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| 2. |
- Abbas, Aamer, 1973, et al.
(author)
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Characterization and mapping of carotenoids in the algae Dunaliella and Phaeodactylum using Raman and target orthogonal partial least squares
- 2011
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In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 107:1, s. 174-177
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Journal article (peer-reviewed)abstract
- A method was developed for the characterisation of carotenoid pigments in algal species using Raman spectroscopy in combination with multivariate hyperspectral analysis. Target orthogonal partial least squares (T-OPLS) operates by designating one known reference spectrum as the target. The target spectrum is put as the single y column in an OPLS regression model where the X matrix consists of the unfolded image spectra as variables in its columns. The spectral shape of the OPLS first orthogonal target score enabled us to verify the peak positions of the standard, and detect new peaks, not present in the reference standard. It was shown that the mixture of carotenoids present in the algae did not fully match the reference spectrum, however, the method provided enough information to make an analysis possible also in this case. The image results were constructed from the OPLS loading vectors that were showing a correlation map for the reference spectrum from the predictive loadings and maps of the occurrence of deviations from the orthogonal loadings.
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| 3. |
- Bouveresse, D. Jouan-Rimbaud, et al.
(author)
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Identification of significant factors by an extension of ANOVA-PCA based on multi-block analysis
- 2011
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In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 106:2, s. 173-182
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Journal article (peer-reviewed)abstract
- 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.
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| 4. |
- Brink, Mattias, et al.
(author)
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On-line predictions of the aspen fibre and birch bark content in unbleached hardwood pulp, using NIR spectroscopy and multivariate data analysis
- 2010
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In: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. - : Elsevier Science B.V., Amsterdam.. - 0169-7439. ; 103:1, s. 53-58
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Journal article (peer-reviewed)abstract
- An on-line fibre-based near-infrared (NIR) spectrometric analyser was adapted for on-site process analysis at an integrated paperboard mill. The analyser uses multivariate techniques for the quantitative predication of the aspen fibre (aspen) and the birch bark contents of sheets of unbleached hardwood pulp. The NIR analyser is a prototype constructed from standard NIR components. The spectroscopic data was processed by using principal component analysis (PCA) and partial least square (PLS) regression. Three sample sets were collected from three experimental designs, each composed of known pulp contents of birch, aspen and birch bark. Sets I and 2 were used for model calibration and set 3 was used to validate the models. The PLS model that produced the best predictions gave an error of prediction (RMSEP) of 13% for aspen and less than 2% for birch bark. Eight components resulted in an (RX)-X-2 of 99.3%, (RY)-Y-2 of 99.6%. and Q(2) of 95.3%. For additional validation of aspen, three unbleached hardwood samples from the mills production were calculated to lie between -7% and +6%, regarding to the PIS model. When vessel cells were counted under a light microscope a value for the aspen content of 4.7% was obtained. The predictive models evaluated were suitable for quality assessments rather than quantitative determination.
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| 5. |
- Carlson, Johan E., et al.
(author)
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Estimation of dielectric properties of crude oils based on IR spectroscopy
- 2014
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In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 139, s. 1-5
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Journal article (peer-reviewed)abstract
- Dielectric properties of crude oils play an important role in characterization and quality control. Measuring permittivity accurately over a wide range of frequencies is, however, a time-consuming task and existing measurement methods are not easily adapted for real-time diagnostics. IR spectroscopy, on the other hand, provides rapid measurements of fundamental molecular properties.In this paper we show that by using multivariate calibration tools such as PLS regression, it is possible to extract dielectric properties of crude oils directly from IR spectra, in addition to conventional interpretation of the spectra, hence reducing the need for direct electrical measurements. Results on 16 different oil samples show that the dielectric parameters obtained with the proposed method agree well with those obtained using direct permittivity measurements. The PLS regression method has also been extended with Monte-Carlo simulation capabilities to account for uncertainties in the data
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| 6. |
- Carlson, Johan E., et al.
(author)
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Extracting homologous series from mass spectrometry data by projection on predefined vectors
- 2012
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In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 114, s. 36-43
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Journal article (peer-reviewed)abstract
- Multivariate statistical methods, such as Principal Component Analysis (PCA), have been used extensively over the past decades as tools for extracting significant information from complex data sets. As such they are very powerful and in combination with an understanding of underlying chemical principles, they have enabled researchers to develop useful models. A drawback with the methods is that they do not have the ability to incorporate any physical / chemical model of the system being studied during the statistical analysis. In this paper we present a method that can be used as a complement to traditional chemometric tools in finding patterns in mass spectrometry data. The method uses a pre-defined set of equally spaced sequences that are assumed to be present in the data. Allowing for some uncertainty in the peak locations due to the uncertainties for the measurement instrumentation, the measured spectra are then projected onto this set. It is shown that the resulting scores can be used to identify homologous series in measured mass spectra that differ significantly between different measured samples. As opposed to PCA, the loading vectors, in this case the pre-defined homologous series, are readily interpretable.
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| 7. |
- Danielsson, Rolf, et al.
(author)
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Exploring liquid chromatography-mass spectrometry fingerprints of urine samples from patients with prostate or urinary bladder cancer
- 2011
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In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 108:1, s. 33-48
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Journal article (peer-reviewed)abstract
- Data processing and analysis have become true rate and success limiting factors for molecular research where a large number of samples of high complexity are included in the data set. In general rather complicated methodologies are needed for the combination and comparison of information as obtained from selected analytical platforms. Although commercial as well as freely accessible software for high-throughput data processing are available for most platforms, tailored in-house solutions for data management and analysis can provide the versatility and transparency eligible for e.g. method development and pilot studies. This paper describes a procedure for exploring metabolic fingerprints in urine samples from prostate and bladder cancer patients with a set of in-house developed Matlab tools. In spite of the immense amount of data produced by the LC-MS platform, in this study more than 1010 data points, it is shown that the data processing tasks can be handled with reasonable computer resources. The preprocessing steps include baseline subtraction and noise reduction, followed by an initial time alignment. In the data analysis the fingerprints are treated as 2-D images, i.e. pixel by pixel, in contrast to the more common list-based approach after peak or feature detection. Although the latter approach greatly reduces the data complexity, it also involves a critical step that may obscure essential information due to undetected or misaligned peaks. The effects of remaining time shifts after the initial alignment are reduced by a binning and [‘]blurring’ procedure prior to the comparative multivariate and univariate data analyses. Other factors than cancer assignment were taken into account by ANOVA applied to the PCA scores as well as to the individual variables (pixels). It was found that the analytical day-to-day variations in our study had a large confounding effect on the cancer related differences, which emphasizes the role of proper normalization and/or experimental design. While PCA could not establish significant cancer related patterns, the pixel-wise univariate analysis could provide a list of about a hundred [‘]hotspots’ indicating possible biomarkers. This was also the limited goal for this study, with focus on the exploration of a really huge and complex data set. True biomarker identification, however, needs thorough validation and verification in separate patient sets.
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| 8. |
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| 9. |
- Lindström, Anton, et al.
(author)
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Bone contrast optimization in magnetic resonance imaging using experimental design of ultra-short echo-time parameters
- 2013
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In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier Science. - 0169-7439 .- 1873-3239. ; 125, s. 33-39
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Journal article (peer-reviewed)abstract
- 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.
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| 10. |
- Lundstedt, Torbjörn, et al.
(author)
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Dynamic modelling of time series data in nutritional metabonomics : A powerful complement to randomized clinical trials in functional food studies
- 2010
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In: Chemometrics and Intelligent Laboratory Systems. - : Elsevier B V. - 0169-7439 .- 1873-3239. ; 104:1, s. 112-120
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Journal article (peer-reviewed)abstract
- 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.
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