Power Law Approach as a Convenient Protocol for Improving Peak Shapes and Recovering Areas from Partially Resolved Peaks

Virtual Instruments for Peak-Overlapping Studies to Determine Low- and High-Concentration Components with Ion Chromatography: Potassium and Sodium

We developed the LabVIEW-based virtual instruments (VIs) to bridge a gap in commercial software and to enable systematic peak-overlapping studies to recognise the concentration levels enabling reliable simultaneous determination of major and minor constituents in samples with wide concentration proportions. The VIs were applied to a case study of the ion chromatographic determination of potassium as minor and sodium as a major ion with an IonPac CS12A column and 50 μL injection loop. Two successive studies based on multilevel two-factorial response surface experimental designs, (1) a model peak-overlapping study based on single-ion injections, and (2) an accuracy and precision study, provided guidelines for real sample analyses. By adjusting sample dilutions so that the sodium mass concentration was set to 340 mg/L, the simultaneous determination of potassium in the presence of sodium was possible in samples with sodium over potassium concentration ratios between 14 and 341. The relative expanded uncertainty associated with potassium ion determination was between 0.52 and 4.4%, and the relative bias was between -3.8 and 1.9%. We analysed Ringer's physiologic solutions, standard sea, trisodium citrate anticoagulant, and buffered citrate anticoagulant solutions. We confirmed that the VI-supported peak-overlapping studies contributed to the quality of results by enabling the evidence-based choices of concentration levels adjusted by a dilution.

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.

A Novel Symmetrical Peak Fitting Method Based on Improved WOA Algorithm for the Analysis of Microchip Electrophoresis Signals

The problem of overlapping peaks has been a challenge in microchip electrophoresis (ME) signal analysis. However, traditional peak fitting algorithms have difficulty analyzing overlapping peaks due to the high dependence on the starting point. In this study, we propose a symmetrical peak fitting method named the tent-mapped whale optimization algorithm and Levenberg–Marquardt (TWOALM), which combines a whale optimization algorithm (WOA) improved by one of the most commonly used chaotic maps, the tent map and the Levenberg–Marquardt (LM) algorithm. Specifically, we first derive the fitted model for the overlapping peaks, showing that it is separable nonlinear least squares, significantly reducing the number of parameters to be optimized. Second, we integrate the tent map into the WOA, which improves the convergence speed of the peak fitting algorithm. Finally, we propose an efficient peak-fitting algorithm that combines the improved WOA and LM. The advantage of the proposed algorithm is that it is significantly faster than WOA and significantly more accurate than the LM algorithm. The results of fitting the synthetic peaks and ME signals showed that the combination of the chaotic map-based WOA algorithm and the LM algorithm can significantly improve the peak fitting performance and provide an effective solution for the analysis of overlapping peaks.

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.

Critical Evaluation of Spectral Resolution Enhancement Methods for Raman Hyperspectra

Overlapping peaks in Raman spectra complicate the presentation, interpretation, and analyses of complex samples. This is particularly problematic for methods dependent on sparsity such as multivariate curve resolution and other spectral demixing as well as for two-dimensional correlation spectroscopy (2D-COS), multisource correlation analysis, and principal component analysis. Though software-based resolution enhancement methods can be used to counter such problems, their performances often differ, thereby rendering some more suitable than others for specific tasks. Furthermore, there is a need for automated methods to apply to large numbers of varied hyperspectral data sets containing multiple overlapping peaks, and thus methods ideally suitable for diverse tasks. To investigate these issues, we implemented three novel resolution enhancement methods based on pseudospectra, over-deconvolution, and peak fitting to evaluate them along with three extant methods: node narrowing, blind deconvolution, and the general-purpose peak fitting program Fityk. We first applied the methods to varied synthetic spectra, each consisting of nine overlapping Voigt profile peaks. Improved spectral resolution was evaluated based on several criteria including the separation of overlapping peaks and the preservation of true peak intensities in resolution-enhanced spectra. We then investigated the efficacy of these methods to improve the resolution of measured Raman spectra. High resolution spectra of glucose acquired with a narrow spectrometer slit were compared to ones using a wide slit that degraded the spectral resolution. We also determined the effects of the different resolution enhancement methods on 2D-COS and on chemical contrast image generation from mammalian cell spectra. We conclude with a discussion of the particular benefits, drawbacks, and potential of these methods. Our efforts provided insight into the need for effective resolution enhancement approaches, the feasibility of these methods for automation, the nature of the problems currently limiting their use, and in particular those aspects that need improvement.

Application of multiplet structure deconvolution to extract scalar coupling constants from 1D nuclear magnetic resonance spectra

Multiplet structure deconvolution provides a robust method to determine the values of the coupling constants in first-order 1D nuclear magnetic resonance (NMR) spectra. Functions simplifying the coupling structure for partners with spin larger than 1 / 2 and for doublets with unequal amplitudes were introduced. The chemical shifts of the coupling partners causing mild second-order effects can, in favourable cases, be calculated from the slopes measured in doublet structures. Illustrations demonstrate that deconvolution can straightforwardly analyse multiplet posing difficulties to humans and, in some cases, extract coupling constants from unresolved multiplets.

Rapid and selective separation of amyloid beta from its stereoisomeric point mutations implicated in neurodegenerative Alzheimer's disease

Extracellular deposition of amyloid beta (Aβ) peptides are a hallmark of Alzheimer's disease. The isomerization and epimerization of Aβ peptides have been linked to the enhanced deposition of Aβ plaques. Therefore, considerable effort has been expended to create effective methods to distinguish such aberrant Aβ peptides from normal Aβ peptides. Herein, we have developed chromatographic retention U-shaped curves to investigate the hydrophobicity of Aβ 1-38, 1-40, 1-42 and fourteen aberrant Aβ 1-42 peptides. Using this information, we developed the first selective and comprehensive method that can easily detect both aberrant and normal Aβ peptides simultaneously using high performance liquid chromatography-mass spectrometry (HPLC-MS). We show for the first time that D-Ser modifications to Aβ cause the peptide to be more hydrophilic, as does D-Asp and L/D-iso-Asp.

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.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Complete identification of all 20 relevant epimeric peptides in β-amyloid: a new HPLC-MS based analytical strategy for Alzheimer's research

Although the underlying cause of Alzheimer's disease (AD) is not known, the extracellular deposition of β-amyloid (Aβ) is considered as a hallmark of AD brains. Evidence has shown the occurrence of d-Asp, isoAsp, and d-Ser residues in Aβ, which may be indicative of and/or contribute to the neurodegeneration in AD patients. Herein, we have developed the first high-throughput profiling technique for all 20 isobaric Aβ peptide epimers containing Asp, isoAsp, and Ser isomers using high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). This new analytical strategy allows the direct detection and identification of all possible Asp, isoAsp, and Ser stereoisomers in Aβ, and may contribute to a better understanding of the pathogenesis of AD.