ABSTRACT
BACKGROUND: Comprehensive two-dimensional chromatography generates complex data sets, and numerous baseline correction and noise removal algorithms have been proposed in the past decade to address this challenge. However, evaluating their performance objectively is currently not possible due to a lack of objective data. RESULT: To tackle this issue, we introduce a versatile platform that models and reconstructs single-trace two-dimensional chromatography data, preserving peak parameters. This approach balances real experimental data with synthetic data for precise comparisons. We achieve this by employing a Skewed Lorentz-Normal model to represent each peak and creating probability distributions for relevant parameter sampling. The model's performance has been showcased through its application to two-dimensional gas chromatography data where it has created a data set with 458 peaks with an RMSE of 0.0048 or lower and minimal residuals compared to the original data. Additionally, the same process has been shown in liquid chromatography data. SIGNIFICANCE: Data analysis is an integral component of any analytical method. The development of new data processing strategies is of paramount importance to tackle the complex signals generated by state-of-the-art separation technology. Through the use of probability distributions, quantitative assessment of algorithm performance of new algorithms is now possible. Therefore, creating new opportunities for faster, more accurate, and simpler data analysis development.
ABSTRACT
Although comprehensive 2-D GC is an established and often applied analytical method, the field is still highly dynamic thanks to a remarkable number of innovations. In this review, we discuss a number of recent developments in comprehensive 2-D GC technology. A variety of modulation methods are still being actively investigated and many exciting improvements are discussed in this review. We also review interesting developments in detection methods, retention modeling, and data analysis.
ABSTRACT
Nano-enabled consumer products are a likely source of nanoparticles in the environment and a number of studies have shown the release of nanoparticles from commercial products. Predicted environmental concentrations have been calculated but there is a need for real measurement data to validate these calculations. However, the detection of engineered nanoparticles in environmental matrices is challenging because of the low predicted environmental concentrations which may be in the ng/L range. In this study nanosized Ag, CeO2 and TiO2 have been measured in multiple surface water samples collected along the rivers Meuse and IJssel in the Netherlands using single-particle ICP-MS as measurement technique. Validation of the analytical method showed its capability to quantitatively determine nanoparticles at low concentrations. Concentration mass detection limits for Ag, CeO2 and TiO2 were 0.1ng/L, 0.05ng/L and 10ng/L respectively. Size detection limits for Ag, CeO2 and TiO2 were 14, 10 and 100nm. The results of the study confirm the presence of nano-sized Ag and CeO2 particles and micro-sized TiO2 particles in these surface waters. n-Ag was present in all samples in concentrations ranging from 0.3 to 2.5ng/L with an average concentration of 0.8ng/L and an average particle size of 15nm. n-CeO2 was found in all samples with concentrations ranging from 0.4 to 5.2ng/L with an average concentration of 2.7ng/L and an average particle size of 19nm. Finally, µ-TiO2 was found in all samples with a concentration ranging from 0.2 to 8.1µg/L with an average concentration of 3.1µg/L and an average particle size of 300nm. The particle sizes that were found are comparable with the particle sizes that are used in nanomaterial applications and consumer products. The nanoparticle concentrations confirm the predicted environmental concentrations values in water for all three nanoparticles.
