Determining groundwater dependence of the Cooloola Patterned Fens in south-eastern Queensland, and threats posed by groundwater extraction

2017 ◽  
Vol 68 (12) ◽  
pp. 2336
Author(s):  
Andrew McDougall ◽  
Sharon Marshall ◽  
Tom Espinoza
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Water Chemistry ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Multivariate Techniques ◽  
Groundwater Extraction ◽  
South Eastern ◽  
Perched Aquifers ◽  
Regional Aquifer

Water extraction from the local aquifer and streams for water supply in the Cooloola area of south-eastern Queensland threatens the groundwater flow for an iconic groundwater-dependent ecosystem, the Cooloola Patterned Fens. Water-chemistry samples were collected from within the fens wetland, bores and local streams. The multivariate techniques of hierarchical cluster analysis (HCA), principal component analysis (PCA) and multidimensional scaling (MDS) were used to discriminate aquifer source of water. Water chemistry of the patterned fens complex was associated with perched aquifers atop an underlying peat aquitard, whereas the water chemistry of two nearby creek systems (Searys Creek and Teewah Creek) was more closely associated with the regional aquifer. The present study highlighted the need for better understanding of the hydrogeology of coastal aquifers and the ecosystems dependent on them.

Quality evaluation of tugaksheeree samples by ATR-FTIR spectroscopy using multicomponent analysis

2020 ◽  
Vol 10 ◽  
Author(s):  
Nikunj D. Patel ◽  
Niranjan S. Kanaki
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Quality Evaluation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Attenuated Total Reflectance ◽  
Chemometric Methods ◽  
Total Reflectance ◽  
Fingerprint Region ◽  
Ftir Technique

Background: Numerous Ayurvedic formulations contains tugaksheeree as key ingredient. Tugaksheereeis the starch gained from the rhizomes of two plants, Curcuma angustifoliaRoxb. (Zingiberaceae) and Marantaarundinacea (MA) Linn. (Marantaceae). Objective: The primary concerns in quality assessment of Tugaksheeree occur due to adulteration or substitution. Method: In current study, Fourier transform infrared (FTIR) technique with attenuated total reflectance (ATR) facility was used to evaluate tugaksheeree samples. Total 10 different samples were studied and transmittance mode was kept to record the spectra devoid of pellets of KBR. Further treatment was given with multi component tools by considering fingerprint region of the spectra. Multivariate analysis was performed by various chemometric methods. Result: Multi component methods like Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA)were used to discriminate the tugaksheeree samples using Minitab software. Conclusion: This method can be used as a tool to differentiate samples of tugaksheeree from its adulterants and substitutes.

scholarly journals Multivariate characteristics of selected grass varieties for seed production

2019 ◽  
Vol 55 (No. 2) ◽  
pp. 83-86
Author(s):  
Marzena Iwańska ◽  
Danuta Martyniak ◽  
Marcin Martyniak ◽  
Dariusz Gozdowski
Keyword(s):  
Principal Component Analysis ◽  
Growth Rate ◽  
Cluster Analysis ◽  
Seed Production ◽  
Plant Resistance ◽  
Hierarchical Cluster Analysis ◽  
Multivariate Analyses ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Festuca Rubra

Data were obtained in a field experiment carried out at Plant Breeding and Acclimatization Institute Radzikow (central Poland) in 2009–2011. The aim of this study was a multivariate evaluation of 13 advanced lines and cultivars of Festuca rubra, taking into account traits important in seed production. Eleven traits of the grasses and plant resistance to diseases were evaluated. On the basis of multivariate analyses, i.e. hierarchical cluster analysis and principal component analysis, groups of varieties were separated and described, relationships between the traits were evaluated as well. The traits with the biggest influence on multivariate diversity of examined varieties were correlated with the first principal component i.e. height of plants, seeds yield, growth rate of plants, leaf width and time to beginning of earing.  

scholarly journals Groundwater quality assessment using principal component analysis and hierarchical cluster analysis in Alaçam, Turkey

2021 ◽  
Author(s):  
Mehmet Taşan ◽  
Yusuf Demir ◽  
Sevda Taşan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Groundwater Quality ◽  
Seawater Intrusion ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Quality Parameters ◽  
Multivariate Statistical

Abstract This study assessed groundwater quality in Alaçam, where irrigations are performed solely with groundwaters and samples were taken from 35 groundwater wells at pre and post irrigation seasons in 2014. Samples were analyzed for 18 water quality parameters. SAR, RSC and %Na values were calculated to examine the suitability of groundwater for irrigation. Hierarchical cluster analysis and principal component analysis were used to assess the groundwater quality parameters. The average EC value of groundwater in the pre-irrigation period was 1.21 dS/m and 1.30 dS/m after irrigation in the study area. It was determined that there were problems in two wells pre-irrigation and one well post-irrigation in terms of RSC, while there was no problem in the wells in terms of SAR. Piper diagram and cluster analysis showed that most groundwaters had CaHCO3 type water characteristics and only 3% was NaCl- as the predominant type. Seawater intrusion was identified as the primary factor influencing groundwater quality. Multivariate statistical analyses to evaluate polluting sources revealed that groundwater quality is affected by seawater intrusion, ion exchange, mineral dissolution and anthropogenic factors. The use of multivariate statistical methods and geographic information systems to manage water resources will be beneficial for both planners and decision-makers.

scholarly journals Discrimination of Thymus, Origanum, Satureja and Thymbra species from the family Labiatae by untargeted metabolomic analysis

2020 ◽  
Vol 38 (No. 3) ◽  
pp. 151-157
Author(s):  
Nurcan Ayşar Güzelsoy ◽  
Filiz Çavuş ◽  
Oya Kaçar
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Single Species ◽  
Metabolomic Analysis ◽  
Methanolic Extracts ◽  
The Family ◽  
Extraction Algorithm

The term “thyme” does not refer to herbs that belong to a single species. The genera Thymus, Origanum, Satureja and Thymbra of the family Labiatae are traditionally named as thyme and locally known as ‘kekik’. Unlike Turkey, these species are globally called differently. Spices made of Origanum, Thymus and Satureja are called oregano, thyme and savory, respectively. It is often difficult to differentiate them because of their similar smell and appearance. Most commercial products traded as a mixture of those genera and the mixing together of different species may lead to economically motivated adulteration and a product of reduced value. The species were analysed by LC-ESI-QTOF-MS and a comprehensive statistical workflow was designed. The data of methanolic extracts were assessed and an extraction algorithm was employed for the processing of raw data. Five species were discriminated using principal component analysis (PCA) and hierarchical cluster analysis (HCA). The results of PCA and HCA were consistent with each other. Twenty-one metabolites were determined for the discrimination.

scholarly journals Analysis of the procedures used in the forensic evaluation of illegal substances: a statistical approach to interpreting reports

2019 ◽  
Vol 8 (1) ◽  
pp. 7-23
Author(s):  
Aline Thaís Bruni ◽  
Ricardo Luís Yoshida ◽  
Arthur Serra Lopes Ferreira ◽  
Jesus Antonio Velho ◽  
Bruno Spinosa De Martinis ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Hierarchical Cluster Analysis ◽  
Labor Demand ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Final Report

ResumoEste estudo utilizou ferramentas estatísticas para avaliar laudos forenses sobre substâncias ilegais. Avaliamos variáveis quanto às características da análise e abordamos a metodologia empregada pelos peritos. Perguntas baseadas no que é necessário para esclarecer a lei foram formuladas. Analisamos 1008 documentos oficiais de diferentes jurisdições, divididos em 504 conjuntos compostos por um laudo preliminar e um laudo definitivo para cada caso. Os laudos foram examinados por uma equação empírica formulada para fornecer um parâmetro denominado “Report Relevance” (Relevância do Laudo), que teve por finalidade classificar cada documento de acordo com uma pontuação relacionada à quantidade de informação contida. A validação do método foi realizada por análise multivariada de dados: Análise de Componentes Principais (Principal Component Analysis, PCA), Análise de Agrupamentos Hierárquicos (Hierarchical Cluster Analysis, HCA), Soft Independent Modeling of Class Analogy (SIMCA) e Mínimos Quadrados Parciais (Partial Least Squares, PLS). A análise quantitativa mostrou que os documentos foram bem produzidos, com boa qualidade, uma vez que a Relevância do Laudo apresentou valores em torno de 0,74 ± 0,08 para aqueles provenientes da Polícia Estadual. Em comparação, os documentos provenientes da Polícia Federal obtiveram valores em torno de 0,87 ± 0,05. Fatores que podem explicar essas diferenças e as melhores pontuações para os laudos federais incluem maior investimento em tecnologia e treinamento de pessoal, e menor demanda de mão-de-obra e rotina. Para ambas as forças policiais, alguns aspectos poderiam ser melhorados, como imagens das evidências coletadas ou procedimentos analíticos laboratoriais. Finalmente, a metodologia neste estudo pode ser adaptada para ser usada em outros tipos de investigação forense.Palavras-chave: Substâncias Ilícitas, Procedimentos Periciais, Análise MultivariadaAbstractThis study used statistical tools to evaluate forensic reports on illegal substances. We evaluated variables regarding the characteristics of the analysis and we addressed the methodology employed by the experts. Questions based on what is required to clarify the law were formulated. We have parsed 1008 official documents from different jurisdictions, divided into 504 sets comprised of a preliminary and a final report for each case. The reports were examined by an empirical equation formulated to provide a parameter called “Report Relevance”, which intended to classify each report according to a score related to the amount of information it contained.  The validation of the method was performed by multivariate data analysis: Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Soft Independent Modeling of Class Analogy (SIMCA) and Partial Least Squares (PLS). Quantitative analysis showed that the expert documents were well produced, with good quality, since the Report Relevance showed values around 0.74 ± 0.08 for the reports from the State Police. By comparison, reports from the Federal Police obtained scores around 0.87 ± 0.05. Factors that might explain these differences and the better scores for the Federal reports include increased investment in technology and training of staff, and a lower labor demand and routine. For both police forces, some aspects could be improved, such as images of the collected evidence or laboratory analytical procedures. Finally, the methodology in this study can be adapted to be used in other kinds of forensic investigation.Keywords: Illegal Substances, Expertise Procedures, Multivariate Analysis.  

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