The novel Outer Plug Standardization (OPS) calibration strategy applied in a new method for the high-throughput determination of Cl-, NO3- and SO4-2 in rainwater samples.

This paper presents the Outer Plug Standardization - OPS, a novel technique as an alternative for the classic internal standardization using multiple injection in capillary zone electrophoresis is proposed herein. This technique was applied in a new method for the determination of chloride, nitrate and sulfate in rainwater samples. After the injection accuracy was tested and proved to be a minor error source (average 1.26% RSD), the OPS was applied and it improved the intra-day and inter-day precision of the analytical method by 32.5% and 24.7%, respectively. Using a capillary with effective length of 23.5 cm the electrophoretic separation of the three inorganic anions could be achieved in <1 min, with detection limits of 0.05, 0.09 and 0.11 mg L-1 for chloride, nitrate and sulfate, respectively. Also, the statistical t-test was applied to the results obtained for 82 rainwater samples that were collected and analyzed applying both the method developed in this study and the official APHA 4140 method where no statistical difference was noted within a 95% confidence level.

Simultaneous determination of aspartame, cyclamate, saccharin and acesulfame-K in powder tabletop sweeteners by FT-Raman spectroscopy associated with the multivariate calibration: PLS, iPLS and siPLS models were compared.

For the first time, a procedure for simultaneous determination of the main artificial sweeteners, aspartame (ASP), cyclamate (CYC), saccharin (SAC), and acesulfame-K (ACSK) by a spectroscopic method associated with the multivariate calibration is proposed. These analytes were quantified in tabletop sweeteners samples using FT-Raman spectroscopy. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used as reference method. Partial least squares (PLS), interval PLS (iPLS), and synergism PLS (siPLS) methods were evaluated in a comparative study where the selected interval models presented better results. Multivariate regression models, such as PLS, iPLS and siPLS were built and the lower root mean square errors for prediction (RMSEP) found were 0.027-0.031% w/w, 0.316-0.363% w/w, 0.082-0.184% w/w, and 0.040-0.049% w/w to ASP, CYC, SAC, and ACSK, respectively. The coefficient of determination for prediction (R2p) varied between 0.978 and 0.979, 0.969-0.977, 0.952-0.994, and 0.959-0.965 for ASP, CYC, SAC and ACSK, respectively. The analysis of model's residues was made by bias and permutation tests to evaluate systematic and trend errors. The selected intervals by iPLS and siPLS were evaluated and the bands related to the vibrational modes of the analytes were assigned with the aid of density functional theory calculations (DFT).