Sven P Jacobsson

Stockholm University, Tukholma, Stockholm, Sweden

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Publications (54)169.87 Total impact

  • K. Magnus Åberg, Sven P. Jacobsson
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    ABSTRACT: A value of class separation can be used as a criterion to optimize data-analytical protocols for multivariate classification problems. The Fisher criterion assumes equal variance–covariance structure, an assumption which is often violated in real datasets. The practical consequences of using the Fisher criterion compared to exploiting the variance–covariance differences has not been studied previously. Here, we show that about 50 samples are required to benefit from using the unequal variance–covariance structures for estimating class separation in two dimensions and more samples are required in higher dimensions. The results show that the Fisher criterion is robust and accurate for selection between pre-treatments compared to a newly derived criterion called the Cooke criterion. Copyright © 2010 John Wiley & Sons, Ltd.
    Journal of Chemometrics 11/2010; 24(11‐12):650 - 654. DOI:10.1002/cem.1326 · 1.80 Impact Factor
  • Carl Grey, Per Edebrink, Maria Krook, Sven P Jacobsson
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    ABSTRACT: In the present study a HPAEC-PAD method is described that was developed for monitoring the consistency of N-glycosylation during the production and purification of recombinant proteins and monoclonal antibodies. The method successfully separated 18 neutral and sialylated oligosaccharides. Results obtained were compared with MALDI-TOF MS and it was shown that both methods gave similar results. In addition, a method validation was performed showing that the HPAEC-PAD analysis was well suited for the mapping and characterization of oligosaccharides. The method was found to be robust and additionally the precision was significantly better compared to the MALDI-TOF MS method.
    Journal of chromatography. B, Analytical technologies in the biomedical and life sciences 08/2009; 877(20-21):1827-32. DOI:10.1016/j.jchromb.2009.05.003 · 2.69 Impact Factor
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    ABSTRACT: Charge state distributions (CSDs) of proteins in nanoESI mass spectra are affected by the instrumental settings and experimental conditions, in addition to the conformations of the proteins in the analyzed solutions. In the presented study, instrumental and experimental parameters--the desolvation gas flow rate, temperature, pH, buffer (ammonium acetate), and organic modifier (methanol) concentrations--were optimized according to a reduced central composite face experimental design to maximize the separation of CSDs of monoclonal IgG1-kappa antibodies produced by two production systems (CHO and GS-NS0 cell lines). Principal component analysis and Fisher linear discriminant analysis were then used to reduce the dimensions of the acquired dataset and quantify the separation of the protein classes, respectively. The results show that the IgG1-kappa molecules produced by the two production systems can be clearly distinguished using the described approach, which could be readily applied to other proteins and production systems.
    Journal of the American Society for Mass Spectrometry 03/2009; 20(6):1030-6. DOI:10.1016/j.jasms.2009.01.008 · 3.19 Impact Factor
  • Sven Jacobsson, Anders Hagman
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    ABSTRACT: Abstract The use of dynamic headspace, a continuous gas extraction technique, for the analysis of volatile compounds in polymers is discussed. The present work presents the principles of dynamic headspace and its use in combination with capillary gas chromatography - mass spectrometry. The merits of dynamic headspace sampling in comparison to static headspace, the predominantly used headspace sampling method, are also discussed. To illustrate the technique, analyses of volatile compounds in three different carbohydrate polymers, commonly used as pharmaceutical excipients, are carried out.
    Drug Development and Industrial Pharmacy 10/2008; 16(17):2547-2560. DOI:10.3109/03639049009058546 · 2.01 Impact Factor
  • Anders Hagman, Sven Jacobsson
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    ABSTRACT: Abstract The possibilities of employing methods of chemometrics in order to characterize macromolecules are described. The review has been limited to chemometric methods concerning multivariate data analysis. Principal component analysis (PCA) has shown to be very useful for pattern recognition problems arising from chromatographic and spectroscopic data. An example of using a classification technique, SIMCA (Soft Independent Modelling of Class Analogy), as a product control method is presented. The suitability of Partial Least Squares (PLS) for relating data of different natures, e.g. chemical data with biological data, is discussed. Moreover, examples ranging from spectroscopic determinations to QSAR (Quantitative Structure Activity Relationships) are illustrated.
    Drug Development and Industrial Pharmacy 10/2008; 16(17):2527-2545. DOI:10.3109/03639049009058545 · 2.01 Impact Factor
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    ABSTRACT: We describe the development of a method in which protein oxidation by H2O2 followed by ultrahigh-pressure liquid chromatography (UHPLC) coupled with electrospray ionization time-of-flight mass spectrometry (ESI-ToFMS) and multivariate analysis are used to detect alterations in conformational states of proteins. In the study reported here, an IgG1 monoclonal antibody in native and denatured conformational states was oxidized by treatment with hydrogen peroxide. Peptide fragments generated by tryptic digestion were then analyzed by UHPLC-ESI-ToFMS. After reducing noise and extracting peaks from the LC–MS data using MzExplorer, software developed in-house and based on Matlab, we were able to distinguish peptides arising from the native and denatured states of the oxidized protein by principal component analysis. Peptides containing residues, which are inclined to undergo oxidation, such as methionine, are founded to be particularly important in this approach. We believe that the methodology could facilitate attempts to characterize the conformational states of recombinant monoclonal antibodies and other proteins.
    Analytical Biochemistry 09/2008; 380(2-380):155-163. DOI:10.1016/j.ab.2008.05.054 · 2.31 Impact Factor
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    ABSTRACT: A method to enhance the multivariate data interpretation of, for instance, metabolic profiles is presented. This was done by correlation scaling of 1H NMR data by the time pattern of drug metabolite peaks identified by LC/MS, followed by parallel factor analysis (PARAFAC). The variables responsible for the discrimination between the dosed and control rats in this model were then eliminated in both data sets. Next, an additional PARAFAC analysis was performed with both LC/MS and 1H NMR data, fused by outer product analysis (OPA), to obtain sufficient class separation. The loadings from this second PARAFAC analysis showed new peaks discriminating between the classes. The time trajectories of these peaks did not agree with the drug metabolites and were detected as possible candidates for markers. These data analyses were also compared with the PARAFAC analysis of raw data, which showed very much the same loading peaks as for the correlation-scaled data, although the intensities differed. Elimination of the variables correlated with the drug metabolites was therefore necessary to be able to select the peaks which were not drug metabolites and which discriminated between the classes.1
    Chemometrics and Intelligent Laboratory Systems 02/2007; 85(2):179-185. DOI:10.1016/j.chemolab.2006.06.012 · 2.38 Impact Factor
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    Jenny Forshed, Helena Idborg, Sven P. Jacobsson
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    ABSTRACT: In the analyses of highly complex samples (for example, metabolic fingerprinting), the data might not suffice for classification when using only a single analytical technique. Hence, the use of two complementary techniques, e.g., LC/MS and 1H-NMR, might be advantageous. Another possible advantage from using two different techniques is the ability to verify the results (for instance, by verifying a time trend of a metabolic pattern).In this work, both LC/MS and 1H-NMR data from analysis of rat urine have been used to obtain metabolic fingerprints. A comparison of three different methods for data fusion of the two data sets was performed and the possibilities and difficulties associated with data fusion were discussed. When comparing concatenated data, full hierarchical modeling, and batch modeling, the first two approaches were found to be the most successful. Different types of block scaling and variable scaling were evaluated and the optimal scaling for each case was found by cross validation. Validations of the final models were performed by means of an external test set.22Copies of the Matlab program files used for this work are available from the authors.
    Chemometrics and Intelligent Laboratory Systems 01/2007; 85(1):102-109. DOI:10.1016/j.chemolab.2006.05.002 · 2.38 Impact Factor
  • Yaoquan Tu, Sven P. Jacobsson, Aatto Laaksonen
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    ABSTRACT: A reliable and highly efficient ab initio tight-binding-like electronic structure calculation method is developed. The method starts from a similar approach outlined by Horsfield [ Phys. Rev. B 56 6594 (1997)], but in this work, the integral evaluations for the exchange-correlation matrix elements are achieved with reasonable accuracy by higher-order many-center expansions. All the integrals are obtained by the use of look-up tables and the efficiency of the calculation is further improved by optimizing the way to choose the integrals in the look-up tables. Calculations on molecular properties (such as equilibrium geometries, dipole moments, and the reaction energies for hydrogenation reactions for a series of molecules containing H, C, N, and O atoms) show that the method thus developed can be used as a general tool for the electronic structure calculations.
    Physical Review B 11/2006; 74(20). DOI:10.1103/PhysRevB.74.205104 · 3.66 Impact Factor
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    ABSTRACT: Neural networks (NNs) belong to 'black box' models and therefore 'suffer' from interpretation difficulties. Four recent methods inferring variable influence in NNs are compared in this paper. The methods assist the interpretation task during different phases of the modeling procedure. They belong to information theory (ITSS), the Bayesian framework (ARD), the analysis of the network's weights (GIM), and the sequential omission of the variables (SZW). The comparison is based upon artificial and real data sets of differing size, complexity and noise level. The influence of the neural network's size has also been considered. The results provide useful information about the agreement between the methods under different conditions. Generally, SZW and GIM differ from ARD regarding the variable influence, although applied to NNs with similar modeling accuracy, even when larger data sets sizes are used. ITSS produces similar results to SZW and GIM, although suffering more from the 'curse of dimensionality'.
    Neural Networks 06/2006; 19(4):500-13. DOI:10.1016/j.neunet.2005.09.002 · 2.08 Impact Factor
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    ABSTRACT: The first step when analyzing multicomponent LC/MS data from complex samples such as biofluid metabolic profiles is to separate the data into information and noise via, for example, peak detection. Due to the complex nature of this type of data, with problems such as alternating backgrounds and differing peak shapes, this can be a very complex task. This paper presents and evaluates a two-dimensional peak detection algorithm based on raw vector-represented LC/MS data. The algorithm exploits the fact that in high-resolution centroid data chromatographic peaks emerge flanked with data voids in the corresponding mass axis. According to the proposed method, only 4 per thousand of the total amount of data from a urine sample is defined as chromatographic peaks; however, 94% of the raw data variance is captured within these peaks. Compared to bucketed data, results show that essentially the same features that an experienced analyst would define as peaks can automatically be extracted with a minimum of noise and background. The method is simple and requires a priori knowledge of only the minimum chromatographic peak width-a system-dependent parameter that is easily assessed. Additional meta parameters are estimated from the data themselves. The result is well-defined chromatographic peaks that are consistently arranged in a matrix at their corresponding m/z values. In the context of automated analysis, the method thus provides an alternative to the traditional approach of bucketing the data followed by denoising and/or one-dimensional peak detection. The software implementation of the proposed algorithm is available at as compiled code for Matlab.
    Analytical Chemistry 03/2006; 78(4):975-83. DOI:10.1021/ac050980b · 5.83 Impact Factor
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    ABSTRACT: This paper addresses the possibility of mathematically partition and process urine 1H-NMR spectra to enhance the efficiency of the subsequent multivariate data analysis in the context of metabolic profiling of a toxicity study. We show that by processing the NMR data with the peak alignment using reduced set mapping (PARS) algorithm and the use of sparse representation of the data results in the information contained in the original NMR data being preserved with retained resolution but free of the problem of peak shifts. We can now describe a method for differential expression analysis of NMR spectra by using prior knowledge, i.e., the onset of dosing, a partitioning not possible to achieve using raw or bucketed data. In addition we also outline a scheme for soft removal of “biological noise” from the aligned data: exhaustive bio-noise subtraction (EBS). The result is a straightforward protocol for detection of peaks that appear as a consequence of the drug response. In other words, it is possible to elucidate peak origin, either from endogenous substances or from the administered drug/biomarkers. The partition of data originating from the normally regulating metabolome can, furthermore, be analyzed free of the superimposed biological noise. The proposed protocol results in enhanced interpretability of the processed data, i.e., a more refined metabolic trace, simplification of detection of consistent biomarkers, and a simplified search for metabolic end products of the administered drug.
    Metabolomics 02/2006; 2(1):1-19. DOI:10.1007/s11306-005-0013-z · 3.97 Impact Factor
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    ABSTRACT: Metabolic fingerprinting of biofluids like urine is a useful technique for detecting differences between individuals. With this approach, it might be possible to classify samples according to their biological relevance. In Part 1 of this work a method for the comprehensive screening of metabolites was described, using two different liquid chromatography (LC) column set-ups and detection by electrospray ionization mass spectrometry (ESI-MS). Data pretreatment of the resulting data described in is needed to reduce the complexity of the data and to obtain useful metabolic fingerprints. Three different approaches, i.e., reduced dimensionality (RD), MarkerLynx, and MS Resolver, were compared for the extraction of information. The pretreated data were then subjected to multivariate data analysis by partial least squares discriminant analysis (PLS-DA) for classification. By combining two different chromatographic procedures and data analysis, the detection of metabolites was enhanced as well as the finding of metabolic fingerprints that govern classification. Additional potential biomarkers or xenobiotic metabolites were detected in the fraction containing highly polar compounds that are normally discarded when using reversed-phase liquid chromatography.
    Journal of Chromatography B 01/2006; 828(1-2):14-20. DOI:10.1016/j.jchromb.2005.07.049 · 2.69 Impact Factor
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    ABSTRACT: Complex biological samples, such as urine, contain a very large number of endogenous metabolites reflecting the metabolic state of an organism. Metabolite patterns can provide a comprehensive signature of the physiological state of an organism as well as insights into specific biochemical processes. Although the metabolites excreted in urine are commonly highly polar, the samples are generally analyzed using reversed-phase liquid chromatography mass spectrometry (RP-LC/MS). In Part 1 of this work, a method for detecting highly polar metabolites by hydrophilic interaction liquid chromatography-electrospray ionization mass spectrometry (HILIC/ESI-MS) is described as a complement to RP-LC/ESI-MS. In addition, in an accompanying paper (Part 2), different multivariate approaches to extracting information from the resulting complex data are described to enable metabolic fingerprints to be obtained. The coverage of the method for the screening of as many metabolites as possible is highly improved by analyzing the urine samples using both a C(18) column and a ZIC-HILIC column. The latter was found to be a good alternative when analyzing highly polar compounds, e.g., hydroxyproline and creatinine, to columns typically used for reversed-phase liquid chromatography.
    Journal of Chromatography B 01/2006; 828(1-2):9-13. DOI:10.1016/j.jchromb.2005.07.031 · 2.69 Impact Factor
  • Hanna Bengter, Charbel Tengroth, Sven P. Jacobsson
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    ABSTRACT: Silver-colloid solutions have been used as the medium for surface-enhanced Raman spectroscopy (SERS) for over two decades. UV-Vis absorption is often used to determine the state of aggregation of the colloid, considered a critical parameter for a successful SERS experiment, especially when using an IR excitation source such as an Nd:YAG laser. We present results from parallel studies of silver colloid aggregation and UV-Vis absorption showing that a significant change in the aggregation state of the colloid does not necessarily translate into a better SERS substrate when using an IR excitation source. Conversely, we show that a small addition of chloride ions sufficient for inducing FT-IR SERS does not produce any detectable colloid particle aggregation. These results may give new insight into how to optimize experimental conditions for SERS when using a near-IR excitation source. Copyright © 2005 John Wiley & Sons, Ltd.
    Journal of Raman Spectroscopy 11/2005; 36(11):1015 - 1022. DOI:10.1002/jrs.1399 · 2.52 Impact Factor
  • Jenny Forshed, Bengt Erlandsson, Sven P. Jacobsson
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    ABSTRACT: This work presents a fast and simple quantitative method for impurity determination of acetaldehyde and propionaldehyde in poloxamer 188 by proton nuclear magnetic resonance spectroscopy (1H NMR). The sample is dissolved in D2O with DCl and analyzed with a 600MHz NMR spectrometer. Data processing, including filtering by convolution of spectra with a triangular function and integration, is performed in MATLAB. The repeated studies of one sample, including automatic gradient shimming and data processing, revealed a relative standard deviation (R.S.D.) of 2.8%. For the reproducibility, also including sample preparation, the R.S.D. was less than 10%. The predictability of a linear calibration model was estimated by the root mean square error of prediction from leave-one-out cross-validation (RMSECV). Using 64 scans, RMSECV was found to be 7.2 and 5.5μgg−1 for acetaldehyde and propionaldehyde respectively for a 4.3-min acquisition time. The limits of detection, defined as three times the noise, reached 19 and 15μgg−1 respectively under the same experimental conditions. These limits are sufficient to quantify 80 and 100μgg−1 of the impurities, which has been found to be the maximum allowed content in the poloxamer for some medical applications. Thus the method has the potential to replace the current liquid chromatography (LC) method for impurity determination of acetaldehyde and propionaldehyde in poloxamer, which is time-consuming and includes a work-up procedure involving many steps.
    Analytica Chimica Acta 11/2005; 552(1):160-165. DOI:10.1016/j.aca.2005.07.050 · 4.52 Impact Factor
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    ABSTRACT: This paper compares the performance of two recently developed algorithms and methods for peak alignment of first-order NMR data of complex biological samples. The NMR spectra of such samples exhibit variations in peak position and peak shape due to variations in the sample matrix and to instrumental instabilities. The first method comprises an alignment of spectral segments with linear interpolation and shift correction to accommodate correspondence between a target and a test spectrum by a beam search or genetic algorithm. The second method is based on peak picking and needle vector representation of the NMR data with subsequent breadth-first search to establish shift corrections between the target and the test spectrum. The two proposed peak alignment methods and their respective merits are discussed for a real metabonomics application. Both alignment methods have been shown to enhance the interpretability of the resulting multivariate models, thereby increasing the prospect of detecting and following the onset of subtle biological changes reflected in the NMR data.
    Journal of Pharmaceutical and Biomedical Analysis 09/2005; 38(5):824-32. DOI:10.1016/j.jpba.2005.01.042 · 2.83 Impact Factor
  • K. Magnus Åberg, Ralf J. O. Torgrip, Sven P. Jacobsson
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    ABSTRACT: Peak alignment using reduced set mapping (PARS) is extended with a new baseline approximation and a new dendrogram alignment scheme, which is designed to avoid the issue of selecting a target chromatogram for the alignment. Two data sets with LC/UV data are studied and it is shown that peak alignment with PARS increases the class separation substantially in the principal component score space. The results indicate that it is possible to use PARS for calibration transfer of multivariate models of chromatographic data. Copyright © 2005 John Wiley & Sons, Ltd.
    Journal of Chemometrics 03/2005; 18(10):465 - 473. DOI:10.1002/cem.892 · 1.80 Impact Factor
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    ABSTRACT: Attenuated total reflection Fourier transform infrared spectroscopy (FTIR) has been used to study cross-linking in hard gelatin capsules induced by exposure to formaldehyde, acetaldehyde, and propionaldehyde. These aldehydes are known to cause cross-linking between the amino acid chains of gelatin. Using FTIR spectroscopy, it is possible to analyze the cross-linking mechanisms by studying changes in the vibrational bands of the gelatin spectrum. The FTIR spectrum changes over time when the capsules are left in an aldehyde-rich environment. Analysis of the spectra shows that the early observed spectral changes conform to reaction intermediates proposed in previous work based on nuclear magnetic resonance experiments, specifically, the formation of amine methyl alcohol of arginine and lysine residues. Further spectral changes appear to be mostly from unreacted aldehydes absorbed to the gelatin, although a minor shift of the amide II peak is attributed to cross-link formation.
    Pharmaceutical Development and Technology 02/2005; 10(3):405-12. DOI:10.1081/PDT-65693 · 1.34 Impact Factor
  • Kent Wiberg, Sven P Jacobsson
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    ABSTRACT: A set of 17 samples containing a constant amount of lidocaine (667 μM) and a decreasing amount of prilocaine (667–0.3 μM) was analysed by LC-DAD at three different levels of separation, followed by parallel factor analysis (PARAFAC) of the data obtained. In Case 1 no column was connected, the chromatographic resolution (Rs) therefore being zero, while Cases 2 and 3 had partly separated peaks (Rs=0.7 and 1.0). The results showed that in Case 1, analysed without any separation, the PARAFAC decomposition with a model consisting of two components gave a good estimate of the spectral and concentration profiles of the two compounds. In Cases 2 and 3, the use of PARAFAC models with two components resolved the underlying chromatographic, spectral and concentration profiles. The loadings related to the concentration profile of prilocaine were used for regression and prediction of the prilocaine content. The results showed that prediction of prilocaine content was possible with satisfactory prediction (RMSEP<0.01). This study shows that PARAFAC is a powerful technique for resolving partly separated peaks into their pure chromatographic, spectral and concentration profiles, even with completely overlapping spectra and the absence or very low levels of separation.
    Analytica Chimica Acta 07/2004; 514(2):203–209. DOI:10.1016/j.aca.2004.03.062 · 4.52 Impact Factor

Publication Stats

1k Citations
169.87 Total Impact Points


  • 2001–2010
    • Stockholm University
      • • Department of Analytical Chemistry
      • • Division of Chemical Physics
      Tukholma, Stockholm, Sweden
  • 2001–2006
    • AstraZeneca
      Tukholma, Stockholm, Sweden
  • 2000
    • Umeå University
      Umeå, Västerbotten, Sweden
  • 1993
    • Folktandvården Stockholm AB
      Tukholma, Stockholm, Sweden