| 1. |
- Björk, Anders, et al.
(författare)
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Modeling of pulp quality parameters from distribution curves extracted from process acoustic measurements on a thermo mechanical pulp (TMP) process
- 2007
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 85:1, s. 63-69
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Tidskriftsartikel (refereegranskat)abstract
- In this paper the feasibility of modeling strength and optical pulp properties from length distribution curves extracted from acoustic data using continuous wavelet transform-fiber length extraction, CWT-FLE (A Björk and L-G Danielsson, 'Extraction of Distribution Curves from Process Acoustic Measurements on a TMP-Process', Pulp and Paper Canada 105 No. 11 (2004), T260-T264) by use of Partial Least Squares (PLS) have been tested. The curves used have earlier been validated against length distribution curves obtained by analyzing pulp samples with a commercial analyzer (FiberMaster). The curves were extracted from acoustic data without any "calibration" against fiber length analyses. The acoustic measurements were performed using an accelerometer affixed to the refiner blow-line during a full-scale trial with a Sunds Defibrator double disc refiner at SCA Ortviken, Sweden. Pulp samples were collected concurrently with the acoustic measurements and extensive physical testing has been made on these samples. For each trial point three pulp samples were collected. PLS1 and PLS2 models were successfully made linking the distribution curves obtained using CWT-FLE to pulp tensile strength properties as well as optical properties. The resulting Root Mean Square Error of Prediction (RMSEP) for all parameters is comparable to what can be obtained by pooling the standard deviations of reference measurements from the different trial points. The results obtained are compared to FiberMaster data modeled in the same fashion, yielding lower prediction errors than the CWT-FLE data. However, this can be partly due to the five-year storage of pulp samples between pulp sampling/acoustic measurement and FiberMaster analyses/sheet testing. The acoustic method is fast and produces results without dead time and could constitute a new tool for improving process control and optimizing the fiber characteristics in a specific process and for a specific purpose. The technique could be implemented in a PC-environment at a fairly low cost.
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| 2. |
- Danielsson, Rolf, et al.
(författare)
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Rapid multivariate analysis of LC/GC/CE data (single or multiple channel detection) without prior peak alignment
- 2006
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 84:1-2, s. 33-39
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Tidskriftsartikel (refereegranskat)abstract
- One- or two-dimensional data obtained with LC/GC/CE and single or multiple channel detection (MS, UV/VIS) are often used as 'fingerprints' in order to characterize complex samples. The relation between samples is then explored by multivariate data analysis (PCA, hierarchical clustering), but inevitable more or less random variation in separation conditions obstructs the analysis. Several methods for peak alignment have been developed, with more or less increase of time and efforts for computations. In this work another approach is presented, based on a correlation measure less sensitive for variations in retention/migration time. The merits of the method as a fast initial data exploration tool are demonstrated for a case study of urine profiling with CE/MS.
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| 3. |
- Eliasson, Charlotte, 1973, et al.
(författare)
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Multivariate methodology for surface enhanced Raman chemical imaging of lymphocytes
- 2006
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 81:1, s. 13-20
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Tidskriftsartikel (refereegranskat)abstract
- Surface enhanced Raman spectroscopy (SERS) was used to study the uptake of rhodamine 6G in human lymphocytes. In total four Raman images of lymphocytes were used. The aim was to find a multivariate methodology capable of separating spectra with chemical information from those that mainly contained the surface enhanced background, in order to create chemical images. The standard PCA procedure was compared with PCA of standard normal variate (SNV) corrected spectra, spectra baseline corrected in the wavelet domain, and variable trimming before PCA, to isolate unique spectra. It was not straightforward to perform a standard PCA for overview, since the small background variation in many variables dominated over the Raman band variation that only occur in few variables. It was shown that wavelet filtering could remove background variations and that variable trimming followed by PCA modelling left the unique Raman spectra as outliers, which facilitated interpretation of the Raman score images.
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| 4. |
- Forshed, Jenny, et al.
(författare)
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Enhanced multivariate analysis by correlation scaling and fusion of LC/MS and 1H-NMR data
- 2007
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier B.V. - 0169-7439 .- 1873-3239. ; 85:2, s. 179-185
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Tidskriftsartikel (refereegranskat)abstract
- A method to enhance the multivariate data interpretation of, for instance, metabolic profiles is presented. This was done by correlation scaling of 1H NMR data by the time pattern of drug metabolite peaks identified by LC/MS, followed by parallel factor analysis (PARAFAC). The variables responsible for the discrimination between the dosed and control rats in this model were then eliminated in both data sets. Next, an additional PARAFAC analysis was performed with both LC/MS and 1H NMR data, fused by outer product analysis (OPA), to obtain sufficient class separation. The loadings from this second PARAFAC analysis showed new peaks discriminating between the classes. The time trajectories of these peaks did not agree with the drug metabolites and were detected as possible candidates for markers. These data analyses were also compared with the PARAFAC analysis of raw data, which showed very much the same loading peaks as for the correlation-scaled data, although the intensities differed. Elimination of the variables correlated with the drug metabolites was therefore necessary to be able to select the peaks which were not drug metabolites and which discriminated between the classes.1
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| 5. |
- Forshed, Jenny, et al.
(författare)
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Evaluation of different techniques for fusion of LC/MS and 1HNMR data
- 2007
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - 0169-7439 .- 1873-3239. ; 85:1, s. 102-109
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Tidskriftsartikel (refereegranskat)abstract
- In the analyses of highly complex samples (for example, metabolic fingerprinting), the data might not suffice for classification when using only a single analytical technique. Hence, the use of two complementary techniques, e.g., LUMS and H-1-NMR, might be advantageous. Another possible advantage from using two different techniques is the ability to verify the results (for instance, by verifying a time trend of a metabolic pattern). In this work, both LC/MS and H-1-NMR data from analysis of rat urine have been used to obtain metabolic fingerprints. A comparison of three different methods for data fusion of the two data sets was performed and the possibilities and difficulties associated with data fusion were discussed. When comparing concatenated data, full hierarchical modeling, and batch modeling, the first two approaches were found to be the most successful. Different types of block scaling and variable scaling were evaluated and the optimal scaling for each case was found by cross validation. Validations of the final models were performed by means of an external test set.(2)
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| 6. |
- Gut, Luiza, et al.
(författare)
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Assessment of a two-step partial nitritation/Anammox system with implementation of multivariate data analysis
- 2007
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 86:1, s. 26-34
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Tidskriftsartikel (refereegranskat)abstract
- Complexity of biological wastewater treatment, in which a variety of physical and (bio)chemical processes concurrently take place, demands appropriate approach in the data analysis. In this study, the data set collected during a 20 month operation of a two-step partial nitritation/Anammox system for nitrogen removal from wastewater (semi-industrial pilot plant scale) are subjected to Principal Component Analysis (PCA) and Partial Least Squares projections to latent structures (PLS) analysis. Interpretation of PCA- and PLS models enable to discern relationships between different factors for the start-up period and stable operation of the pilot plant. Variables like conductivity, pH value, dissolved oxygen concentration and nitrite-to-ammonium ratio (NAR) appear to be the key factors in the process control and monitoring. Extension of the Anammox reactor capacity demands accurate monitoring, principally by scrutinizing nitrite nitrogen concentration in the reactor. These findings suggest that the two methods complement each other in assessing the partial nitritation/Anammox system. This study demonstrated that multivariate data analysis provides the powerful implement in the field of wastewater treatment, especially in investigating novel systems.
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| 7. |
- Hedenström, Mattias, et al.
(författare)
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Visualization and interpretation of OPLS models based on 2D NMR Data
- 2008
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier. - 0169-7439 .- 1873-3239. ; 92:2, s. 110-117
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Tidskriftsartikel (refereegranskat)abstract
- Multivariate analysis on spectroscopic 1H NMR data is well established in metabolomics and other fields where the composition of complex samples is studied. However, biomarker identification can be hampered by overlapping resonances. 2D NMR data provides a more detailed “fingerprint” of the chemical structure and composition of the sample with greatly improved spectral resolution compared to 1H NMR data. In this report, we demonstrate a procedure for the construction of multivariate models based on frequency domain 2D NMR data where the loadings can be visualized as highly informative 2D loading spectra. This method is based on the analysis of raw spectral data without any need for peak picking or integration prior to analysis. Spectral features such as line widths and peak positions are thus retained. Hence, the loadings can be visualized and interpreted on a molecular level as pseudo 2D spectra in order to identify potential biomarkers. To demonstrate this strategy we have analyzed HSQC spectra acquired from populus phloem plant extracts originating from a set of designed experiments with OPLS regression.
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| 8. |
- Lindström, Anton, et al.
(författare)
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Quantitative protein descriptors for secondary structure characterization and protein classification
- 2009
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 95:1, s. 74-85
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 9. |
- Lundstedt-Enkel, Katrin, et al.
(författare)
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Different multivariate approaches to material discovery, process development, PAT and environmental process monitoring
- 2006
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 84:1-2, s. 201-207
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Tidskriftsartikel (refereegranskat)abstract
- The aim with the present paper is to illustrate the use of multivariate strategies (i.e. integration of different multivariate methods) with five examples, four from the pharmaceutical industry and one from environmental research. In the first part, two examples wherein hierarchical models are applied to quality control (QC) and process control are discussed. In the second part a more complex problem and a strategy for material discovery/development are presented wherein a combination of multivariate calibration, multivariate analysis and multivariate design is needed. In the third part, a process analytical/optimization problem is illustrated with a two-step process, demanding that different multivariate tools are combined in a sequential way so that a useful model can be established and the process can be understood. In the final part the usefulness of principal component analysis followed by soft independent modelling of class analogy is illustrated with an example from environmental process monitoring. The five examples from quite different areas show that the chemometric tools are even more powerful if used integrated. However, different strategies and combinations of the tools have to be applied, depending on the problem and the aim.
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| 10. |
- Nyström, Josefina, et al.
(författare)
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Objective measurement of Radiation Induced Erythema by nonparametric hypothesis testing on indices from multivariate data
- 2008
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439 .- 1873-3239. ; 90:1, s. 43-8
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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