Fast gas chromatography for pesticide residues analysis using analyte protectants.

Fast GC-MS with narrow-bore columns combined with effective sample preparation technique (QuEChERS method) was used for evaluation of various calibration approaches in pesticide residues analysis. In order to compare the performance of analyte protectants (APs) with matrix-matched standards calibration curves of selected pesticides were searched in terms of linearity of responses, repeatability of measurements and reached limit of quantifications utilizing the following calibration standards in the concentration range 1-500 ng mL(-1)(the equivalent sample concentration 1-500 microg kg(-1)): in neat solvent (acetonitrile) with/without addition of APs, matrix-matched standards with/without addition of APs. For APs results are in a good agreement with matrix-matched standards. To evaluate errors of determination of concentration synthetic samples at concentration level of pesticides 50 ng mL(-1) (50 microg kg(-1)) were analyzed and quantified using the above given standards. For less troublesome pesticides very good estimation of concentration was obtained utilizing APs, while for more troublesome pesticides such as methidathion, malathion, phosalone and deltamethrin significant overestimation reaching up to 80% occurred. According to presented results APs can be advantegously used for "easy" pesticides determination. For "difficult" pesticides an alternative calibration approach is required for samples potentially violating MRLs. An example of real sample measurement is shown. In this paper also the use of internal standards (triphenylphosphate (TPP) and heptachlor (HEPT)) for peak areas normalization is discussed in terms of repeatability of measurements and quantitative data obtained. TPP normalization provided slightly better results than the use of absolute peak areas measurements on the contrary to HEPT.

Chemometrical classification of pumpkin seed oils using UV-Vis, NIR and FTIR spectra.

The main outcome of this work is elaboration of classification models for edible oil samples representing the most widespread brands of Austrian pumpkin seed oil. A complete spectral characterisation of the pumpkin seed oil samples by UV-Vis, NIR and FTIR spectra was obtained together with their basic sensorial classification. Chemometrical processing of the measured data enabled the detection of the most important spectral features, which are crucial for categorising the oils into two or three classes according to their sensory quality evaluated by a panel of experts. The elaborated models thus make it possible to predict the category into which a hitherto unclassified oil sample belongs--considering classification into either two categories, containing oils with overall acceptable scores or oils that were not accepted, or three categories, involving oils fulfilling all quality criteria, oils with good scores and not accepted oils. This will perspectively facilitate the determination of chemical substances responsible for bad taste, odour and colour of the respective oil brands, as well as finding substances contributing to the excellent sensorial perception of some tested products.

QSRR models for potential local anaesthetic drugs using high performance liquid chromatography.

Quantitative structure-retention relationships (QSRR) were proposed for Separon SGX C18 and Separon SGX Phenyl columns using physico-chemical molecular descriptors for the compounds, which are potential local anaesthetic drugs. Chemometrical methods were used for the QSRR studies of the HPLC retention factor k of 59 esters of alkoxyphenylcarbamic acid, which exhibit surface and/or infiltration anaesthetic activity. Four separation systems were used: phenyl column and acetonitrile/water mobile phase, phenyl column and methanol/water mobile phase, C18 column and acetonitrile/water mobile phase, and C18 column and methanol/water mobile phase. The values of logP and logS and ¹³C and ¹H NMR chemical shifts were simulated and utilized in calculating the corresponding QSRR models and predicting the retention factors by artificial neural networks (ANN). In addition, principal component analysis and cluster analysis were used for a closer characterization of alkoxyphenylcarbamic acid esters. The proposed ANN models, based on optimally selected species descriptors, showed a high degree of correlation between k predicted and k measured. The intercepts and the slopes of the obtained dependences were close to the theoretically expected values of 0 and 1, respectively.

Effect of the statin therapy on biochemical laboratory tests--a chemometrics study.

Statins are the first-line choice for lowering total and LDL cholesterol levels and very important medicaments for reducing the risk of coronary artery disease. The aim of this study is therefore assessment of the results of biochemical tests characterizing the condition of 172 patients before and after administration of statins. For this purpose, several chemometric tools, namely principal component analysis, cluster analysis, discriminant analysis, logistic regression, KNN classification, ROC analysis, descriptive statistics and ANOVA were used. Mutual relations of 11 biochemical laboratory tests, the patient's age and gender were investigated in detail. Achieved results enable to evaluate the extent of the statin treatment in each individual case. They may also help in monitoring the dynamic progression of the disease.

Use of multidimensional data analysis for prediction of lung malignity.

Diagnosis of lung malignity can be predicted or confirmed not only according to the values of appropriate laboratory tests but also using multidimensional statistical analysis, which uses simultaneously all performed tests in the form of their optimal combination. The developed new way of diagnosis prediction is applied here to the results of laboratory analysis of lung tumor markers in serum as well as pleural effusion (exudate). Four laboratory tests were used and investigated in detail: carcinoembryonic antigen, CEA, in serum as well as in pleural exudate, and cytokeratin 19 fragment, CYFRA, in serum and exudate, as well. Each test represents one dimension in the investigated biomedical problem from the statistical point of view. Joint utilization of the performed laboratory tests is based on their optimized combination into a new statistical variable using a selected chemometric principle (principal component, discriminant function, or logit in logistic regression). This approach results in enhancement of diagnostic effectiveness applied for the specified purpose.

Comparison of negative chemical ionization and electron impact ionization in gas chromatography-mass spectrometry of endocrine disrupting pesticides.

The study of pesticide residues belonging to endocrine disrupting chemicals (EDCs) (23 analytes of different chemical classes--organochlorines, organophosphates, pyrethroids, dicarboximides, phtalamides, dinitroanilines, pyrazole, triazinone) in apple matrix with conventional capillary GC-NCI-MS (with methane as reagent gas) in comparison to EI ionization is presented. For sample preparation QuEChERS method was applied. The lowest calibration levels (LCLs) for all pesticides were determined in both modes. Calibration in the NCI mode was performed at the concentration levels from 0.1 to 500 microg kg(-1) (R(2) > 0.999) and for EI in the range from 5 to 500 microg kg(-1) (R(2) > 0.99). From LCLs the instrumental limits of detection (LODs) and quantification (LOQs) were calculated. Chemometric study of pesticide signals in two MS modes was performed. Repeatability of all measurements, expressed by the relative standard deviations of absolute peak areas was better than 10% for the majority of compounds. Significantly lower values were obtained for the NCI mode.