Compound-specific 81Br/79Br analysis by capillary gas chromatography/multicollector inductively coupled plasma mass spectrometry

Brominated organic compounds (BOCs) are of great interest in atmospheric chemistry, natural products, geochemistry, and marine chemistry. The Br isotope ratios ((81)Br and (79)Br; average (81)Br/(79)Br = 0.97277) have significant potential for the study of the source, transport, and fate of these compounds. Currently, there is no published method for determining the Br isotopic content of BOCs in complex mixtures. To measure the ratios of stable Br in BOCs on a compound-specific basis, we assembled a gas chromatography/multicollector inductively coupled plasma mass spectrometry (GC/MCICPMS) system. Background signals, the formation of Ar dimers from organic hydrogen, and the solvent peak did not affect the results. The analysis of three brominated benzenes revealed that 0.3 per thousand precision can be attained when 0.3 nmol of Br are injected. For samples larger than 0.3 nmol Br, the isotopic precision was within a factor of 3 of the shot-noise limit, which is the best possible statistical precision attainable avoiding all other sources of noise.     

Tutorial on a chemical model building by least-squares non-linear regression of multiwavelength spectrophotometric pH-titration data

Although the modern instrumentation enables for the increased amount of data to be delivered in shorter time, computer-assisted spectra analysis is limited by the intelligence and by the programmed logic tool applications. Proposed tutorial covers all the main steps of the data processing which involve the chemical model building, from calculating the concentration profiles and, using spectra regression, fitting the protonation constants of the chemical model to multiwavelength and multivariate data measured. Suggested diagnostics are examined to see whether the chemical model hypothesis can be accepted, as an incorrect model with false stoichiometric indices may lead to slow convergence, cyclization or divergence of the regression process minimization. Diagnostics concern the physical meaning of unknown parameters beta(qr) and epsilon(qr), physical sense of associated species concentrations, parametric correlation coefficients, goodness-of-fit tests, error analyses and spectra deconvolution, and the correct number of light-absorbing species determination. All of the benefits of spectrophotometric data analysis are demonstrated on the protonation constants of the ionizable anticancer drug 7-ethyl-10-hydroxycamptothecine, using data double checked with the SQUAD(84) and SPECFIT/32 regression programs and with factor analysis of the INDICES program. The experimental determination of protonation constants with their computational prediction based on a knowledge of chemical structures of the drug was through the combined MARVIN and PALLAS programs. If the proposed model adequately represents the data, the residuals should form a random pattern with a normal distribution N(0, s2), with the residual mean equal to zero, and the standard deviation of residuals being near to experimental noise. Examination of residual plots may be assisted by a graphical analysis of residuals, and systematic departures from randomness indicate that the model and parameter estimates are not satisfactory.     

Outliers detection in the statistical accuracy test of a p<Emphasis Type="Italic">K</Emphasis><Subscript>a</Subscript> prediction

The regression diagnostics algorithm REGDIA in S-Plus is introduced to examine the accuracy of pK _a predicted with four programs: PALLAS, MARVIN, PERRIN and SYBYL. On basis of a statistical analysis of residuals, outlier diagnostics are proposed. Residual analysis of the ADSTAT program is based on examining goodness-of-fit via graphical diagnostics of 15 exploratory data analysis plots, such as bar plots, box-and-whisker plots, dot plots, midsum plots, symmetry plots, kurtosis plots, differential quantile plots, quantile-box plots, frequency polygons, histograms, quantile plots, quantile-quantile plots, rankit plots, scatter plots, and autocorrelation plots. Outliers in pK _a relate to molecules which are poorly characterized by the considered pK _a program. Of the seven most efficient diagnostic plots (the Williams graph, Graph of predicted residuals, Pregibon graph, Gray L–R graph, Index graph of Atkinson measure, Index graph of diagonal elements of the hat matrix and Rankit Q–Q graph of jackknife residuals) the Williams graph was selected to give the most reliable detection of outliers. The six statistical characteristics, F_exp,R²,R_P²,MEP,AIC{F_{\rm exp},R^{2},R_{\rm P}^{2},{\it MEP},{\it AIC}}, and s in pK _a units, successfully examine the specimen of 25 acids and bases of a Perrin’s data set classifying four pK _a prediction algorithms. The highest values F_exp,R²,R_P²{F_{\rm exp},R^{2},R_{\rm P}^{2}} and the lowest value of MEP and s and the most negative AIC have been found for PERRIN algorithm of pK _a prediction so this algorithm achieves the best predictive power and the most accurate results. The proposed accuracy test of the REGDIA program can also be extended to test other predicted values, as log P, log D, aqueous solubility or some physicochemical properties.