scholarly journals Distribution and Taxonomic Significance of Secondary Metabolites Occurring in the Methanol Extracts of the Stonecrops (Sedum L., Crassulaceae) from the Central Balkan Peninsula

2015 ◽  
Vol 10 (6) ◽  
pp. 1934578X1501000
Author(s):  
Gordana S. Stojanović ◽  
Snežana Č. Jovanović ◽  
Bojan K. Zlatković
Keyword(s):  
Cluster Analysis ◽  
Chemical Composition ◽  
Secondary Metabolites ◽  
Hplc Analysis ◽  
Methanol Extract ◽  
Hierarchical Cluster ◽  
Balkan Peninsula ◽  
Taxonomic Significance ◽  
Methanol Extracts ◽  
Agglomerative Hierarchical Cluster Analysis

The present study is engaged in the chemical composition of methanol extracts of Sedum taxa from the central part of the Balkan Peninsula, and representatives from other genera of Crassulaceae ( Crassula, Echeveria and Kalanchoe) considered as out-groups. The chemical composition of extracts was determined by HPLC analysis, according to retention time of standards and characteristic absorption spectra of components. Identified components were considered as original variables with possible chemotaxonomic significance. Relationships of examined plant samples were investigated by agglomerative hierarchical cluster analysis (AHC). The obtained results showed how the distribution of methanol extract components (mostly phenolics) affected grouping of the examined samples. The obtained clustering showed satisfactory grouping of the examined samples, among which some representatives of the Sedum series, Rupestria and Magellensia, are the most remote. The out-group samples were not clearly singled out with regard to Sedum samples as expected; this especially applies to samples of Crassula ovata and Echeveria lilacina, while Kalanchoe daigremontiana was more separated from most of the Sedum samples.

scholarly journals The Measurement of Antioxidant Activity of Velvet Beans (Mucuna pruriens) and Velvet Beans (Mucuna pruriens) in Coffee Preparations

Biomedika ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 117-122
Author(s):  
Indah Tri Susilowati ◽  
Purwati Purwati
Keyword(s):  
Antioxidant Activity ◽  
Secondary Metabolites ◽  
Methanol Extract ◽  
Antioxidant Activities ◽  
Mucuna Pruriens ◽  
Methanol Extracts ◽  
Activity Test ◽  
Antioxidant Content ◽  
Ic50 Values

Antioxidant activity tests of methanol extracts of velvet beans (Mucuna pruriens) and velvet beans (Mucuna pruriens) in coffee preparations have been previously carried out by several researchers. This present study aims to examine the phytochemical and antioxidant activities of methanol extracts of Mucuna pruriens and Mucuna pruriens in coffee preparations. The methanol extract was tested for the phytochemical and antioxidant activities by applying DPPH (1,1-Dhiphenyl-2-picrylhydrazil) method. The results of the phytochemical test showed that the methanol extracts of the Mucuna pruriens and Mucuna pruriens in coffee preparations contained secondary metabolites of flavonoids, alkaloids, saponins, terpenoids, and tannins. The outcomes of the antioxidant activity test revealed that the methanol extracts of the Mucuna pruriens and Mucuna pruriens in coffee preparations had antioxidant activities, as indicated by the IC50 values of 42.09 ppm and 37.23 ppm. It was also revealed that the antioxidant content of Mucuna pruriens in coffee preparations was stronger than the content in the velvet beans alone.

Diversity Assessment of Indian Sunnhemp (Crotalaria juncea L.) Accessions for Enhanced Biomass and Fibre Yield using Geographic Information System Approach

2021 ◽  
Author(s):  
R.T. Maruthi ◽  
A. Anil Kumar ◽  
S.B. Choudhary ◽  
H.K. Sharma ◽  
J. Mitra
Keyword(s):  
Cluster Analysis ◽  
Germplasm Collection ◽  
Hierarchical Cluster ◽  
High Biomass ◽  
Lignocellulosic Substrate ◽  
Phenotypic Data ◽  
Diversity Pattern ◽  
Diversity Assessment ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Fibre Yield

Background: Sunnhemp, a rapid growing, high biomass yielding bast fibre crop has a tremendous potentiality in biofuels sector as a lignocellulosic substrate. In order to capitalize the new found area there is a need to identify high biomass and fibre yielding sunnhemp genotypes. The present study provides details of morphological diversity and geographical distribution pattern of Indian sunnhemp accessions. Methods: A total of 42 germplasm accessions collected from ten different states were evaluated for fibre yield and attributing traits in April-June cropping season. Based on phenotypic data agglomerative hierarchical cluster analysis was performed. Geographical coordinates of germplasm collection site were utilized to derive the spatial genetic diversity pattern for green biomass yield and fibre yield.Result: Phenotypic evaluation revealed significant genetic variability among the genotypes for biomass and fibre yield leading to identification of several promising accessions. Cluster analysis and PCA grouped the 42 sunnhemp accessions into three clusters. Cluster II and III are highly divergent harboring contrasting phenotypes. DIVA-GIS approach identified eastern Rajasthan, western Jharkhand and border area between Bihar and Jharkhand as sites of highest sunnhemp diversity. 

scholarly journals The Essential Oil Compositions of Three Teucrium Taxa Growing Wild in Sicily: HCA and PCA Analyses

Molecules ◽  
2021 ◽  
Vol 26 (3) ◽  
pp. 643
Author(s):  
Giorgia Catinella ◽  
Natale Badalamenti ◽  
Vincenzo Ilardi ◽  
Sergio Rosselli ◽  
Laura De Martino ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Chemical Composition ◽  
Essential Oils ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
The Other ◽  
Qualitative And Quantitative ◽  
Geographical Variations ◽  
Sesquiterpene Hydrocarbons ◽  
Principal Component Analyses

The chemical composition and the qualitative and quantitative variability of the essential oils of three taxa belonging to the Teucrium genus were studied. The investigated taxa, that grow wild in Sicily, were Teucrium flavum L. (section Chamaedrys (Mill.) Scheb.), Teucrium montanum and Teucrium capitatum L. of section Polium (Mill.) Scheb. Essential oils were extracted by hydrodistillation and analyzed by GC-MS. In total, 74 compounds were identified. Sesquiterpene hydrocarbons were found to be the main group for T. flavum (48.3%). T. capitatum consisted essentially of monoterpene hydrocarbons (72.7%), with α-pinene (19.9%), β-pinene (27.6%) and sylvestrene (16.6%) as the most abundant compounds whereas ledene oxide (12.1%), epiglobulol (13.5%) and longifolenaldehyde (14.5%) were identified as the main constituents among the oxygenated sesquiterpenes (63.5%) of T. montanum. Furthermore, a complete literature review on the composition of the essential oils of all the other accessions of these Teucrium taxa, studied so far, was performed. Hierarchical Cluster Analysis (HCA) and Principal Component Analyses (PCA) were used in order to demonstrate geographical variations in the composition of the essential oils.

scholarly journals Use of cluster analysis to monitor novel coronavirus-19 infections in Maharashtra, India

2020 ◽  
Vol 72 ◽  
pp. 44-48 ◽  
Author(s):  
Sanjay Kumar
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster ◽  
Healthcare Management ◽  
The State ◽  
Optimum Number ◽  
Box Plots ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Medical Facilities ◽  
Novel Coronavirus ◽  
Almost All

Objectives: A novel coronavirus disease (COVID-19) has been continuously spreading in almost all the districts of the state Maharashtra in India. As a part of the healthcare management development, it is very important to monitor districts affected due to novel coronavirus (COVID-19). The main objective of this study was to identify and classify affected districts into real clusters on the basis of observations of similarities within a cluster and dissimilarities among different clusters so that government policies, decisions, medical facilities (ventilators, testing kits, masks, treatment etc.), etc. could be improved for reducing the number of infected and deceased persons and hence cured cased could be increased. Material and Methods: In the study, we focused on COVID-19 affected districts of the state Maharashtra of India. We applied agglomerative hierarchical cluster analysis, one of data mining techniques to fulfill the objective. Elbow method was used for obtaining an optimum number of clusters for further analysis. The study of variations among various clusters for each of the variables was performed using box plots. Results: Results obtained from the Elbow method suggested three optimum numbers of clusters for each of the variables. For confirmed and cured cases, cluster I corresponded to the districts BI, GO, ND, PA, SI, WS, JN, CH, OS, HI, NB, JG, RT, LA, KO, AM, ST, BU, DH, AK, YTL, SN, AH, SO, AU, RG, NG, NS and PL. Cluster II corresponded to the districts TH and PU and cluster III corresponded to the district MC. For the death cases, cluster I corresponded to the districts BI, GO, ND, PA, SI, WS, JN, CH, OS, HI, NB, JG, RT, LA, KO, AM, ST, BU, DH, AK, YTL, SN, AH, SO, AU, RG, NG, NS, PL and TH. Cluster II corresponded to the district PU and cluster III corresponded to the district MC. Conclusions: The study showed that the district MC under cluster III was affected severely with COVID-19 which had high number of confirmed cases. A good percentage of cured cases were found in some of the districts under cluster I where six districts (GO, SI, CH, OS, SN) had 100% success rate to cure patients. It was observed that the districts TH, PU and MC under clusters II and III had severe conditions which need optimization of medical facilities and monitoring techniques like screening, closedown, curfews, lockdown, evacuations, legal actions, etc.

scholarly journals In Vitro Trials of Dittrichia graveolens Essential Oil Combined with Antibiotics

2016 ◽  
Vol 11 (6) ◽  
pp. 1934578X1601100 ◽  
Author(s):  
Dragoljub L. Miladinović ◽  
Budimir S. Ilić ◽  
Branislava D. Kocić ◽  
Marija S. Marković ◽  
Ljiljana C. Miladinović
Keyword(s):  
Cluster Analysis ◽  
Gas Chromatography ◽  
Antibacterial Activity ◽  
Chemical Composition ◽  
Essential Oil ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Bacterial Strains ◽  
Components Analysis

The chemical composition and antibacterial activity of Dittrichia graveolens (L.) Greuter essential oil were examined. Gas chromatography and gas chromatography/mass spectrometry were used to analyze the chemical composition of the essential oil. The antibacterial activity was investigated by the broth microdilution method against thirteen bacterial strains. The interactions of the essential oil and three standard antibiotics: chloramphenicol, tetracycline and streptomycin toward five selected strains were evaluated using the microdilution checkerboard assay in combination with chemometric methods: principal components analysis and hierarchical cluster analysis. Oxygenated monoterpenes were the most abundant compound class in the essential oil (40.6%), with bornyl acetate (21.7%) as the major compound. The essential oil exhibited slight antibacterial activity against the tested bacterial strains in vitro, but the combinations D. graveolens essential oil-chloramphenicol and D. graveo/ens-tetracycline exhibited mostly synergistic or additive interactions. These combinations reduced the minimum effective dose of the antibiotics and, consequently, minimized their adverse side effects. In contrast, the association of D. graveolens essential oil and streptomycin was characterized by strong antagonistic interactions against E. coli ATCC 25922, S. aureus ATCC 29213 and P. aeruginosa ATCC 27853. In the principal components analysis (PCA) and hierarchical cluster analysis (HCA), streptomycin against these bacterial strains stood out and formed a separate group.

scholarly journals Discrimination and classification of tobacco wastes by identification and quantification of polyphenols with LC-MS/MS

2010 ◽  
Vol 75 (7) ◽  
pp. 875-891 ◽  
Author(s):  
Jun Wang ◽  
Dingqiang Lu ◽  
Hui Zhao ◽  
Ben Jiang ◽  
Jiali Wang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Chemical Composition ◽  
Active Compound ◽  
Methanolic Extract ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Chlorogenic Acids ◽  
Tobacco Waste

The chemical composition of polyphenols in tobacco waste was identified by HPLC-PAD-ESI/MS/MS and the contents of chlorogenic acids and rutin in 10 varieties of tobacco wastes were determined by HPLC-UV. The relationships between the contents of active polyphenols and the varieties of tobacco wastes were interpreted by hierarchical cluster analysis (HCA) and principal component analysis (PCA). The results showed that 15 polyphenols were identified in a methanolic extract of dried tobacco waste. The tobacco wastes were characterized by high levels of chlorogenic acids (3-CQA, 5-CQA, and 4-CQA) and rutin; their ranges in the 10 tobacco varieties were 0.116-0.196, 0.686-1.781, 0.094- 0.192, and 0.413-0.998 %, respectively. According to multivariate statistics models, two active compound variables can be considered important for the discrimination of the varieties of tobacco wastes: chlorogenic acids and rutin. Consequently, samples of 10 tobacco varieties were characterized into three groups by HCA based on the PCA pattern. In conclusion, tobacco waste could be used as a new pharmaceutical material for the production of natural chlorogenic acids and rutin in the ethnopharmacological industry.

scholarly journals Geostatistical Analysis of Groundwater Data

2019 ◽  
Vol 9 (1) ◽  
pp. 2734-2741
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Hierarchical Cluster ◽  
Geostatistical Analysis ◽  
Water Requirements ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Observation Wells ◽  
Surface And Groundwater

Water resources are stressed because of the country's increasing population and increased water requirements. Even though a good understanding of both surface and groundwater hydrological systems make it possible to manage these resources properly. To study the main characteristics of formation of clusters of groundwater levels, statistical analysis has been used. Geostatistics is a class of statistics used to analyze and predict the values associated with spatial or spatiotemporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. The Statistical analysis is applied to monthly groundwater levels fluctuation data over a period of 2004-2017 in Mysuru, Mandya, Chamarajanagara and Hassan districts of Southern Karnataka in India. The groundwater levels data is collected from 197 Observation Wells from the districts. The Statistical methods like K-Means Clustering and Agglomerative Hierarchical Cluster Analysis is used to perform the datasets. Grouping is made using AHC method, during this process results are obtained by graph called Dendrogram. The obtained results are compared with the LULC maps of all 4 districts. Different grouping (cluster) is made for groundwater level fluctuations for proper conclusion to arrive.

Sign in / Sign up

Export Citation Format

Share Document