Biomedical subjects

Fredrik O Andersson

Publications and source records attributed to Fredrik O Andersson.

3 recordsLinked to original sources

PubMed2005

Cross-linking of gelatin capsules with formaldehyde and other aldehydes: an FTIR spectroscopy study.

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.

Aldehydes↗

PubMed2004

Data preprocessing by wavelets and genetic algorithms for enhanced multivariate analysis of LC peptide mapping.

Peptide mapping by means of liquid chromatography is a powerful technique used for the characterisation and analysis of the primary structure of proteins. Subtle changes in the covalent structure of the protein can be detected by means of the chromatographic profile (fingerprint). Chromatographic methods, however, display variations in the chromatographic profile even at identical instrumental settings and sample conditions. These variations may be due to changes of the chromatographic conditions, e.g. slight shifts in column temperature, and degradation or alterations of the stationary phase or small changes in the trifluoroacetic acid (TFA) concentration. Such variations may result in varying retention times and peak shapes of the analytes and differences in the chromatographic baseline, thereby having a detrimental impact on the results obtained on multivariate analysis of peptide maps. In order to reduce the non-sample-related variations and to be able to more fully extract the information in peptide mapping, approaches for achieving this objective are outlined in the present study. These methods are denoising and data compression of the chromatograms by wavelets, baseline corrections by linear interpolation, and peak shift alignments towards a target chromatogram by means of a genetic algorithm. Visual inspections of preprocessed chromatograms and principal component analysis (PCA) score plots demonstrate the efficiency of the methodology used. Furthermore, deliberately added changes, e.g. insertions of small Gaussian peaks (outliers), are more easily detected by the proposed methods than from the original chromatograms by multivariate analysis.

Algorithms↗

PubMed2002

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.

Acetaminophen↗