scholarly journals On establishing ceramic chemical groups: exploring the influence of data analysis methods and the role of the elements chosen in analysis

2013 ◽  
Vol 1 (1) ◽  
pp. 1 ◽  
Author(s):  
Kostalena Michelaki ◽  
Michael J. Hughes ◽  
Ronald G.V. Hancock
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Compositional Data ◽  
Principal Component ◽  
Component Analysis ◽  
Data Exploration ◽  
Short Paper ◽  
Bivariate Data ◽  
Chemical Groups ◽  
Log Ratio

Since the 1970s, archaeologists have increasingly depended on archaeometric rather than strictly stylistic data to explore questions of ceramic provenance and technol- ogy, and, by extension, trade, exchange, social networks and even identity. It is accepted as obvious by some archaeometrists and statisti- cians that the results of the analyses of compo- sitional data may be dependent on the format of the data used, on the data exploration method employed and, in the case of multivari- ate analyses, even on the number of elements considered. However, this is rarely articulated clearly in publications, making it less obvious to archaeologists. In this short paper, we re- examine compositional data from a collection of bricks, tiles and ceramics from Hill Hall, near Epping in Essex, England, as a case study to show how the method of data exploration used and the number of elements considered in multivariate analyses of compositional data can affect the sorting of ceramic samples into chemical groups. We compare bivariate data splitting (BDS) with principal component analysis (PCA) and centered log ratio-principal component analysis (CLR-PCA) of different unstandardized data formats [original concen- tration data and logarithmically transformed (i.e. log10 data)], using different numbers of elements. We confirm that PCA, in its various forms, is quite sensitive to the numbers and types of elements used in data analysis.

Reaction Monitoring in Explorative Organic Synthesis Using Fiber-Optical NIR Spectroscopy and Principal Component Analysis

1996 ◽  
Vol 50 (12) ◽  
pp. 1541-1544 ◽  
Author(s):  
Hans-René Bjørsvik
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Latent Variables ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Quantitative Information ◽  
Chemistry Laboratory ◽  
Reaction Profile

A method of combining spectroscopy and multivariate data analysis for obtaining quantitative information on how a reaction proceeds is presented. The method is an approach for the explorative synthetic organic laboratory rather than the analytical chemistry laboratory. The method implements near-infrared spectroscopy with an optical fiber transreflectance probe as instrumentation. The data analysis consists of decomposition of the spectral data, which are recorded during the course of a reaction by using principal component analysis to obtain latent variables, scores, and loading. From the scores and the corresponding reaction time, it is possible to obtain a reaction profile. This reaction profile can easily be recalculated to obtain the concentration profile over time. This calculation is based on only two quantitative measurements, which can be (1) measurement from the work-up of the reaction or (2) chromatographic analysis from two withdrawn samples during the reaction. The method is applied to the synthesis of 3-amino-propan-1,2-diol.

Statistical approaches in literature: An application of principal component analysis and factor analysis to analyze the different arrangements about the Quran’s Suras

2020 ◽  
Author(s):  
Yanwen Wang ◽  
Javad Garjami ◽  
Milena Tsvetkova ◽  
Nguyen Huu Hau ◽  
Kim-Hung Pho
Keyword(s):  
Data Mining ◽  
Principal Component Analysis ◽  
Factor Analysis ◽  
Data Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Holy Quran ◽  
The Holy Quran ◽  
Statistical Approaches

Abstract Data mining, statistics, and data analysis are popular techniques to study datasets and extract knowledge from them. In this article, principal component analysis and factor analysis were applied to cluster thirteen different given arrangements about the Suras of the Holy Quran. The results showed that these thirteen arrangements can be categorized in two parts such that the first part includes Blachère, Davood, Grimm, Nöldeke, Bazargan, E’temad-al-Saltane and Muir, and the second part includes Ebn Nadim, Jaber, Ebn Abbas, Hazrat Ali, Khazan, and Al-Azhar.

Entwicklung, Beobachtung und Steuerung integrierter, quasi-kontinuierlicher pharmazeutischer Produktionsprozesse mit Methoden der Multivariaten Datenverarbeitung

2016 ◽  
Author(s):  
Sven-Oliver Borchert
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Process Analytical Technology ◽  
Multivariate Data ◽  
Principal Component ◽  
Component Analysis ◽  
Analytical Technology

Die vorliegende Arbeit befasst sich mit Aspekten einer modernen Bioverfahrenstechnik am ­Beispiel von Prozessen zur Herstellung rekombinanter potentieller Malariavakzine. Dabei ­wurden zwei quasi-kontinuierliche Prozesse aus herkömmlichen Batch-Unit Operationen auf­gebaut, in denen die Anwendung von Process Analytical Technology im Vordergrund steht. Das Hauptaugenmerk dieser Arbeit lag dabei auf einer Implementierung der Multivariate Data ­Analysis zum Monitoring und zur Evaluierung des zyklischen Prozessablaufes und seiner Reproduzierbarkeit. Im Bereich der Principal Component Analysis wurde die Methode der Prozessüberwachung mit dem Golden Batch-Tunnel angewendet. Mit dem Golden Batch-Ansatz ­wurden Methoden zur Prozessprädiktion implementiert und mit einer Model Predictive Multi­variate Control auch zur Steuerung von realen Prozesses erprobt. Darüber hinaus wurde die MVDA zur Prädiktion von Medienkomponenten sowie deren zellspezifische Reaktionsraten aus klassischen Onli...

Three-way principal component analysis applied to noodles sensory data analysis

2011 ◽  
Vol 106 (1) ◽  
pp. 125-130 ◽  
Author(s):  
Christophe B.Y. Cordella ◽  
Riccardo Leardi ◽  
Douglas N. Rutledge
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sensory Data

Non-negative principal component analysis for NMR-based metabolomic data analysis

2012 ◽  
Vol 118 ◽  
pp. 51-61 ◽  
Author(s):  
Lingli Deng ◽  
Kian-Kai Cheng ◽  
Jiyang Dong ◽  
Julian L. Griffin ◽  
Zhong Chen
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Metabolomic Data

scholarly journals From Multicriterial Data Analysis Up to the Exclusive Contract

2018 ◽  
Vol 72 (1) ◽  
pp. 17-29
Author(s):  
Erika Fecková Škrabul’áková
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Principal Component ◽  
Theoretical Work ◽  
Component Analysis ◽  
Actual Practice ◽  
Evaluation Tool ◽  
Exclusive Contract ◽  
A Company

Abstract In the present paper, we propose a heuristic for identifying the key supplier of a company. In order to develop the supplier’s evaluation tool, we use the multicriterial data analysis. The main statistical instrument here is the principal component analysis. Using the data from all realized orders of a chosen company in Slovakia from the year 2017, we identify its key supplier. Hence, this study helps to close the gap between theoretical work on principal component analysis and actual practice. The importance of the present work underlines the fact that the company is planning to sign an exclusive contract with the recommended supplier.

scholarly journals FaDA: A Shiny web application to accelerate common lab data analyses

2020 ◽  
Author(s):  
Yodit Feseha ◽  
Quentin Moiteaux ◽  
Estelle Geffard ◽  
Gérard Ramstein ◽  
Sophie Brouard ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Rna Sequencing ◽  
Quantitative Pcr ◽  
Principal Component ◽  
Component Analysis ◽  
Receiver Operating Curve ◽  
Web Based ◽  
Data Analyses ◽  
Receiver Operating

AbstractBackgroundWeb-based data analysis and visualization tools are mostly designed for specific purposes, such as data from whole transcriptome RNA sequencing or single-cell RNA sequencing. However, limited efforts have been made to develop tools designed for data of common laboratory data for non-computational scientists. The importance of such web-based tool is stressed by the current increased samples capacity of conventional laboratory tools such as quantitative PCR, flow cytometry or ELISA.ResultsWe provide a web-based application FaDA, developed with the R Shiny package providing users to perform statistical group comparisons, including parametric and non-parametric tests, with multiple testing corrections suitable for most of the standard wet-lab analyses. FaDA provides data visualization such as heatmap, principal component analysis (PCA) and receiver operating curve (ROC). Calculations are performed through the R language.ConclusionsFaDA application provides a free and intuitive interface allowing biologists without bioinformatic skills to easily and quickly perform common lab data analyses. The application is freely accessible at https://shiny-bird.univ-nantes.fr/app/FadaAbbreviationsAUC: Area Under the Curve; FaDA: Fast Data Analysis; GEO: Gene Expression Omnibus; ELISA: enzyme-linked immunosorbent assay; PCA: Principal Component Analysis; qPCR: quantitative PCR; ROC: Receiver Operating Curve.

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