

Maize landraces are important genetic resources for maize breeding. Many of these landrace varieties have not yet been properly studied in order to be distinguished from the others.  In this study, multivariate statistical methods were used, beyond the analysis of variance, for estimating genetic dissimilarity among 27 maize landrace accessions. Principal component analysis and clustering analysis were performed using 16 evaluated quantitative characters. The ANOVA results reported the existence of significant differences among the tested accessions for 14 evaluated characters. Two principal components almost explained 49% of found experimental variance. Four different clusters were formed by the used clustering analysis, whose results were plotted into a dendrogram. The graphical integration of this dendrogram with the PCA allowed to conclude that the variation found may be due to the genotypic distinctions existing among the four groups of accesses determined in this study.

Classification of wine distillates using multivariate statistical methods based on their direct GC-MS analysis

2015 ◽  
Vol 69 (3) ◽  
Author(s):  
Ivan Špánik ◽  
Luboš Čirka ◽  
Pavel Májek
Keyword(s):  
Geographical Origin ◽  
Direct Injection ◽  
Principal Component ◽  
Processing Technology ◽  
Time Interval ◽  
Multivariate Statistical ◽  
Ms Analysis ◽  
Peak Areas ◽  
Chromatographic Peaks

AbstractThis work describes a novel methodology for the recognition of brandies based on direct injection of a raw sample followed by GC-MS analysis. Direct injection was chosen for its simplicity and the fact that the composition of the samples analysed remains unchanged compared to original brandy. The repeatability of the analytical procedure was evaluated by a comparison of the peak areas for randomly selected compounds obtained from 10 parallel measurements. A novel chemometric procedure was investigated in order to separate the samples studied on the basis of their geographical origin, processing technology or maturation time. In this procedure, a principal component analysis was applied to full chromatograms to select the time interval that shows the significant differences between the samples studied. It was shown that the chromatogram recorded at 36-39 min bore the maximal differences, hence it could be used to classify the brandy samples. The chromatographic peaks found within this time interval were identified and their peak areas determined. These compounds could be used as specific markers for determining geographical origin or processing technology.

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.

Sign in / Sign up

Export Citation Format

Share Document