Multivariate autofluorescence of intact food systems.
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Journal Article↗
Biomedical subjects
Lars Nørgaard
Publications and source records attributed to Lars Nørgaard.
Chemometric exploration of an amperometric biosensor array for fast determination of wastewater quality.
Four wastewater samples of different treatment qualities; untreated, alarm, alert and normal, from a Swedish chemi-thermo-mechanical pulp mill and pure water were investigated using an amperometric bio-electronic tongue in a batch cell. The aim was to explore enzymatically modified screen-printed amperometric sensors for the discrimination of wastewater quality and to counteract the inherent drift. Seven out of eight platinum electrodes on the array were modified with four different enzymes; tyrosinase, horseradish peroxidase, acetyl cholinesterase and butyryl cholinesterase. At a constant potential the current intensity on each sensor was measured for 200s, 100s before injection and 100s after injection of the sample. The dynamic biosensor response curves from the eight sensors were used for principal component analysis (PCA). A simple baseline and sensitivity correction equivalent to multiplicative drift correction (MDC), using steady state intensities of reference sample (catechol) recordings, was employed. A clear pattern emerged in perfect agreement with prior knowledge of the samples explaining 97% of the variation in the data by two principal components (PCs). The first PC described the treatment quality of the samples and the second PC described the difference between treated and untreated samples. Horseradish peroxidase and pure platinum sensors were found to be the determinant sensors, while the rest did not contribute much to the discrimination. The wastewater samples were characterized by the chemical oxygen demand (COD), biological oxygen demand (BOD), total organic carbon (TOC), inhibition of nitrification, inhibition of respiration and toxicity towards Vibrio fischeri using Microtox, the freshwater alga Pseudokirchneriella subcapita and the freshwater crustacean Daphnia magna.
Algorithms↗
Quantification of lipoprotein subclasses by proton nuclear magnetic resonance-based partial least-squares regression models.
BACKGROUND: Cardiovascular disease risk can be estimated in part on the basis of the plasma lipoprotein profile. Analysis of lipoprotein subclasses improves the risk evaluation, but the traditional methods are very time-consuming. Novel, rapid, and productive methods are therefore needed. METHODS: We obtained plasma samples from 103 fasting people and determined the plasma lipoprotein subclass profiles by an established ultracentrifugation-based method. Proton nuclear magnetic resonance (NMR) spectra were obtained from replicate samples on a 600 MHz NMR spectrometer. From the ultracentrifugation-based reference data and the NMR spectra, we developed partial least-squares (PLS) regression models to predict cholesterol and triglyceride (TG) concentrations in plasma as well as in VLDL, intermediate-density lipoprotein (IDL), LDL, 3 LDL fractions, HDL, and 3 HDL subclasses. RESULTS: The correlation coefficients (r) between the plasma TG and cholesterol concentrations measured by the 2 methods were 0.98 and 0.91, respectively. For LDL- and HDL-cholesterol concentrations, r = 0.90 and 0.94, respectively. For cholesterol concentrations in the LDL-1, LDL-2, and LDL-3 fractions, r = 0.74, 0.78, and 0.69, respectively, and for HDL subclasses HDL(2b), HDL(2a), and HDL(3), cholesterol concentrations were predicted with r = 0.92, 0.94, and 0.75, respectively. TG concentrations in VLDL, IDL, LDL, and HDL were predicted with correlations of 0.98, 0.85, 0.77, and 0.74, respectively. The cholesterol and TG concentrations in the main lipoprotein fractions and in LDL fractions and HDL subclasses predicted by the PLS models were 94%-100% of the concentrations obtained by ultracentrifugation. CONCLUSION: NMR-based PLS regression models are appropriate for use in research in which analyses of the plasma lipoprotein profile, including LDL and HDL subclasses, are required in large numbers of samples.
Adult↗
Multivariate analysis to separate the signal given by cross-reactants in immunoassay with sample matrix dilution.
This paper describes a new approach to achieve selectivity in an immunoassay by separating the signals given by two cross-reactive compounds present simultaneously in a complex sample matrix. The method is based on the sequential dilution of the sample containing a mixture of the two analytes, spiking each diluted sample with a reference compound, and the detection by enzyme-linked immunosorbent assay (ELISA). The obtained multivariate response was used for the individual calibrations of the assay for each of the two cross-reactants simultaneously by using principal component analysis (PCA) and partial least squares regression (PLSR) data modeling. The calibration models showed that the signal separation due the analytes 2,4-dinitrophenol (2,4-DNP) and 4-nitrophenol (4-NP) was possible with a prediction concentration error of 1.4 microM and 72 microM, respectively.
2,4-Dinitrophenol↗
Prediction of in vitro metabolic stability of calcitriol analogs by QSAR.
The metabolic stability of a drug is an important property for potential drug candidates. Measuring this property, however, can be costly and time-consuming. The use of quantitative structure-activity relationships (QSAR) to estimate the in vitro stability is an attractive alternative to experimental measurements. A data set of 130 calcitriol analogs with known values of in vitro metabolic stability was used to develop QSAR models. The analogs were encoded with molecular structure descriptors computed mainly with the commercial software QikProp and DiverseSolutions. Variable selection was carried out by five different variable selection techniques and Partial Least Squares Regression (PLS) models were generated from the 130 analogs. The models were used for prediction of the metabolic stability of 244 virtual calcitriol analogs. Twenty of the 244 analogs were selected and the in vitro metabolic stability was determined experimentally. The PLS models were able to predict the correct metabolic stability for 17 of the 20 selected analogs, corresponding to a prediction performance of 85%. The results clearly demonstrate the utility of QSAR models in predicting the in vitro metabolic stability of calcitriol analogs.
Calcitriol↗
Exploratory multivariate spectroscopic study on human skin.
BACKGROUND/AIMS: Spectroscopy on human skin is a field that is being adopted increasingly because of its rapidity and high reproducibility. Infrared reflectance (IR), near-infrared reflectance (NIR), and fluorescence spectroscopy have previously been applied to human skin in vivo to compare healthy and sick skin, including skin cancer, atopy, and leprosy. Exploratory data analysis/chemometrics is a tool for evaluating multivariate data such as spectroscopic measurements. The objective of this study was to explore the spectral variance spanned by people with normal integument, and to demonstrate the advantages of multivariate analysis to skin research. METHODS: IR, NIR and fluorescence spectroscopy have been carried out in vivo on 216 volunteers' forearms before and after four tape strippings. The subjects were asked to fill in a questionnaire regarding factors suspected to influence the measurement results. Principal Component Analysis (PCA) was used to investigate whether the population can be divided into groups on the basis of their skin chemistry. Unless otherwise stated, the results are from the measurements prior to stripping. RESULTS: In contrast to IR and fluorescence spectra, NIR spectra proved able to detect gender differences. By use of PCA, classifications on male and female subjects were observed from the IR and NIR measurements, and as an indication from the fluorescence measurements. The NIR and fluorescence measurements varied between elderly and young subjects. The largest variance in the fluorescence landscapes was seen between pigmented and non-pigmented skin. No connection was found between the spectroscopic measurements and smoking or drinking habits. CONCLUSIONS: Future spectroscopic skin investigations should be balanced as regards to gender and age, as these can possibly affect the measurement results. Chemometrics proved to be superior to traditional attempts of interpreting the spectra.
Adult↗