Using second-order calibration to identify and quantify aromatic sulfonates in water by high-performance liquid chromatography in the presence of coeluting interferences

We used the Generalized Rank Annihilation Method (GRAM), a second-order calibration method, to quantify aromatic sulfonates in water with high-performance liquid chromatography (HPLC) when interferences coeluted with the analytes of interest. With GRAM, we can quantify in only two chromatographic analyses, one for a calibration sample and one for the unknown sample. The calculated concentrations were not statistically different to those obtained when the chromatographic separation of the unknown sample was modified in order to completely separate the analyte from the interferences before univariate calibration. With GRAM, the concentrations are determined much more quickly because a complete resolution is not required.     

An optimized direct method for the determination of carboxylic acids in beverages by HPLC

Summary A direct method for the simultaneous determination of tartaric, malic, lactic, acetic, citric, shikimic, fumaric and succinic acids in fruit juices and wines by isocratic reversed phase HPLC is reported.The variables (pH, ionic strength, flow and temperature) have been optimized by a modification of the original simplex method. The separation factor (s) and calibrated resolution product (r^*) have been used as criteria for selectivity optimization. After validation, the method has been applied to the determination of carboxylic acids in apple, orange and lemon juices, white and red wines and musts during the fermenation process.     

Time shift correction in second-order liquid chromatographic data with iterative target transformation factor analysis

When the generalized rank annihilation method (GRAM) is applied to liquid chromatographic data with diode-array detection, an important problem is the time shift of the peak of the analyte in the test sample. This problem leads to erroneous predictions. This time shift can be corrected if a time window is selected so that the chromatographic profile of the analyte in the test sample is trilinear with the peak of the analyte in the calibration sample. In this paper we present a new method to determine when this condition is met. This method is based on the curve resolution with iterative target transformation factor analysis (ITTFA). The calibration and test matrices are independently decomposed into profiles and spectra, and aligned before GRAM is applied. Here we study two situations: first, when the calibration matrix has one analyte and second, when it has two analytes. When the calibration matrix has two analytes, we selectively determine the time window for the analyte to be quantified. There were considerably fewer prediction errors after correction.     

Limit of detection estimator for second-order bilinear calibration

A new approach is developed for estimating the limit of detection in second-order bilinear calibration with the generalized rank annihilation method (GRAM). The proposed estimator is based on recently derived expressions for prediction variance and bias. It follows the latest IUPAC recommendations in the sense that it concisely accounts for the probabilities of committing both types I and II errors, i.e. false positive and false negative declarations, respectively. The estimator has been extensively validated with simulated data, yielding promising results.     

Insertion reactions of [M(SR)3(PMe2Ph)2] with CS2 (M = Ru, Os; R = C6F4H-4, C6F5). X-ray structures of [Ru(S2CSC6F4H-4)2(PMe2Ph)2], trans-thiolates [M(SR)2(S2CSR)(PMe2Ph)2] (M = Ru; R = C6F5 and M = Os; R = C6F4H-4), and trans-thiolate-phosphine [Os(SC6F5)2(S2CSC6F5)(PMe2Ph)2

Reactions of [M(SR)(3)(PMe(2)Ph)(2)] (M = Ru, Os; R = C(6)F(4)H-4, C(6)F(5)) with CS(2) in acetone afford [Ru(S(2)CSR)(2)(PMe(2)Ph)(2)] (R = C(6)F(4)H-4, 1; C(6)F(5), 3) and trans-thiolates [Ru(SR)(2)(S(2)CSR)(PMe(2)Ph)(2)] (R = C(6)F(4)H-4, 2; C(6)F(5), 4) or the isomers trans-thiolates [Os(SR)(2)(S(2)CSR)(PMe(2)Ph)(2)] (R = C(6)F(4)H-4, 5; C(6)F(5), 7) and trans-thiolate-phosphine [Os(SR)(2)(S(2)CSR)(PMe(2)Ph)(2)] (R = C(6)F(4)H-4, 6; C(6)F(5), 8) through processes involving CS(2) insertion into M-SR bonds. The ruthenium(III) complexes [Ru(SR)(3)(PMe(2)Ph)(2)] react with CS(2) to give the diamagnetic thiolate-thioxanthato ruthenium(II) and the paramagnetic ruthenium(III) complexes while osmium(III) complexes [Os(SR)(3)(PMe(2)Ph)(2)] react to give the paramagnetic thiolate-thioxanthato osmium(III) isomers. The single-crystal X-ray diffraction studies of 1, 4, 5, and 8 show distorted octahedral structures. (31)P [(1)H] and (19)F NMR studies show that the solution structures of 1 and 3 are consistent with the solid-state structure of 1.     

Insensitivity of leptogenesis with flavor effects to low energy leptonic CP violation

If the baryon asymmetry of the Universe is produced by leptogenesis, CP violation is required in the lepton sector. In the seesaw extension of the standard model with three hierarchical right-handed neutrinos, we show that the baryon asymmetry is insensitive to the Pontecorvo-Maki-Nagakawa-Sakata phases: thermal leptogenesis can work for any value of the observable phases. This result was well known when there were no flavor effects in leptogenesis; we show that it remains true when flavor effects are included.     

Effect of non-significant proportional bias in the final measurement uncertainty

The trueness of an analytical method can be assessed by calculating the proportional bias of the method in terms of apparent recovery. If the apparent recovery does not differ significantly from one, the analytical method has not a significant bias. If this is the case, the bias is neglected and the uncertainty associated with this bias is included in the uncertainty budget of results. However, when assessing trueness there is always a probability of incorrectly concluding that the proportional bias is not significant. Therefore, the uncertainty of results may be underestimated. In this paper, we study how non-significant bias affects the uncertainty of analytical results. Moreover, we study how to avoid the underestimation of uncertainty by including the non-significant bias calculated in the uncertainty budget. To answer these questions, we have used the Monte-Carlo method to simulate the process of estimating the apparent recovery of a biased analytical method and, subsequently, the future results this method provides. The results of the simulation show that non-significant bias may underestimate the uncertainty of analytical results when bias contributes in more than 20% to the overall uncertainty. Uncertainty is specially underestimated when bias contributes in more than 50% to the overall uncertainty.