Influence of theoretical and semi-empirical peak models on the efficiency calculation in chiral chromatography.

Height equivalent to theoretical plate (H) equations, such as the van Deemter or Knox-Saleem equations, and other efficiency vs. linear velocity equations (u), provide kinetic insights into chromatographic separations phenomena and column performance. In enantioselective separations, the peak shape of the two enantiomers can differ significantly and are often asymmetric. The peak efficiency calculations heavily impact these efficiency-flow profiles, leading to erroneous estimations of eddy diffusion, longitudinal diffusion, and mass transfer terms. In this work, new asymmetric peak functions are employed for modeling enantiomer peaks based on the Haarhoff-Van der Linde function, its generalized variant (GHVL), once Generalized Asymmetric Gaussian (AGN), and Twice Generalized Gaussian (TGN). The new models (AGN, TGN, and GHVL) incorporate higher statistical moments besides the zeroth, first, and second moments to account for two-sided asymmetry (fronting or tailing). The fit results are compared with the traditional efficiency calculation methods endorsed by official pharmacopeia and numerical estimation of moments from the raw data. Enantiomeric separations of ibuprofen and dl-homophenylalanine were chosen as probe molecules. The results demonstrate that non-linear least squares fitted functions provide better estimations of peak efficiency data even in the presence of high noise. In particular, the generalized models consistently offered the best quality fits for various peak shapes in chiral separations. Conversely, the half-height Gaussian method greatly overpredicted skewed peak efficiencies. This investigation reveals that the commonly held assumptions of peak shape and numerical integration of raw data are highly insufficient for chiral chromatography. The impact of asymmetry on plate height should not be overlooked when accurate data from efficiency-flow rate curves is derived. We advocate for the broader adoption of these new generalized peak (AGN, TGN, GHVL) models because they provide robustness at various SNRs that account for right or left asymmetry while accurately representing peak geometry.

Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain.

Magnetic resonance spectroscopy (MRS) is one of the few non-invasive imaging modalities capable of making neurochemical and metabolic measurements in vivo. Traditionally, the clinical utility of MRS has been narrow. The most common use has been the "single-voxel spectroscopy" variant to discern the presence of a lactate peak in the spectra in one location in the brain, typically to evaluate for ischemia in neonates. Thus, the reduction of rich spectral data to a binary variable has not classically necessitated much signal processing. However, scanners have become more powerful and MRS sequences more advanced, increasing data complexity and adding 2 to 3 spatial dimensions in addition to the spectral one. The result is a spatially- and spectrally-variant MRS image ripe for image processing innovation. Despite this potential, the logistics for robustly accessing and manipulating MRS data across different scanners, data formats, and software standards remain unclear. Thus, as research into MRS advances, there is a clear need to better characterize its image processing considerations to facilitate innovation from scientists and engineers. Building on established neuroimaging standards, we describe a framework for manipulating these images that generalizes to the voxel, spectral, and metabolite level across space and multiple imaging sites while integrating with LCModel, a widely used quantitative MRS peak-fitting platform. In doing so, we provide examples to demonstrate the advantages of such a workflow in relation to recent publications and with new data. Overall, we hope our characterizations will lower the barrier of entry to MRS processing for neuroimaging researchers.

Rapid Vector-Based Peak Fitting and Resolution Enhancement for Correlation Analyses of Raman Hyperspectra.

Spectroscopic peak parameters are important since they provide information about the analyte under study. Besides obtaining these parameters, peak fitting also resolves overlapped peaks. Thus, the obtained parameters should permit the construction of a higher-resolution version of the original spectrum. However, peak fitting is not an easy task due to computational reasons and because the true nature of the analyte is often unknown. These difficulties are major impediments when large hyperspectral data sets need to be processed rapidly, such as for manufacturing process control. We have developed a novel and relatively fast two-part algorithm to perform peak fitting and resolution enhancement on such data sets. In the first part of the algorithm, estimates of the total number of bands and their parameters were obtained from a representative spectrum in the data set, using a combination of techniques. Starting with these parameter estimates, all the spectra were then iteratively and rapidly fitted with Gaussian bands, exploiting intrinsic features of the Gaussian distribution with vector operations. The best fits for each spectrum were retained. By reducing the obtained bandwidths and commensurately increasing their amplitudes, high-resolution spectra were constructed that greatly improved correlation-based analyses. We tested the performance of the algorithm on synthetic spectra to confirm that this method could recover the ground truth correlations between highly overlapped peaks. To assess effective peak resolution, the method was applied to low-resolution spectra of glucose and compared to results from high-resolution spectra. We then processed a larger spectral data set from mammalian cells, fixed with methanol or air drying, to demonstrate the resolution enhancement of the algorithm on complex spectra and the effects of resolution-enhanced spectra on two-dimensional correlation spectroscopy and principal component analyses. The results indicated that the algorithm would allow users to obtain high-resolution spectra relatively fast and permit the recovery of important aspects of the data's intrinsic correlation structure.

A new UltraScan module for the characterization and quantification of analytical buoyant density equilibrium experiments to determine AAV capsid loading.

A method for characterizing and quantifying peaks formed in an analytical buoyant density equilibrium (ABDE) experiment is presented. An algorithm is derived to calculate the concentration of the density forming gradient material at every point in the cell, provided the rotor speed, temperature, meniscus position, bottom of the cell position, and the loading concentration, molar mass, and partial specific volume of the density gradient-forming material are known. In addition, a new peak fitting algorithm has been developed which allows the user to automatically quantify the peaks formed in terms of density, apparent partial specific volume, and relative abundance. The method is suitable for both ionic and non-ionic density forming materials and can be used with data generated from the UV optical system as well as the AVIV fluorescence optical system. These methods have been programmed in a new UltraScan-III module (us_abde). Examples are shown that demonstrate the application of the new module to adeno-associated viral vector preparations and proteins.

Study on the Occurrence Difference of Functional Groups in Coals with Different Metamorphic Degrees.

In order to quantitatively study the difference in occurrence content of functional groups in coals with different metamorphic degrees, the samples of long flame coal, coking coal, and anthracite of three different coal ranks were characterized by FTIR and the relative content of various functional groups in different coal ranks was obtained. The semi-quantitative structural parameters were calculated, and the evolution law of the chemical structure of the coal body was given. The results show that with the increase in the metamorphic degree, the substitution degree of hydrogen atoms on the benzene ring in the aromatic group increases with the increase in the vitrinite reflectance. With the increase in coal rank, the content of phenolic hydroxyl, carboxyl, carbonyl, and other active oxygen-containing groups gradually decreased, and the content of ether bonds gradually increased. Methyl content increased rapidly first and then increased slowly, methylene content increased slowly first and then decreased rapidly, and methylene content decreased first and then increased. With the increase in vitrinite reflectance, the OH-π hydrogen bond gradually increases, the content of hydroxyl self-association hydrogen bond first increases and then decreases, the oxygen-hydrogen bond of hydroxyl ether gradually increases, and the ring hydrogen bond first significantly decreases and then slowly increases. The content of the OH-N hydrogen bond is in direct proportion to the content of nitrogen in coal molecules. It can be seen from the semi-quantitative structural parameters that with the increase in coal rank, the aromatic carbon ratio fa, aromatic degree AR and condensation degree DOC increase gradually. With the increase in coal rank, A(CH2)/A(CH3) first decreases and then increases, hydrocarbon generation potential 'A' first increases and then decreases, maturity 'C' first decreases rapidly and then decreases slowly, and factor D gradually decreases. This paper is valuable for analyzing the occurrence form of functional groups in different coal ranks and clarifying the evolution process of structure in China.

Bacterial Metabolites and Particle Size Determine Cerium Oxide Nanomaterial Biotransformation.

Soil is a major receptor of manufactured nanomaterials (NMs) following unintentional releases or intentional uses. Ceria NMs have been shown to undergo biotransformation in plant and soil organisms with a partial Ce(IV) reduction into Ce(III), but the influence of environmentally widespread soil bacteria is poorly understood. We used high-energy resolution fluorescence-detected X-ray absorption spectroscopy (HERFD-XAS) with an unprecedented detection limit to assess Ce speciation in a model soil bacterium (Pseudomonas brassicacearum) exposed to CeO2 NMs of different sizes and shapes. The findings revealed that the CeO2 NM's size drives the biotransformation process. No biotransformation was observed for the 31 nm CeO2 NMs, contrary to 7 and 4 nm CeO2 NMs, with a Ce reduction of 64 ± 14% and 70 ± 15%, respectively. This major reduction appeared quickly, from the early exponential bacterial growth phase. Environmentally relevant organic acid metabolites secreted by Pseudomonas, especially in the rhizosphere, were investigated. The 2-keto-gluconic and citric acid metabolites alone were able to induce a significant reduction in 4 nm CeO2 NMs. The high biotransformation measured for <7 nm NMs would affect the fate of Ce in the soil and biota.

Development of Solvent-Free Co-Ground Method to Produce Terbinafine Hydrochloride Cyclodextrin Binary Systems; Structural and In Vitro Characterizations.

Molecular complexation with cyclodextrins (CDs) has long been a known process for modifying the physicochemical properties of problematic active pharmaceutical ingredients with poor water solubility. In current times, the focus has been on the solvent-free co-grinding process, which is an industrially feasible process qualifying as a green technology. In this study, terbinafine hydrochloride (TER), a low solubility antifungal drug was used as a model drug. This study aimed to prepare co-ground products and follow through the preparation process of the co-grinding method in the case of TER and two amorphous CD derivatives: (2-hydroxypropyl)-ß-cyclodextrin (HPBCD); heptakis-(2,6-di-O-methyl)-ß-cyclodextrin (DIMEB). For this evaluation, the following analytical tools and methods were used: phase solubility studies, differential scanning calorimetry (DSC), X-ray powder diffractometry (XRPD), hot-stage X-ray powder diffractometry (HOT-XRPD), Fourier-transform infrared (FT-IR), Raman spectroscopy, and Scanning Electron Microscopy (SEM). Furthermore, in vitro characterization (dissolution and diffusion studies) was performed in two kinds of dissolution medium without enzymes. In the XRPD and SEM studies, it was found that the co-grinding of the components resulted in amorphous products. FT-IR and Raman spectroscopies confirmed the formation of an inclusion complex through the unsaturated aliphatic chain of TER and CDs. In vitro characterization suggested better dissolution properties for both CDs and decreased diffusion at higher pH levels in the case of HPBCD.

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.

Applying Log-Normal Peak Fitting to Parallel Reaction Monitoring Data Analysis.

Chromatographic separation is often an important part of mass-spectrometry-based proteomic analysis. It reduces the complexity of the initial samples before they are introduced to mass-spectrometric detection and chromatographic characteristics (such as retention time) add analytical features to the analyte. The acquisition and analysis of chromatographic data are thus of great importance, and specialized software is used for the extraction of quantitative information in an efficient and optimized manner. However, occasionally, automatic peak picking and correct peak boundary setting is challenged by, for instance, aberration of peak shape, peak truncation, and peak tailing, and a manual review of a large number of peaks is frequently required. To support this part of the analysis, we present here a software tool, Peakfit, that fits acquired chromatographic data to the log-normal peak equation and reports the calculated peak parameters. The program is written in R and can easily be integrated into Skyline, a popular software packages that is frequently used for proteomic parallel reaction monitoring applications. The program is capable of processing large data sets (>10 000 peaks) and detecting sporadic outliers in peak boundary selection performed, for instance, in Skyline. In an example data set, available via ProteomeXchange with identifier PXD026875, we demonstrated the capability of the program to characterize chromatographic peaks and showed an example of its ability to objectively and reproducibly detect and solve problematic peak-picking situations.

THORONDOR: a software for fast treatment and analysis of low-energy XAS data.

THORONDOR is a data treatment software with a graphical user interface (GUI) accessible via the browser-based Jupyter notebook framework. It aims to provide an interactive and user-friendly tool for the analysis of NEXAFS spectra collected during in situ experiments. The program allows on-the-fly representation and quick correction of large datasets from single or multiple experiments. In particular, it provides the possibility to align in energy several spectral profiles on the basis of user-defined references. Various techniques to calculate background subtraction and signal normalization have been made available. In this context, an innovation of this GUI involves the usage of a slider-based approach that provides the ability to instantly manipulate and visualize processed data for the user. Finally, the program is characterized by an advanced fitting toolbox based on the lmfit package. It offers a large selection of fitting routines as well as different peak distributions and empirical ionization potential step edges, which can be used for the fit of the NEXAFS rising-edge peaks. Statistical parameters describing the goodness of a fit such as χ2 or the R-factor together with the parameter uncertainty distributions and the related correlations can be extracted for each chosen model.

Resolving overlapped signals with automated FitNMR analytical peak modeling.

Nuclear magnetic resonance (NMR) is a valuable tool for determining the structures of molecules and probing their dynamics. A longstanding problem facing both small-molecule and macromolecular NMR is overlapped signals in crowded spectra. To address this, we have developed a method that extracts peak features by fitting analytically derived models of NMR lineshapes. The approach takes into account the effects of truncation, apodization, and the resulting artifacts, while avoiding systematic errors that have affected other models. Even severely overlapped peaks, beyond the point of coalescence, can be distinguished in both simulated and experimental data. We show that the method can measure unresolved backbone scalar couplings directly from a 2D proton-nitrogen spectrum of a de novo designed mini protein. The algorithm is implemented in the FitNMR open-source R package and can be used to analyze nearly any type of single or multidimensional data from small molecules or biomolecules.

Modified Gaussian models applied to the description and deconvolution of peaks in chiral liquid chromatography.

The description of the profiles of chromatographic peaks has been studied extensively, with a large number of proposed mathematical functions. Among them, the accuracy achieved with modified Gaussian models that describe the deviation of an ideal Gaussian peak as a change in the peak variance or standard deviation over time, has been highlighted. These models are, in fact, a family of functions of different complexity with great flexibility to adjust chromatographic peaks over a wide range of asymmetries and shapes. However, an uncontrolled behaviour of the signal may occur outside the region being fitted, forcing the use of different strategies to overcome this problem. In this work, the performance of the LMG (Linear Modified Gaussian), PVMG (Parabolic Variance Modified Gaussian), and PLMG (Parabolic-Lorentzian Modified Gaussian) models is compared with variants obtained by combination of the modified Gaussian models with an equation that adds an exponential tail and with other functions that limit the growth of the independent variable. The behaviour of the approaches is checked through the simultaneous fitting of enantiomeric peaks showing a wide range of characteristics, obtained in the separation of drugs with chiral activity by liquid chromatography using enantioselective columns. The study is also carried out with the purpose of performing the deconvolution of the peaks of the enantiomers, when these are not completely resolved, in order to evaluate the enantiomeric fraction.

Exploring the accuracy of isotopic analyses in atom probe mass spectrometry.

Atom probe tomography (APT) can theoretically deliver accurate chemical and isotopic analyses at a high level of sensitivity, precision, and spatial resolution. However, empirical APT data often contain significant biases that lead to erroneous chemical concentration and isotopic abundance measurements. The present study explores the accuracy of quantitative isotopic analyses performed via atom probe mass spectrometry. A machine learning-based adaptive peak fitting algorithm was developed to provide a reproducible and mathematically defensible means to determine peak shapes and intensities in the mass spectrum for specific ion species. The isotopic abundance measurements made with the atom probe are compared directly with the known isotopic abundance values for each of the materials. Even in the presence of exceedingly high numbers of multi-hit detection events (up to 80%), and in the absence of any deadtime corrections, our approach produced isotopic abundance measurements having an accuracy consistent with values limited predominantly by counting statistics.

Influencing Factors for Determining the Crystallinity of Native Cellulose by X-ray Diffraction.

Full width at half maximum (FWHM) of diffraction peaks of native cellulose is larger than 1°. The diffraction peaks of the crystalline phase largely coincide with those of the amorphous phase of cellulose, leading to the low resolution. Therefore, when calculating the crystallinity of natural cellulose by fitting the peaks, only relying on the single evaluation factor of Rwp (R-weighted Pattern) may lead to great randomicity of calculation results. Due to the special crystal structure of natural cellulose and the characteristics of crystallinity determination by peak separation method, the XRD Rietveld fitting method is adopted to determine the crystallinity of native cellulose. Through limiting the convergence conditions of fitting functions, we firstly discuss the effects of peak shape functions, scanning range, and the positions of amorphous peaks on crystallinity determination, which helps to reduce the randomness of XRD in solving crystallinity of cellulose and improve the precision of calculation. Then, three evaluation indexes (Rwp, FWHM, and RSD) are used to evaluate the rationality of crystallinity calculation results. Moreover, the reproducibility and precision of the crystallinity of three kinds of natural cellulose are tested under the optimized conditions. At the same time, the optimized fitting method is adopted to calculate the content of cellulose I and II, which can be used to guide the selection of alkali concentration and alkali treatment time in the spinning process of native cellulose.

FTIR investigation of the secondary structure of type I collagen: New insight into the amide III band.

This work presents a thorough study on the Amide III band in fibrous proteins using Fourier Transformed Infrared Spectroscopy (FTIR). Type I collagen was chosen as a model for this family of proteins, not only because of its important role in mammalian tissues, but also for its involvement in several pathologies. In order to disclose the conformational information contained in the collagen bands, the spectral characteristics of Amide III of type I collagen were related to the ones of Amide I band, performing experiments of thermal denaturation of the protein in acidic solution. Data acquired allowed to observe the protein unfolding and retrieve information about its structural arrangements during the thermal cycle. Taking as guideline the well-known behaviour of the Amide I band, we correlated the structural changes deducible from Amide I analysis with the ones detectable for Amide III band, by exploiting three spectral analysis techniques, namely 2D-correlation analysis, second derivative analysis, and peak-fitting. This approach enabled us to jointly support the obtained results and finally to assign the components of the Amide III of a typical fibrous protein, such as type I collagen, to its characteristic secondary structure.

Fate and UF fouling behavior of algal extracellular and intracellular organic matter under the influence of copper ions.

Copper ion plays an important role in the outbreak of algal blooms. The aim of this research was to investigate the fate and fouling behavior of algal extracellular organic matter (EOM) and intracellular organic matter (IOM) under different copper concentrations during ultrafiltration (UF). Under the lowest copper concentration of 0.01 µmol/L, both the EOM and IOM caused the largest decrease in filtration flux, followed by EOM under high copper concentrations of 0.3 µmol/L and 0.1 µmol/L; less membrane fouling was induced by IOM under a copper concentration of 0.3 µmol/L than under a copper concentration of 0.1 µmol/L. More reversible fouling (Rre) was induced by EOM/IOM at the lowest copper concentration of 0.01 µmol/L than under the other two copper concentrations, whereas more membrane fouling was induced by EOM than by IOM under the different copper concentrations. Fluorescence excitation-emission matrices-parallel factor analysis (PARAFAC) indicated that fouling by the protein-like components in EOM was more irreversible under 0.01 µmol/L copper than under 0.1 µmol/L and 0.3 µmol/L copper. However, the fouling by this component was more reversible in the IOM solutions than in the EOM solutions. The amount of macromolecular biopolymers was highly correlated with the total and reversible membrane fouling during the EOM/IOM filtration, whereas a weaker correlation (r2) between the humic-like organics and membrane fouling was determined by Pearson's correlation matrix analysis. High-performance size exclusion chromatography (HPSEC) combined with peak-fitting prediction was suggested to be more suitable for membrane fouling implication in algal water treatment.

Automated single-ion peak fitting as an efficient approach for analyzing complex chromatographic data.

Chromatography provides important detail on the composition of environmental samples and their chemical processing. However, the complexity of these samples and their tendency to contain many structurally and chemically similar compounds frequently results in convoluted or poorly resolved data. Data reduction from raw chromatograms of complex environmental data into integrated peak areas consequently often requires substantial operator interaction. This difficulty has led to a bottleneck in analysis that increases analysis time, decreases data quality, and will worsen as advances in field-based instrumentation multiply the quantity and informational density of data produced. In this work, we develop and validate an automated approach to fitting chromatographic data within a target retention time window with a combination of multiple idealized peaks (Gaussian peaks either with or without an exponential decay component). We compare this single-ion peak fitting approach to drawn baseline integration methods of more than 70,000 peaks collected by field-based chromatographs spanning across a wide range of volatilities and functionalities. Accuracy of peak fitting under real-world conditions is found to be within 10%. The quantitative parameters describing the fit (e.g. coefficients, fit residuals, etc.) are found to provide valuable information to increase the efficiency of quality control and provide constraints to accurately integrate peaks that are significantly convoluted with neighboring peaks. Implementation of the peak fitting method is shown to yield accurate integration of peaks otherwise too poorly resolved to separate into individual compounds and improved quantitative metrics to determine the fidelity of the data reduction process, while substantially decreasing the time spent by operators on data reduction.

An Empirical Study on Raman Peak Fitting and Its Application to Raman Quantitative Research.

Fitting experimentally measured Raman bands with theoretical model profiles is the basic operation for numerical determination of Raman peak parameters. In order to investigate the effects of peak modeling using various algorithms on peak fitting results, the representative Raman bands of mineral crystals, glass, fluids as well as the emission lines from a fluorescent lamp, some of which were measured under ambient light whereas others under elevated pressure and temperature conditions, were fitted using Gaussian, Lorentzian, Gaussian-Lorentzian, Voigtian, Pearson type IV, and beta profiles. From the fitting results of the Raman bands investigated in this study, the fitted peak position, intensity, area and full width at half-maximum (FWHM) values of the measured Raman bands can vary significantly depending upon which peak profile function is used in the fitting, and the most appropriate fitting profile should be selected depending upon the nature of the Raman bands. Specifically, the symmetric Raman bands of mineral crystals and non-aqueous fluids are best fit using Gaussian-Lorentzian or Voigtian profiles, whereas the asymmetric Raman bands are best fit using Pearson type IV profiles. The asymmetric O-H stretching vibrations of H2O and the Raman bands of soda-lime glass are best fit using several Gaussian profiles, whereas the emission lines from a florescent light are best fit using beta profiles. Multiple peaks that are not clearly separated can be fit simultaneously, provided the residuals in the fitting of one peak will not affect the fitting of the remaining peaks to a significant degree. Once the resolution of the Raman spectrometer has been properly accounted for, our findings show that the precision in peak position and intensity can be improved significantly by fitting the measured Raman peaks with appropriate profiles. Nevertheless, significant errors in peak position and intensity were still observed in the results from fitting of weak and wide Raman bands having unnormalized intensity/FWHM ratios lower than 200 counts/cm-1.

Quick AS NEXAFS Tool (QANT): a program for NEXAFS loading and analysis developed at the Australian Synchrotron.

An analysis program for near-edge X-ray absorption fine-structure (NEXAFS) spectra has been developed and implemented at the soft X-ray beamline of the Australian Synchrotron. The program allows for instant viewing of corrected data channels including normalizations to a standard, double normalizations when the standard itself has an undesired spectral response, and background subtraction. The program performs simple compositional analysis and peak fitting and includes rapid common calculations such as the average tilt angle of molecules with respect to the surface, and the determination of the complex index of refraction, which previously required intensive manual analysis. These functionalities make common manipulations carried out with NEXAFS data quick and straightforward as spectra are collected, greatly increasing the efficiency and overall throughput of NEXAFS experiments.

Comparative quantification of dietary supplemented neural creatine concentrations with (1)H-MRS peak fitting and basis spectrum methods.

Magnetic resonance spectroscopy (MRS) is an analytical procedure that can be used to non-invasively measure the concentration of a range of neural metabolites. Creatine is an important neurometabolite with dietary supplementation offering therapeutic potential for neurological disorders with dysfunctional energetic processes. Neural creatine concentrations can be probed using proton MRS and quantified using a range of software packages based on different analytical methods. This experiment examines the differences in quantification performance of two commonly used analysis packages following a creatine supplementation strategy with potential therapeutic application. Human participants followed a seven day dietary supplementation regime in a placebo-controlled, cross-over design interspersed with a five week wash-out period. Spectroscopy data were acquired the day immediately following supplementation and analyzed with two commonly-used software packages which employ vastly different quantification methods. Results demonstrate that neural creatine concentration was augmented following creatine supplementation when analyzed using the peak fitting method of quantification (105.9%±10.1). In contrast, no change in neural creatine levels were detected with supplementation when analysis was conducted using the basis spectrum method of quantification (102.6%±8.6). Results suggest that software packages that employ the peak fitting procedure for spectral quantification are possibly more sensitive to subtle changes in neural creatine concentrations. The relative simplicity of the spectroscopy sequence and the data analysis procedure suggest that peak fitting procedures may be the most effective means of metabolite quantification when detection of subtle alterations in neural metabolites is necessary. The straightforward technique can be used on a clinical magnetic resonance imaging system.