| 1. |
- Galindo-Prieto, Beatriz, 1981-
(författare)
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Novel variable influence on projection (VIP) methods in OPLS, O2PLS, and OnPLS models for single- and multi-block variable selection : VIPOPLS, VIPO2PLS, and MB-VIOP methods
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Multivariate and multiblock data analysis involves useful methodologies for analyzing large data sets in chemistry, biology, psychology, economics, sensory science, and industrial processes; among these methodologies, partial least squares (PLS) and orthogonal projections to latent structures (OPLS®) have become popular. Due to the increasingly computerized instrumentation, a data set can consist of thousands of input variables which contain latent information valuable for research and industrial purposes. When analyzing a large number of data sets (blocks) simultaneously, the number of variables and underlying connections between them grow very much indeed; at this point, reducing the number of variables keeping high interpretability becomes a much needed strategy.The main direction of research in this thesis is the development of a variable selection method, based on variable influence on projection (VIP), in order to improve the model interpretability of OnPLS models in multiblock data analysis. This new method is called multiblock variable influence on orthogonal projections (MB-VIOP), and its novelty lies in the fact that it is the first multiblock variable selection method for OnPLS models.Several milestones needed to be reached in order to successfully create MB-VIOP. The first milestone was the development of a single-block variable selection method able to handle orthogonal latent variables in OPLS models, i.e. VIP for OPLS (denoted as VIPOPLS or OPLS-VIP in Paper I), which proved to increase the interpretability of PLS and OPLS models, and afterwards, was successfully extended to multivariate time series analysis (MTSA) aiming at process control (Paper II). The second milestone was to develop the first multiblock VIP approach for enhancement of O2PLS® models, i.e. VIPO2PLS for two-block multivariate data analysis (Paper III). And finally, the third milestone and main goal of this thesis, the development of the MB-VIOP algorithm for the improvement of OnPLS model interpretability when analyzing a large number of data sets simultaneously (Paper IV).The results of this thesis, and their enclosed papers, showed that VIPOPLS, VIPO2PLS, and MB-VIOP methods successfully assess the most relevant variables for model interpretation in PLS, OPLS, O2PLS, and OnPLS models. In addition, predictability, robustness, dimensionality reduction, and other variable selection purposes, can be potentially improved/achieved by using these methods.
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| 2. |
- Ljunggren, Joel
(författare)
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Some Approaches to Eco-Friendly Products from Natural Matrices
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Since the onset of the industrial and chemical revolution, humans have caused immense damages to the surrounding flora and fauna. Effective methods for wood protection measures proved to be toxic; fossil fuels contribute to global warming and pesticides can be detected in the air, water, and soil. It is abundantly clear that efforts to find eco-friendly products are needed, while simultaneously providing the necessary incentives for sustainable worldwide development. Using renewable resources play a critical role in this shift towards circular economies.Wood has long been used as a renewable resource in high demand, but its susceptibility to attack by wood-decaying fungi mean that most European woods need to be protected against these fungi before outdoor use. We showed that fractionating turpentine, a pulp and paper mill by-product, increased antifungal efficacy by concentrating bioactive oxygenated sesquiterpenes. Based on this result, recombinations of the fractions were shown to exhibit synergistic effects that enable a more efficient product utilisation. In addition, this approach enabled putative identifications of previously unknown Picea abies turpentine constituents present at low levels.For a carbon-neutral society, production of biofuels using oleaginous yeast to convert lignocellulosic biomass into fuel has been hailed as a next-generation source of bioenergy. However, lignocellulose biofuel production by microorganisms is not straightforward and one challenge is the formation of microbe-toxic monomers, such as vanillin, during lignin degradation. The oleaginous yeast Cystobasidium laryngis and other potential oil-producing yeasts were screened for their viability and vanillin biotransformation capabilities. To this end, a mass chromatographic peak extraction tool termed TMATE was developed. Vanillyl alcohol was found to be the main product following vanillin degradation.The detrimental health and ecological effects of pesticides highlight the urgency for alternative crop protection measures, such as biological insect control and semiochemicals. In this regard, we present an essential step towards understanding the varied chemical ecology of microbe-insect interactions. Our methodology and findings provide cues with high information value that can be used to develop well-informed and potentially sustainable pest management regimes by, for example, the push-pull methodology using live yeasts.
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| 3. |
- Wiklund, Susanne, 1973-
(författare)
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Spectroscopic data and multivariate analysis : tools to study genetic perturbations in poplar trees
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In our society in the 21st century one of the greatest challenges is to provide raw materials to the industry in a sustainable way. This requires increased use of renewable raw materials such as wood. Wood is widely used in pulp, paper and textile industries and ongoing research efforts aim to extend the use of wood in e.g. liquid biofuels and green chemicals. At Umeå Plant Science Center (UPSC) poplar trees are used as model systems to study wood formation. The objective is to understand the function of the genes underlying the wood forming process. This knowledge could result in improved chemical and physical wood properties suitable for different industrial processes. This will in turn meet the demands for a sustainable development. This thesis presents tools and strategies to unravel information regarding genetic perturbations in poplar trees by the use of nuclear magnetic resonance (NMR) spectroscopy and multivariate analysis (MVA). Furthermore, gas chromatography/mass spectrometry (GC/MS) is briefly discussed in this context. Multivariate methods to find patterns and trends in NMR data have been used for more than 30 years. In the beginning MVA was applied in pattern recognition studies in order to characterize chemical structures with different ligands and in different solvents. Today, the multivariate methods have developed and the research have changed focus towards the study of biofluids from plant extracts, urine, blood plasma, saliva etc. NMR spectra of biofluids can contain thousands of resonances, originating from hundreds of different compounds. This type of complex data can be hard to summarize and interpret without appropriate tools and require sophisticated strategies for data evaluation. Related fields of research are rapidly growing and are here referred to as metabolomics. Five different research projects are presented which includes analysis of poplar samples where macromolecules such as pectin and also small molecules such as metabolites were analysed by high resolution magic angle spinning (HR/MAS) NMR spectroscopy, 1H-13C HSQC NMR and GC/MS. The discussion topics include modelling of metabolomic time dependencies in combination with genetic variation, validation of orthogonal projections to latent structures (OPLS) models, selection of putative biomarkers related to genetic modification from OPLS-discriminant analysis (DA) models, measuring one of the main components found in the primary cell-walls of poplar i.e. pectin, the use of Fourier transformed two-dimensional (2D) NMR data in OPLS modelling and model complexity in a PLS model.
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| 4. |
- Andersson, Karl, 1972-
(författare)
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Characterization of Biomolecular Interactions Using a Multivariate Approach
- 2004
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis presents a novel bioinformatic methodology denoted the bio-chemometric approach. The methodology is designed for generation of detailed descriptions and predictions of biomolecular interactions. It is based on multivariate analysis of the sensitivity of a biomolecular interaction to multiple minor changes in the experimental conditions. In this work, either the chemical environment where the interaction takes place, or the molecular structure of one of the interacting molecules, was varied. The sensitivity of the interaction to the performed variations was presented as a vector called the sensitivity fingerprint. The bio-chemometric approach was tested on several biomolecular interactions. Useful descriptions of the interactions were obtained by measuring binding kinetics for each interaction in 12-20 different buffers and correlating buffer composition to binding kinetics. The obtained chemical sensitivity fingerprints were reproducible, significantly different and showed a weak correlation to binding site properties for the tested interactions. The results indicate that the fingerprints contained useful information about the binding site. The predictive ability of the bio-chemometric approach was tested on two different biomolecular interactions where one of the binding partners was slightly modified into multiple analogues by amino acid exchanges. In one example, interactions of 18 peptide analogues with an antibody gave data that could be used for accurate prediction of the dissociation rates of novel analogues. Reliable predictions of binding kinetics and affinity were also obtained for single domain camel antibody analogues binding to a protein antigen. By using the three-dimensional structure of camel antibodies and data obtained using the bio-chemometric approach, even the importance of non-exchanged amino acids for the binding could be estimated. The bio-chemometric approach can potentially improve the development of peptides and proteins for therapeutic and diagnostic use. It is suggested to be valid for general use in biochemistry.
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| 5. |
- Moberg, Ludvig, 1966-
(författare)
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A multivariate approach to the analysis of phytoplankton pigments
- 2001
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Photosynthetic pigments are routinely determined in investigations of marine and freshwater environments, chlorophyll is used as an estimator of phytoplankton biomass. Photosynthetic pigments are also widely used to characterise phytoplankton taxonomic composition. To estimate biomass from the chlorophyll a concentration, in vitro absorption and fluorimetric spectroscopic methods are used. However, these methods are known to be prone to interference from chlorophyll degradation products. If a taxonomical characterisation is required, i.e. identification of the phytoplankton classes present in a sample, these methods are inadequate.The objective of this thesis has been to develop qualitative and quantitative multivariate spectroscopic methods for the analysis of phytoplankton pigments. To evaluate the data in these studies, three chemometric methods have been used; principal component analysis (PCA), partial least squares regression (PLS) and parallel factor analysis (PARAFAC).A multivariate regression model, based on the absorption spectra of standards, was developed for the determination of the six key analytes (the chlorophylls and their degradation products), the chlorophylls were accurately determined in the presence of degradation products (Paper I). This method is validated, with real samples and samples harvested from laboratory cultures. The prediction results obtained with the proposed method were in good agreement with HPLC analysis, and for the cultures the results were consistent with prior knowledge about the pigment composition (Paper II). Absorption spectra and multivariate models were used for qualitative analysis of phytoplankton cultures and samples. Nine species representing six phytoplankton classes are analysed and the results demonstrated the possibility to assess the gross phytoplankton composition (Paper III). From samples collected in the Baltic Sea during one year, it was shown that a PCA model of the spectra yielded similar results as a PCA model of microscopy counts, which outlines the possibility to monitor and screen samples (Paper IV). Fluorescence excitation emission matrices were registered for a set of laboratory prepared standards, these were decomposed with a PARAFAC model and the obtained results were used for regression analysis. The second-order advantage was exploited which suspends the requirement of measuring all interfering constituents (Paper V). Excitation emission matrices are also measured in vivo for cultures and samples. From decomposition of the data it was found that four components attributed to the variation in the data. Two components were similar to chlorophyll and carotenoids and two components to phycobilines (manuscript). In conclusion, the use of full spectrum techniques enhances the information acquired for phytoplankton samples, and chemometric methods have shown to be a valuable tool to extract this information.
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