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  • 1. Bouveresse, D. Jouan-Rimbaud
    et al.
    Pinto, Rui Climaco
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Schmidtke, L. M.
    Locquet, N.
    Rutledge, D. N.
    Identification of significant factors by an extension of ANOVA-PCA based on multi-block analysis2011In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 106, no 2, p. 173-182Article in journal (Refereed)
    Abstract [en]

    A modification of the ANOVA-PCA method, proposed by Harrington et al. to identify significant factors and interactions in an experimental design, is presented in this article. The modified method uses the idea of multiple table analysis, and looks for the common dimensions underlying the different data tables, or data blocks, generated by the "ANOVA-step" of the ANOVA-PCA method, in order to identify the significant factors. In this paper, the "Common Component and Specific Weights Analysis" method is used to analyse the calculated multi-block data set. This new method, called AComDim, was compared to the standard ANOVA-PCA method, by analysing four real data sets. Parameters computed during the AComDim procedure enable the computation of F-values to check whether the variability of each original data block is significantly greater than that of the noise.

  • 2.
    Galindo-Prieto, Beatriz
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Eriksson, Lennart
    MKS Umetrics AB, Umeå, Sweden.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Variable influence on projection (VIP) for OPLS models and its applicability in multivariate time series analysis2015In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 146, p. 297-304Article in journal (Refereed)
    Abstract [en]

    Abstract Recently a new parameter to infer variable importance in orthogonal projections to latent structures (OPLS) was presented. Called OPLS-VIP (variable influence on projection), this parameter is here applied in multivariate time series analysis to achieve an improved diagnosis of process dynamics. To this end, OPLS-VIP has been tested in three real-world industrial data sets; the first data set corresponds to a pulp manufacturing process using a continuous digester, the second one involves data from an industrial heater that experienced problems, and the third data set contains measures of the chemical oxygen demand into the effluent of a newsprint mill. The outcomes obtained using OPLS-VIP are benchmarked against classical PLS-VIP results. It is demonstrated how OPLS-VIP provides a better diagnosis and understanding of the time series behavior than PLS-VIP.

  • 3.
    Galindo-Prieto, Beatriz
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry. MKS Instruments AB, Umeå, Sweden.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry. MKS Instruments AB, Umeå, Sweden.
    Geladi, Paul
    A new approach for variable influence on projection (VIP) in O2PLS models2017In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 160, p. 110-124Article in journal (Refereed)
    Abstract [en]

    A novel variable influence on projection approach for O2PLS® models, named VIPO2PLS, is presented in this paper. VIPO2PLS is a model-based method for judging the importance of variables. Its cornerstone is the 2-way formalism of the O2PLS models; i.e. the use of both predictive and orthogonal normalized loadings of the two modelled data matrices, and also a new weighting system based on the sum of squares of both data blocks (X, Y). The VIPO2PLS algorithm has been tested in one synthetic data set and two real cases, and the outcomes have been compared to the PLS-VIP, VIPOPLS, and i-PLS methods. The purpose is to achieve a sharper and enhanced model interpretation of O2PLS models by using the new VIPO2PLS method for assessing the importance of both X- and Y- variables.

  • 4. Krupinska, Karolina
    et al.
    Geladi, Paul
    Vikholm-Lundin, Inger
    Lindholm-Sethson, Britta
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Detection of low levels of Escherichia coli by electrochemical impedance spectroscopy and singular value decomposition2017In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 163, p. 49-54Article in journal (Refereed)
    Abstract [en]

    The first steps are reported in the development of a new ultrasensitive electrochemical biosensor for detection of Escherichia coli (E. coli) in water. Two gold electrodes in a sandwich flow-cell were modified with E. coli polyclonal antibody and exposed to three different concentrations of E. coli. Electrochemical Impedance Spectroscopy was used in combination with Singular Value Decompostion of the complex numbers to monitor the interactions at the electrode surfaces. A linear regression line in the concentration range 10-1000 CFU⁎ml-1 was obtained without use of redox probes or metal nanoparticles for signal amplification.

  • 5.
    Lindström, Anton
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Andersson, C. David
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Johansson, Adam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Bone contrast optimization in magnetic resonance imaging using experimental design of ultra-short echo-time parameters2013In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 125, p. 33-39Article in journal (Refereed)
    Abstract [en]

    For the purpose of improved planning and treatment by radiation of tumours, we present work exploring the effect of controllable ultra-short echo-time (UTE) sequence settings on the bone contrast in magnetic resonance (MR) imaging, using design of experiments (DoE). Images were collected using UTE sequences from MR imaging and from standard computed tomography (CT). CT was used for determining the spatial position of the bony structures in an animal sample and co-registered with the MR images. The effect of the UTE sequence parameter flip angle (Flip), repetition time (T-R), echo time (T-E), image matrix size (Vox) and number of radial sampling spokes (Samp) were studied. The parameters were also investigated in a healthy voluntary and it was determined that the optimal UTE settings for high bone contrast in a clinically relevant set up were: Flip similar to 9 degrees and T-E = 0.07 ms, while T-R was kept at 8 ms, Vox at 192 and Samp at 30,000. The use of response surface maps, describing the modelled relation between bone contrast and UTE settings, founded in the DoE, may provide information and be a tool to more appropriately select suitable UTE sequence settings.

  • 6.
    Lindström, Anton
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Pettersson, Fredrik
    The Wellcome Trust Center for Human Genetics, Oxford University, Oxford, UK.
    Linusson, Anna
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Quantitative protein descriptors for secondary structure characterization and protein classification2009In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 95, no 1, p. 74-85Article in journal (Refereed)
    Abstract [en]

    In this study protein chains were characterized based on alignment-independent protein descriptors using three types of structural and sequence data; (i) C-α atom Euclidean distances, (ii) protein backbone ψ and φ angles and (iii) amino acid physicochemical properties (zz-scales). The descriptors were analyzed using principal component analysis (PCA) and further elucidated using the multivariate methods partial least-squares projections to latent structures discriminant-analysis (PLS-DA) and hierarchical-PLS-DA. The descriptors were applied to three protein chain datasets: (i) 82 chains classified, according to the structural classification of proteins (SCOP) scheme, as either all-α or all-β; (ii) 96 chains classified as either α + β or α/β and (iii) 6590 chains of all aforementioned classes selected from the PDB-select database. Results showed that the descriptors related to the secondary structure of the chains. The C-α Euclidean distances, and as expected, the protein backbone angles were found to be most important for the characterization and classification of chains. Assignment of SCOP classes using PLS-DA based on all descriptor types was satisfactory for all-α and all-β chains with more than 93% correct classifications of a large external test set, while the protein chains of types α/β and α + β was harder to discriminate between, resulting in 74% and 54% correct classifications, respectively.

  • 7.
    Lundstedt, Torbjörn
    et al.
    AcurePharma AB, Uppsala, Sweden.
    Hedenström, Mattias
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Soeria-Atmadja, D
    Division of Toxicology, National Food Administration, Uppsala, Sweden.
    Hammerling, U
    Division of Toxicology, National Food Administration, Uppsala, Sweden.
    Gabrielsson, Jon
    AcureOmics AB, Umeå, Sweden.
    Olsson, J
    KPL Good Food Practice AB, Uppsala, Sweden.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Dynamic modelling of time series data in nutritional metabonomics: A powerful complement to randomized clinical trials in functional food studies2010In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 104, no 1, p. 112-120Article in journal (Refereed)
    Abstract [en]

    Functional foods are foods or dietary ingredients that provide a health benefit beyond basic nutrition. A new legislation, known as the Nutrition and Health Claims Regulation, defines the legal framework for such claims within the European Union. Any claim about the nutritional or physiological effects of a product must be scientifically demonstrated. In this study, we have focused on the exploration of metabonomics as a complementary profiling technology to establish monitoring/data analysis procedures of randomized nutritional trials. More specifically, a combined intake of soybean and grapefruit in a human intervention study was analyzed with respect to both pharmacological and physiological effects. Resulting multivariate models showed a diet-induced decrease of lactate, cholesterols and triglycerides. The most drastically elevated metabolite, myo-inositol, was found to accompany a marked reduction of triglyceride levels. Suggestively, this is due to the biotransformation of myo-inositol to phosphatidylinositol, which results in a decrease of available precursors to form triglycerides. Strong inter-subject variation was present that required special attention. Dynamic modelling of collected time series data that provided the opportunity to identify slow, medium or fast responders as well as groups of subjects showing different response profiles, was also highlighted in the study. The applied strategy of time series data has proven to be a powerful complement to randomized nutritional studies adopting a clinical trial design.

  • 8.
    Löfstedt, Tommy
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Hanafi, Mohamed
    Unité de Recherches "Sensometrics and Chemometrics", ONIRIS, Site de la Géraudière, BP 82 225 Nantes 44322 Cedex 03, France.
    Mazerolles, Gérard
    INRA-UMR 1083 SPO, INRA, 2 Place Viala, 34060 Montpellier, France.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    OnPLS path modelling2012In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 118, p. 139-149Article in journal (Refereed)
    Abstract [en]

    OnPLS was recently presented as a general extension of O2PLS to the multiblock case. OnPLS is equivalent to O2PLS in the case of two matrices, but generalises symmetrically to cases with more than two matrices, i.e. without giving preference to any one of the matrices.

    This article presents a straight-forward extension to this method and thereby also introduces the OPLS framework to the field of PLS path modelling. Path modelling links a number of data blocks to each other, thereby establishing a set of paths along which information is considered to flow between blocks, representing for instance a known time sequence, an assumed causality order, or some other chosen organising principle. Compared to existing methods for path analysis, OnPLS path modelling extracts a minimum number of predictive components that are maximally covarying with maximised correlation. This is a significant contribution to path modelling, because other methods may yield score vectors with variation that obstructs the interpretation. The method achieves this by extracting a set of "orthogonal" components that capture local phenomena orthogonal to the variation shared with all the connected blocks.

    Two applications will be used to illustrate the method. The first is based on a simulated dataset that show how the interpretation is improved by removing orthogonal variation and the second on a real data process for monitoring of protein structure changes during cheese ripening by analysing infrared data.

  • 9.
    Nyström, Josefina
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Geladi, Paul
    Lindholm-Sethson, Britta
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Larson, Johan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Svensk, Ann-Christin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Franzén, Lars
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Objective measurement of Radiation Induced Erythema by nonparametric hypothesis testing on indices from multivariate data2008In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 90, no 1, p. 43-8Article in journal (Refereed)
    Abstract [en]

    Three instrumental measurement techniques: Laser-Doppler Imaging (LDI), Digital Colour Photography (DCP) and Near InfraRed (NIR) spectroscopy were tested for their potential to objectively measure radiation-based erythema in breast cancer patients. The irradiation dose intervals were 0, 8-16, 18-26, 28-34, 36-44 and 46-50 Gy. In addition, two types of skin lotion for reducing erythema were tested on the patients and these were compared to using no lotion. The measured results had very skew distributions for all three techniques making nonparametric testing necessary. The Wilcoxon Signed Rank Sum Test (WSRST) was used for this purpose. LDI was performed to produce univariate average perfusion values leading to a perfusion increment ratio. These ratios showed a good sensitivity to erythema, with a median detection limit of 18 Gy. DCP was used to extract average red-green-blue (RGB) values that were used in multivariate models. Results for a combination of principal component score values showed a marked increase in median erythema from 8 Gy on. The Multivariate data from NIR spectroscopy were data-reduced to principal component scores and combinations of these were tested. The score combinations were used to show median detection limits down to 8 Gy. The difference between the lotions and using no lotion gave no significant result for the WSRST paired comparison for any used measurement technique.

  • 10.
    Olsson, Ing-Marie
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Johansson, Erik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Berntsson, Martin
    Eriksson, Lennart
    Gottfries, Johan
    Wold, Svante
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Rational DOE-protocols for 96-well plates2006In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 83, no 1, p. 66-74Article in journal (Refereed)
    Abstract [en]

    The use of 96-well plates for chemical and biological applications has rapidly increased as new applicable domains have been discovered and new laboratory instruments developed. There are 96, 384, 1536, etc. plates customized for diverse applications such as biological assays, sample preparation, solid-phase extraction and crystallization. Multi-pipettes as well as automated pipette systems accelerate the preparation of plates resulting in even faster evaluation systems. A bottleneck in the use of multi-unit plates is method development and optimization. By applying rational experimental design, the optimization could be made more efficient and less time-consuming. Unfortunately, the workload related to manual preparation of multi-unit plates according to an experimental design is often considered overwhelming. The present study introduces a new approach for experimental design in 96-well plates that minimizes the manual workload without compromising the quality of the experimental design. This approach is scalable to larger rectangular formats such as 384- and 1536-well plates. The optimal combinations will be delineated and applied experimentally to a reporter-gene assay.

  • 11.
    Stenlund, Hans
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Madsen, Rasmus
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Vivi, Antonio
    Nuclear Magnetic Resonance Centre of Siena University, Siena, Italy.
    Calderisi, Marco
    Nuclear Magnetic Resonance Centre of Siena University, Siena, Italy.
    Lundstedt, Torbjörn
    AcureOmics AB, Umeå, Sweden.
    Tassini, Maria
    Nuclear Magnetic Resonance Centre of Siena University, Siena, Italy.
    Carmellini, Mario
    Department of Surgery and Bioengineering, University of Siena, Siena, Italy.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Monitoring kidney-transplant patients using metabolomics and dynamic modeling2009In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 98, no 1, p. 45-50Article in journal (Refereed)
    Abstract [en]

    A kidney transplant provides the only hope for a normal life for patients with end-stage renal disease, i.e., kidney failure. Unfortunately, the lack of available organs leaves some patients on the waiting list for years. In addition, the post-transplant treatment is extremely important for the final outcome of the surgery, since immune responses, drug toxicity and other complications pose a real and present threat to the patient. In this article, we describe a novel strategy for monitoring kidney transplanted patients for immune responses and adverse drug effects in their early recovery. Nineteen patients were followed for two weeks after renal transplantation, two of them experienced problems related to kidney function, both of whom were correctly identified by means of nuclear magnetic resonance spectroscopic analysis of urine samples and multivariate data analysis.

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