Role of the Power Function Value in Linearity and Universality for Charged Aerosol Detectors: Theoretical Elucidations from a Validated Model

High-throughput drug discovery on the microgram scale is now common, making analyte quantitation without molecule-specific calibration imperative. The charged aerosol detector (CAD) was invented to be a next-generation universal liquid chromatography (LC) detector with excellent response universality for nonvolatile analytes as well as sensitivity for nonchromophoric compounds. Although the CAD is a mass flow-sensitive detector, its response to mass is inherently nonlinear, which challenges traditional quantitation. In CAD software, there is a "power function value" (p) setting that can be used to linearize the signal through digital signal processing. The exact workings of this power function value algorithm remain unknown; however, its optimization is a crucial aspect of analytical method development for LC-CAD. Herein, we developed a theoretical relationship that can be used to predict the chromatogram (plus peak area, width, and height) at any p if the data are collected at p = 1. This model was validated using a diverse dataset comprising 1440 measurements including peak heights, areas, and widths. Predicted areas had an average error of less than 2% showing excellent agreement between calculated and experimental results. An open-access automated code is tested and provided, which predicts the power function value that produces the most linear response. It is vital to note that optimizing the power function value affects peaks of different heights disproportionately. Low-level impurities were shown to be minimized and eventually eliminated by increasing the power function value. This model provides an easy-to-implement tool (MATLAB or Excel) that assists in choosing the optimal p for each LC-CAD method, increasing the speed of method development and improving the accuracy of quantitative workflows.

Pink noise removal and spectral distortion correction based fiber Bragg grating demodulation algorithm

Pink noise and spectral distortions affect the demodulation accuracies of fiber Bragg gratings (FBGs). A noise reduction algorithm with Savitzky-Golay filtering on each intrinsic mode function component decomposed by CEEMDAN, followed by signal reconstruction from the filtered components, is presented herein. Simulations show that the algorithm retains FBG signal details while reducing noise, avoids measurement errors from excessive smoothing, and suppresses white and pink noises adequately. A resolution-enhanced peak detection algorithm with distortion spectrum correction is also proposed with high accuracy and simple calculation. The experimental results show that the goodness of fit of the FBG temperature-wavelength curve R² improves from 0.9826 to 0.9999.

Iterative Multivariate Peaks Fitting—A Robust Approach for The Analysis of Non-Baseline Resolved Chromatographic Peaks

Selectivity in separation science is defined as the extent to which a method can determine the target analyte free of interference. It is the backbone of any method and can be enhanced at various steps, including sample preparation, separation optimization and detection. Significant improvement in selectivity can also be achieved in the data analysis step with the mathematical treatment of the signals. In this manuscript, we present a new approach that uses mathematical functions to model chromatographic peaks. However, unlike classical peak fitting approaches where the fitting parameters are optimized with a single profile (one-way data), the parameters are optimized over multiple profiles (two-way data). Thus, it allows high confidence and robustness. Furthermore, an iterative approach where the number of peaks is increased at each step until convergence is developed in this manuscript. It is demonstrated with simulated and real data that this algorithm is: (1) capable of mathematically separating each component with minimal user input and (2) that the peak areas can be accurately measured even with resolution as low as 0.5 if the peak’s intensities does not differ by more than a factor 10. This was conclusively demonstrated with the quantification of diterpene esters in standard mixtures.

Advances in ultra-high-pressure and multi-dimensional liquid chromatography instrumentation and workflows

The present contribution discusses recent advances in ultra-high-pressure liquid chromatography (UHPLC) and multi-dimensional liquid chromatography (MDLC) technology. First, new developments in UHPLC column technology and system design are highlighted. The latter includes a description of a novel injector concept enabling method speed-up, emerging detectors, and instrument diagnostics approaches. Next, online MDLC workflows are reviewed and advances in modulation technology are highlighted. Finally, key applications published in 2020 are reviewed.

The theory and practice of ultrafast liquid chromatography: A tutorial

Modern high-throughput experimentation and challenging analytical problems of academic/industrial research have put the responsibility on separation scientists to develop new fast separation approaches. With the availability of high-pressure pumps, small particles with hydrolytically stable surface chemistries, reduced extra-column band broadening, and low volume detectors with fast signal processing, it is now feasible to do sub-minute to sub-second chromatography. Herein, the fundamental theoretical principles of ultrafast chromatography, along with practical solutions, are reviewed. Approaches for rapid separations in packed beds, narrow open tubular columns, and monoliths are demonstrated, along with the challenges that were faced. The instrumentation requirements (pumps, injection systems, detectors, column packing process) for using short columns ranging from 0.5 to 5 cm are examined, followed by real applications. One of the main problems in ultrafast chromatography is partial or complete peak overlap. As per Gidding's statistical overlap theory, peak overlap cannot be avoided for a completely random sample for a column with a given peak capacity. Signal processing techniques based on Fourier transform deconvolution of band broadening, power laws, derivatives, and iterative curve fitting are explained to help improve the chromatographic resolution. An example of ten peaks separated in under a second is shown and discussed. Other ultrafast separations in supercritical fluid chromatography or capillary electrophoresis are briefly mentioned to provide a complete understanding of this emerging field.