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1.
  • Ahlinder, Jon, et al. (författare)
  • Chemometrics comes to court: evidence evaluation of chem–bio threat agent attacks
  • 2015
  • Ingår i: Journal of Chemometrics. - : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 29:5, s. 267-276
  • Tidskriftsartikel (refereegranskat)abstract
    • Forensic statistics is a well-established scientific field whose purpose is to statistically analyze evidence in order to support legal decisions. It traditionally relies on methods that assume small numbers of independent variables and multiple samples. Unfortunately, such methods are less applicable when dealing with highly correlated multivariate data sets such as those generated by emerging high throughput analytical technologies. Chemometrics is a field that has a wealth of methods for the analysis of such complex data sets, so it would be desirable to combine the two fields in order to identify best practices for forensic statistics in the future. This paper provides a brief introduction to forensic statistics and describes how chemometrics could be integrated with its established methods to improve the evaluation of evidence in court.The paper describes how statistics and chemometrics can be integrated, by analyzing a previous know forensic data set composed of bacterial communities from fingerprints. The presented strategy can be applied in cases where chemical and biological threat agents have been illegally disposed.
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2.
  • Amiri, Saeid, et al. (författare)
  • Assessing the coefficient of variations of chemical data using bootstrap method
  • 2011
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 25:6, s. 295-300
  • Tidskriftsartikel (refereegranskat)abstract
    • The coefficient of variation is frequently used in the comparison and precision of results with different scales. This work examines the comparison of the coefficient of variation without any assumptions about the underlying distribution. A family of tests based on the bootstrap method is proposed, and its properties are illustrated using Monte Carlo simulations. The proposed method is applied to chemical experiments with iid and non-iid observations.
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3.
  • Andersson, Carin, et al. (författare)
  • A chemometrical approach to study interactions between ethynylestradiol and an AhR-agonist in stickleback (Gasterosteus aculeatus)
  • 2010
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 24:11-12, s. 768-778
  • Tidskriftsartikel (refereegranskat)abstract
    • Quantifiable responses in fish, such as induction of certain proteins, can be used as indicators of chemical contamination of waterways. In order to evaluate differences in ethoxyresorufin-O-deethylase (EROD) induction capacity of the gill and the liver and effects on organs and biomarker proteins, e.g. gill and liver EROD, hepatosomatic index (HSI), nephrosomatic index (NSI), gonadosomatic index (GSI), spiggin, vitellogenin and sperm motility were analysed in male three-spined sticklebacks (Gasterosteus aculeatus) exposed for 21 days to β-naphthoflavone (βNF) alone (Exp 1) or in combination with 17α-ethynylestradiol (EE2) (Exp 2). The sperm motility variables were studied using computer-assisted sperm analysis (CASA). Exp 1: Gill EROD activity was significantly induced in fish exposed to ≥1.2 µg/l and hepatic EROD activity in fish exposed to ≥6 µg/l. No significant effect of ßNF on the production of spiggin or vitellogenin or on sperm variables was found. Exp 2: A significant additative effect of EE2 + βNF was shown for gill EROD. A significant antagonistic effect of the two compounds was found on NSI where an increased EE2 concentration led to an increase in NSI while an increased concentration of βNF led to a decreased NSI. Interestingly, the results showed that exposure to intermediate concentrations of EE2 and ßNF led to a significant increase in the sperm variables. In the aquatic environment mixtures of numerous chemicals with oestrogenic activity are present, so if the capacity to induce gill EROD activity is a general property of oestrogen-acting chemicals, our findings are important.
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4.
  • Andersson, C David, et al. (författare)
  • Multivariate assessment of virtual screening experiments
  • 2010
  • Ingår i: Journal of Chemometrics. - : John Wiley & Sons, Ltd. - 0886-9383 .- 1099-128X. ; 24:11-12, s. 757-767
  • Tidskriftsartikel (refereegranskat)abstract
    • Discovering molecules with a desired biological function is one of the great challenges in drug research. To discover new lead molecules, virtual screens (VS) are often conducted, in which databases of molecules are screened for potential binders to a specific protein, using molecular docking. The choice of docking software and parameter settings within the software can significantly influence the outcome of a VS. In this study, we have applied chemometric methods such as design of experiments, principal component analysis and partial least-square projections to latent structure (PLS) to simulated VS experiments to find and compare suitable conditions for performing VS against six protein targets selected from the DUD databases. The docking parameters in FRED, and scoring functions in both FRED and GOLD docking software, were varied according to a statistical experimental design and a PLS model was calculated to correlate the experimental setup to the VS outcome. The study revealed that the choice of scoring function has the greatest influence on VS outcome, and that other parameters have varying influence, depending on the protein target. We also found that substantial bias can be introduced by the lack of variation of molecular properties in the databases used in the screening. Our results provide indications that docking experiments could be tailored to the protein target in order to obtain satisfactory VS results.
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5.
  • Andersson, Per M, et al. (författare)
  • Strategies for subset selection of parts of an in-house chemical library
  • 2001
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383. ; 15:4, s. 353-69
  • Tidskriftsartikel (refereegranskat)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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6.
  • Andersson, Patrik, et al. (författare)
  • Ultraviolet absorption characteristics and calculated semi-empirical parameters as chemical descriptors in multivariate modelling of polychlorinated biphenyls
  • 1996
  • Ingår i: Journal of Chemometrics. - 0886-9383 .- 1099-128X. ; 10:2, s. 171-185
  • Tidskriftsartikel (refereegranskat)abstract
    • The structural variation within the polychlorinated biphenyls (PCBs) was characterized by using principal component analysis (PCA). A multivariate model was evolved from 52 physicochemical descriptors including measured ultraviolet (UV) absorption spectra, calculated semiempirical parameters (AM1) and properties captured from the literature. Parameters calculated by using the AM1-Hamiltonian were e.g. heat of formation, dipole moments, ionization potential and the barrier of internal rotation. The UV spectra were measured and digitized in the range 200-300 nm. The multivariate model revealed that most of the information within the set of physicochemical parameters was related to molecular size. Descriptors depending on size were e.g. GC retention times, partition coefficients and a subset of semiempirically derived energy terms. Important also were parameters reflecting differences in substitution patterns and related to electronic and steric properties, such as UV absorption in the wavelength region 245-300 nm, the barrier of internal rotation and the ionization potential. The developed model describes the large variation in physicochemical characteristics within the PCBs. The importance of a broad chemical characterization is illustrated by a quantitative structure-activity relationship (QSAR) for the potency of inhibition of intercellular communication for 27 structurally diverse tetra- to heptachlorinated PCBs.
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7.
  • Antti, Henrik, et al. (författare)
  • Batch statistical processing of 1H NMR-derived urinary spectral data
  • 2002
  • Ingår i: Journal of Chemometrics: Special Issue: Proceedings of the 7th Scandinavian Symposium on Chemometrics. Issue Edited by Lars Nørgaard. - : Wiley. ; 16:8-10, s. 461-8
  • Tidskriftsartikel (refereegranskat)abstract
    • Multivariate statistical batch processing (BP) analysis of 1H nuclear magnetic resonance (NMR) urine spectra was employed to establish time-dependent metabolic variations in animals treated with the model hepatotoxin hydrazine. Hydrazine was administered orally to rats (at 90 mg kg-1), and urine samples were collected from dosed rats and matched control animals (n = 5 per group) at time points up to 168 h post-dose. Urine samples were analysed via 1H NMR spectroscopy and partial least squares-based batch processing analysis, treating each rat as an individual batch comprising a series of timed urine samples. A model defining the mean urine profile was established for the control group, and samples obtained from hydrazine-treated animals were assessed using this model. Time-dependent deviations from the control model were evident in all hydrazine-treated animals, and hepatotoxicity was manifested by increased urinary excretion of taurine, creatine, 2-aminoadipate, citrulline and -alanine together with depletion of urinary levels of citrate, succinate and hippurate. The experiment was repeated at six different pharmaceutical centres in order to assess the robustness of the technology and to establish the natural variability in the data. Results were consistent across the data for all centres. BP plots showed a characteristic pattern for each toxin, allowing the time points at which there were maximum metabolic differences to be determined and providing a means of visualizing the net toxin-induced metabolic movement of urinary metabolism. BP may prove to be a powerful metabonomic tool in defining time-dependent metabolic consequences of toxicity and is an efficient means of visualizing inter-animal variations in response as well as defining multivariate statistical limits of normality in terms of biofluid composition.
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8.
  • Artursson, Tom, et al. (författare)
  • Drift correction for gas sensors using multivariate methods
  • 2000
  • Ingår i: Journal of Chemometrics. - 0886-9383 .- 1099-128X. ; 14:5-6, s. 711-723
  • Tidskriftsartikel (refereegranskat)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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9.
  • Axrup, L, et al. (författare)
  • Using miniature diode array NIR spectrometers for analysing wood chips and bark samples in motion
  • 2000
  • Ingår i: JOURNAL OF CHEMOMETRICS. - : JOHN WILEY & SONS LTD. - 0886-9383. ; 14:5-6, s. 561-572
  • Tidskriftsartikel (refereegranskat)abstract
    • The chemical and physical properties of the wood chips entering the Kraft pulping process are of high interest for many different reasons. The most important one is the possibility to establish an effective and dynamic forward process control. The same is
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10.
  • Berglund, Anders, 1970-, et al. (författare)
  • The GIFI approach to non-linear PLS modeling
  • 2001
  • Ingår i: Journal of Chemometrics. - : Wiley Inter Science. - 0886-9383 .- 1099-128X. ; 15:4, s. 321-36
  • Tidskriftsartikel (refereegranskat)abstract
    • The GIFI approach to non-linear modeling involves the transformation of quantitative variables to a set of 1/0 dummies in a similar manner to the way qualitative variables are coded. This is followed by analyzing the sets of 1/0 dummies by principal component analysis, multiple regression or, as discussed here, PLS. The patterns of the resulting coefficients indicate the nature of the non-linearities in the data. Here the potential uses and limitations of PLS regression, in combination with four variants of GIFI coding, are investigated using both simulated and empirical data sets.
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11.
  • Björck, Åke, et al. (författare)
  • Fast and stable partial least squares modelling: A benchmark study with theoretical comments
  • 2017
  • Ingår i: Journal of Chemometrics. - : WILEY. - 0886-9383 .- 1099-128X. ; 31:8
  • Tidskriftsartikel (refereegranskat)abstract
    • Algorithms for partial least squares (PLS) modelling are placed into a sound theoretical context focusing on numerical precision and computational efficiency. NIPALS and other PLS algorithms that perform deflation steps of the predictors (X) may be slow or even computationally infeasible for sparse and/or large-scale data sets. As alternatives, we develop new versions of the Bidiag1 and Bidiag2 algorithms. These include full reorthogonalization of both score and loading vectors, which we consider to be both necessary and sufficient for numerical precision. Using a collection of benchmark data sets, these 2 new algorithms are compared to the NIPALS PLS and 4 other PLS algorithms acknowledged in the chemometrics literature. The provably stable Householder algorithm for PLS regression is taken as the reference method for numerical precision. Our conclusion is that our new Bidiag1 and Bidiag2 algorithms are themethods of choice for problems where both efficiency and numerical precision are important. When efficiency is not urgent, the NIPALS PLS and the Householder PLS are also good choices. The benchmark study shows that SIMPLS gives poor numerical precision even for a small number of factors. Further, the nonorthogonal scores PLS, direct scores PLS, and the improved kernel PLS are demonstrated to be numerically less stable than the best algorithms. PrototypeMATLAB codes are included for the 5 PLS algorithms concluded to be numerically stable on our benchmark data sets. Other aspects of PLS modelling, such as the evaluation of the regression coefficients, are also analyzed using techniques from numerical linear algebra.
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12.
  • Björk, Anders, et al. (författare)
  • Spectra of wavelet scale coefficients from process Acoustic Measurements as input for PLS modelling of pulp quality
  • 2002
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 16:8-10, s. 521-528
  • Tidskriftsartikel (refereegranskat)abstract
    • Acoustic and vibration signals are captured by simple standard accelerometers. These can often be mounted directly on operative process equipment, creating a completely non-invasive measurement system. The signals from the accelerometer are then amplified, digitized by an analogue-to-digital converter and stored in some suitable format in a PC. The method most often used for signal processing of acoustic data has been to apply variants of fast Fourier transform (FFT) on sampled data to produce a frequency domain representation. An alternative way tried here is to use the fast wavelet transform (FWT) in combination with FFT. The FWT has the advantage that it produces time-resolved representations and, on each time scale, different features can be extracted. However, in this case, time resolution has no meaning, since the starting points for data acquisitions were not fixed. The wavelet step can be seen as a series of pre-filters and it is here followed by FFT on coefficients at each wavelet scale. The results are compared to those obtained after FFT on the complete time series. We have used spectra of wavelet scale coefficients in an attempt to model pulp quality with PLS. In this case the number of points in the resulting wavelet multiresolution spectrum (WT-MRS) can be limited to a low number, e.g. 255 compared to 1025 with direct FFT on the time series. In the PLS modelling step the advantage is that the first two components describe Y much better than when using the conventional approach, e.g. 72% explained Y variance compared to 40%. A second advantage is that the model requires fewer coefficients.
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13.
  • Björkström, Anders, et al. (författare)
  • A two-parametric class of predictors in multivariate regression
  • 2007
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 21:5-6, s. 215-226
  • Tidskriftsartikel (refereegranskat)abstract
    • We demonstrate that a number of well-established multivariate regression methods for prediction are related in that they are special cases of basically one general procedure. We try a more general method based on this procedure with two metaparameters. In a simulation study, based on a latent structure model, we compare this method to ridge regression (RR), multivariate partial least squares regression (PLSR) and repeated univariate PLSR. For most types of data sets studied, all methods do approximately equally well. There are some cases where RR and least squares ridge regression (LSRR) yield larger errors than the other methods, and we conclude that one-factor methods are not adequate for situations where more than one latent variable are needed to describe the data. Among those based on latent variables, none of the methods tried is superior to the others in any obvious way.
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14.
  • Brandmaier, Stefan, et al. (författare)
  • An evaluation of experimental design in QSAR modelling utilizing the k-medoid clustering
  • 2012
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 26:10, s. 509-517
  • Tidskriftsartikel (refereegranskat)abstract
    • A reliable selection of a representative subset of chemical compounds has been reported to be crucial for numeroustasks in computational chemistry and chemoinformatics. We investigated the usability of an approach on the basisof the k-medoid algorithm for this task and in particular for experimental design and the split between training andvalidation set. We therefore compared the performance of models derived from such a selection to that of modelsderived using several other approaches, such as space-filling design and D-optimal design. We validated the performance on four datasets with different endpoints, representing toxicity, physicochemical properties and others.Compared with the models derived from the compounds selected by the other examined approaches, those derivedwith the k-medoid selection show a high reliability for experimental design, as their performance was constantlyamong the best for all examined datasets. Of all the models derived with all examined approaches, those derivedwith the k-medoid approach were the only ones that showed a significantly improved performance compared witha random selection, for all datasets, the whole examined range of selected compounds and for each dimensionalityof the search space.
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15.
  • Bro, Rasmus, et al. (författare)
  • PLS works
  • 2009
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 23:1-2, s. 69-71
  • Tidskriftsartikel (refereegranskat)abstract
    • In a recent paper, claims were made that most current implementations of PLS provide wrong and misleading residuals [1]. In this paper the relation between PLS and Lanczos bidiagonalization is described and it is shown that there is a good rationale behind current implementations of PLS. Most importantly, the residuals determined in current implementations of PLS are independent of the scores used for predicting the dependent variable(s). Oppositely, in the newly suggested approach, the residuals are correlated to the scores and hence may be high due to variation that is actually used for predicting. It is concluded that the current practice of calculating residuals be maintained.
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16.
  • Brydegaard, Mikkel, et al. (författare)
  • Chemometric approach to chromatic spatial variance. Case study: patchiness of the Skyros wall lizard
  • 2012
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383. ; 26:6, s. 246-255
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper, we demonstrate how to take advantage of the large number of spatial samples provided by commercial multispectral RGB imagers. We investigate the possibility to use various multidimensional histograms and probability distributions for decomposition and predictive models. We show how these methods can be used in an example using images of different Skyros wall lizards and demonstrate improved performance in prediction of color morph compared with traditional parameterization techniques of spatial variance. Copyright (c) 2012 John Wiley & Sons, Ltd.
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17.
  • Bylesjö, Max, et al. (författare)
  • OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification
  • 2006
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 20:8-10, s. 341-351
  • Tidskriftsartikel (refereegranskat)abstract
    • The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS-DA). We demonstrate how class-orthogonal variation can be exploited to augment classification performance in cases where the individual classes exhibit divergence in within-class variation, in analogy with soft independent modelling of class analogy (SIMCA) classification. The prediction results will be largely equivalent to traditional supervised classification using PLS-DA if no such variation is present in the classes. A discriminatory strategy is thus outlined, combining the strengths of PLS-DA and SIMCA classification within the framework of the OPLS-DA method. Furthermore, resampling methods have been employed to generate distributions of predicted classification results and subsequently assess classification belief. This enables utilisation of the class-orthogonal variation in a proper statistical context. The proposed decision rule is compared to common decision rules and is shown to produce comparable or less class-biased classification results.
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18.
  • Carlson, Johan E., et al. (författare)
  • Ultrasonic measurement of the reaction kinetics of the setting of calcium sulfate bone cements using implicit calibration
  • 2008
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 22:11-12, s. 752-757
  • Tidskriftsartikel (refereegranskat)abstract
    • Injectable bone cements based on calcium sulfate and calcium phosphates are being developed for use as bone defect filling and reinforcement of osteoporotic bones. For developers and end-users, kinetic properties of the setting reaction of such cements are of great interest. Existing standards for setting time measurement are based on visual examination of the cement surface and thus suffer from poor repeatability and subjectivity. Implicit calibration provides the means of determining parameters of a physical model at the same time as a calibration based on regression. This enables the use of indirect observations for the determination of implicit model parameters. In this paper, we study the hydration of calcium sulfate hemihydrate into calcium sulfate dihydrate, by combining multivariate calibration with a physical model of the reaction kinetics. The physical model contains three parameters, the reaction rate, the reaction order and a time delay. These parameters are estimated from ultrasound amplitude spectra. The resulting model fit has an R2 value of 99.9%.
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19.
  • Carlson, Rolf, et al. (författare)
  • A novel approach for screening discrete variations in organic synthesis
  • 2001
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 15:5, s. 455-474
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper we present a general strategy for screening discrete variations in organic synthesis. The strategy is based upon principal properties, i.e. principal component characterization of the constituents defining the reaction system. The first step is to select subsets of test items from each class of constituents defining the reaction space, i.e. substrates, reagents, solvents, catalysts, etc., so that the selected items from each class cover the properties considered. The second step is to construct a candidate matrix which contains all possible combinations of the items in the subsets. This matrix is a full multilevel factorial design. The third step is to assign a tentative model for the screening experiment and to construct the corresponding candidate model matrix. The fourth step is to select experiments to yield an experimental design that spans the variable space efficiently and that also gives good estimates of the model parameters. We present an algorithm that uses singular value decomposition to select experiments. The proposed strategy is then illustrated with an example of the Fischer indole synthesis.
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20.
  • Dumarey, Melanie, et al. (författare)
  • OPLS methods for the analysis of hyperspectral images—comparison with MCR-ALS
  • 2014
  • Ingår i: Journal of Chemometrics. - : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 28:8, s. 687-696
  • Tidskriftsartikel (refereegranskat)abstract
    • Two new orthogonal projections to latent structures (OPLS) based methods were proposed to analyze hyperspectral images, enabling the visualization ofmultiple chemical compounds in onematrix without the need of extensive preprocessing. Both proposed methods delivered images representing the chemical distribution in the ribbon similar to the more traditional multivariate curve resolution–alternating least squares (MCR-ALS) method, but their image background was less dynamic resulting in a stronger chemical contrast. This indicated that the methods successfully removed structured variation orthogonal to the chemical information (pure spectra of individual compounds), which was confirmed by the fact that physical scattering effects caused by grooves and edges were captured in the images visualizing the orthogonal components of the model. Hereby, the OPLS-based method employing the pure spectra as weights in the OPLS algorithm was more successful in distinguishing compounds with a similar spectral signal than the transposed OPLS algorithm(pure spectra of individual compounds were used as response in OPLS model). It should be noted that for the main compounds, the MCR-ALS method enabled easier visual interpretation compared to the OPLS-based methods by setting all values below zero to zero, resulting in a higher contrast between pixels containing the studied compound and pixels not containing that compound.
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21.
  • Eriksson, L., et al. (författare)
  • A chemometrics toolbox based on projections and latent variables
  • 2014
  • Ingår i: Journal of Chemometrics. - : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 28:5, s. 332-346
  • Tidskriftsartikel (refereegranskat)abstract
    • A personal view is given about the gradual development of projection methods-also called bilinear, latent variable, and more-and their use in chemometrics. We start with the principal components analysis (PCA) being the basis for more elaborate methods for more complex problems such as soft independent modeling of class analogy, partial least squares (PLS), hierarchical PCA and PLS, PLS-discriminant analysis, Orthogonal projection to latent structures (OPLS), OPLS-discriminant analysis and more. From its start around 1970, this development was strongly influenced by Bruce Kowalski and his group in Seattle, and his realization that the multidimensional data profiles emerging from spectrometers, chromatographs, and other electronic instruments, contained interesting information that was not recognized by the current one variable at a time approaches to chemical data analysis. This led to the adoption of what in statistics is called the data analytical approach, often called also the data driven approach, soft modeling, and more. This approach combined with PCA and later PLS, turned out to work very well in the analysis of chemical data. This because of the close correspondence between, on the one hand, the matrix decomposition at the heart of PCA and PLS and, on the other hand, the analogy concept on which so much of chemical theory and experimentation are based. This extends to numerical and conceptual stability and good approximation properties of these models. The development is informally summarized and described and illustrated by a few examples and anecdotes.
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22.
  • Eriksson, Lennart, et al. (författare)
  • A graphical index of separation (GIOS) in multivariate modeling
  • 2010
  • Ingår i: Journal of Chemometrics. - : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 24:11-12, s. 779-789
  • Tidskriftsartikel (refereegranskat)abstract
    • We introduce a new measure for the importance of predictor variables, X, for the separation of two groups (classes) of observations. The measure is a Graphical Index of Separation (GIOS), and is, for each predictor, determined from the distribution of all possible pairs of observations with one from each group. GIOS is quantitative, intuitively simple and easy to interpret. The GIOS is straightforward to visualize in bivariate plots, and line or bar plots for larger number of variables. The approach applies both to discriminant analyses such as LDA, SIMCA, PLS-DA, OPLS-DA and to quantitative modeling such as MLR, PLS and OPLS. In the latter case, the observations are first divided into two groups based on their response values, Y. The GIOS approach is illustrated by PLS-DA/OPLS-DA and SIMCA-classification of a number of multivariate data sets with few and many variables relative to the number of observations.
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23.
  • Eriksson, Lennart, et al. (författare)
  • CV-ANOVA for significance testing of PLS and OPLS® models
  • 2008
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 22:11-12, s. 594-600
  • Tidskriftsartikel (refereegranskat)abstract
    • This report describes significance testing for PLS and OPLS® (orthogonal PLS) models. The testing is applicable to single-Y cases and is based on ANOVA of the cross-validated residuals (CV-ANOVA). Two variants of the CV-ANOVA are introduced. The first is based on the cross-validated predictive residuals of the PLS or OPLS model while the second works with the cross-validated predictive score values of the OPLS model. The two CV-ANOVA diagnostics are shown to work well in those cases where PLS and OPLS work well, that is, for data with many and correlated variables, missing data, etc. The utility of the CV-ANOVA diagnostic is demonstrated using three datasets related to (i) the monitoring of an industrial de-inking process; (ii) a pharmaceutical QSAR problem and (iii) a multivariate calibration application from a sugar refinery. Copyright © 2008 John Wiley & Sons, Ltd.
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24.
  • Eriksson, Lennart, et al. (författare)
  • Editorial
  • 2007
  • Ingår i: Journal of Chemometrics. - : John Wiley & Sons, Ltd. - 0886-9383 .- 1099-128X. ; 21:10-11, s. 397-
  • Tidskriftsartikel (populärvet., debatt m.m.)abstract
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25.
  • Eriksson, Lennart, et al. (författare)
  • Multivariate analysis of congruent images (MACI)
  • 2005
  • Ingår i: Journal of Chemometrics. - : John Wiley & Sons, Ltd. - 0886-9383 .- 1099-128X. ; 19:5-7, s. 393-403
  • Tidskriftsartikel (refereegranskat)abstract
    • The multivariate analysis of congruent images (MACI) is discussed. Here, each image represents one observation and the data set contains a set of congruent images. With congruent images we mean a set of images, properly pre-processed, oriented and aligned, so that each data element (feature, pixel) corresponds to the same element across all images. An example may be a set of frames from a fixed video camera looking at a stable process. The purpose of a MACI is to find and express patterns over a set of images for the purpose of classification or quantitative regression-like relationships. This is in contrast to standard image analysis, which is usually concerned with a single image and the identification of parts of the image, for example tumour tissue versus normal. We also extend MACI to the case with a set of images that initially are not fully congruent, but are made so by the use of wavelet analysis and the distributions of the wavelet coefficients. Thus, the resulting description forms a set of congruent vectors amenable to multivariate data analysis. The MACI approach will be illustrated by four data sets, three easy-to-understand tutorial image data sets and one industrial image data set relating to quality control of steel rolls.
  •  
26.
  • Eriksson, Lennart, et al. (författare)
  • Multivariate biological profiling and principal toxicity regions of compounds: the PCB case study
  • 2002
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 16:8-10, s. 497-509
  • Tidskriftsartikel (refereegranskat)abstract
    • The chemometric QSAR strategy, as applied in environmental sciences and drug design, is based on (1) multivariate characterization of chemical structure, (2) multivariate design in the principal properties of a set of compounds to select a representative training set, and (3) multivariate modelling of the structure-activity relationships. A multivariate QSAR investigation is often commenced by applying a screening design, and the selected compounds are tested biologically in a broad battery of test systems (multivariate biological profiling). In many cases the result is such that for certain biological end-points only some of the tested compounds are active, while for another set of biological end-points other tested chemicals are active. In other words, when looking at the chemical property space, there may be both responding and non-responding toxicity regions, or even regions of very specific toxicity mechanisms. This may lead to loss of resolution and balance in the resulting QSAR models. Therefore it might sometimes be worthwhile to focus the QSAR modelling on parts of the chemical space where high toxicity is expected or known to be the case. In this paper we describe a multi-stage modification of the chemometric QSAR strategy, aimed at identifying focused sets of compounds that provide a good mapping of such principal toxicity regions. This strategy is based on PCA, PLS and multivariate design in several stages. The strategy is illustrated using a data set of polychlorinated biphenyls, a set of compounds for which seven biological end-points were determined. Copyright © 2002 John Wiley & Sons, Ltd.
  •  
27.
  • Eriksson, Lennart, et al. (författare)
  • PLS-trees (R), a top-down clustering approach
  • 2009
  • Ingår i: Journal of Chemometrics. - Chichester : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 23:11, s. 569-580
  • Tidskriftsartikel (refereegranskat)abstract
    • A hierarchical clustering approach based on a set of PLS models is presented. Called PLS-Trees (R), this approach is analogous to classification and regression trees (CART), but uses the scores of PLS regression models as the basis for splitting the clusters, instead of the individual X-variables. The split of one cluster into two is made along the sorted first X-score (t(1)) of a PLS model of the cluster, but may potentially be made along a direction corresponding to a combination of scores. The position of the split is selected according to the improvement of a weighted combination of (a) the variance of the X-score, (b) the variance of Y and (c) a penalty function discouraging an unbalanced split with very different numbers of observations. Cross-validation is used to terminate the branches of the tree, and to determine the number of components of each cluster PLS model. Some obvious extensions of the approach to OPLS-Trees and trees based on hierarchical PLS or OPLS models with the variables divided in blocks depending on their type, are also mentioned. The possibility to greatly reduce the number of variables in each PLS model on the basis of their PLS w-coefficients is also pointed out. The approach is illustrated by means of three examples. The first two examples are quantitative structure-activity relationship (QSAR) data sets, while the third is based on hyperspectral images of liver tissue for identifying different sources of variability in the liver samples.
  •  
28.
  • Eriksson, Lennart, et al. (författare)
  • Separating Y-predictive and Y-orthogonal variation in multi-block spectral data
  • 2006
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 20, s. 352-61
  • Tidskriftsartikel (refereegranskat)abstract
    • Spectral data (X) may contain (a) variation that is correlated to concentrations or properties (Y) of samples and (b) variation that is unrelated to the same Y. This paper outlines an approach by which both such sources of variation may be resolved. The approach is based on a combination of hierarchical modelling and orthogonal partial least squares (OPLS). OPLS is first used at the base hierarchical level. The output is a labelling of the resulting score vectors as representing Y-predictive or Y-orthogonal variation. OPLS is then also used at the top hierarchical level together with principal components analysis (PCA). With PCA the Y-orthogonal X-variation is analysed and interpreted. With OPLS the Y-predictive X-variation is examined. The applicability of the proposed strategy is illustrated using one multi-block spectral data set.
  •  
29.
  • Forshed, J, et al. (författare)
  • Herman Wold medal winners 2007-2009
  • 2010
  • Ingår i: JOURNAL OF CHEMOMETRICS. - : Wiley. - 0886-9383. ; 24:11-12, s. 635-635
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
  •  
30.
  • Gabrielsson, Jon, et al. (författare)
  • The OPLS methodology for analysis of multi-block batch process data
  • 2006
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 20:8-10, s. 362-9
  • Tidskriftsartikel (refereegranskat)abstract
    • With increasing availability of different process analysers multiple data sources are commonly available and this will impose new challenges and enable new types of investigations. The ability to separate joint, complementary and redundant information in multiple block data will be of increasing importance. In this study data from a batch mini plant were collected and O2PLS was implemented to facilitate a combined analysis of spectroscopic and process data. This enables assessment of both the joint and complementary variations in the respective data sets. The different types of variation that were separated were then modelled together to evaluate their individual correlation to a time response. By combining data of different origin an uncomplicated summary of the variation was accomplished and a deeper understanding of process interactions was gained. The analysis of separated variation with a response variable proved useful for verifying the supposed correlation between the joint variation and time.
  •  
31.
  • Galindo-Prieto, Beatriz, et al. (författare)
  • Variable influence on projection (VIP) for orthogonal projections to latent structures (OPLS)
  • 2014
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 28:8, s. 623-632
  • Tidskriftsartikel (refereegranskat)abstract
    • A new approach for variable influence on projection (VIP) is described, which takes full advantage of the orthogonal projections to latent structures (OPLS) model formalism for enhanced model interpretability. This means that it will include not only the predictive components in OPLS but also the orthogonal components. Four variants of variable influence on projection (VIP) adapted to OPLS have been developed, tested and compared using three different data sets, one synthetic with known properties and two real-world cases.
  •  
32.
  • Geladi, Paul, et al. (författare)
  • Monitoring of a batch organic synthesis by near-infrared spectroscopy : modeling and interpretation of three-way data
  • 2002
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383. ; 16:7, s. 329-38
  • Tidskriftsartikel (refereegranskat)abstract
    • Three-way data of the type batch × time × NIR wavelength were obtained by NIR spectroscopic multivariate monitoring of an organic synthesis as a batch process. The model synthesis, an ester synthesis, was carried out as an experimental design. Unexpected technical problems caused a blocking effect that forced a modification of the design. After preprocessing of a reduced three-way array, the spectral data in the three-way array were subjected to parallel factor analysis (PARAFAC). The loadings from this analysis could be interpreted and explained as a function of the synthesis studied. For the spectral interpretation, spectra of pure chemicals were needed. The paper is an illustration of what can be done with three-way modeling in order to increase the understanding of a reaction, and it attempts to show how the results can be interpreted and presented. The data sets are available from the authors.
  •  
33.
  • Geladi, Paul (författare)
  • Principles of Proper Validation: use and abuse of re-sampling for validation
  • 2010
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 24, s. 168-187
  • Tidskriftsartikel (refereegranskat)abstract
    • Validation in chemometrics is presented using the exemplar context of multivariate calibration/prediction. A phenomenological analysis of common validation practices in data analysis and chemometrics leads to formulation of a set of generic Principles of Proper Validation (PPV), which is based on a set of characterizing distinctions: (i) Validation cannot be understood by focusing on the methods of validation only; validation must be based on full knowledge of the underlying definitions, objectives, methods, effects and consequences which are all outlined and discussed here. (ii) Analysis of proper validation objectives implies that there is one valid paradigm only: test set validation. (iii) Contrary to much contemporary chemometric practices (and validation myths), cross-validation is shown to be unjustified in the form of monolithic application of a one-for-all procedure (segmented cross-validation) on all data sets. Within its own design and scope, cross-validation is in reality a sub-optimal simulation of test set validation, crippled by a critical sampling variance omission, as it manifestly is based on one data set only (training data set). Other re-sampling validation methods are shown to suffer from the same deficiencies. The PPV are universal and can be applied to all situations in which the assessment of performance is desired: prediction-, classification-, time series forecasting-, modeling validation. The key element of PPV is the Theory of Sampling (TOS), which allow insight into all variance generating factors, especially the so-called incorrect sampling errors, which, if not properly eliminated, are responsible for a fatal inconstant sampling bias, for which no statistical correction is possible. In the light of TOS it is shown how a second data set (test set, validation set) is critically necessary for the inclusion of the sampling errors incurred in all 'future' situations in which the validated model must perform. Logically, therefore, all one data set re-sampling approaches for validation, especially cross-validation and leverage-corrected validation, should be terminated, or at the very least used only with full scientific understanding and disclosure of their detrimental variance omissions and consequences. Regarding PLS-regression, an emphatic call is made for stringent commitment to test set validation based on graphical inspection of pertinent t-u plots for optimal understanding of the X-Y interrelationships and for validation guidance. OSAR/QSAP forms a partial exemption from the present test set imperative with no generalization potential. Copyright (C) 2010 John Wiley & Sons, Ltd.
  •  
34.
  •  
35.
  • Ghorbanzadeh, Mehdi, et al. (författare)
  • Binary classification model to predict developmental toxicity of industrial chemicals in zebrafish
  • 2016
  • Ingår i: Journal of Chemometrics. - : Wiley-Blackwell. - 0886-9383 .- 1099-128X. ; 30:6, s. 298-307
  • Tidskriftsartikel (refereegranskat)abstract
    • The identification of industrial chemicals, which may cause developmental effects, is of great importance for an early detection of hazardous chemicals. Accordingly, categorical quantitative structure-activity relationship (QSAR) models were developed, based on developmental toxicity profile data for zebrafish from the ToxCast Phase I testing, to predict the toxicity of a large set of high and low production volume chemicals (H/LPVCs). QSARs were created using linear (LDA), quadratic, and partial least squares-discriminant analysis with different chemical descriptors. The predictions of the best model (LDA) were compared with those obtained by the freely available QSAR model VEGA, created based on a dataset with a different chemical domain. The results showed that despite similar accuracy (AC) of both models, the LDA model is more specific than VEGA and shows a better agreement between sensitivity (SE) and specificity (SP). Applying a 90% confidence level on the Lou model led to even better predictions showing SE of 0.92, AC of 0.95, and geometric mean of SE and SP (G) of 0.96 for the prediction set. The LDA model predicted 608 H/LPVCs as toxicants among which 123 chemicals fall inside the AD of the VEGA model, which predicted 112 of those as toxicants. Among the 112 chemicals predicted as toxic H/LPVCs, 23 have been previously reported as developmental toxicants. The here presented LDA model could be used to identify and prioritize H/LPVCs for subsequent developmental toxicity assessment, as a screening tool of potential developmental effects of new chemicals, and to guide synthesis of safer alternative chemicals.
  •  
36.
  • Gottfries, Johan, et al. (författare)
  • On the impact of uncorrelated variation in regression mathematics
  • 2008
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 22:11-12, s. 565-70
  • Tidskriftsartikel (refereegranskat)abstract
    • The objective of the present study is to investigate if, and if so, how uncorrelated variation relates to regression mathematics as exemplified by partial least squares (PLS) methodology. In contrast to previous methods, orthogonal partial least squares (OPLS) method requires a multi-focus, in the sense that in parallel to calculation of correlation it requires an analysis of orthogonal variation, i.e. the uncorrelated structure in a comprehensive way. Subsequent to the estimation of the correlation is the remaining orthogonal variation, i.e. uncorrelated data, divided into uncorrelated structure and stochastic noise by the OPLS component. Thus, it appears obvious that it is of interest to understand how the uncorrelated variation can influence the interpretation of the regression model. We have scrutinized three examples that pinpoint additional value from OPLS regarding the modelling of the orthogonal, i.e. uncorrelated, variation in regression mathematics. In agreement with the results, we conclude that uncorrelated variations do impact interpretations of regression analyses output and provides not only opportunities by OPLS but also an obligation for the user to maximize benefit from OPLS.
  •  
37.
  •  
38.
  • Holcomb, Tyler R., et al. (författare)
  • Significance regression : A statistical approach to partial least squares
  • 1997
  • Ingår i: Journal of Chemometrics. - 0886-9383 .- 1099-128X. ; 11:4, s. 283-309
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents a formal framework for deriving partial least squares algorithms from statistical hypothesis testing. This new formulation, significance regression (SR), leads to partial least squares for scalar output problems (PLS1), to a close approximation of a common multivariable partial least squares algorithm (PLS2) under certain model assumptions and to more general methods under less restrictive model assumptions. For models with multiple outputs, SR will be shown to have certain advantages over PLS2. Using the new formulation, a significance test is advanced for determining the number of directions to be used. The prediction and estimation properties of SR are discussed. A brief numerical example illustrates the relationship between SR and PLS2. © 1997 by John Wiley & Sons, Ltd.
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39.
  •  
40.
  • Jonsson, Pär, et al. (författare)
  • Strategies for implementation and validation of on-line models for multivariate monitoring and control of wood chip properties
  • 2004
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 18:3-4, s. 203-7
  • Tidskriftsartikel (refereegranskat)abstract
    • Here we present an approach for on-line control and monitoring of pulpwood chip properties based on near infrared (NIR) spectroscopy and multivariate data analysis. In addition, this paper suggests how to deal with large multivariate data sets in order to extract information which can be used as a basis for changes in raw material or process conditions in the drive towards more optimal intermediate or end product properties within the pulp and paper industry. The pulpwood chips used as raw material in a pulp and paper making process were characterized at- and on-line using NIR spectroscopic measurements. Collected NIR spectra were used in multivariate calibration models for prediction of the moisture content as well as the between- and within-species variation in the studied raw material. Statistical experimental design was used to form a calibration data set including most of the variation occurring in a real on-line situation. NIR spectra for all designed samples were measured at-line and the estimated calibration models were used for carrying out predictions on-line. Predictions of the moisture content (% dry weight) as well as the percentage contents of pine and sawmill chips in the raw material were carried out using partial least squares projections to latent structures (PLS) methodology. NIR spectra were collected subsequently on-line once every minute, and, to reduce the problem with noise in the time series predictions, the measured signals were filtered using a moving average of 100 predicted values. This provided smoother predictions more suitable for process monitoring and control. To validate the quality of the predictions, wood chips from the studied process were sampled and analysed in the laboratory before being subjected to predictions in the on-line model. Comparison of the filtered on-line predictions with the results obtained from the laboratory measurements indicated that moisture and pine chip contents could be well predicted by the on-line model, while predictions of sawmill chip content showed less promising results.
  •  
41.
  • Karlsson, Peter S., 1968-, et al. (författare)
  • A Liu estimator for the beta regression model and its application to chemical data
  • 2020
  • Ingår i: Journal of Chemometrics. - : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 34:10, s. 1-16
  • Tidskriftsartikel (refereegranskat)abstract
    • Abstract Beta regression has become a popular tool for performing regression analysis on chemical, environmental, or biological data in which the dependent variable is restricted to the interval [0, 1]. For the first time, in this paper, we propose a Liu estimator for the beta regression model with fixed dispersion parameter that may be used in several realistic situations when the degree of correlation among the regressors differs. First, we show analytically that the new estimator outperforms the maximum likelihood estimator (MLE) using the mean square error (MSE) criteria. Second, using a 'simulation study, we investigate the properties in finite samples of six different suggested estimators of the shrinkage parameter and compare it with the MLE. The simulation results indicate that in the presence of multicollinearity, the Liu estimator outperforms the MLE uniformly. Finally, using an empirical application on chemical data, we show the benefit of the new approach to applied researchers.
  •  
42.
  • Lindberg, Nils-Olof, et al. (författare)
  • Use of software to facilitate pharmaceutical formulation : experiences from a tablet formulation
  • 2004
  • Ingår i: Journal of Chemometrics. - Chichester : John Wiley & Sons, Ltd. - 0886-9383 .- 1099-128X. ; 18:3-4, s. 133-138
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper exemplifies the benefits of using experimental design together with software to facilitate the formulation of a tablet for specific purposes, from screening to robustness testing. By applying a multivariate design for the screening experiments, many excipients were evaluated in comparatively few experiments. The formulation work was generally based on designed experiments. Most of the experiments were fractional or full factorial designs, generated and evaluated in Modde with the centre point replicated. The robustness of the formulation was evaluated with experimental designs on two different occasions. Tested flavours were found to have limited influence on the important responses, which was key information in order to proceed with that particular composition. The formulation was also robust towards normal batch-to-batch variation of the excipients and the active pharmaceutical ingredient. A process step was investigated and, by applying experimental design and keeping in mind previous findings, important information could be gained from the study. The different studies yielded good and very useful models. Established relationships between design factors and responses provided information that was vital for the project. In cases of poor models, essential information regarding robustness was obtained.
  •  
43.
  • Liu, Tao, et al. (författare)
  • Modelling of partition constants : Linear solvation energy relationships or PLS regression?
  • 2009
  • Ingår i: Journal of Chemometrics. - New York : Wiley. - 0886-9383 .- 1099-128X. ; 23:5, s. 254-262
  • Tidskriftsartikel (refereegranskat)abstract
    • Estimation methods for partition constants are needed in many fields of engineering and science. The partitioning between phases is determined by the free energy of the transfer and all estimation methods must therefore describe the same entity. Linear solvation energy relationships (LSERs) try to split the contributions to van der Waals and polar interactions into directly interpretable solute descriptors, while projection-based regression methods can accomplish a similar dimensionality reduction from a set of theoretical descriptors. Here, we use the partitioning between octanol and water (Kow) and water solubility (Sw) to investigate similarities and differences between LSER and partial least squares regression (PLSR) models. The similarities in model structure are described, and shown to transform into a comparable prediction performance. We also demonstrate the opportunity to accomplish an analogous chemical interpretation of a PLSR model - either directly or through a linear transformation of the PLS factors - as with an LSER model. Much of the alleged difference between the mechanistic or semi-empirical LSER and the statistical PLSR models will then disappear. The choice of a modelling approach should therefore primarily be driven by the availability of data and predictive performance.
  •  
44.
  • Lukmanov, Rustam A., et al. (författare)
  • Chemical identification of microfossils from the 1.88-Ga Gunflint chert : Towards empirical biosignatures using laser ablation ionization mass spectrometer
  • 2021
  • Ingår i: Journal of Chemometrics. - : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 35:10
  • Tidskriftsartikel (refereegranskat)abstract
    • In this contribution, we investigated the chemical composition of Precambrian microfossils from the Gunflint chert (1.88 Ga) using a miniature laser ablation ionization mass spectrometer (LIMS) developed for in situ space applications. Spatially resolved mass spectrometric imaging (MSI) and depth profiling resulted in the acquisition of 68,500 mass spectra. Using single mass unit spectral decomposition and multivariate data analysis techniques, we identified the location of aggregations of microfossils and surrounding inorganic host mineral. Our results show that microfossils have unique chemical compositions that can be distinguished from the inorganic chert with high fidelity. Chemical depth profiling results also show that with LIMS microprobe data, it is possible to identify chemical differences between individual microfossils, thereby providing new insights about nature of early life. Analysis of LIMS spectra acquired from the individual microfossils reveals complex mineralization, which can reflect the metabolic diversity of the Gunflint microbiome. An intensity-based machine learning model trained on LIMS Gunflint data might be applied for the future investigations of putative microfossils from silicified matrices, where morphological integrity of investigated structures is lost, and potentially in the investigation of rocks acquired from the Martian surface.
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45.
  • Lundstedt-Enkel, Katrin, et al. (författare)
  • Interaction study with rats given two flame retardants : polybrominated diphenyl ethers (Bromkal 70-5 DE) and chlorinated paraffins (Cereclor 70L)
  • 2010
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 24:11-12, s. 710-718
  • Tidskriftsartikel (refereegranskat)abstract
    • This study explored possible interaction effects on animal liver microsomal enzymes and thyroid hormones of two flame retardants: Bromkal 70-5 DE, a mixture of polybrominated diphenyl ethers (hereafter called PBDE); and Cereclor 70L, a mixture of chlorinated paraffins (hereafter called CP). Female Sprague-Dawley rats were exposed to these compounds in dose ranges of 1.3–18.7 mg/kg bw/day (PBDE) and 1–55 mg/kg bw/day (CP), by gavage for 14 days. Biological responses were measured on liver somatic index (LSI) and hepatic enzyme activity of (a) ethoxyresorufin-O-deethylase (EROD) (indicating CYP1A1 activity), (b) pentoxyresorufin-O-depentylase (PROD) (indicating CYP2B activity) and (c) the phase II conjugation enzyme uridine diphosphoglucuronosyl transferase (UDP-GT). The levels of total and free thyroxine hormone in rat plasma (TT4 and FT4, respectively) were also measured. In the experimental work, a Doehlert uniform shell design was used in order to select the combination of concentrations of PBDE and CP administered to the rats. Eight different combinations were used, including a control. The measured responses were modelled with multiple linear regression (MLR), giving response surface plots. The results showed strong synergism between the two flame retardants at one particular exposure combination, resulting in increased hepatic microsomal enzyme responses and decreased serum T4 concentrations. Notably, the exposure combination causing the most marked effects represented intermediate doses of both substances. The mechanisms behind the observed effects are unknown, but may involve induction or inhibition of enzyme systems.
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46.
  • Lundstedt-Enkel, Katrin, et al. (författare)
  • QSBMR - Quantitative Structure Biomagnification Relationships : Physicochemical and Structural Descriptors Important for the Biomagnification of Organochlorines and Brominated Flame Retardants
  • 2006
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 20:8-10, s. 392-401
  • Tidskriftsartikel (refereegranskat)abstract
    • The aim of this project is to establish models to predict the biomagnification of contaminants present in Baltic Sea biota. In this paper a quantitative model that we term QSBMR-Quantitative Structure Biomagnification Relationships is presented. This model describes the relationship between the biomagnification factors (BMFs) for several organochlorines (OCs) and brominated flame retardants (BFRs), for example, polychlorinated biphenyls (PCBs), polybrominated diphenylethers (PBDEs) and hexabromocyclododecane (HBCD), and their descriptors, for example, physico-chemical properties and structural descriptors. The concentrations of contaminants in herring (Clupea harengus) muscle and guillemot (Uria aalge) egg from the Baltic Sea were used. The BMFs were calculated with the randomly sampled ratios (RSR) method that denotes the BMFs with a measure of the variation. In order to describe the physico-chemical properties and chemical structures, approximately 100 descriptors for the contaminants were generated: (a), by using the software (TSAR); (b) finding log Kow values from the literature, and (c) creating binary fingerprint variables that described the position of the chlorine and bromine for the respective PCB and PBDE molecules. Partial least squares (PLS) regression was used to model the relationship between the contaminants' BMF and the descriptors and the resulting QSBMR revealed that more than 20 descriptors in combination were important for the biomagnification of OCs and BFRs between herring and guillemot. The model including all contaminants (R2X=0.73, R2Y=0.87 and Q2=0.63, three components) explained approximately as much of the variation as the model with the PCBs alone (R2X=0.83, R2Y=0.87 and Q2=0.58, two components). The model with the BFRs alone (R2X=0.68, R2Y=0.88 and Q2 = 0.41, two components) had a slightly lower Q2 than the model including all contaminants. For validation, a training set of seven contaminants was selected by multivariate design (MVD) and a model was established. This model was then used to predict the BMFs of the test set (seven contaminants not included in the model). The resulting R2 for the regression Observed BMF versus Predicted BMF was high (0.65). The good models showed that descriptors important for the biomagnification of OCs and BFRs had been used. These types of models will be useful for in silico predictions of the biomagnification of new, not yet investigated, compounds as an aid in risk assessments.
  •  
47.
  • Lundstedt, Torbjörn, et al. (författare)
  • Editorial
  • 2006
  • Ingår i: Journal of Chemometrics. - : John Wiley & Sons, Ltd. - 0886-9383 .- 1099-128X. ; 20:8-10, s. 323-324
  • Tidskriftsartikel (populärvet., debatt m.m.)
  •  
48.
  • Löfstedt, Tommy, et al. (författare)
  • Bi-modal OnPLS
  • 2012
  • Ingår i: Journal of Chemometrics. - : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 26:6, s. 236-245
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents an extension to the recently published OnPLS data analysis method. Bi-modal OnPLS allows for arbitrary block relationships in both columns and rows and is able to extract orthogonal variation in both columns and rows without bias towards any particular direction or matrix: the method is fully symmetric with regard to both rows and columns. Bi-modal OnPLS extracts a minimal number of globally predictive score vectors that exhibit maximal covariance and correlation in the column space and a corresponding set of predictive loading vectors that exhibit maximal correlation in the row space. The method also extracts orthogonal variation (i.e. variation that is not related to all other matrices) in both columns and rows. The method was applied to two synthetic datasets and one real data set regarding sensory information and consumer likings of dairy products. It was shown that Bi-modal OnPLS greatly improves the intercorrelations between both loadings and scores while still finding the correct variation. This facilitates interpretation of the predictive components and makes it possible to study the orthogonal variation in the data.
  •  
49.
  • Löfstedt, Tommy, et al. (författare)
  • OnPLS—a novel multiblock method for the modelling of predictive and orthogonal variation
  • 2011
  • Ingår i: Journal of Chemometrics. - : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 25:8, s. 441-455
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents a new multiblock analysis method called OnPLS, a general extension of O2PLS to the multiblock case. The proposed method is equivalent to O2PLS in cases involving only two matrices, but generalises to cases involving more than two matrices without giving preference to any particular matrix: the method is fully symmetric. OnPLS extracts a minimal number of globally predictive components that exhibit maximal covariance and correlation. Furthermore, the method can be used to study orthogonal variation, i.e. local phenomena captured in the data that are specific to individual combinations of matrices or to individual matrices. The method's utility was demonstrated by its application to three synthetic data sets. It was shown that OnPLS affords a reduced number of globally predictive components and increased intercorrelations of scores, and that it greatly facilitates interpretation of the predictive model.
  •  
50.
  • Muthas, Daniel, et al. (författare)
  • Focused hierarchical design of peptide libraries - follow the lead
  • 2007
  • Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 21:10-11, s. 486-495
  • Tidskriftsartikel (refereegranskat)abstract
    • A novel design strategy based on the hierarchical design of experiments (HDoE) method named focused hierarchical design of experiments (FHDoE) is presented. FHDoE combine two design layers and use focused substitutions to increase the probability of obtaining active peptides when designing libraries through a selection of compounds biased towards a lead structure. Increasing the number of peptides with measurable activity will increase the information gained and the likelihood of constructing good quantitative structure-activity relationship (QSAR) models. The utility of the novel design method is verified using two different approaches. First, a library designed with the novel FHDoE method was compared with libraries generated from classical positional scanning techniques (e.g., alanine scan) as well as with general and centered minimum analog peptide sets (MAPS) libraries by using an example found in the literature. Secondly, the same design strategies were applied to a dataset of 58 angiotensin converting enzyme (ACE) dipeptide inhibitors. QSAR models were generated from designed sublibraries and the activities of the remaining compounds were predicted. These two examples show that the use of FHDoE renders peptide libraries close in physicochemical space to the native ligand, yielding a more thorough screening of the area of interest as compared to the classical positional scans and fractional factorial design (FFD). It is also shown that an FHDoE library of six dipeptides could produce a QSAR model that better described the requisites of high activity ACE inhibitors than could QSAR models built from either a nine-dipeptide library designed with MAPS or a 58-dipeptide library.
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