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.

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.

An energy dispersive X-ray fluorescence spectrometry approach for the identification of geographical origin of wheat flour

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Tong Chen ◽  
Xingpu Qi ◽  
Zaiyong Si ◽  
Qianwei Cheng ◽  
Hui Chen
Keyword(s):  
Principal Component Analysis ◽  
Wheat Flour ◽  
Principal Component ◽  
Component Analysis ◽  
Mineral Elements ◽  
Energy Dispersive ◽  
Fluorescence Spectrometry ◽  
Energy Channel ◽  
X Ray ◽  
Different Origins

Abstract In this work, a method was established for discriminating geographical origins of wheat flour based on energy dispersive X-ray fluorescence spectrometry (ED-XRF) and chemometrics. 68 wheat flour samples from three different origins were collected and analyzed using ED-XRF technology. Firstly, the principal component analysis method was applied to analyze the feasibility of discrimination and reduce data dimensionality. Then, Competitive Adaptive Reweighted Sampling (CARS) was used to further extract feature variables, and 12 energy variables (corresponding to mineral elements) were identified and selected to characterize the geographical attributes of wheat flour samples. Finally, a non-linear model was constructed using principal component analysis and quadratic discriminant analysis (QDA). The CARS-PCA-QDA model showed that the accuracy of five-fold cross-validation was 84.25%. The results showed that the established method was able to select important energy channel variables effectively and wheat flour could be classified based on geographical origins with chemometrics, which could provide a theoretical basis for unveiling the relationship between mineral element composition and wheat origin.

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.

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