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ABSTRACT In the effort of analysing multivariate images, image PLS has been considered interesting. In this paper, image PLS (MIR) is compared with image PCA (MIA) by studying a comparison data set. While MIA has been commercially... more
ABSTRACT In the effort of analysing multivariate images, image PLS has been considered interesting. In this paper, image PLS (MIR) is compared with image PCA (MIA) by studying a comparison data set. While MIA has been commercially available for some time, image PLS has not. The kernel PLS algorithm of Lindgren has been implemented in a development environment which is a combination of G (LabVIEW) and MATLAB. In this presentation the power of this environment, as well as an early example in image regression, will be demonstrated. With kernel PLS, all PLS vectors (eigenvectors and eigenvalues) can be calculated from the joint variance–covariance (X′Y and Y′X) and association (Y′Y and X′X) matrices. The dimensions of the kernel matrices X′YY′X and Y′XX′Y are K × K (K is the number of X-variables) and M × M (M is the number of Y-variables) respectively. Hence their size is dependent only on the number of X and Y-variables and not on the number of observations (pixels), which is crucial in image analysis. The choice of LabVIEW as development platform has been based on our experience of a very short implementation time combined with user-friendly interface possibilities. Integrating LabVIEW with MATLAB has speeded up the decomposition calculations, which otherwise are slow. Also, algorithms for matrix calculations are easier to formulate in MATLAB than in LabVIEW. Applying this algorithm on a representative test image which shows many of the typical features found in technical imagery, we have shown that image PLS (MIR) decomposes the data differently than image PCA (MIA), in accordance with chemometric experience from ordinary two-way matrices. In the present example the Y-reference texture-related image used turned out to be able to force a rather significant ‘tilting’ compared with an ‘ordinary MIA’ of the primary structures in the original, spectral R/G image. Copyright © 2000 John Wiley & Sons, Ltd.
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When using hyphenated methods in analytical chemistry, the data obtained for each sample are given as a matrix. When a regression equation is set up between an unknown sample (a matrix) and a calibration set (a stack of matrices), the... more
When using hyphenated methods in analytical chemistry, the data obtained for each sample are given as a matrix. When a regression equation is set up between an unknown sample (a matrix) and a calibration set (a stack of matrices), the residual is a matrix R.The regression equation is usually solved by minimizing the sum of squares of R. If the sample contains some constituent not calibrated for, this approach is not valid. In this paper an algorithm is presented which partitions R into one matrix of low rank corresponding to the unknown constituents, and one random noise matrix to which the least squares restrictions are applied. Properties and possible applications of the algorithm are also discussed.In Part 2 of this work an example from HPLC with diode array detection is presented and the results are compared with generalized rank annihilation factor analysis (GRAFA).
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... 21 K. Esbensen, P. Geladi and S. Wold, BAMID - Bilinear Analysis of Multivariate Image Data, in N. Raun (Editor), Proc. ... 31 S. Bryan, W. Woodward, D. Griffis and R. Linton, A microcomputer based digital imaging system for ion... more
... 21 K. Esbensen, P. Geladi and S. Wold, BAMID - Bilinear Analysis of Multivariate Image Data, in N. Raun (Editor), Proc. ... 31 S. Bryan, W. Woodward, D. Griffis and R. Linton, A microcomputer based digital imaging system for ion microanalysis, Journal of Microscopy, 138 (1985) 15 ...
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Abstract The concepts data matrix and multivariate data analysis are rapidly becoming popular and well-known words in chemistry. Many methods used in the laboratory can produce data arrays of a greater complexity than the data matrix. The... more
Abstract The concepts data matrix and multivariate data analysis are rapidly becoming popular and well-known words in chemistry. Many methods used in the laboratory can produce data arrays of a greater complexity than the data matrix. The broad picture easily gets lost here, not least because of the confusing nomenclature. There is a need for systematization and generalization. Methods available in psychometrics and methods used in chemical research are described and compared in this paper. The goal is to provide a systematic overview and a simple introduction to the subject. References are made to more detailed descriptions in the literature.
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Analytica Chimica Acta, 185 (1986) 117 Elsevier Science Publishers BV, Amsterdam Printed in The Netherlands PARTIAL LEASTSQUARES REGRESSION: A TUTORIAL PAUL GELADI*a and BRUCE R. KOWALSKI Laboratory for Chemometrics and Center for ...
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ABSTRACT Adulteration of food products remains a food security risk. Spices are food components with a high value per unit mass due to their desired flavour attributes and are therefore economically worthwhile targets for adulteration.... more
ABSTRACT Adulteration of food products remains a food security risk. Spices are food components with a high value per unit mass due to their desired flavour attributes and are therefore economically worthwhile targets for adulteration. Vibrational spectroscopy techniques could be ideal to detect adulterants due to benefits such as speed of analysis. Near infrared (NIR) and mid-infrared (mid-IR) spectra were used to quantify the amount of adulterant (buckwheat or millet) in ground black pepper. NIR spectra used for calibration were the average spectra of individual hyperspectral images recorded with a spatial resolution of 300 mu m x 300 mu m from 1000 nm to 2500 nm at 6.3 nm intervals; hyperspectral images were collected to cope with the heterogeneity of the samples. The calibrations calculated, using the averaged NIR spectra, were more accurate than those calculated from mid-IR spectra. This was a direct result of sample heterogeneity and the insufficient sampling area in the mid-IR measurements. NIR-based calibrations were suitable for process control when the appropriate spectral data pre-treatment was used: standard normal variate followed by first derivative pre-processing of the 1100-2500 nm spectral range resulted in a root mean square error of prediction equal to 2.7% w/w and a ratio of standard error of prediction to standard deviation (validation set) of 11.1.
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ABSTRACT Any data table produced in a chemical investigation can be analysed by bilinear projection methods, i. e. principal components and factor analysis and their extensions. Representing the table rows (objects) as points in a... more
ABSTRACT Any data table produced in a chemical investigation can be analysed by bilinear projection methods, i. e. principal components and factor analysis and their extensions. Representing the table rows (objects) as points in a p-dimensional space, these methods project the point swarm of the data set or parts of it down on a F-dimensional subspace (plane or hyperplane). Different questions put to the data table correspond to different projections. This provides an efficient way to convert a data table to a few informative pictures showing the relations between objects (table rows) and variables (table columns). The methods are presented geometrically and mathematically in parallell with chemical illustrations. more dangerous in the long run than methods that are conservative with respect to the amount of extracted information.
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A model-free multivariate analysis using singular value decomposition is employed to refine an equivalent electrical circuit model in order to probe the electrochemical properties of the hematite/water interface in dilute NaCl and NH4Cl... more
A model-free multivariate analysis using singular value decomposition is employed to refine an equivalent electrical circuit model in order to probe the electrochemical properties of the hematite/water interface in dilute NaCl and NH4Cl solutions using electrochemical impedance spectroscopy. The result shows that the surface protonation is directly related to the mobility and trapping of charge carriers at the mineral surface. Moreover, the point of zero charge can be found at pH where the charge transfer resistance is the highest, in addition to the minimum double layer capacitance. The inner-sphere interaction of the NH4(+) ion with the surface is indicated by an increase of capacitance for charge carrier trapping from the protonated surface as well as lower double layer capacitance and open circuit potential. It is clear that the intrinsic electrochemical activity of hematite depends on the degree of surface (de)protonation and other inner-sphere adsorption, as these processes af...
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Two types of high flow rate sampling heads and two versions of a low flow rate sampling head for collecting airborne dust on filter media are examined critically. The errors of the sampling train and sources of error in sample handling... more
Two types of high flow rate sampling heads and two versions of a low flow rate sampling head for collecting airborne dust on filter media are examined critically. The errors of the sampling train and sources of error in sample handling are described. The total precision for each of the four samplers is 5–6% r.s.d. as determined experimentally by gravimetrical
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Early, reliable and fast diagnosis of melanoma is particularly important as the number of cases is increasing. In this paper, the potential of using near infrared spectroscopy for melanoma diagnosis is studied. The classification task is... more
Early, reliable and fast diagnosis of melanoma is particularly important as the number of cases is increasing. In this paper, the potential of using near infrared spectroscopy for melanoma diagnosis is studied. The classification task is complicated by a low signal-to-noise ratio and the high dimensionality of the spectral data. Thus pre- selection of wavelength variables is required. Atypical naevi
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ABSTRACT The use of imaging and images in chemical and technological applications is treated in two parts. Part I focuses on the generation and properties of images. The result of image registration is an analog or digital image, a... more
ABSTRACT The use of imaging and images in chemical and technological applications is treated in two parts. Part I focuses on the generation and properties of images. The result of image registration is an analog or digital image, a representation of intensities as a function of position coordinates x, y (and sometimes also z) in a non-homogeneous material. Certain univariate operations for reducing noise and errors, improving contrast, measuring and counting particles etc. can be carried out on digitized images. A simple example from bioenergy research is given. In this first part a multivariate image is defined and an explanation is given of how it can be obtained. Operations on multivariate images and interpretations of multivariate results are presented in part II.
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A method is reported for the determination of aluminium in aerosols collected on Whatman 41 cellulose filters. In the destruction procedure, provision was made to eliminate silica by hydrofluoric acid treatment. Analysis of the solutions... more
A method is reported for the determination of aluminium in aerosols collected on Whatman 41 cellulose filters. In the destruction procedure, provision was made to eliminate silica by hydrofluoric acid treatment. Analysis of the solutions was performed by the flameless atomic-absorption technique. The accuracy was checked by using data from instrumental neutron-activation analysis (INAA) and standard reference materials. An analogue integrator gave a better linearity and detection limit than conventional peak-reading.
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The linear and non-linear relationships of acute toxicity (as determined on five aquatic non-vertebrates and humans) to molecular structure have been investigated on 38 structurally-diverse chemicals. The compounds selected are the... more
The linear and non-linear relationships of acute toxicity (as determined on five aquatic non-vertebrates and humans) to molecular structure have been investigated on 38 structurally-diverse chemicals. The compounds selected are the organic chemicals from the 50 priority chemicals prescribed by the Multicentre Evaluation of In Vitro Cytotoxicity (MEIC) programme. The models used for the evaluations are the best combination of physico-chemical properties that could be obtained so far for each organism, using the partial least squares projection to latent structures (PLS) regression method and backpropagated neural networks (BPN). Non-linear models, whether derived from PLS regression or backpropagated neural networks, appear to be better than linear models for describing the relationship between acute toxicity and molecular structure. BPN models, in turn, outperform non-linear models obtained from PLS regression. The predictive power of BPN models for the crustacean test species are better than the model for humans (based on human lethal concentration). The physico-chemical properties found to be important to predict both human acute toxicity and the toxicity to aquatic non-vertebrates are the n-octanol water partition coefficient (Pow) and heat of formation (HF). Aside from the two former properties, the contribution of parameters that reflect size and electronic properties of the molecule to the model is also high, but the type of physico-chemical properties differs from one model to another. In all of the best BPN models, some of the principal component analysis (PCA) scores of the 13C-NMR spectrum, with electron withdrawing/accepting capacity (LUMO, HOMO and IP) are molecular size/volume (VDW or MS1) parameters are relevant. The chemical deviating from the QSAR models include non-pesticides as well as some of the pesticides tested. The latter type of chemical fits in a number of the QSAR models. Outliers for one species may be different from those of other test organisms.
Research Interests: Principal Component Analysis, Nonlinear dynamics, Magnetic Resonance Spectroscopy, Neural Network, Linear models, and 19 moreBiological Sciences, Linear Model, Environmental Sciences, Humans, Animals, Electronic properties, Partition Coefficient, CHEMICAL SCIENCES, HYDROCARBONS, Spectrum, Xenobiotics, Toxicity Tests, Lethal Dose, Structure activity Relationship, Physico chemical Properties, Acute Toxicity, Partial Least Square, Cytotoxins, and Molecular Structure(Biological Sciences, Linear Model, Environmental Sciences, Humans, Animals, Electronic properties, Partition Coefficient, CHEMICAL SCIENCES, HYDROCARBONS, Spectrum, Xenobiotics, Toxicity Tests, Lethal Dose, Structure activity Relationship, Physico chemical Properties, Acute Toxicity, Partial Least Square, Cytotoxins, and Molecular Structure)
(Biological Sciences, Linear Model, Environmental Sciences, Humans, Animals, Electronic properties, Partition Coefficient, CHEMICAL SCIENCES, HYDROCARBONS, Spectrum, Xenobiotics, Toxicity Tests, Lethal Dose, Structure activity Relationship, Physico chemical Properties, Acute Toxicity, Partial Least Square, Cytotoxins, and Molecular Structure)
... northern Sweden G. Petterson ~, I. Renberg 1, p. Geladi 2, A. Lindberg 2 & F. Lindgren 2 1Department of Ecological Botany, University of Umed, S-901 87 Umed, Sweden; 2Department of Organic Chemistry, University of Umed, S-901 87... more
... northern Sweden G. Petterson ~, I. Renberg 1, p. Geladi 2, A. Lindberg 2 & F. Lindgren 2 1Department of Ecological Botany, University of Umed, S-901 87 Umed, Sweden; 2Department of Organic Chemistry, University of Umed, S-901 87 Umed, Sweden ...
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... Another way to adjust the Class Size Training set Test set Unbalanced Bread 58476 6902 Flies 279 200 Balanced Bread 279 6902 Flies 279 200 Table 1 ... References 1. H. Grahn and P. Geladi (Eds), Techniques and Applications of... more
... Another way to adjust the Class Size Training set Test set Unbalanced Bread 58476 6902 Flies 279 200 Balanced Bread 279 6902 Flies 279 200 Table 1 ... References 1. H. Grahn and P. Geladi (Eds), Techniques and Applications of Hyperspectral Image Analysis. ...
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ABSTRACT Part 1 explained multiplicative scatter correction (MSC), the building of a principal component regression (PCR) model and how the test data can be used in prediction. Emphasis was on data pretreatment for linearistion and on... more
ABSTRACT Part 1 explained multiplicative scatter correction (MSC), the building of a principal component regression (PCR) model and how the test data can be used in prediction. Emphasis was on data pretreatment for linearistion and on spectral/chemical interpretation of the results. Part 2 discusses partial least squares (PLS or PLSR) regression. The data set prepared in Part 1 is also used here. Details on data pretreatment are, therefore, not repeated. Some details of PLS modeling are explained using the calculations of the example. Also, the interpretation of the PLS model gets some attention. Neural network calculation results are included for comparison. Artifical neural networks (ANN) are non-linear, so linearisation is not considered necessary. Latent variable regression methods such as PLS and PCR and ANNs are all successive approximations to the unknown function y = f(x) that forms the basis of all calibration methods. In latent variable regression, the rank of the model determines the degree of approximation. In ANNs, the number of hidden nodes and the number of iterations determine the degree of approximation.
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Research Interests: Biomedical Engineering, Biomedical, Skin Cancer, Melanoma, Squamous Cell Carcinoma, and 11 moreOral Squamous Cell Carcinoma (OSCC), Malignant Melanoma, Spectrum analysis, General practitioner, Basal cell carcinoma, Reproducibility of Results, Sensitivity and Specificity, Electrical And Electronic Engineering, Electric Impedance, Skin Neoplasms, and Electrical Impedance
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Five acute bioassays consisting of three cyst-based tests (with Artemia salina, Streptocephalus proboscideus and Brachionus calyciflorus), the Daphnia magna test and the bacterial luminescence inhibition test (Photobacterium phosphoreum)... more
Five acute bioassays consisting of three cyst-based tests (with Artemia salina, Streptocephalus proboscideus and Brachionus calyciflorus), the Daphnia magna test and the bacterial luminescence inhibition test (Photobacterium phosphoreum) are used to determine the acute toxicity of the 50 priority chemicals of the Multicentre Evaluation of In Vitro Cytotoxicity (MEIC) programme. These tests and five physiocochemical properties (n-octanol-water partition coefficient, molecular weight, melting point, boiling point and density) are evaluated either singly or in combination to predict human acute toxicity. Acute toxicity in human is expressed both as oral lethal doses (HLD) and as lethal concentrations (HLC) derived from clinical cases. A comparison has also been made between the individual tests and the conventional rodent tests, as well as between rodent tests and the batteries resulting from partial least squares (PLS), with regard to their predictive power for acute toxicity in humans. Results from univariate regression show that the predictive potential of bioassays (both ecotoxicological and rodent tests) is generally superior to that of individual physicochemical properties for HLD. For HLC prediction, however, no consistent trend could be discerned that indicated whether bioassays are better estimators than physicochemical parameters. Generally, the batteries resulting from PLS regression seem to be more predictive than rodent tests or any of the individual tests. Prediction of HLD appears to be dependent on the phylogeny of the test species: cructaceans, for example, appear to be more important components in the test battery than rotifers and bacteria. For HLC prediction, one anostracan and one cladoceran crustacean are considered to be important. When considering both ecotoxicological tests and physicochemical properties, the battery based on the molecular weight and the cladoceran crustacean predicts HLC substantially better than any other combination.
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Positive Matrix Factorization (PMF) was used to identify and apportion candidate sources of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/F) in samples of offshore and coastal surface sediments from the Baltic... more
Positive Matrix Factorization (PMF) was used to identify and apportion candidate sources of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/F) in samples of offshore and coastal surface sediments from the Baltic Sea. Atmospheric deposition was the dominant source in offshore and pristine areas, in agreement with previous studies. Earlier chlorophenol use and a source suggested origins from pulp and paper production and related industries were identified as important coastal sources. A previously presumed major source, chlorine bleaching of pulp, was of only minor importance for modern Baltic surface sediments. The coastal source impacts were mostly local or regional, but pattern variations in offshore samples indicate that coastal sources may have some importance for offshore areas. Differences between sub-basins also indicated that local and regional air emissions from incineration or other high-temperature processes are more important in the southern Baltic Sea compared to those in northerly areas. These regional differences demonstrated the importance of including offshore sediments from the Bothnian Bay, Gulf of Finland, and other areas of the Baltic Sea in future studies to better identify the major PCDD/F sources to the Baltic Sea.