Dissertations / Theses on the topic 'Chemometrics'
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g, Fong Siong Fong. "Chemometrics and chromatography." Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535216.
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Huang, Jun. "Development in Applied Chemometrics : AMT, acoustic chemometrics and N-way image analysis." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Natural Sciences and Technology, 2001. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-2143.
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Kittiwachana, Sila. "Application of chemometrics for process monitoring." Thesis, University of Bristol, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.529846.
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Ampiah-Bonney, Richmond Jerry. "Application of chemometrics in process analysis." Thesis, University of Hull, 2006. http://hydra.hull.ac.uk/resources/hull:5861.
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The acid catalysed esterification of ethanol by acetic acid, a batch process, has been investigated on a laboratory scale at the high temperature range of 78 - 80°C. The data has been collected by Raman Spectroscopy and successfully de-noised using Principal Components Analysis. The first principal component (PCI) was found to describe the fluorescence and other sources of noise in the data and the reconstituted data due to the variation captured in the second principal component (PC2) contained the actual Raman spectra. Thus the reaction profile as well as the profiles of individual reaction components have been clearly mapped out. Validation of this denoising technique has been done by calculating the kinetics of the reaction with the reconstituted data, which has been found to follow the theoretical first order reaction kinetics. The effect of variable selection procedures on model building has been investigated using data from a continuous industrial process, for which reaction profiling as was done for the batch system is not applicable. Two variable selection techniques, General Randomised PRESS-based Elimination (GRAPE) and the genetic algorithm (GA), improve the prediction ability of MLR models by a great deal, indicated by Root Mean Square Error of Cross-Validation (RMSECV) values of 1.0649 - 1.1277 and 1.0977 - 2.0064 respectively. Predicted concentrations are a good estimate for the actual concentrations.
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Zappi, Alessandro <1990>. "Chemometrics applied to direct multivariate analysis." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amsdottorato.unibo.it/8898/1/Zappi_Alessandro_Thesis.pdf.
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The present Ph.D. Thesis is focused on applications and developments of chemometrics. After a short introduction about chemometrics (Chapter 1), the present work is divided in three Chapters, reflecting the research activities addressed during the three-year PhD work:
• Chapter 2 concerns the application of classification tools to food traceability (Chapter 2.1), plant metabolomics (Chapter 2.2), and food-frauds detection (Chapter 2.3) problems.
• Chapter 3 concerns the application of design of experiments for a bio-remediation research (Chapter 3.1) and for machine optimization (Chapter 3.2).
• Chapter 4 concerns the development of the net analyte signal (NAS) procedure and its application to several analytical problems. The main aim of this research is to face the matrix-effect problem using a multivariate approach.
Chemometrics is the science that extracts useful information from chemical data. The development of instruments and computers is bringing to analytical methodologies ever more sophisticated, and the consequence is that huge amounts of data are collected. In parallel with this rapid evolution, it is, therefore, important to develop chemometric methods able to handle and process the data. Moreover, the attention is also focusing on analytical techniques that do not destroy the analyzed samples. Chemometrics and its application to non-destructive analytical methods are the main topics of this research project.
Several analytical techniques have been used during this project: gas-chromatography (GC), bioluminescence, atomic absorption spectroscopy (AAS), liquid chromatography (HPLC), near-infrared spectroscopy, UV-Vis spectroscopy, Raman spectroscopy, X-ray powder diffraction (XRPD), attenuated total reflectance (ATR) spectroscopy.
Moreover, this research activity was carried out in collaboration with several external research groups and companies
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Thurston, Tom. "Chemometrics, kinetics and software for reaction monitoring." Thesis, University of Bristol, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.411114.
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Zhu, Lifeng. "Pharmaceutical reaction and process monitoring using chemometrics." Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437267.
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Airiau, Christian Y. "Application of chemometrics to hyphenated liquid chromatography." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.392922.
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Oliveira, De Souza Danilo. "Quick-EXAFS and hydrotreating catalysts : chemometrics contribution." Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10061/document.
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L’hydrodésulfuration (HDS) est un procédé catalytique utilisé pour éliminer le soufre des carburants. La demande mondiale de carburants propres a stimulé les recherches sur autour de ce procédé afin de mieux comprendre les mécanismes réactionnels et de produire des catalyseurs plus efficaces. Deux axes de recherches peuvent être dégagés : d’une part la formulation de nouvelles voies de synthèse permettant la production des catalyseurs plus efficaces et d’autre part la compréhension du processus catalytique au niveau moléculaire. La compréhension des transformations structurales du catalyseur au niveau moléculaire pendant la réaction ainsi que pendant la genèse de la phase active est une nécessité pour améliorer les propriétés des catalyseurs. Dans ce contexte, ce travail propose deux objectifs. En premier lieu, il présente nouvelle méthode de synthèse de catalyseurs d’HDS à base de CoMo supporté dans TiO2 par voie sol-gel. Dans un deuxième temps, le travail présente la mise-en-œuvre de la chimiometrie pour traiter des données in situ de spectroscopie d’absorption de rayons-X (XAS) qui permet d’obtenir des informations sur la structure moléculaire du catalyseur pendant son activation. Les installations synchrotron de dernière génération permettent en effet d’enregistrer des données expérimentales avec résolution temporelle de l’ordre de la seconde (Quick-EXAFS) et la chimiometrie fournit des outils d’analyse et d’interprétation pour extraire des informations sur les cinétiques de réaction et sur les transformations structurales menant à la formation de la phase active du catalyseurHydrodesulfurization (HDS) is catalytic process used to remove sulfur from petroleum feedstock. The world claim for clean fuel boosted scientists to get new insights on the catalytic reaction in order to understand the mechanisms of the process and, thus, produce catalysts that are more efficient. Such researches are based mainly in to lines: by one hand, in the formulation of new routes that lead to tailored catalysts and, by the other hand, in a better understanding of the catalytic process at the molecular and atomic level. Particularly, the later leads to an optimization of the formulation and better catalytic performance, for which is required further understanding of the molecular structure, its transformations during the reaction, the nature of active species and its genesis. In this picture, the goal of this work is twofold. First, to present a new route for produce HDS CoMo-based catalysts via one-pot sol-gel method, which revealed to have suitable macro- and microscopic properties making promising solids for further applications. Second, to adapt and use chemometrics method to treat in situ measurements, particularly, X-ray absorption spectra (XAS), to get new insights on the genesis of the catalytic active phase at the molecular level. XAS techniques is suitable to probe local atomic structure, and last generation synchrotron facilities provide conditions to perform such in situ experiments with very fast acquisition (Quick-EXAFS). Chemometrics provide a brand new scope on data analysis and interpretation for extract information on the kinetics of reaction and structure transformation that leads to the active phase of the catalysts
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Pell, Randall James. "Chemometrics and infrared emission spectroscopy for remote analysis /." Thesis, Connect to this title online; UW restricted, 1990. http://hdl.handle.net/1773/11545.
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Chen, Zhaomin. "Human Liver Metastases: Chemometrics of Imaging FTIR Data." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437662269.
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Seasholtz, Mary Beth. "Parsimonious construction of multivariate calibration models in chemometrics /." Thesis, Connect to this title online; UW restricted, 1992. http://hdl.handle.net/1773/8705.
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Harang, Valérie. "Aspects of Optimisation of Separation of Drugs by Chemometrics." Doctoral thesis, Uppsala University, Analytical Pharmaceutical Chemistry, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3738.
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Statistical experimental designs have been used for method development and optimisation of separation. Two reversed phase HPLC methods were optimised. Parameters such as the pH, the amount of tetrabutylammonium (TBA; co-ion) and the gradient slope (acetonitrile) were investigated and optimised for separation of erythromycin A and eight related compounds. In the second method, a statistical experimental design was used, where the amounts of acetonitrile and octane sulphonate (OSA; counter ion) and the buffer concentration were studied, and generation of an α-plot with chromatogram simulations optimised the separation of six analytes.
The partial filling technique was used in capillary electrophoresis to introduce the chiral selector Cel7A. The effect of the pH, the ionic strength and the amount of acetonitrile on the separation and the peak shape of R- and S-propranolol were investigated.
Microemulsion electrokinetic chromatography (MEEKC) is a technique similar to micellar electrokinetic chromatography (MEKC), except that the microemulsion has a core of tiny droplets of oil inside the micelles. A large number of factors can be varied when using this technique. A screening design using the amounts of sodium dodecyl sulphate (SDS), Brij 35, 1-butanol and 2-propanol, the buffer concentration and the temperature as factors revealed that the amounts of SDS and 2-propanol were the most important factors for migration time and selectivity manipulation of eight different compounds varying in charge and hydrophobicity. SDS and 2-propanol in the MEEKC method were further investigated in a three-level full factorial design analysing 29 different compounds sorted into five different groups. Different optimisation strategies were evaluated such as generating response surface plots of the selectivity/resolution of the most critical pair of peaks, employing chromatographic functions, simplex optimisation in MODDE and 3D resolution maps in DryLab™.
Molecular descriptors were fitted in a PLS model to retention data from the three-level full factorial design of the MEEKC system. Two different test sets were used to study the predictive ability of the training set. It was concluded that 86 – 89% of the retention data could be predicted correctly for new molecules (80 – 120% of the experimental values) with different settings of SDS and 2-propanol.
Statistical experimental designs and chemometrics are valuable tools for the development and optimisation of analytical methods. The same chemometric strategies can be employed for all types of separation techniques.
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Harang, Valérie. "Aspects of optimisation of separation of drugs by chemometrics /." Uppsala : Acta Universitatis Upsaliensis :, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3738.
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Kownarumit, Supaporn. "Multiplex screening using enzyme inhibition, fluorescence detection and chemometrics." Thesis, Loughborough University, 2006. https://dspace.lboro.ac.uk/2134/12308.
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Fluorescence enzyme inhibition assays have been established for a number of years as valuable methods of analysis in clinical chemistry and other fields. Those in common use are normally single analyte assays. However, in many cases (e.g. drug screening) dual or multiplex assays would be much more valuable, with the advantages of increased information content, saving in time and costs, and the elimination of some sources of sampling variance. This project has investigated single and dual screening assays of enzyme inhibitors, namely 3-nitrophenylboronic acid (3-NPBA), phenylethyl /3-0- thiogalactopyranoside (PETG) and sodium vanadate (VI), using flow injection fluorescence spectroscopy and chemometric methods to resolve strongly overlapping fluorescence spectra. The single and dual screening assays have been based on flow injection analysis methodology, with immobilised enzymes on solid phase reactors to investigate the enzyme inhibitors. The assays were rapid, allowing around 15-25 measurements to be made per hour. The inhibitions of alkaline protease, alkaline phosphatase and /3-galactosidase with their inhibitors at flg/ml levels were achieved. An alternative approach to these dual assays has been investigated by the use of multivariate techniques. Such techniques allow accurate and reliable results to be obtained even from spectra that contain extremely overlapping signals. Moreover, preliminary investigation of three fluorophores which gave strongly overlapping spectra, using flow injection fluorescence spectroscopy and partial least squares (PLS-1) model has been successful. By combination of this flow injection fluorescence spectroscopy with the use of chemometrics, many applications can be envisaged in biochemical, clinical, and pharmaceutical industries. With the findings of this research the system described here can be developed for use in high throughput screening of candidate drug molecules and many screening processes throughout the different industries.
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Caldicott, Kenneth James. "Application of trace analysis and chemometrics to environmental problems." Thesis, University of South Wales, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.480930.
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Guillo, Christelle. "Application of capillary electropheries and chemometrics to urine profiling." Thesis, King's College London (University of London), 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.408612.
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Deane, John Michael. "Novel application of chemometrics in the petro-chemical industry." Thesis, University of Bristol, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.281870.
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Fisher, Amanda Kaye. "Raman Chemometrics and Application to Enzyme Kinetics and Urinalysis." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/92590.
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Raman spectroscopy records the inelastic scattering of photons originating from striking a sample with monochromatic light. Inelastic, or Raman, scattered photons shift in wavelength due to excitation of the vibrational modes of molecules struck by the incident light. The Raman scattered photons are representative of all of the covalent bonds contained within a sample. Raman spectra taken of biological systems such as proteins, bacterial colonies, and liquid waste, are difficult to interpret due to the complexity of their covalent bond landscape and mixtures of molecules in highly variable concentrations. Rather than deconstructing Raman spectra to attempt assignment of specific bonds and functional groups to wavenumber peaks, here we have developed a chemometric analysis pipeline for quantifying the similarities and differences among a set of Raman spectra. This quantification aids in both classification of samples, and in measuring how samples change over time. The chemometric approach for interpretation of Raman spectra was made freely available in a user-friendly format via a MATLAB add-on called the Raman Data Analysis (RDA) Toolbox. Demonstrations of the RDA Toolbox functionalities on Raman spectra taken of various common biological systems are included, such as determination of protein concentration and monitoring bacterial culture growth. The RDA Toolbox and Raman spectroscopy are also used to initiate research in novel areas. Fast and accurate evaluation of enzyme specific activity is required for engineering enzymes, and results of Raman assays, evaluated in the RDA Toolbox, are successfully correlated to absorbance activity assays of an enzyme WT and mutant library. Further development of this research could alleviate the bottleneck of screening mutant libraries in enzyme engineering projects. The Toolbox is then used in a distinctly different application for evaluating urine and spent dialysate samples from patients with end stage renal disease. Categorization between samples from healthy volunteers and patients is accomplished with close to 100% accuracy, and evidence indicating that Raman spectroscopy can serve as an early diagnostic tool for infections of the peritoneal membrane is presented.PHD
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Lloyd, Gavin Rhys. "Chemometrics and Pattern Recoqnition for the Analysis of Multivariate Datasets." Thesis, University of Bristol, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.520174.
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Griffin, Angela Jane. "Development of a solid state IR analyser system using chemometrics." Thesis, University of Hull, 2003. http://hydra.hull.ac.uk/resources/hull:5536.
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There are many applications for gas and vapour measuring systems in environmental and industrial analysis. Different analytical techniques and detectors employed include; chromatography, catalytic filaments and pellistors, metal oxide and semiconductors, electrochemical sensors, piezoelectric and surface acoustic wave detectors, and spectrometry. The technique employed in this work is infrared spectrometry and it is part of a joint project between the Analytical Science and Optoelectronic Groups at the University of Hull. The aim of the project is to develop a robust multi-component infrared gas analyser using a fixed array of quantum well intersubband photodetectors QWIPs in which the photodetectors are tailored to measure at specific wavelengths. In order to provide a low cost portable detector, only a limited number of detectors can be incorporated into the array, therefore a method of selecting suitable wavelengths to measure is needed. To achieve this, FTIR spectra were collected for mixtures of CH4, CO, CO2 and N20. A variable selection algorithm based on multivariate linear regression (VS-MLR) was applied to the spectra to reduce the number of measurements required down to five. It was found that it was possible to build prediction models using low resolution and that baseline variations could be compensated for by using an internal standard. Based on the wavenumber selection models, the concentrations of CH4, CO, CO2 and N20 in new validation data could be predicted using wavenumbers 2129,2284,3071,3148 and 3488 cm 1, with prediction errors averaging less than 14 %. This gave comparable results to PLS models built from the entire spectra. An exponential dilution system was constructed to enable different concentrations of a gas standard to be prepared and a GC was included into the system to act as a reference method for IR measurements. Good correlation was achieved between IR and GC responses for CH4, C02 and N20 with RZ values greater than 0.988. A prototype system was built (which incorporated the dilution system), utilising narrowband filters (at wavelengths 2.9,3.25,4.3 5,4.73 and 10.62 μm) combined with general IR detectors, based on the results of the wavelength selection. This was shown to give promising results and future work will be to replace the IR detectors with QWIPs.# Also explored in this work was the feasibility of developing a system to measure volatile organicc ompounds(V OCs). This was investigatedb y collecting FTIR spectra for a variety of VOCs and employing PCA and cluster analysis to see if it was possible to distinguish between different types of compounds. Partial least squares (PLS) and VS-MLR was performed on a simple four component vapour mixture to demonstrate that the same methods employed on the CH4, CO, CO2 and N20 mix could also be applied to other mixtures. In conclusion, the project demonstrated the role chemometrics can play in the development of a solid state gas analyser, from experimental design to data treatment and analysis. It was proved the VS-MLR could be successfully utilised to determine suitable wavelengths to measure in the array for gas and vapour applications and a prototype system was constructed using narrow band filters to demonstrate the operation of such a system.
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Kavianpour, Keyhandokht. "Study of degradation of chlorophylls using DAD-HPLC and chemometrics." Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262839.
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Elbergali, Abdalla Kh. "Factor analysis : theory and applications to evolutionary problems in chemometrics." Thesis, University of Bristol, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282138.
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Xu, Yun. "Chemometrics pattern recognition with applications to genetic and metabolomics data." Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435733.
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Chen, Zewei. "Authentication of Complex Botanical Materials by Chemometrics and Chemical Profiling." Ohio University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1617010785195628.
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Loades, Victoria Catherine. "The application of chemometrics to spectroscopic and process analytical data." Thesis, University of Hull, 2003. http://hydra.hull.ac.uk/resources/hull:13971.
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The research has included collaboration with number of different companies and consortiums involving spectroscopic measurements with the application of chemometric techniques. For the 'European Framework 5', Standards Measurements and Testing (SMT) chemometrics network consortium a certified reference dataset based on visible metals complex spectra was developed. An inter-laboratory study was carried out which demonstrated the between subject significant difference for chemometric data analysis. An industrial collaboration with BNFL, Springfield's, this work consisted of producing a PLS regression model which could be used to predict levels of uranyl and nitrate in uranyl nitrate liquors samples, which were analysed by Raman spectroscopy which was insensitive to temperature. A substantial amount of work has been in the development of GMS with multivariate calibration for process analysis. The GMS is designed for the analysis of flowing mixtures, slurries and moisture content. The method is currently hindered by the existing calibration method; here PCA, PLS and weighted ridge regression (WRR) have been applied to the broadband, complex spectra to successfully allow measurement of a range of samples including; aqueous, organic, fermentation and non-homogeneous samples.
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Asquith, Ilse Bernadette. "An investigation of statistical methodologies for evaluating natural herbicides for the control of yellow nutsedge (Cyperus esculentus)." Thesis, Nelson Mandela Metropolitan University, 2007.
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The present study was undertaken with the view to evaluate methodologies based on traditional Scheffé experimental designs that study mixtures as a tool for discovery research particularly when seeking new and or improved uses of existing mixtures. For the purpose of this study, the topic of controlling the problematic weed known as Yellow Nutsedge (Cyperus esculentus L. var. esculentus) or “Geel Uintjie”, was selected on a rather ad hoc basis. Yellow Nutsedge is a troublesome perennial weed found in most agricultural countries in the world. Herbicidal control is often difficult because of the weeds’ ability to propagate via tubers, which can remain dormant for a number of years and are also resistant to most synthetic herbicide controls. As a first step the study involved the selection of a group of chemical compounds that would be used in suppressing the germination of Yellow Nutsedge tubers. Treatment with various combinations of these chemical compounds as determined by statistical experimental designs was carried out. A review of the literature, particularly literature concerned with the study of the phenomenon of allelopathy, suggested that various phenolic-D-glucopyranosides could show promise in the suppressing the germination of Yellow Nutsedge tubers. This led to the selection of this group of compounds as the target group of “active” substances for the study. Since the group of phenolic-D-glucopyranosides is quite large, and in order to keep the study to a reasonable size, only four phenolic-D-glucopyranosides were selected namely: 4-nitrophenyl-D-glucopyranoside, 4-chlorophenyl--Dglucopyranoside, arbutin and salicin. This selection was based firstly based on a particular phenolic-D-glucopyranoside being a suspected allelochemical, and secondly the ease of technical synthesis using a catalytic process. In addition to the four selected phenolic-D-glucopyranosides, their aglycones (4,nitrophenol, 4,chlorophenol, hydroquinone and salicyl alcohol) were also included as potential “active” substances in order to discern any potential activity between the phenolic-D-glucopyranosides and the aglycones. iii The selected “active substances” were combined in various combinations according to various mixture designs in such a manner that the sum of the proportions of the various actives in any one mixture was always equal to 1. The mixtures of actives were then used in various germination experiments and three experimental responses were measured namely the germination, average dry mass and length of longest shoot. From the results of these germination studies the canonical form of the polynomial equation describing the variation in each of the three germination responses was calculated and evaluated statistically. These equations were then used to estimate the presence of, and the magnitude of synergism between the various active substances. The results from these screening experiments and their detailed statistical analysis indicated that the response surface model for the germination response contains three synergistic blends (4-nitrophenyl--D-glucopyranoside + arbutin; 4-nitrophenyl--Dglucopyranoside + hydroquinone; and 4-chlorophenyl--D-glucopyranoside + salicin) and one antagonistic blend (4-nitrophenyl--D-glucopyranoside + 4- chlorophenol--D-glucopyranoside). The response surface model for the average dry mass response contains two synergistic blends (4-nitrophenyl--Dglucopyranoside + hydroquinone; and 4-chlorophenol--D-glucopyranoside + salicin) and the same antagonistic blend as for germination response. For both germination and average dry mass responses, the most synergistic blend was found to be the combination of 4-chlorophenyl--D-glucopyranoside and salicin. Two additional tests were conducted and both confirmed the results obtained from the screening designs. These tests involved the identification of the two components responsible for the synergistic activity that resulted in the suppression of the germination of the tubers and growth of the seedlings. The experimental response measuring the longest shoot proved to be erroneous and was excluded from the statistical analysis. In summary, this study has clearly shown that statistically designed experiments based on mixture designs can be used as a powerful tool in identifying and quantifying synergistic (or antagonistic) effects of chemicals on the germination ability of plant seeds.
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Gromski, Piotr Sebastian. "Application of chemometrics for the robust analysis of chemical and biochemical data." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/application-of-chemometrics-for-the-robust-analysis-of-chemical-and-biochemical-data(3049006f-e218-4286-83a8-e1fd85004366).html.
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In the last two decades chemometrics has become an essential tool for the experimental biologist and chemist. The level of contribution varies strongly depending on the type of research performed. Therefore, chemometrics may be used to interpret and explain results, to compare experimental data with real-word ‘unseen’ data, to accurately detect certain chemical vapour, to identify cancerous related metabolites, to identify and rank potentially relevant/important variables or simply just for a pictorial interpretation and understanding of the results. Whilst many chemometrics methods are well-established in the area of chemistry and metabolomics many scientists are still using them with what is often referred to as a ‘black-box’ approach, that is without prior knowledge of the methods and well-recognised statistical properties. This lack of knowledge is thanks to the wide availability of powerful computers and – perhaps more notably – up-to-date, easy to use and reliable software. The main aim of this study is to reduce this gap by providing extensive demonstration of several approaches applied at different stages of the data analysis pipeline highlighting the importance of appropriate method selection. The comparisons are based both on chemical and biochemical (metabolomics) data and construct a firm basis for the researchers in terms of understanding of chemometric methods and the influence of parameter selection. Consequently, in this thesis the exploration and comparison of different approaches employed for various statistical steps are investigated. These include pre-treatment steps such as dealing with missing data and scaling. First, different substitution of missing values and their influence on unsupervised and supervised learning have been compared, where it has been shown that metabolites that display skewness in distribution can have a significant impact on the replacement approach. The scaling approaches were compared in terms of effect on classification accuracy for variety of metabolomics data sets. It was shown that the most standard option which is autoscaling is not always the best. In the next step a comparison of various variable selection methods which are commonly used for the analysis of chemical data has been carried out. The results revealed that random forests, with its variable selection techniques, and support vector machines, combined with recursive feature elimination as a variable selection method, displayed the best results in comparison to other approaches. Moreover, in this study a double cross-validation procedure was applied to minimize the consequence of over-fitting. Finally, seven different algorithms and two model validation procedures based on either 10-fold cross-validation or bootstrapping were investigated in order to allow direct comparison between different classification approaches.
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Mobley, Paul R. "Use of a priori information to produce more effective, automated chemometrics methods /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/8549.
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Fernández, Barrat Cristina. "Analytical methodologies based on chemometrics to optimize the photodegradation of dyes." Doctoral thesis, Universitat Rovira i Virgili, 2012. http://hdl.handle.net/10803/76336.
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El objeto de la presente tesis es el desarrollo de metodologías analíticas rápidas para optimizar los procesos de degradación de colorantes orgánicos presentes en aguas residuales. Se ha llevado a cabo una revisión bibliográfica acerca de dichos procesos y las técnicas analíticas utilizadas para su monitorización y la identificación de posibles intermedios. Se han establecido nuevas metodologías analíticas basadas en herramientas quimiométricas tales como diseño de experimentos para optimizar procesos de fotodegradación con y sin catalizador. Se ha empleado resolución de curvas multivariante para la determinación simultánea de colorantes e intermedios durante estos procesos. Dado que la velocidad de degradación es el parámetro mas utilizado para evaluar la eficiencia de las fotodegradaciones se han evaluado todas las constantes involucradas en la degradación y/o la adsorción sobre el catalizador mediante HS-MCR-ALS. Se ha establecido una metodología analítica rápida para la determinación de colorantes basada en cromatografía de inyección secuencial (SIC).The main aim of this thesis is the development of rapid analytical methodologies to optimize the photodegradation processes of organic azo dyes from industrial wastewater. A bibliographic review has been done concerning the most employed processes for dye removal and the analytical techniques used for the monitoring and the identification of intermediates. New analytical methodologies based on chemometric tools such as experimental designs have been established to optimize photodegradation processes with and without using heterogeneous catalysts. Multivariate curve resolution has been employed to determine simultaneously different dyes and intermediates along degradation processes. Since the degradation rate is the most employed parameter to evaluate the photodegradation efficiency all the kinetic constants involved in the degradation and/or the adsorption of the dye onto the catalyst have been evaluated by HS-MCR-ALS. Furthermore a rapid analytical methodology for dye determination based on sequential injection chromatography (SIC) has been established.
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Marcos-Dominguez, Ana Maria. "Investigation of chemometrics methods for characterising drift phenomena in ICP-AES." Thesis, University of Plymouth, 2001. http://hdl.handle.net/10026.1/1679.
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The objective of this study was to fully characterise drift phenomena in inductively coupled plasma atomic emission spectroscopy (ICP-AES) in order to develop novel correction procedures to aid routine analysis. Long-term drift of the analytical signal continues to be a potential disadvantage when using ICP-AES and often necessitates regular recalibration. The long-term stability of three commercially available Instruments was studied using In each case a range of analyte and intrinsic plasma emission lines. Long-term fluctuations were observed which generated drift bias of up to 20% on the initial values. The drift pafterns were characterised and found to be qualitatively reproducible. In most cases, similar long-term fluctuations were observed independent of the analyte or nature of the emission line. In addition, high inter-element correlation was observed on the long-term fluctuations even when sequential acquisition was employed. In order to study the fundamental causes of drift, the effect of two key instrumental parameters, i.e. the RF power and the nebutiser gas flow rate were studied with respect to the stability of the signal. Different drift patterns were found depending on the working conditions. Classical statistical methods and a multi-way approach. PARAFAC. were then employed to describe the system. The use of intemal standards to correct for drift has also been investigated, but found to be of benefit only under certain defined conditions (i.e. robust conditions, high RF power and low nebuliserflow rate). At soft conditions, low RF power and medium to high nebuliser flow rate, the system Is very unstable and intemal standardisation is not fully effective as a correction method. For such conditions, a novel correction procedure has been developed, which employs the drift pattem of one intrinsic plasma line (i.e. an argon line) and a correction factor which is specific for each emission line. The drift values were reduced from around 20% before correction to better than ±2% following the described protocol. Finally, the effects of chemical matrices on the long-term stability of the emission signals have been evaluated. Three synthetic matrices were prepared simulating nitric, soil and water matrices. The stability of the instrument when working wrth these matrices at both robust and soft conditions was found to be poor, especially when the solution was matched with the soil matrix. The use of more robust conditions did not improve the long-temi stability of the emission signals. The outcome of this study proved to be a better understanding of drift phenomena and a novel method for drift correction.
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O'Connor, J. "Use of chemometrics in the prognosis of patients with myocardial infarction." Thesis, University of Brighton, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234289.
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Wasim, Mohammad. "Application of chemometrics to liquid chromatography with nuclear magnetic resonance spectroscopy." Thesis, University of Bristol, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412363.
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Zissis, Konstantinos D. "Application of chemometrics for the analysis of mixtures by coupled chromatography." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310782.
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Emerton, Guy. "Data-driven methods for exploratory analysis in chemometrics and scientific experimentation." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86366.
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Thesis (MSc)--Stellenbosch University, 2014.ENGLISH ABSTRACT: Background New methods to facilitate exploratory analysis in scientific data are in high demand. There is an abundance of available data used only for confirmatory analysis from which new hypotheses can be drawn. To this end, two new exploratory techniques are developed: one for chemometrics and another for visualisation of fundamental scientific experiments. The former transforms large-scale multiple raw HPLC/UV-vis data into a conserved set of putative features - something not often attempted outside of Mass-Spectrometry. The latter method ('StatNet'), applies network techniques to the results of designed experiments to gain new perspective on variable relations. Results The resultant data format from un-targeted chemometric processing was amenable to both chemical and statistical analysis. It proved to have integrity when machine-learning techniques were applied to infer attributes of the experimental set-up. The visualisation techniques were equally successful in generating hypotheses, and were easily extendible to three different types of experimental results. Conclusion The overall aim was to create useful tools for hypothesis generation in a variety of data. This has been largely reached through a combination of novel and existing techniques. It is hoped that the methods here presented are further applied and developed.
AFRIKAANSE OPSOMMING: Agtergrond Nuwe metodes om ondersoekende ontleding in wetenskaplike data te fasiliteer is in groot aanvraag. Daar is 'n oorvloed van beskikbaar data wat slegs gebruik word vir bevestigende ontleding waaruit nuwe hipoteses opgestel kan word. Vir hierdie doel, word twee nuwe ondersoekende tegnieke ontwikkel: een vir chemometrie en 'n ander vir die visualisering van fundamentele wetenskaplike eksperimente. Die eersgenoemde transformeer grootskaalse veelvoudige rou HPLC / UV-vis data in 'n bewaarde stel putatiewe funksies - iets wat nie gereeld buite Massaspektrometrie aangepak word nie. Die laasgenoemde metode ('StatNet') pas netwerktegnieke tot die resultate van ontwerpte eksperimente toe om sodoende ân nuwe perspektief op veranderlike verhoudings te verkry. Resultate Die gevolglike data formaat van die ongeteikende chemometriese verwerking was in 'n formaat wat vatbaar is vir beide chemiese en statistiese analise. Daar is bewys dat dit integriteit gehad het wanneer masjienleertegnieke toegepas is om eienskappe van die eksperimentele opstelling af te lei. Die visualiseringtegnieke was ewe suksesvol in die generering van hipoteses, en ook maklik uitbreibaar na drie verskillende tipes eksperimentele resultate. Samevatting Die hoofdoel was om nuttige middele vir hipotese generasie in 'n verskeidenheid van data te skep. Dit is grootliks bereik deur 'n kombinasie van oorspronklike en bestaande tegnieke. Hopelik sal die metodes wat hier aangebied is verder toegepas en ontwikkel word.
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Reilly, Michael Anthony. "Characterisation of HFC-134a by gas chromatography-mass spectrometry and chemometrics." Thesis, University of Edinburgh, 1999. http://hdl.handle.net/1842/14268.
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1,1,1,2-tetrafluoroethane (HFC 134a) is one of the major replacement compounds for chlorofluorocarbons and is therefore of great industrial importance. This work describes a method of characterisation for HFC 134a. The method profiled the levels of synthesis by-products present in samples of HFC 134a using gas chromatography (GC) with detection by flame ionisation detector (FID) or electron capture detector (ECD). The principal method for identification of the by-products was EI GC-mass spectrometry. The multi-variate data produced by the profiling of samples were analysed using chemometric techniques. A training set of samples of HFC 134a, with known origins of production, was analysed by both GC-FID and GC-ECD. This training data set was used to investigate the different methods of chemometric analysis as applied to the raw data, normalised data and principal component analysed data. K-means clustering and Hierarchical clustering were investigated to find the optimum methods for the identification of samples' origins of production based on their chromatographic profiles. The FID chromatographic traces could be correctly identified by applying a two step principal component analysis (PCA) using a hierarchical clustering method to classify the samples. The ECD chromatographic data could be correctly identified by applying a PCA followed by classification using a hierarchical clustering method. The two classification techniques were used to identify further samples of HFC 134a into clusters belonging to the known origins of production or into new clusters representing samples of unknown origin. Classification of samples using the ECD data required the least amount of operator interpretation and provided the least amount of ambiguity in sample identification.
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Zhang, Mengliang. "Determination of Environmental Pollutants by Gas Chromatography/Mass Spectrometry with Chemometrics." Ohio University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1418386921.
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Aloglu, Ahmet Kemal. "Characterization of Foods by Chromatographic and Spectroscopic Methods Coupled to Chemometrics." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou152293360889416.
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Benoudjit, Nabil. "Variable selection and neural networks application in infrared spectroscopy and chemometrics." Saarbrücken VDM Verlag Dr. Müller, 2003. http://d-nb.info/988752271/04.
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Qian, Jue. "Glucose Monitoring in Various Matrices with Near-Infrared Spectrometry and Chemometrics." Diss., University of Iowa, 2013. https://ir.uiowa.edu/etd/4901.
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The long-term complications of diabetes can be dramatically reduced with tight glycemic control. Although the current invasive technology for measuring blood glucose is effective, it not well suited for the real-time measurements necessary for tight control. Near infrared (NIR) absorption spectroscopy, coupling with multivariate calibration modeling, can potentially provide portable, rugged and low-cost instrumentation for continuous glucose sensing. An optical microsensor that can be used in conjunction with an ultrafiltration sampling probe is under development for continuous glucose measurements in interstitial fluid (ISF) collected from subcutaneous tissue.
The first part of this research focused on the development of an algorithm for eliminating of effect of temperature variance on NIR glucose measurements. Spectra of 80 bovine blood ultrafiltrate samples were collected under five different temperatures by using a Fourier transform (FT) NIR spectrometer. Based on the fundamental properties of digital Fourier filtering, baseline variations created by difference in the temperature of the blood ultrafiltrate samples were shown to be eliminated by using an optimized Gaussian shape filter response function. PLS calibration models combined with digital Fourier filtering provided standard errors of prediction in the range of 0.3-0.4 mM for sample with temperatures between 25-40 °C.
Before applying the microsensor to animal or human measurements, a testing platform was designed and constructed for the eventual purpose of evaluating the ability of the microsensor to follow glucose concentration transients. A series of computer-controlled pumps were used in combination with an ultrafiltration probe to create glucose transients and deliver the corresponding samples to the spectrometer for analysis. NIR spectra were collected continuously as the concentrations of glucose, urea, and lactate were varied independently. Glucose transients were followed over periods of days by using either partial least squares (PLS) or net analyte signal (NAS) calibration methods.
The NAS calibration method and a modified Hybrid Linear Analysis (HLA) method were investigated for monitoring the concentrations of glucose and lactate during microbial fermentations. An UF-sampling probe is used to collect samples of the fermentation broth and deliver these samples to the spectrometer for continuous analysis. The established NAS and modified-HLA calibration models provided glucose and lactate concentration measurements with mean percentage errors of 2 and 3%, respectively. These calibration functions were demonstrated capable of accurate concentration measurements several days beyond the formal calibration process.
Lastly, NIR spectra of whole bovine blood samples were used to demonstrate the ability to measure glucose in blood with different levels of hematocrit. Calibration functions were based on PLS modeling and the effective models were developed for measurements from absorbance and single-beam NIR spectra. The method of multiplicative scatter correction was found to be particularly effective in reducing the impact of light scattering caused by the red blood cells at different hematocrit levels. These findings imply that nondestructive NIR spectroscopy has the potential to measure glucose without consuming blood, thereby reducing phlebotomy blood loss in neonates and potentially decreasing the frequency of red blood cell transfusion for this fragile patient population.
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BACCOLO, GIACOMO. "Chemometrics approaches for the automatic analysis of metabolomics GC-MS data." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/374731.
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La metabolomica, che consiste nella identificazione di tutti i metaboliti presenti all’interno dei campioni biologici analizzati, è un approccio ampiamente applicato in diversi campi di ricerca quali: identificazione di biomarcatori, sviluppo di nuovi farmaci, scienze alimentari e ambientali. La metabolomica è strettamente legata alla capacità di tecniche analitiche fra queste una delle più applicate è la gas cromatografia accoppiata alla spettrometria di massa. Moderne piattaforme analitiche possono generare centinaia di migliaia di spettri, rilevando una quantità impressionante di molecole distinte. Nonostante i progressi tecnici raggiunti sul lato sperimentale, la conversione dei segnali misurati dagli strumenti in informazioni utili non è un passaggio scontato in studi metabolomici. Per ogni composto identificato, l’obbiettivo è ottenere la concentrazione relativa tra tutti i campioni analizzati e lo spettro di massa associato al composto, necessario per l’identificazione della molecola stessa. I software disponibili per l’analisi dei dati sperimentali sono stati ripetutamente indicati come una fonte importante di incertezza, limitando fortemente sia la quantità che la qualità delle informazioni estratte. Gli strumenti più applicati richiedono l’impostazione di diversi parametri da parte dell’operatore, influenzando il risultato dell’analisi. In questa tesi è descritto un nuovo approccio, chiamato AutoDise, per l’analisi dei dati GC-MS. L’elaborazione dei segnali sperimentali si basa su PARAFAC2. PARAFAC2 è un modello che scompone dati multidimensionali, discriminando tra i diversi segnali nei campioni.
Grazie alle sue proprietà, PARAFAC2 non ha bisogno che i dati siano pretrattati e non richiede di impostare parametri, mentre software utilizzati in questo ambito richiedono di definire diversi parametri e un laborioso pretrattamento dei dati, richiedendo l’intervento di un utente esperto, inoltre la riproducibilità dei risultati è limitata, dipendendo i parametri scelti dall’utente. Tuttavia, il fitting di modelli PARAFAC2 coinvolge diverse fasi ed è necessario un esperto analista per l’analisi e l’interpretazione dei modelli. AutoDise è un sistema esperto in grado di gestire tutti i passaggi riguardanti la modellazione e di generare una tabella dei picchi in cui ogni composto è identificato in modo univoco, con risultati completamente riproducibili. Questo è possibile grazie alla combinazione di diversi strumenti diagnostici e grazie all’ applicazione di modelli d’intelligenza artificiale. Le prestazioni dell’approccio sono state testate su un complesso dataset di oli d’oliva ottenuto tramite analisi GC-MS. I dati sono stati analizzati sia manualmente, da utenti esperti, sia automaticamente con il metodo AutoDise proposto e le tabelle dei picchi risultanti sono state confrontate. I risultati mostrano che AutoDise supera l’analisi manuale sia in termini di numero di composti identificati che di qualità dell’identificazione e della quantificazione. Inoltre, è stata sviluppata una GUI per rendere l’algoritmo più accessibile a persone non esperte nel linguaggio di programmazione. La tesi include un tutorial che mostra le caratteristiche principali e come utilizzare l’interfaccia grafica. Un’altra parte importante della tesi è stata dedicata al test e allo sviluppo di nuove reti neurali artificiali da implementare nel software AutoDise per rilevare quali componenti PARAFAC2 stanno fornendo informazioni chimicamente utili. A tal fine, più di 170.000 profili sono stati etichettati manualmente, al fine di addestrare, validare e testare una rete neurale convoluzionale e una rete bilineare con memoria a breve termine e un modello k-nearest neighbour. I risultati suggeriscono che le reti di deep learning possono essere efficacemente applicate per la classificazione automatica dei profili cromatografici.Metabolomics, which consists of identifying all the metabolites present in the biological samples analysed, is an approach widely applied in various research fields such as biomarker identification, new drug development, food and environmental sciences. Metabolomics is closely linked to the ability of analytical techniques, one of the most widely applied being gas chromatography coupled to mass spectrometry. Modern analytical platforms can generate hundreds of thousands of spectra, detecting an impressive number of distinct molecules. Despite the technical progress achieved on the experimental side, the conversion of signals measured by instruments into useful information is not an obvious step in metabolomic studies. For each identified compound, the goal is to obtain the relative concentration among all analysed samples and the mass spectrum associated with the compound needed to identify the molecule itself. The software available for analysing experimental data has repeatedly been cited as a major source of uncertainty, severely limiting both the quantity and quality of the information extracted. The most applied tools are based on univariate data analysis, considering each sample separately from the others and requiring the operator to set several parameters, affecting the result of the analysis. In this thesis, a new approach, called AutoDise, for the analysis of GC-MS data is described. The processing of the experimental signals is based on PARAFAC2. PARAFAC2 is a model that decomposes multidimensional data, discriminating between different signals in the samples. Due to its properties, PARAFAC2 does not need the data to be pre-processed and does not require parameters to be set, whereas software used in this field requires several parameters to be defined and laborious pre-processing of the data, requiring the intervention of an expert user, and the reproducibility of the results is limited, depending on the parameters chosen by the user. However, fitting PARAFAC2 models involves several steps and an experienced analyst is needed to analyse and interpret the models. AutoDise is an expert system capable of handling all modelling steps and generating a peak table in which each compound is uniquely identified, with fully reproducible results. This is possible thanks to the combination of different diagnostic tools and the application of artificial intelligence models. The performance of the approach was tested on a complex dataset of olive oils obtained by GC-MS analysis. The data were analysed both manually, by experienced users, and automatically with the proposed AutoDise method and the resulting peak tables were compared. The results show that AutoDise outperforms manual analysis both in terms of the number of compounds identified and the quality of identification and quantification. In addition, a GUI was developed to make the algorithm more accessible to people not skilled in the programming language. The thesis includes a tutorial showing the main features and how to use the GUI. Another important part of the thesis was devoted to testing and developing new artificial neural networks to be implemented in the AutoDise software to detect which PARAFAC2 components are providing chemically useful information. To this end, more than 170,000 profiles were manually labelled in order to train, validate and test a convolutional neural network and a bilinear network with short-term memory and a k-nearest neighbour model. The results suggest that deep learning networks can be effectively applied for the automatic classification of chromatographic profiles.
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Pierce, Karisa M. "Objectively obtaining information from gas chromatographic separations of complex samples using novel data processing and chemometric techniques /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/8575.
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Zhang, Xin. "Application of chemometrics to hyperspectral imaging analysis of environmental and agricultural samples." Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/301275.
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This Thesis deals with the resolution of hyperspectral imaging data by using chemometric methods, in particular by using appropriate data pretreatment methods and by using Multivariate Curve Resolution (MCR) methods. The main contribution of the present Thesis is the study and implementation of the MCR-ALS (Multivariate Curve Resolution Alternating Least Squares) method for the resolution of hyperspectral images, collected by remote sensing (airborne or space borne Earth observation instrument) and by micro-spectroscopy imaging. Specifically, in this Thesis work, we explore the combination of chemometric and hyperspectral imaging methods for the resolution of spectra (signatures) and spatial distribution maps of the chemical constituents of a sample. The ultimate goal of this study is to improve the analysis and interpretation of hyperspectral imaging data by taking advantage of different chemometric powerful tools. Local rank/selectivity properties describing the spatial information of spectroscopic images can be used as a constraint to increase the performance of MCR methods significantly, decreasing rotation ambiguity uncertainties. Different multivariate resolution methods were compared, such as MCR-ALS, Principal Component Analysis (PCA), and Minimum Volume Simplex Analysis (MVSA), Multivariate Curve Resolution-Function Minimization (MCR-FMIN), MCR-BANDS and FAC-PACK. All these approaches have been used for the evaluation of the extension of rotation ambiguities remaining in the results after their application. Several hyperspectral images provided by standard and widely used instruments such as NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS), Raman hyperspectral imaging Spectrometer, and Infrared hyperspectral imaging Spectrometer have been used as example of data sets to test the different methods, in particular to test the MCR-ALS method.
The results obtained in this Thesis show that MCR-ALS method can be successfully used for hyperspectral image resolution purposes. The spectra signatures of the pure constituents present in hyperspectral images and their concentration distribution at a pixel level can be estimated. Constituents identification can be performed using the resolved pure spectra signatures and comparing them to reference spectra from spectral libraries or from experimental spectra of reference samples. Application of image data pretreatment methods reduce significantly the presence of strong fluorescence background in Raman hyperspectral images. In contrast, infrared hyperspectral imaging is not affected by fluorescence. Kramers-Kronig transform enables to calculate absorption spectra in case only reflectance spectra can be measured for infrared spectra. The extent of rotation ambiguity associated to MCR-ALS and other resolution methods can be rather high when they are applied for hyperspectral image resolution with high noise. The correct resolution of hyperspectral images can only be guaranteed if additional constraints are applied, such as those providing information about the local rank properties of the image, i.e. about the presence or absence of the different constituents (components) in the image pixels. Only in this way it is possible to increase the reliability of the solutions provided by MCR methods and decrease the uncertainties associated to them. Appropriate use of local rank and selectivity constraints can improve significantly the quality of the pure spectra (signatures) and of the constituent distribution maps resolved by MCR-ALS analysis of hyperspectral images in remote sensing studies. Use of correlation coefficients between selected spectra and image pixel spectra is shown to provide an alternative way for the application of the selectivity constraint in hyperspectral images for the first time. This alternative method resulted to be satisfactory when pure pixels exist. MCR-BANDS method can be used to get estimations of the extension of rotation ambiguities in MCR resolved results. The Area of Feasible Solutions represents feasible solutions geometrically. The range of rotation ambiguity calculated by MCR-BANDS and AFS are in agreement. MCR-ALS with the trilinearity constraint is an effective way to characterize and resolve Excitation-Emission Matrix fluorescence spectra (EEM).Esta Tesis trata de la resolución de datos de imágenes hiperespectrales utilizando métodos quimiométricos, en particular mediante el uso de métodos de pretratamiento de datos y utilizando métodos de resolución multivariante de curvas (MCR). La principal contribución de la presente Tesis es el estudio y la aplicación del método MCR-ALS (resolución multivariante de curvas mediante mínimos cuadrados alternados) para la resolución de imágenes hiperespectrales, adquiridas mediante técnicas de teledetección y mediante técnicas de micro-espectroscopia. Específicamente, en el trabajo de esta Tesis, se explora la combinación de los métodos quimiométricos y de los métodos de análisis de imágenes hiperespectrales, para la resolución de los espectros (firmas) y de los mapas de distribución de los componentes químicos de la muestra. El objetivo final de este estudio es mejorar el análisis y la interpretación de los datos de imágenes hiperespectrales mediante el aprovechamiento de diferentes herramientas quimiométricas poderosas. La detección del rango local y las propiedades de selectividad que describen la información espacial de los componentes presentes en las imágenes espectroscópicas. Se han comparado diferentes métodos de resolución, tales como MCR-ALS, MVSA (Mínimo Volumen Simplex Análisis), PCA (Análisis de Componentes Principales), y MCR-FMIN. Los métodos MCR-BANDS y FAC-PACK se han utilizado para la evaluación de la extensión de las ambigüedades rotacionales existentes en los resultados después de la aplicación de estos métodos de resolución multivariante. En esta Tesis se han analizado diversos conjuntos de datos compuestos por varias imágenes hiperespectrales proporcionadas por instrumentos estándar tales como el espectrómetro de imágenes hiperespectrales en el visible y en el infrarrojo AVIRIS de la NASA, y diversos espectrómetros de imágenes hiperespectrales Raman y infrarrojo de laboratorio. La eficacia del procedimiento MCR-ALS se ilustra proporcionando comparaciones exhaustivas con otros métodos de resolución de mezclas espectrales a partir de conjuntos de datos hiperespectrales simulados y reales.
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Ferreira, Diana Santos. "Application of chemometrics to process monitoring with online high performance liquid chromatography." Thesis, University of Bristol, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.544423.
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Walmsley, Anthony David. "The application of chemometrics for the development of metal oxide sensor arrays." Thesis, University of Greenwich, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294060.
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Zomer, Simeone. "Application of chemometrics for feature extraction and pattern recognition in hyphenated chromatography." Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432960.
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Malecha, Michael Markus. "Identification of lubricant contaminants in beverage cans using spectroscopic analysis and chemometrics." Thesis, Cranfield University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269638.
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Gendrin, Christelle. "Chemical imaging and chemometrics for the analysis of pharmaceutical solid dosage forms." Université Louis Pasteur (Strasbourg) (1971-2008), 2008. https://publication-theses.unistra.fr/public/theses_doctorat/2008/GENDRIN_Christelle_2008.pdf.
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Des systèmes d'imagerie hyperspectrale ont été mis sur le marché pour l'analyse chimique microscopique et macroscopique. Constitués d'un détecteur plan présentant une matrice de pixels, ou d'un détecteur point lié à une plateforme mobile, ils peuvent acquérir des spectres localisés spatialement [. . . ]. Les données générées ont donc deux dimensions spatiales et une dimension spectrale, formant ainsi un cube de données. Elles permettent à la fois l'identification des constituants des échantillons et la visualisation de leur distribution spatiale. L'industrie pharmaceutique s'est vivement intéressée à ce nouveau mode d'analyse chimique. En effet, l'homogénéité des différents constituants est essentielle pour la qualité globale des comprimés pharmaceutiques. Par exemple, un mélange non homogène peut engendrer des problèmes au niveau de la production des comprimés comme une adhérence de la poudre sur les poinçons de la presse, ou produire des comprimés n'ayant pas la quantité nominale en Principe Actif (PA), la molécule soignant la maladie, ou encore amener des différences de dissolution et donc influencer l'efficacité du principe actif. De plus, depuis 2002, l'administration américaine imposant les normes de qualité sur les produits pharmaceutiques et alimentaires, La Food and Drug Administration (FDA), promeut la mise en place d'outils de suivi de la production et de contrôle qualité tout au long de la chaîne de fabrication, lançant ainsi l'initiative PAT (Process Analytical Technology). Afin d'améliorer la qualité de leur production et de diminuer le nombre de lots rejetés, les industries pharmaceutiques suivent aujourd'hui ces instructions et tendent à implémenter des nouveaux outils de contrôle. Pour ces raisons, les systèmes d'imagerie chimique non destructifs ont toute leur place dans l'industrie pharmaceutique. . . [etc]By combining both spectral and spatial information Near Infrared Chemical Imaging (NIR-CI) allows the identification of the chemical species and their localization. Since the distribution of chemical species influences greatly the quality attributes of the medicine this kind of instrumentation has naturally shown to be very useful for the development of pharmaceutical products. However, each analysis generates thousands of spatially resolved spectra which need to be processed for objective comparison of the data. In the present work, the extraction of distribution maps and their characterization is firstly addressed. In that case the samples' composition is fully known and specific wavelengths or the full reference spectra are used to localize the chemical species. Histogram analysis is performed to assess the homogeneity of the first intermediates revealing a batch with greater inhomogeneity. In the second intermediates, a difference in the particle sizes of two batches is enhanced using a segmentation scheme based on Otsu thresholding and watershed refinement. The usefulness of NIR-CI and image processing to study and compare the quality of intermediates is demonstrated. In a second part, the simultaneous extraction of spectra and distribution maps without a priori information is proven. The accuracy of NMF, BPSS, MCRALS and PMF algorithms are compared. The latter proves to extract both spectral profile and concentration with the best accuracy especially when rotational tools are used to investigate the space of feasible solutions. The last chapter deals with the quantification of API in binary mixtures and pharmaceutical tablets. The quantification without a priori reveals to be quite challenging. With homogeneous sample, the multivariate curve resolution algorithms fail to recover the pure spectra. A segmentation scheme is appropriate only in specific cases if the chemical species particles are larger than the spatial resolution of the device. If a full range of tablets with known concentration is provided, PLS algorithm gives the most accurate quantification results. However, it is demonstrated that with the only knowledge of reference spectra, PLS-DA provides an estimation of the concentration which allows semi-quantitative analysis of samples when the construction of a full range is not possible for instance during the analysis of development samples
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Gendrin, Christelle Hirsch Ernest. "Chemical imaging and chemometrics for the analysis of pharmaceutical solid dosage forms." Strasbourg : Université de Strasbourg, 2009. http://eprints-scd-ulp.u-strasbg.fr:8080/1031/01/GENDRIN_Christelle_2008.pdf.
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Guirguis, Amira. "Identification and classification of new psychoactive substances using Raman spectroscopy and chemometrics." Thesis, University of Hertfordshire, 2017. http://hdl.handle.net/2299/18331.
Full textAbstract:
The sheer number, continuous emergence, heterogeneity and wide chemical and structural diversity of New Psychoactive Substance (NPS) products are factors being exploited by illicit drug designers to obscure detection of these compounds. Despite the advances in analytical techniques currently used by forensic and toxicological scientists in order to enable the identification of NPS, the lack of a priori knowledge of sample content makes it very challenging to detect an 'unknown' substance. The work presented in this thesis serves as a proof-of-concept by combining similarity studies, Raman spectroscopy and chemometrics, underpinned by robust pre-processing methods for the identification of existing or newly emerging NPS. It demonstrates that the use of Raman spectroscopy, in conjunction with a 'representative' NPS Raman database and chemometric techniques, has the potential for rapidly and non-destructively classifying NPS according to their chemical scaffolds. The work also demonstrates the potential of indicating the purity in formulations typical of those purchased by end users of the product i.e. 'street-like' mixtures. Five models were developed, and three of these provided an insight into the identification and classification of NPS depending on their purity. These are: the 'NPS and non-NPS/benchtop' model, the 'NPS reference standards/handheld' model and the 'NPS and non-NPS/handheld' model. In the 'NPS and non-NPS/benchtop' model (laser λex = 785 nm), NPS internet samples were projected onto a PCA model derived from a Raman database comprising 'representative' NPSs and cutting agent/ adulterant reference standards. This proved the most successful in suggesting the likely chemical scaffolds for NPS present in samples bought from the internet. In the 'NPS reference standards/handheld' model (laser λex = 1064 nm), NPS reference standards were projected onto a PCA model derived from a Raman database comprising 'representative' NPSs. This was the most successful of the three models with respect to the accurate identification of pure NPS. This model suggested chemical scaffolds in 89% of samples compared to 76% obtained with the benchtop instrument, which generally had higher fluorescent backgrounds. In the 'NPS and non-NPS/handheld' model (laser λex = 1064 nm), NPS internet samples were projected onto a PCA model derived from a Raman database comprising 'representative' NPSs and cutting agent/ adulterant reference standards. This was the most successful in differentiating between NPS internet samples dependent on their purity. In all models, the main challenges for identification of NPS were spectra displaying high fluorescent backgrounds and low purity profiles. The 'first pass' matching identification of NPS internet samples on a handheld platform was improved to ~50% using a laser source of 1064 nm because of a reduction in fluorescence at this wavelength. We outline limitations in using a handheld platform that may have added to problems with appropriate identification of NPS in complex mixtures. However, the developed models enabled the appropriate selection of Raman signals crucial for identification of NPS via data reduction, and the extraction of important patterns from noisy and/or corrupt data. The models constitute a significant contribution in this field with respect to suggesting the likely chemical scaffold of an 'unknown' molecule. This insight may accelerate the screening of newly emerging NPS in complex matrices by assigning them to: a structurally similar known molecule (supercluster/ cluster); or a substance from the same EMCDDA/EDND class of known compounds. Critical challenges in instrumentation, chemometrics, and the complexity of samples have been identified and described. As a result, future work should focus on: optimising the pre-processing of Raman data collected with a handheld platform and a 1064 nm laser λex; and optimising the 'representative' database by including other properties and descriptors of existing NPS.