Triolein, a triglyceride containing oleic acid as the only acid moiety in the glyceride molecules has been isothermally treated at 280, 300, and 325 o C in glass vials under nitrogen atmosphere. The products formed during the thermal treatment at each temperature have been analysed both by infrared spectrometry and GC-MS. The GC-MS analysis was performed after derivatisation of the fatty acids into their methyl esters (FAMEs).Chemometric tools were used in determining the concentrations of the main products namely triolein and trieaidin in the thermally treated mixtures. The concentration profiles of the trielaidin formed during thermal treatment at the above three temperatures were used in determining activation energy for the cis-trans isomerisation of triolein The combined analysis reveals that the thermal treatment induces not only cis-trans isomerisation but also fission and fusion in the molecules. Furthermore, migration of the double bond in oleic and elaidic acids forming cis and trans isomers of the 18:1 acid was also observed. The heat induced isomerisation in triolein follows a zeroeth order reaction with an activation energy 41±5 kcal/mol.
Described here is a mass spectrometry-based covalent labeling protocol that utilizes the amine reactive reagent, s-methyl thioacetimidate (SMTA), to study the chemical denaturant-induced equilibrium unfolding/refolding properties of proteins and protein-ligand complexes in solution. The protocol, which involves evaluating the rate at which globally protected amine groups in a protein are modified with SMTA as a function of chemical denaturant concentration, is developed and applied to the analysis of eight protein samples including six purified protein samples (ubiquitin, BCAII, RNaseA, 4OT, and lysozyme with, and without GlcNAc), a five-protein mixture comprised of ubiquitin, BCAII, RNaseA, Cytochrome C, and lysozyme, and a yeast cell lysate. In ideal cases the folding free energies of proteins and the dissociation constants of protein-ligand complexes can be accurately evaluated using the protocol. A direct MALDI-TOF readout is demonstrated for analysis of purified protein samples. Bottom-up proteomic strategies involving gel-based and/or LC-MS-based shotgun proteomic platforms are also demonstrated for the analyses of complex protein samples. Analysis of proteins in a yeast cell lysate suggests the SMTA-labeling protocol expands the peptide and protein coverage in chemical modification- and shotgun proteomics-based strategies for making thermodynamic measurements of protein folding and stability on the proteomic scale.
Monitoring the changes of jet fuel physical properties is important because fuel used in high-performance aircraft must meet rigorous specifications. Near-infrared (NIR) spectroscopy is a fast method to characterize fuels. Because of the complexity of NIR spectral data, chemometric techniques are used to extract relevant information from spectral data to accurately classify physical properties of complex fuel samples. In this work, discrimination of fuel types and classification of flash point, freezing point, boiling point (10%, v/v), boiling point (50%, v/v), and boiling point (90%, v/v) of jet fuels (JP-5, JP-8, Jet A, and Jet A1) were investigated. Each physical property was divided into three classes, low, medium, and high ranges, using two evaluations with different class boundary definitions. The class boundaries function as the threshold to alarm when the fuel properties change. Optimal partial least squares discriminant analysis (oPLS-DA), fuzzy rule-building expert system (FuRES), and support vector machines (SVM) were used to build the calibration models between the NIR spectra and classes of physical property of jet fuels. OPLS-DA, FuRES, and SVM were compared with respect to prediction accuracy. The validation of the calibration model was conducted by applying bootstrap Latin partition (BLP), which gives a measure of precision. Prediction accuracy of 97 ± 2% of the flash point, 94 ± 2% of freezing point, 99 ± 1% of the boiling point (10%, v/v), 98 ± 2% of the boiling point (50%, v/v), and 96 ± 1% of the boiling point (90%, v/v) were obtained by FuRES in one boundaries definition. Both FuRES and SVM obtained statistically better prediction accuracy over those obtained by oPLS-DA. The results indicate that combined with chemometric classifiers NIR spectroscopy could be a fast method to monitor the changes of jet fuel physical properties.
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