| 1. |
- Artursson, Tom, et al.
(author)
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Drift correction for gas sensors using multivariate methods
- 2000
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In: Journal of Chemometrics. - 0886-9383 .- 1099-128X. ; 14:5-6, s. 711-723
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Journal article (peer-reviewed)abstract
- Drift is one of the most serious impairments afflicting gas sensors. It can be seen as a gradual change in the sensor response over a long period of time when the external conditions an constant. This paper presents a new simple drift counteraction method based on PCA and PLS. The basic idea is to remove the drift direction component from the measurements. The direction of the drift, p, is calculated from measurements for a reference gas. Projecting the sample gas measurements on this vector gives the score vector t. The drift component tp(T) can then he removed from the sample gas data, which we call component correction (CC). The method is tested on a data set based on a reduced factorial design with four gases and a concentration gradient of hydrogen. It is found that the method works efficiently for both cases. Copyright (C) 2000 John Wiley & Sons, Ltd.
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| 2. |
- Andersson, Per M, et al.
(author)
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Comparison between physicochemical and calculated molecular descriptors
- 2000
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In: Journal of Chemometrics: Special Issue: Proceedings of the SSC6, August 1999, HiT/TF, Norway . Issue Edited by Kim Esbensen. ; 14:5-6, s. 629-42
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Journal article (peer-reviewed)abstract
- It has earlier been proven that measured physicochemical properties are useful in the selection of building blocks for combinatorial chemistry as well as for investigation of the scope and limitations of organic reactions. However, measured physicochemical properties are only available for small subsets of reagents, starting materials or building blocks; therefore it is necessary to use calculated descriptors and it is essential that the descriptors are relevant. The objective was to investigate whether three different descriptor data sets contained similar information about the chemical structure, with the major aim to investigate whether calculated descriptors contain similar information as experimental data. A total of 205 heterogeneous primary amines were characterized using three different data sets of molecular descriptor variables. The first set consisted of four physicochemical variables compiled from the literature and commercially available chemicals in chemical catalogues. From these four descriptors together with molecular weight, three additional descriptors could be calculated, resulting in a total of eight descriptor variables in the first data set. The second data set consisted of 81 calculated molecular descriptor variables relating to size, connectivity, atom count, topology and electrotopology indices. The third data set consisted of 10 semi-empirical variables (AM1). All the calculated variables were generated using the software Tsar 3.11. The descriptor variable sets were compared using principal component analysis (PCA) and partial least squares projections to latent structures (PLS). The following result shows that the different descriptor sets do contain similar latent information and that the different types of calculated variables do correlate well with the experimental data, making them suitable to use for e.g. combinatorial library design.
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| 3. |
- Andersson, Per M, et al.
(author)
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Strategies for subset selection of parts of an in-house chemical library
- 2001
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In: Journal of Chemometrics. - : Wiley. - 0886-9383. ; 15:4, s. 353-69
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Journal article (peer-reviewed)abstract
- When a company decides to perform biological testing of their in-house library, i.e. compounds which have been synthesized or purchased over the years, it is usually not feasible or desirable to test all of them using e.g. high-throughput screening (HTS). The limitation is the usually high number of compounds to test (104-106) leading to practical limitations and high costs in terms of both material costs and disposal considerations. Therefore it is often desirable to make a selection of which compounds to include in the biological testing. A challenge is how to make this selection in order to cover the structural space of the in-house library as well as possible. Here we present and discuss different selection strategies based mainly on statistical molecular design (SMD). These methods require different prior information about the compounds under investigation, e.g. characterization of the chemical structure, affinity/biological activity data or neither of these. Which method to be used is largely problem-dependent, i.e. the composition and origin of the library, and hence the structural space, are of great importance. Chemical and biological knowledge about the system under investigation should as far as possible be considered when making the final decision on which method to apply.
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| 4. |
- Antti, Henrik, et al.
(author)
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Detection of kappa number distributions in kraft pulps using nir spectroscopy and multivariate calibration
- 2000
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In: TAPPI Journal. ; 83:3, s. 102-8
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Journal article (peer-reviewed)abstract
- Chemical pulp is characterized by its average lignin content, commonly expressed as the pulp-kappa number. However, this average kappa number provides no information about the distribution of kappa number within the pulp . This study proposes a new method of pulp characterization using near-infrared reflectance (NIR) spectroscopy to measure distributions of kappa numbers within pulp samples. Pure pulps with different kappa numbers were mixed to create blended samples with a known nonuniformity of kappa number distribution. NIR spectroscopy-combined with multi-variate calibration methods was used to detect distributions of kappa numbers in the pulps. Models calculated from these data gave good predictions of the average kappa number as well as the standard deviation around the average. The results imply that NIR spectroscopy can provide information about the average kappa number as well as the distribution of kappa number within the pulp.
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| 5. |
- Ekwall, Björn, et al.
(author)
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MEIC evaluation of acute systemic toxicity : Part VIII. Multivariate partial least squares evaluation, including the selection of a battery of cell line tests with a good prediction of human acute lethal peak blood concentrations for 50 chemicals
- 2000
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In: Atla-Alternatives to Laboratory Animals. - 0261-1929. ; 28, s. 201-34
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Journal article (peer-reviewed)abstract
- The Multicenter Evaluation of In vitro Cytotoxicity (MEIC) programme was set up to evaluate the relevance for human acute toxicity of in vitro cytotoxicity tests. A total of 61 assays were used to test all 50 reference chemicals. The results of all the tests and the human database were presented in the first five papers of this series. An evaluation of the relevance for human acute toxicity of all submitted test results with use of hard linear regression modelling was presented in the next two papers, and demonstrated a high relevance of in vitro tests, notably tests involving human cell lines. In the present study, multivariate partial least square (PLS) modelling with latent variables analysis has been used to reach two objectives. The first objective was to study the prediction of human acute toxicity by the 61 assays. The second objective was to select a practical battery from the 61 assays, with an optimal prediction of lethal blood concentrations from human acute poisonings of the chemicals. A two-component PLS model of all 61 assays predicted three sets of lethal blood concentrations (clinical, forensic and peak concentrations) very well (R-2 = 0.77, 0.76 and 0.83, Q(2) = 0.74, 0.72 and 0.81, respectively), providing correlative evidence for a high relevance for human acute toxicity of most of the assays. The assays with human cells were highly predictive, whereas assays with Very short incubation times and non-fish ecotoxicological assays were least predictive. These findings confirm the previous results from linear regression analysis. To select an optimal battery, 24 successive PLS models of in vitro data were compared with lethal peak concentrations. The battery selection was based on 38 chemicals with reliable and relevant lethal peak concentrations. An initial PLS model of all 61 assays was used to select the 15 most predictive and most distinct assays. Subsequent PLS models were used to measure the decrease in prediction when assays were deleted from the 15-test battery, as well as the increase in prediction when some extra-predictive assays (as identified by the deletion process) were added later to an optimal two-test battery. The most predictive three-test battery (R-2 = 0.79 and Q(2) = 0.78 for all 50 chemicals) included two circumstantial assays. The most predictive and most cost-effective battery consisted of three human cell line assays, with four endpoints and two exposure times, i.e. protein content (24 hours), ATP content (24 hours), inhibition of elongation of cells (24 hours), and pH-change (7 days). This 1, 5, 9, 16 battery exclusively measures basal cytotoxicity, and is highly predictive (R-2 = 0.77 and Q(2) = 0.76 for 50 chemicals) of the actual lethal peak blood concentrations from acute poisonings in humans. The battery prediction compares favourably with the prediction of human lethal dose by a PLS model of rat and mouse 50% lethal dose (LD50) values for the 50 chemicals (R-2 = 0.65 and Q(2) = 0.64). The three assays of the battery and other promising MEIC assays should be formally validated as soon as possible. The battery can be used immediately for several non-regulatory purposes, including the high-throughput screening of potential pharmaceuticals.
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| 6. |
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| 7. |
- Elg Christoffersson, Kristina, et al.
(author)
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Reactivity of dissolving pulp: characterisation using chemical properties, NMR spectroscopy and multivariate data analysis
- 2002
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In: Cellulose. ; 9:2, s. 159-70
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Journal article (peer-reviewed)abstract
- The reactivity of dissolving pulp was experimentally determined in termsof residual cellulose in viscose. The correlations between 11 chemicalproperties of pulp and filter values and residual cellulose contents of viscosewere then investigated by multivariate data analysis. Both the viscose filtervalue and the residual cellulose were well modelled from the 11 propertiesby partial least squares regression. The results show that pulps with highacetone extractable fractions, high magnesium contents, low alkali resistanceand low viscosity, gave low viscose filter values and low residual cellulosecontents. Pulps with low residual cellulose contents also had low carboxylgroupcontents and low polydispersity. The results are interpreted as that in pulpwith high reactivity, the hemicellulose content is low and that the cellulosechains are shorter and more soluble in alkali. An explanation of the positiveeffect from the high extractive content is that the extractives facilitate thediffusion of carbon disulfide. A principal component analysis of CP/MAS13C-NMR spectral data of six pulp samples showed that differences inreactivity between the pulps could be explained by variations in the hydrogenbonds in the cellulose and/or changes in the glucosidic bonds. In a separatestudy electron beam processing enhanced the reactivity, i.e. lowered theresidual cellulose content, of the investigated pulps. The magnitude of theelectron dose, within the tested range (5.4–23.7 kGy), didnotseem to be important, but the reactivity within pulp sheets tended to be ratherinhomogeneous.
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| 8. |
- Eriksson, Lennart, et al.
(author)
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Megavariate analysis of hierarchical QSAR data
- 2002
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In: Journal of Computer-Aided Molecular Design. ; 16:10, s. 711-26
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Journal article (peer-reviewed)abstract
- Multivariate PCA- and PLS-models involving many variables are often difficult to interpret, because plots and lists of loadings, coefficients, VIPs, etc, rapidly become messy and hard to overview. There may then be a strong temptation to eliminate variables to obtain a smaller data set. Such a reduction of variables, however, often removes information and makes the modelling efforts less reliable. Model interpretation may be misleading and predictive power may deteriorate.A better alternative is usually to partition the variables into blocks of logically related variables and apply hierarchical data analysis. Such blocked data may be analyzed by PCA and PLS. This modelling forms the base-level of the hierarchical modelling set-up. On the base-level in-depth information is extracted for the different blocks. The score vectors formed on the base-level, here called `super variables', may be linked together in new matrices on the top-level. On the top-level superficial relationships between the X- and the Y-data are investigated.In this paper the basic principles of hierarchical modelling by means of PCA and PLS are reviewed. One objective of the paper is to disseminate this concept to a broader QSAR audience. The hierarchical methods are used to analyze a set of 10 haloalkanes for which K = 30 chemical descriptors and M = 255 biological responses have been gathered. Due to the complexity of the biological data, they are sub-divided in four blocks. All the modelling steps on the base-level and the top-level are reported and the final QSAR model is interpreted thoroughly.
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| 9. |
- Gabrielsson, Jon, 1973-
(author)
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Multivariate methods in tablet formulation
- 2004
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Doctoral thesis (other academic/artistic)abstract
- This thesis describes the application of multivariate methods in a novel approach to the formulation of tablets for direct compression. It begins with a brief historical review, followed by a basic introduction to key aspects of tablet formulation and multivariate data analysis. The bulk of the thesis is concerned with the novel approach, in which excipients were characterised in terms of multiple physical or (in most cases) spectral variables. By applying Principal Component Analysis (PCA) the descriptive variables are summarized into a few latent variables, usually termed scores or principal properties (PP’s). In this way the number of descriptive variables is dramatically reduced and the excipients are described by orthogonal continuous variables. This means that the PP’s can be used as ordinary variables in a statistical experimental design. The combination of latent variables and experimental design is termed multivariate design or experimental design in PP’s. Using multivariate design many excipients can be included in screening experiments with relatively few experiments. The outcome of experiments designed to evaluate the effects of differences in excipient composition of formulations for direct compression is, of course, tablets with various properties. Once these properties, e.g. disintegration time and tensile strength, have been determined with standardised tests, quantitative relationships between descriptive variables and tablet properties can be established using Partial Least Squares Projections to Latent Structures (PLS) analysis. The obtained models can then be used for different purposes, depending on the objective of the research, such as evaluating the influence of the constituents of the formulation or optimisation of a certain tablet property. Several examples of applications of the described methods are presented. Except in the first study, in which the feasibility of this approach was first tested, the disintegration time of the tablets has been studied more carefully than other responses. Additional experiments have been performed in order to obtain a specific disintegration time. Studies of mixtures of excipients with the same primary function have also been performed to obtain certain PP’s. Such mixture experiments also provide a straightforward approach to additional experiments where an interesting area of the PP space can be studied in more detail. The robustness of a formulation with respect to normal batch-to-batch variability has also been studied. The presented approach to tablet formulation offers several interesting alternatives, for both planning and evaluating experiments.
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| 10. |
- Gabrielsson, Jon, et al.
(author)
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Multivariate methods in the development of a new tablet formulation : optimization and validation
- 2004
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In: Drug Development and Industrial Pharmacy. - New York : M. Dekker. - 0363-9045 .- 1520-5762. ; 30:10, s. 1037-1049
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Journal article (peer-reviewed)abstract
- In a previous study of the development of a tablet formulation approximately 100 excipients were characterized in screening experiments using multivariate design. Acceptable values for important responses were obtained with some of the formulations. The relationships between the properties of the excipients and the responses were evaluated using PLS. In this study additional experiments were performed in order to validate models obtained from the screening study and to find a formulation of suitable composition with desired tablet properties. A formulation with the desired disintegration time was found with the additional experiments and the agreement between observed and predicted values was fair for the tablets that did disintegrate. A limitation of this study was that tablets from four experiments did not disintegrate within the set time limit. The lack of agreement between observed and predicted values of these four experiments was probably due to the nature of one of the factors in the design. Considering the reduced experimental design the results are still encouraging.
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