Gas chromatographic (GC) determination of quantitative fatty acid composition: which derivatization reagent is the best?
In this study, quantitative gas chromatography‒mass spectrometry (GC–MS) analysis was used to evaluate the influence of pigment concentration on the drying of oil paints. Seven sets of artificially aged self-made paints with different pigments (yellow ochre, red ochre, natural cinnabar, zinc white, Prussian blue, chrome oxide green, hematite + kaolinite) and linseed oil mixtures were analysed. In the pigment + linseed oil mixtures, linseed oil concentration varied in the range of 10 to 95 g/100 g. The results demonstrate that the commonly used palmitic acid to stearic acid ratio (P/S) to distinguish between drying oils varied in a vast range (from especially low 0.6 to a common 1.6) even though the paints contained the same linseed oil. Therefore, the P/S ratio is an unreliable parameter, and other criteria should be included for confirmation. The pigment concentration had a substantial effect on the values used to characterise the degree of drying (azelaic acid to palmitic acid ratio (A/P) and the relative content of dicarboxylic acids (∑D)). The absolute quantification showed that almost all oil paint mock-ups were influenced by pigment concentration. Therefore, pigment concentration needs to be considered as another factor when characterising oil-based paint samples based on the lipid profile.
Monoaminoacridines (1-, 2-, 3-, 4-, and 9-aminoacridine) were studied for suitability as matrices in the negative ion mode matrix-assisted laser desorption/ionization mass spectrometry (MALDI(−)-MS) analysis of various samples. This is the first study to examine 1-, 2-, and 4-aminoacridine as potential matrix material candidates for MALDI(−)-MS. In addition, spectral (UV−Vis absorption and fluorescence), proton transfer-related (basicity and autoprotolysis), and crystallization properties of these compounds were characterized experimentally and/or computationally. For testing the capabilities of these aminoacridines as matrix materials, four samples related to cultural heritage materialsstearic acid, colophony resin, dyer's madder dye, and a resinous case-study sample from a shipwreckwere analyzed with MALDI(−)-MS. A novel algorithm (implemented as an executable Python script) for MS data analysis was developed to compare the five matrix materials and to help mass spectrometrists rapidly identify peaks originating from the sample and matrix material. It was determined that all five of the studied aminoacridines can successfully be used as matrix materials in MALDI(−)-MS analysis. As an interesting finding, in several cases, the best mass spectra were obtained by using a relatively small amount of matrix material mixed with an excess amount of sample. 3-and 4-aminoacridine outperformed the other aminoacridines in the ease of obtaining acceptable spectra, average number of ions identified in the mass spectra, and low dependence of the sample-to-matrix mass ratio on experimental results.
RationaleThe purpose of the current work is to realistically assess the uncertainty contribution in gas chromatography/mass spectrometry (GC/MS) analysis originating from less‐than‐ideal derivatization efficiency.MethodsAs the exemplary analytical method a two‐step derivatization method with KOH and BSTFA (N,O‐bis(trimethylsilyl)trifluoroacetamide), applied for the analysis of fatty acid triglycerides (using real measurement data), was selected. The derivatization efficiencies were in the range 0.89–1.04. In this study, two approaches for bottom‐up uncertainty evaluation were compared: the traditional GUM approach and the Monte Carlo method (MCM). Both were used with and without taking correlation between input quantities into account.ResultsThe most reliable uncertainty estimates were in the range 0.07–0.08 (expanded uncertainties at 95% coverage probability). A strong negative correlation was found between the slope and intercept of the calibration graph (r = −0.71) and it markedly influenced the uncertainty estimate of derivatization efficiency. The MCM was found to give somewhat higher uncertainty estimates, which are considered more realistic.ConclusionsDerivatization directly affects the analysis result. Thus, in the case of this exemplary analysis, just derivatization alone (i.e. if all other uncertainty sources are neglected) causes relative expanded uncertainty around 8%, being thus an important and in some cases the dominant uncertainty contributor.
Rhodotorula toruloides is a non-conventional, oleaginous yeast able to naturally accumulate high amounts of microbial lipids. Constraint-based modeling of R. toruloides has been mainly focused on the comparison of experimentally measured and model predicted growth rates, while the intracellular flux patterns have been analyzed on a rather general level. Hence, the intrinsic metabolic properties of R. toruloides that make lipid synthesis possible are not thoroughly understood. At the same time, the lack of diverse physiological data sets has often been the bottleneck to predict accurate fluxes. In this study, we collected detailed physiology data sets of R. toruloides while growing on glucose, xylose, and acetate as the sole carbon source in chemically defined medium. Regardless of the carbon source, the growth was divided into two phases from which proteomic and lipidomic data were collected. Complemental physiological parameters were collected in these two phases and altogether implemented into metabolic models. Simulated intracellular flux patterns demonstrated the role of phosphoketolase in the generation of acetyl-CoA, one of the main precursors during lipid biosynthesis, while the role of ATP citrate lyase was not confirmed. Metabolic modeling on xylose as a carbon substrate was greatly improved by the detection of chirality of D-arabinitol, which together with D-ribulose were involved in an alternative xylose assimilation pathway. Further, flux patterns pointed to metabolic trade-offs associated with NADPH allocation between nitrogen assimilation and lipid biosynthetic pathways, which was linked to large-scale differences in protein and lipid content. This work includes the first extensive multi-condition analysis of R. toruloides using enzyme-constrained models and quantitative proteomics. Further, more precise kcat values should extend the application of the newly developed enzyme-constrained models that are publicly available for future studies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.