Sven P. Jacobsson

Peak alignment of NMR signals by means of a genetic algorithm

Nuclear magnetic resonance (NMR) analysis of complex samples, such as biofluid samples is accompanied by variations in peak position and peak shape not directly related to the sample. This is due to variations in the background matrix of the sample and to instrumental instabilities. These variations complicate and limit the interpretation and analysis of NMR data by multivariate methods. Alignment of the NMR signals may circumvent these limitations and is an important preprocessing step prior to multivariate analysis. Previous aligning methods reduce the spectral resolution, are very computer-intensive for this kind of data (65k data points in one spectrum), or rely on peak detection. The method presented in this work requires neither data reduction nor preprocessing, e.g. peak detection. The alignment is achieved by taking each segment of the spectrum individually, shifting it sidewise, and linearly interpolating it to stretch or shrink until the best correlation with a corresponding reference spectrum segment is obtained. The segments are automatically picked out with a routine, which avoids cutting in a peak, and the optimization process is accomplished by means of a genetic algorithm (GA). The peak alignment routine is applied to NMR metabonomic data.1

Algorithmic approaches for studies of variable influence, contribution and selection in neural networks

Abstract Two methods of studying variable influence and contribution in neural network (NN) models are examined in this work. The first approach, a variable sensitivity analysis method, is based on sequential zeroing of weights (SZW) of the connection between the input variables and the first hidden layer of an established NN model. The second approach is based on systematic variation of variables (SVV) while the other variables are either kept constant or systematically varied synchronously. It is shown that there is a close resemblance between the results obtained by the proposed method for studies on variable influence and contribution in artificial NN models and the nature of the functions used to generate these synthetic data sets. The standard NN models are thus suitable not only for function approximation and nonlinear relationships, but also to a high degree able to represent the nature of the input variables. We are thus able to demonstrate that highly interconnected NN models, which are sometimes considered to be black boxes, can be highly transparent. The information generated about the variables, using the methods proposed in this work, can thus serve as a guide to the interpretation of influence, contribution, and selection. The methods proposed in this study are further compared to other sensitivity analysis methods as statistical sensitivity analysis (SSA) and β-tests. Furthermore, the methods applied to the synthetic data sets were used on three real data sets, giving, for instance, additional information on the effect of principal component (PC) regularization of input variables.

NMR and Bayesian regularized neural network regression for impurity determination of 4-aminophenol

A method for the determination of 4-aminophenol as an impurity in paracetamol (N-(4-hydroxyphenyl)-acetamide) by proton nuclear magnetic resonance ((1)H-NMR) spectroscopy has been developed. The (13)C-satellite from the protons in the ortho position from the hydroxyl group in paracetamol was used as an internal standard, although these peaks interfered with the peaks from the protons in 4-aminophenol. Because of interference in the spectra and non-linearity over a wide calibration range, a Bayesian regularized neural network model was used for calibration. Various kinds of data preprocessing were examined: zero filling, multiplication by a negative exponential function (line broadening), followed by Fourier transformation of the free induction decay (FID). The NMR spectral data were automatically phased and shift-adjusted by means of a genetic algorithm. Multiplicative scatter correction and data compression by wavelets and sequential zeroing of weights variable selection were performed to obtain an optimal calibration model. Neither zero filling of the FID nor line broadening improved the calibration models with regard to error of prediction, so these processes were excluded in the final model. The generated Bayesian regularized network model was evaluated with an independent test set. Four different models with different test sets were constructed to explore the quality of the calibration. The mean error of the optimal calibration model was 25.3 x 10(-6) weight of 4-aminophenol per weight paracetamol. The method is characterized by being relative fast, simple and sufficient sensitive for typical pharmaceutical impurity determinations.

Development of a high performance anion exchange chromatography analysis for mapping of oligosaccharides

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.

Determination of solvation free energies by adaptive expanded ensemble molecular dynamics

A new method of calculating absolute free energies is presented. It was developed as an extension to the expanded ensemble molecular dynamics scheme and uses probability density estimation to continuously optimize the expanded ensemble parameters. The new method is much faster as it removes the time-consuming and expertise-requiring step of determining balancing factors. Its efficiency and accuracy are demonstrated for the dissolution of three qualitatively very different chemical species in water: methane, ionic salts, and benzylamine. A recently suggested optimization scheme by Wang and Landau [Phys. Rev. Lett. 86, 2050 (2001)] was also implemented and found to be computationally less efficient than the proposed adaptive expanded ensemble method.

Miniaturized on-line proteolysis–capillary liquid chromatography–mass spectrometry for peptide mapping of lactate dehydrogenase

In this study, methodology was developed for on-line and miniaturized enzymatic digestion with liquid chromatographic (LC) separation and mass spectrometric (MS) detection. A packed capillary LC-MS system was combined with on-line trypsin cleavage of a model protein, lactate dehydrogenase, to provide an efficient system for peptide mapping. The protein was injected onto an enzymatic capillary reactor and the resulting peptides were efficiently trapped on a capillary trapping column. Different trapping columns were evaluated to achieve a high binding capacity for the peptides generated in the enzyme reactor. The peptides were further eluted from the pre-column and separated on an analytical capillary column by a buffer more suitable for the following an electrospray ionisation (ESI) MS process. An important aspect of the on-line approach was the desalting of peptides performed in the trapping column to avoid detrimental signal suppression in the ESI process. The developed on-line system was finally compared to a classical digestion in solution, with reference to peptide sequence coverage and sensitivity. It was shown that the on-line system gave more than 100% higher peptide sequence coverage than traditional digestion methods.

Studies on contamination of intravenous solutions from PVC-bags with dynamic headspace GC-MS and LC-diode array techniques

Abstract The contamination of intravenous solutions from PVC-bags was studied by: (1) dynamic headspace GC-MS of the PVC-material; and (2) screening and identification of the impurities in the intravenous solution by gradient liquid chromatography with a diode array UV-detector (LC-UV-DAD). In the study of the PVC-material, the plastic was heated to 120°C for 5–20 min in an oven, conditions similar to the heat sterilization process of the product. The volatile compounds were transferred into a GC-MS system. The intravenous solution in the PVC-bag was studied by injecting 1 ml of the solution into a gradient LC-UV-DAD system. Both studies show that a wide range of compounds including many unidentified are released from the PVC-material. The following compounds have been identified in a brand of Swedish intravenous solutions in 100 ml PVC-bags: di(2-ethylhexyl)phthalate, mono(2-ethylhexyl)phthalate, phthalic acid, 2-ethylhexanol, phthalide, butylhydroxyanisol, benzaldehyde, benzole acid and phenol. In solutions from another manufacturer bisphenol-A and cyclohexanon were detected as well. The components were present at μg/l levels except for cyclohexanon which was in the mg/1 level.

Simultaneous determination of albumin and immunoglobulin G with fluorescence spectroscopy and multivariate calibration

A method is proposed for the simultaneous determination of albumin and immunoglobulin G (IgG1) with fluorescence spectroscopy and multivariate calibration with partial least squares regression (PLS). The influence of some instrumental parameters were investigated with two experimental designs comprising 19 and 11 experiments, respectively. The investigated parameters were excitation and emission slit, detection voltage and scan rate. When a suitable instrumental setting had been found, a minor calibration and test set were analysed and evaluated. Thereafter, a larger calibration of albumin and IgG1 was made out of 26 samples (0-42mugml(-1) albumin and 0-12.7mugml(-1) IgG1). This calibration was validated with a test set consisting of 14 samples in the same concentration range. The precision of the method was estimated by analysing two test set samples for six times each. The scan modes tested were emission scan and synchronous scan Delta60nm. The results showed that the method could be used for determination of albumin and IgG1 (albumin, root mean square error of prediction (RMSEP) <2, relative standard error of prediction (RSEP) <6% and IgG1, RMSEP <1, RSEP <8%) in spite of the overlapping fluorescence of the two compounds. The estimated precision was relative standard deviation (R.S.D.) <1.7%. The method was finally applied for the analysis of some sample fractions from an albumin standard used in affinity chromatography.

Optimization of capillary electrophoresis conditions for coupling to a mass spectrometer via a sheathless interface

When optimizing a capillary electrophoresis/electrospray ionization mass spectrometry (CE/ESI-MS) system, consideration has to be given not only to the separation but also to the electrospray stability. Methods developed for CE/UV analysis of drugs and peptides were considered and modified to be suitable for a CE/MS system with a robust sheathless interface. Different concentrations of the organic modifiers acetonitrile, methanol and 2-propanol were used in the separation buffer. The type and concentrations of these modifiers were also compared with reference to electrospray stability, sensitivity and time of analysis. In addition, different ionic strengths in the buffers were evaluated with reference to electrospray stability. The repeatability was used for the estimation of electrospray stability. The degree to which these parameters influenced the separation and the ESI stability was studied using a nine-peptide standard mixture and the antibiotic drugs bacampicillin and ampicillin as test substances. The analysis time and resolution were used as measures of the efficiency of the separation. A time-of-flight MS analyzer was used since it has the potential advantages of becoming a better fit for integration of CE with MS owing to the speed and sensitivity of this mass analyzer. The detection limit, i.e. 1 microM, for bacampicillin was comparable to what could be achieved with CE/MS on a quadrupole instrument using selected ion monitoring and sheath flow ESI.

Study of Preprocessing Methods for the Determination of Crystalline Phases in Binary Mixtures of Drug Substances by X-ray Powder Diffraction and Multivariate Calibration:

In this paper, various preprocessing methods were tested on data generated by X-ray powder diffraction (XRPD) in order to enhance the partial least-squares (PLS) regression modeling performance. The preprocessing methods examined were 22 different discrete wavelet transforms, Fourier transform, Savitzky–Golay, orthogonal signal correction (OSC), and combinations of wavelet transform and OSC, and Fourier transform and OSC. Root mean square error of prediction (RMSEP) of an independent test set was used to measure the performance of the various preprocessing methods. The best PLS model was obtained with a wavelet transform (Symmlet 8), which at the same time compressed the data set by a factor of 9.5. With the use of wavelet and X-ray powder diffraction, concentrations of less than 10% of one crystal from could be detected in a binary mixture. The linear range was found to be in the range 10–70% of the crystalline form of phenacetin, although semiquantitative work could be carried out down to a level of approximately 2%. Furthermore, the wavelet-pretreated models were able to handle admixtures and deliberately added noise.