Biomedical subjects

John Mortensen

Publications and source records attributed to John Mortensen.

6 recordsLinked to original sources

PubMed2005

Multivariate statistical methods for evaluating biodegradation of mineral oil.

Two methods were developed for evaluating natural attenuation and bioremediation of mineral oil after environmental spills and during in vitro experiments. Gas chromatography-mass spectrometry (GC-MS) in selected ion monitoring (SIM) mode was used to obtain compound-specific data. The chromatographic data were then preprocessed either by calculating the first derivative, retention time alignment and normalization or by peak identification, quantification and calculation of diagnostic ratios within homologue series of polycyclic aromatic compounds (PACs). Finally, principal component analysis (PCA) was applied to the preprocessed chromatograms or diagnostic ratios to study the fate of the oil. The methods were applied to data from an in vitro biodegradation experiment with a North Sea crude oil exposed to three mixtures of bacterial strains: R (alkane degraders and surfactant producers), U (PAC degraders) and M (mixture of R- and U-strains) over a 1-year-period with five sampling times. Assessment of variation in degradability within isomer groups of methylfluorenes (m/z 180), methylphenanthrenes (m/z 192) and methyldibenzothiophenes (m/z 198) was used to evaluate the effects of microbial degradation on the composition of the oil. The two evaluation methods gave comparable results. In the objective pattern matching approach, principal component 1 (PC1) described the general changes in the isomer abundances, whereas M samples were separated from U and R samples along PC2. Furthermore, in the diagnostic ratio approach, a third component (PC3) could be extracted; although minor, it separated R samples from U and M samples. These results demonstrated that the two methods were able to differentiate between the effects due to the different bacterial activities, and that bacterial strain mixtures affected the PAC isomer patterns in different ways in accordance with their different metabolic capabilities.

Bacteria↗

PubMed2005

Characterization and matching of oil samples using fluorescence spectroscopy and parallel factor analysis.

A novel approach for matching oil samples by fluorescence spectroscopy combined with three-way decomposition of spectra is presented. It offers an objective fingerprinting based on the relative composition of polycyclic aromatic compounds (PACs) in oils. The method is complementary to GC-FID for initial screening of oil samples but can also be used for prescreening in the field, onboard ships, using a portable fluorescence spectrometer. Parallel factor analysis (PARAFAC) was applied to fluorescence excitation-emission matrixes (EEMs) of heavy fuel oils (HFOs), light fuel oils, lubricating oils, crude oils, unknown oils, and a sample collected in the spill area two weeks after the Baltic Carrier oil spill (Denmark, 2001). A total of 112 EEMs were decomposed into a five-factor PARAFAC model using excitation wavelengths from 245 to 400 nm and emission wavelengths from 280 to 550 nm. The PARAFAC factors were compared to EEMs of PAC standards with two to five rings, and the comparisons indicate that each of the factors can be related to a mixture of PACs with similar fluorescence characteristics: a mixture of naphthalenes and dibenzothiophenes, fluorenes, phenanthrenes, chrysenes, and five-ring PACs, respectively. Oils were grouped in score plots according to oil type. Except for HFOs and crude oils, the method easily discriminated between the four oil types. Minor overlaps of HFOs and crude oils were observed along all five PARAFAC factors, and the variability of crude oils was large along factor 2 due to a varying content of five-ring PACs. The spill sample was correctly assigned as a HFO with similar PAC pattern as oil from the cargo tank of the Baltic Carrier by comparing the correlation coefficient of scores for the oil spill sample and possible source oils (i.e., oils in the database).

Databases, Factual↗

PubMed2005

Chromatographic preprocessing of GC-MS data for analysis of complex chemical mixtures.

Hyphenated analytical techniques such as gas chromatography-mass spectrometry (GC-MS) can provide extensive amounts of analytical data when applied to environmental samples. Quantitative analyses of complex contaminant mixtures by commercial preprocessing software are time-consuming, and baseline distortion and incomplete peak resolution increase the uncertainty and subjectivity of peak quantification. Here, we present a semi-automatic method developed specific for processing complex first-order chromatographic data (e.g. selected ion monitoring in GC-MS) prior to chemometric data analysis. Chromatograms are converted into semi-quantitative variables (e.g. diagnostic ratios (DRs)) that can be exported directly to appropriate softwares. The method is based on automatic peak matching, initial parameterization, alternating background noise reduction and peak estimation using mathematical functions (Gaussian and exponential-Gaussian hybrid) with few (i.e. three to four) parameters. It is capable of resolving convoluted peaks, and the exponential-Gaussian hybrid improves the description of asymmetric peaks (i.e. fronting and tailing). The optimal data preprocessing suggested in this article consists of estimation of Gaussian peak parameters and subsequent calculation of diagnostic ratios from peak heights. We tested the method on chromatographic data from 20 replicate oil samples and found it to be less time-consuming and subjective than commercial software, and with comparable data quality.

Gas Chromatography-Mass Spectrometry↗

PubMed2004

Integrated methodology for forensic oil spill identification.

A new integrated methodology for forensic oil spill identification is presented. It consists of GC-MS analysis, chromatographic data processing, variable-outlier detection, multivariate data analysis, estimation of uncertainties, and statistical evaluation. The methodology was tested on four groups of diagnostic ratios composed of petroleum biomarkers and ratios within homologous PAH categories. Principal component analysis (PCA) was employed and enabled the simultaneous analysis of many diagnostic ratios. Weathering was taken into account by considering the sampling uncertainties estimated from replicate spill samples. Statistical evaluation ensured an objective matching of oil spill samples with suspected source oils as well as classification into positive match, probable match, and nonmatch. The data analysis is further refined if two or more source oils are classified as probable match by using weighted least squares fitting of the principal components, local PCA models, and additional information relevant to the spill case. The methodology correctly identified the source of two spill samples (i.e., crude oils from Oseberg East and Oseberg Field Centre) and distinguished them from closely related source oils.

Denmark↗

PubMed2004

Potentiometric and theoretical studies of the carbonate sensors based on 3-bromo-4-hexyl-5-nitrotrifluoroacetophenone.

Novel carbonate ionophore, trifluoroacetophenone derivative (TFA) substituted by two acceptor substituents in the phenyl ring (3-bromo-4-hexyl-5-nitrotrifluoroacetophenone), was synthesized. Solvent polymeric membrane sensors based on this ionophore exhibited heightened selectivity to carbonate ions in the presence of the most important interfering anions. A wide range of potentiometric properties were studied and compared with those of sensors based on mono-substituted ionophores. Special attention was paid to pH dependence of sensor responses and to elaboration of appropriate conditions for carbonate analysis. A segmented-sandwich membrane method was applied for determination of the stoichiometry of ionophore-carbonate complexes, which was determined to be 1:3, and apparent complex formation constants which were 14.4 and 13.6 for DOS- and NPOE-plasticized membranes, respectively. Theoretical studies on TFA derivatives by semi-empirical (AM1 and PM3) and ab initio(6-31+G*) methods were performed, considering different types of possible ionophore-ion interactions. The formation of hydrogen bonds between carbonate and hydrated TFA was proved to be much more favourable in terms of energy compared to tetrahedral nucleophilic adducts that earlier were postulated to being formed in the membrane phase. The final conclusion on the mechanism of carbonate sensing by TFA-based solvent polymeric membrane sensors was made on the basis of computational data and detailed analysis of the literature.

Journal Article↗

PubMed2003

Fermentation monitoring using multisensor systems: feasibility study of the electronic tongue.

The electronic tongue based on an array of 30 non-specific potentiometric chemical sensors has been applied to qualitative and quantitative monitoring of a batch fermentation process of starting culture for light cheese production. Process control charts were built by using PLS regression and data from fermentations run under "normal" operating conditions. Control charts allow discrimination of samples from fermentation batches run under "abnormal" operating conditions from "normal" ones at as early as 30-50% of fully evolved fermentations. The capability of the electronic tongue to quantify concentrations of important organic acids (citric, lactic and orotic) in the present type of fermentation media was demonstrated. Average prediction errors were assessed in the range 5-13% based on test set validation. Correlation between peptide profiles determined using HPLC and the electronic tongue output was also established. The electronic tongue was demonstrated to be a promising tool for fermentation process monitoring and quantitative analysis of growth media.

Journal Article↗