scholarly journals Hydrochemical assessment of Semarang area using multivariate statistics: A sample based dataset

2016 ◽  
Author(s):  
Dasapta Erwin Irawan ◽  
Thomas Triadi Putranto
Keyword(s):  
Cluster Analysis ◽  
Statistical Treatment ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Data Set ◽  
Anal Ysis ◽  
R Packages ◽  
Point Data ◽  
Depth Water ◽  
Water Elevation

Abstract. The following paper describes in brief the data set related to our project "Hydrochemical assessment of Semarang Groundwater Quality". All of 58 samples were taken in 1992, 1993, 2003, 2006, and 2007 using well point data from several reports from Ministry of Energy and Min- eral Resources and independent consultants. We provided 20 parameters in each samples (sample id, coord X, coord Y, well depth, water level, water elevation, TDS, pH, EC, K, Ca, Na, Mg, Cl, SO4, HCO3, year, ion balance, screen location, and chemical facies). The chemical composi- tion were tested in the Water Quality Laboratory, Universitas Diponegoro using mas spectrofotometer method. The statistical treatment for the dataset (available on Zenodo doi:10.5281/zenodo.57293) were described as follows: (1) data preparation in to csv file format, load it in to R environment; (2) data treatment, including: correlation matrix, cluster analysis using kmeans and hierarchical cluster analysis, and principal component analysis. For anal- ysis and visualizations, We used the following R packages: ggplot2, dplyr, factomineR, factoExtra, cluster, ggcorrplot, and ape.

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.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis

2007 ◽  
Vol 56 (6) ◽  
pp. 75-83 ◽  
Author(s):  
X. Flores ◽  
J. Comas ◽  
I.R. Roda ◽  
L. Jiménez ◽  
K.V. Gernaey
Keyword(s):  
Cluster Analysis ◽  
Control Strategies ◽  
Treatment Plant ◽  
Principal Component ◽  
Statistical Techniques ◽  
Data Sets ◽  
Data Set ◽  
Casual Relation ◽  
Evaluation Matrix ◽  
Natural Groups

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

scholarly journals Structured Laser Illumination Planar Imaging Based Classification of Ground Coffee Using Multivariate Chemometric Analysis

2016 ◽  
Vol 8 (3) ◽  
pp. 32 ◽  
Author(s):  
Olivier K. Bagui ◽  
Kenneth A. Kaduki ◽  
Edouard Berrocal ◽  
Jeremie T. Zoueu
Keyword(s):  
Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Planar Imaging ◽  
Reliable Measurement ◽  
Laser Illumination ◽  
Coffee Beans ◽  
Chemometric Techniques ◽  
Ground Coffee

<p class="1Body">Most commercially available ground coffees are processed from Robusta or Arabica coffee beans. In this work, we report on the potential of Structured Laser Illumination Planar Imaging (SLIPI) technique for the classification of five types of Robusta and Arabica commercial ground coffee samples (Familial, Belier, Brazil, Colombia and Malaga). This classification is made, here, from the measurement of the extinction coefficient µ<sub>e</sub> and of the optical depth OD by means of SLIPI. The proposed technique offers the advantage of eliminating the light intensity from photons which have been multiply scattered in the coffee solution, leading to an accurate and reliable measurement of µ<sub>e</sub>. Data analysis uses the chemometric techniques of Principal Component Anaysis (PCA) for variable selection and Hierarchical Cluster Analysis (HCA) for classification. The chemometric model demonstrates the potential of this approach for practical assessment of coffee grades by correctly classifying the coffee samples according to their species.</p>

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 Polymorphism analysis in some Algerian Opuntia species using morphological and phenological UPOV descriptors

2017 ◽  
Vol 95 (3) ◽  
pp. 391 ◽  
Author(s):  
Boubakr Hadjkouider ◽  
Ammar Boutekrabt ◽  
Bahia Lallouche ◽  
Salim Lamine ◽  
Néjia Zoghlami
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Phenotypic Diversity ◽  
Principal Component ◽  
Morphological Diversity ◽  
Hierarchical Cluster ◽  
Relative Magnitude ◽  
Polymorphism Analysis ◽  
Opuntia Ficus Indica ◽  
New Varieties

<p><strong>Background: </strong>In the present study, we have investigated the morphological variation in a set of five <em>Opuntia</em> species from the Algerian steppes using 49 UPOV descriptors.</p><p><strong>Questions: </strong>which of the 49 descriptors that can be used as powerful estimators of the phenotypic diversity within <em>Opuntia</em> species? How is the morphological diversity patterned in Algerian <em>Opuntia</em>?</p><p><strong>Species study/ Mathematical model: </strong><em>Opuntia ficus-indica, Opuntia amycleae, Opuntia streptacantha, Opuntia engelmannii, Opuntia robusta</em><strong>.</strong> Principal Components Analysis (PCA) and Hierarchical Cluster Analysis were used.</p><p><strong>Study site: </strong>Four counties were studied located in the Algerian steppes. The present research was carried out during 2014.</p><p><strong>Methods:</strong> 49 descriptors adopted by the International Union for the Protection of New Varieties of Plants (UPOV) were employed in the present research, where cladode, flower and fruit traits were used to determine the overall degree of polymorphism among 5 <em>Opuntia</em> species.</p><p><strong>Results:</strong> Principal Component Analysis and Hierarchical Cluster Analysis indicated a consistent differentiation between all studied species. The relative magnitude of the first two PCA eigenvectors showed that 8 descriptors out of 49 were identified as the most important descriptors for the classification of the species. The dendrogram performed on the calculated Euclidean distances between all species pairs allowed the identification of 3 groups, unlike the PCA that identified 4 groups. The species <em>Opuntia ficus-indica </em>and <em>Opuntia amycleae</em> were identified as very close morphologically.</p><p><strong>Conclusions: </strong>The present outcome represents a paramount step towards the fast selection of interesting species and for their best management and conservation.</p>

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