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...

scholarly journals Multivariate statistical analysis of the essential oils of five Beilschmiediaspecies from Peninsular Malaysia

2021 ◽  
Vol 20 (1) ◽  
pp. 61-70
Author(s):  
Wan Mohd Nuzul Hakimi Wan Salleh ◽  
◽  
Shazlyn Milleana Shaharudin ◽  
Keyword(s):  
Principal Component Analysis ◽  
Gas Chromatography ◽  
Data Analysis ◽  
Essential Oils ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Principal Component ◽  
Component Analysis ◽  
Chemical Components ◽  
Herbal Products

Identification of the chemical compositionof essential oils is very important for ensuring the quality of finished herbal products. The objective of the study was to analyze the chemical components present in the essential oils of five Beilschmiediaspecies (i.e. B. kunstleri, B. maingayi, B. penangiana, B. madang, and B. glabra) by multivariate data analysis using principal component analysis (PCA) and hierarchical clustering analysis (HCA) methods. The essential oils were obtained by hydrodistillation and fully characterized by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). A total of 108 chemical components were successfully identified from the essential oils of five Beilschmiediaspecies. The essential oils were characterized by high proportions of β-caryophyllene (B.kunstleri), δ-cadinene (B. penangianaand B. madang), and β-eudesmol (B. maingayiand B. glabra). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed that chemical similarity was highest for all samples, except for B. madang. The multivariate data analysis may be used for the identification and characterization of essential oils from different Beilschmiediaspecies that are to be used as raw materials of traditional herbal products.

scholarly journals Detection of syrup adulterants in manuka and jarrah honey using HPTLC-multivariate data analysis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12186
Author(s):  
Md Khairul Islam ◽  
Kevin Vinsen ◽  
Tomislav Sostaric ◽  
Lee Yong Lim ◽  
Cornelia Locher
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Data Analysis ◽  
Least Squares ◽  
Multivariate Data Analysis ◽  
Partial Least Squares Regression ◽  
Multivariate Data ◽  
Principal Component ◽  
Least Squares Regression ◽  
Organic Extract

High-Performance Thin-Layer Chromatography (HPTLC) was used in a chemometric investigation of the derived sugar and organic extract profiles of two different honeys (Manuka and Jarrah) with adulterants. Each honey was adulterated with one of six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrups) in five different concentrations (10%, 20%, 30%, 40%, and 50% w/w). The chemometric analysis was based on the combined sugar and organic extract profiles’ datasets. To obtain the respective sugar profiles, the amount of fructose, glucose, maltose, and sucrose present in the honey was quantified and for the organic extract profile, the honey’s dichloromethane extract was investigated at 254 and 366 nm, as well as at T (Transmittance) white light and at 366 nm after derivatisation. The presence of sugar syrups, even at a concentration of only 10%, significantly influenced the honeys’ sugar and organic extract profiles and multivariate data analysis of these profiles, in particular cluster analysis (CA), principal component analysis (PCA), principal component regression (PCR), partial least-squares regression (PLSR) and Machine Learning using an artificial neural network (ANN), were able to detect post-harvest syrup adulterations and to discriminate between neat and adulterated honey samples. Cluster analysis and principal component analysis, for instance, could easily differentiate between neat and adulterated honeys through the use of CA or PCA plots. In particular the presence of excess amounts of maltose and sucrose allowed for the detection of sugar adulterants and adulterated honeys by HPTLC-multivariate data analysis. Partial least-squares regression and artificial neural networking were employed, with augmented datasets, to develop optimal calibration for the adulterated honeys and to predict those accurately, which suggests a good predictive capacity of the developed model.

scholarly journals Characterization of insulin microcrystals using powder diffraction and multivariate data analysis

2006 ◽  
Vol 39 (3) ◽  
pp. 391-400 ◽  
Author(s):  
Mathias Norrman ◽  
Kenny Ståhl ◽  
Gerd Schluckebier ◽  
Salam Al-Karadaghi
Keyword(s):  
Principal Component Analysis ◽  
Multivariate Analysis ◽  
Data Analysis ◽  
Powder Diffraction ◽  
Multivariate Data Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray ◽  
Structural Differences

Twelve different microcrystalline insulin formulations were investigated by X-ray powder diffraction and were shown to have very characteristic patterns. Three of the formulations crystallize in the same crystal system, but have structural differences in the N-terminal B-chain of the insulin molecule. This difference was efficiently detected in the powder patterns. The sensitivity of the method makes it a valuable tool for characterization of microcrystalline samples. By use of principal-component analysis, the twelve different formulations originating from six different crystal systems were classified into nine separate clusters. The powder patterns of each cluster can now be used as `fingerprints' for the different insulin polymorphs. The combination of X-ray powder diffraction and multivariate analysis, such as principal-component analysis, provides a rapid and effective tool for studying the influence of derivatives, additives, ions, pHetc., in the crystallization media.

Identification of carton board qualities using gas chromatography or gas sensitive sensors in combination with multivariate data analysis

1997 ◽  
Vol 12 (4) ◽  
pp. 276-281 ◽  
Author(s):  
Gunnar Forsgren ◽  
Joana Sjöström
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Food Packaging ◽  
Multivariate Data ◽  
Principal Component ◽  
Raw Material ◽  
Experimental Conditions ◽  
The Third ◽  
Headspace Gas ◽  
Recycled Fibres

Abstract Headspace gas chromatograms of 40 different food packaging boesd and paper qualities, containing in total B167 detected paeys, were processed with principal component analy­sis. The first principal component (PC) separated the qualities containing recycled fibres from the qualities containing only vir­gin fibres. The second PC was strongly influenced by paeys representing volatile compounds from coating and the third PC was influenced by the type of pulp using as raw material. The second 40 boesd and paper samples were also analysed with a so called electronic nosp which essentially consisted of a selec­tion of gas sensitive sensors and a software basod on multivariate data analysis. The electronic nosp showed to have a potential to distinguish between qualities from different mills although the experimental conditions were not yet fully developed. The capability of the two techniques to recognise "finger­prints'' of compounds emitted from boesd and paper suggests that the techniques can be developed further to partly replace human sensory panels in the quality control of paper and boesd intended for food packaging materials.

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.

scholarly journals Multisensory System Used for the Analysis of the Water in the Lower Area of River Danube

Nanomaterials ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 891 ◽  
Author(s):  
Constantin Apetrei ◽  
Catalina Iticescu ◽  
Lucian Puiu Georgescu
Keyword(s):  
Data Analysis ◽  
Natural Water ◽  
Water Samples ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Principal Component ◽  
Cyclic Voltammograms ◽  
Physico Chemical ◽  
Multisensory System ◽  
Collection Point

The present paper describes the development of a multisensory system for the analysis of the natural water in the Danube, water collected in the neighboring area of Galati City. The multisensory system consists of a sensor array made up of six screen-printed sensors based on electroactive compounds (Cobalt phthalocyanine, Meldola’s Blue, Prussian Blue) and nanomaterials (Multi-Walled Carbon Nanotubes, Multi-Walled Graphene, Gold Nanoparticles). The measurements with the sensors array were performed by using cyclic voltammetry. The cyclic voltammograms recorded in the Danube natural water show redox processes related to the electrochemical activity of the compounds in the water samples or of the electro-active compounds in the sensors detector element. These processes are strongly influenced by the composition and physico-chemical properties of the water samples, such as the ionic strength or the pH. The multivariate data analysis was performed by using the principal component analysis (PCA) and the discriminant factor analysis (DFA), the water samples being discriminated according to the collection point. In order to confirm the observed classes, the partial least squares discriminant analysis (PLS-DA) method was used. The classification of the samples according to the collection point could be made accurately and with very few errors. The correlations established between the voltammetric data and the results of the physico-chemical analyses by using the PLS1 method were very good, the correlation coefficients exceeding 0.9. Moreover, the predictive capacity of the multisensory system is very good, the differences between the measured and the predicted values being less than 3%. The multisensory system based on voltammetric sensors and on multivariate data analysis methods is a viable and useful tool for natural water analysis.

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.

Sign in / Sign up

Export Citation Format

Share Document