Browse
Submit Data
Databases
API
Help

Dataset Information

31 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Discrimination between vegetable oil and animal fat by a metabolomics approach using gas chromatography-mass spectrometry combined with chemometrics.

ABSTRACT: Adulteration of olive oil with the other cheap oils and fats plays an important role in economics and has nutritional benefits. In this work, metabolite profiling was performed using gas chromatography-mass spectrometry to identify and quantify animal fat (lard) adulteration in vegetable oil (olive oil). Principal component analysis could correctly identify and clustering olive oil, sunflower oil, sesame oil, lard, and adulterated samples through the changes in their fatty acid methyl esters (FAMEs) profile. A targeted metabolomics method was then optimized and validated through construction of calibration curves of known FAMSs in olive oil and lard. The method was presented high linearity (R² > 0.96) and good intra and inter day accuracy and precision (79-101 and 86-102% and 2-7 and 3-7, respectively) for determination of FAMEs. Afterwards the absolute concentration and relative percentage of FAMEs were successfully determined in 12 commercial olive oils and 3 lards samples. Methyl myristate, methyl palmitate, methyl oleate, and methyl stearate were selected as discriminant markers to identify and quantify lard adulteration even at a low level of lard (5%w/w), with errors less than 2% in the comparison of the absolute or relative concentrations of FAMEs using several statistical methods. The proposed methodology allowed us to quantify the FAMEs simultaneously and also could predict small amount of lard in the adulterated olive oil samples.

SUBMITTER: Heidari M

PROVIDER: S-EPMC7374695 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Color discrimination and gas chromatography-mass spectrometry fingerprint based on chemometrics analysis for the quality evaluation of Schizonepetae Spica.

Project description:Schizonepetae Spica (SS), the dried spike of Schizonepeta tenuifolia Briq., is a traditional Chinese medicinal herb. According to the color of persistent calyx, SS is categorized into two classes: the yellowish-green-type and the brownish-type. Based on the chemometrics analysis of gas chromatography-mass spectrometry (GC-MS), a novel model of identifying and evaluating the quality of SS in different colors was constructed for the first time in this work. 20 batches SS samples of different colors were collected and used to extract essential oils. The average essential oils yield of SS in yellowish-green color was significantly higher than that of SS in brownish color from the same origin (p<0.05). The GC-MS fingerprints of 20 batches SS samples whose correlation coefficients were over 0.964 demonstrated SS samples were consistent to some extent in spite of slightly different chemical indexes. A total of 39 common volatiles compounds were identified. Hierarchical clustering analysis (HCA), principal component analysis (PCA) and partial least-squares discriminate analysis (PLS-DA) were developed to distinguish SS samples characterized by different colors. Consistent results were obtained to show that SS samples could be successfully grouped according to their color. Finally, 4,5,6,7-tetrahydro-3,6-dimethyl-benzofuran and pulegone were detected as the key variables for discriminating SS samples of different colors and for quality control. The obtained results proved that SS of good quality were often yellowish-green and those of poor quality were often brownish.

| S-EPMC6946158 | biostudies-literature

Identification of potential diagnostic biomarkers of cerebral infarction using gas chromatography-mass spectrometry and chemometrics.

Project description:Cerebral infarction (CI) is one of the most common cerebrovascular diseases and remains a major health problem worldwide. In this study, we evaluated the potential diagnostic biomarkers and important relevant metabolic pathways associated with CI. Metabolomics based on gas chromatography-mass spectrometry coupled with the multivariate pattern recognition technique were used to characterize the potential serum metabolic profiles of CI. Forty healthy controls and thirty-three cerebral infarction patients were recruited for the nontargeted global metabolites' study and subsequent targeted fatty acid analysis. Overall, thirty-four endogenous metabolites were found in serum from the untargeted global study, four of which were detected to be significantly different between the CI group and healthy controls, including l-lysine, octadecanoic acid (fatty acid), l-tyrosine and lactic acid. Additionally, fourteen free fatty acids were identified by the subsequent targeted fatty acid analysis, and seven of them were detected to be significantly different between the CI group and healthy controls, which were mainly associated with arachidonic acid metabolism and fatty acid metabolism. Our results suggest several potential diagnostic biomarkers, and serum metabolism research is demonstrated as a powerful tool to explore the pathogenesis of CI.

| S-EPMC9081573 | biostudies-literature

Chemical discrimination in turbulent gas mixtures with MOX sensors validated by gas chromatography-mass spectrometry.

Project description:Chemical detection systems based on chemo-resistive sensors usually include a gas chamber to control the sample air flow and to minimize turbulence. However, such a kind of experimental setup does not reproduce the gas concentration fluctuations observed in natural environments and destroys the spatio-temporal information contained in gas plumes. Aiming at reproducing more realistic environments, we utilize a wind tunnel with two independent gas sources that get naturally mixed along a turbulent flow. For the first time, chemo-resistive gas sensors are exposed to dynamic gas mixtures generated with several concentration levels at the sources. Moreover, the ground truth of gas concentrations at the sensor location was estimated by means of gas chromatography-mass spectrometry. We used a support vector machine as a tool to show that chemo-resistive transduction can be utilized to reliably identify chemical components in dynamic turbulent mixtures, as long as sufficient gas concentration coverage is used. We show that in open sampling systems, training the classifiers only on high concentrations of gases produces less effective classification and that it is important to calibrate the classification method with data at low gas concentrations to achieve optimal performance.

| S-EPMC4239943 | biostudies-literature

Integrating comprehensive two-dimensional gas chromatography mass spectrometry and parallel two-dimensional liquid chromatography mass spectrometry for untargeted metabolomics.

Project description:The diverse characteristics and large number of entities make metabolite separation challenging in metabolomics. To date, there is not a singular instrument capable of analyzing all types of metabolites. In order to achieve a better separation for higher peak capacity and accurate metabolite identification and quantification, we integrated GC × GC-MS and parallel 2DLC-MS for analysis of polar metabolites. To test the performance of the developed system, 13 rats were fed different diets to form two animal groups. Polar metabolites extracted from rat livers were analyzed by GC × GC-MS, parallel 2DLC-MS (-) and parallel 2DLC-MS (+), respectively. By integrating all data together, 58 metabolites were detected with significant change in their abundance levels between groups (p≤ 0.05). Of the 58 metabolites, three metabolites were detected in two platforms and two in all three platforms. Manual examination showed that discrepancy of metabolite regulation measured by different platforms was mainly caused by the poor shape of chromatographic peaks resulting from low instrument response. Pathway analysis demonstrated that integrating the results from multiple platforms increased the confidence of metabolic pathway assignment.

| S-EPMC6677244 | biostudies-literature

Urinary Metabolomics Study of Patients with Gout Using Gas Chromatography-Mass Spectrometry.

Project description:ObjectivesGout is a common type of inflammatory arthritis. The aim of this study was to detect urinary metabolic changes in gout patients which may contribute to understanding the pathological mechanism of gout and discovering potential metabolite markers.MethodsUrine samples from 35 gout patients and 29 healthy volunteers were analyzed by gas chromatography-mass spectrometry (GC-MS). Orthogonal partial least-squares discriminant analysis (OPLS-DA) was performed to screen differential metabolites between two groups, and the variable importance for projection (VIP) values and Student's t-test results were combined to define the significant metabolic changes caused by gout. Further, binary logistic regression analysis was performed to establish a model to distinguish gout patients from healthy people, and receiver operating characteristic (ROC) curve was made to evaluate the potential for diagnosis of gout.ResultA total of 30 characteristic metabolites were significantly different between gout patients and controls, mainly including amino acids, carbohydrates, organic acids, and their derivatives, associated with perturbations in purine nucleotide synthesis, amino acid metabolism, purine metabolism, lipid metabolism, carbohydrate metabolism, and tricarboxylic acid cycle. Binary logistic regression and ROC curve analysis showed the combination of urate and isoxanthopterin can effectively discriminate the gout patients from controls with the area under the curve (AUC) of 0.879.ConclusionThus, the urinary metabolomics study is an efficient tool for a better understanding of the metabolic changes of gout, which may support the clinical diagnosis and pathological mechanism study of gout.

| S-EPMC6206583 | biostudies-literature

High-resolution gas chromatography/mass spectrometry metabolomics of non-human primate serum.

Project description:RationaleMetabolomics analyses using gas chromatography/mass spectrometry (GC/MS)-based metabolomics are heavily impeded by the lack of high-resolution mass spectrometers and limited spectral libraries to complement the excellent chromatography that GC platforms offer, a challenge that is being addressed with the implementation of high-resolution (HR) platforms such as 1D-GC/Orbitrap-MS.MethodsWe used serum samples from a non-human primate (NHP), a baboon (Papio hamadryas), with suitable quality controls to quantify the chemical space using an advanced HRMS platform for confident metabolite identification and robust quantification to assess the suitability of the platform for routine clinical metabolomics research. In a complementary approach, we also analyzed the same serum samples using two-dimensional gas chromatography/time-of-flight mass spectrometry (2D-GC/TOF-MS) for metabolite identification and quantification following established standard protocols.ResultsOverall, the 2D-GC/TOF-MS (~5000 peaks per sample) and 1D-GC/Orbitrap-MS (~500 peaks per sample) analyses enabled identification and quantification of a total of 555 annotated metabolites from the NHP serum with a spectral similarity score Rsim ≥ 900 and signal-to-noise (S/N) ratio of >25. A common set of 30 metabolites with HMDB and KEGG IDs was quantified in the serum samples by both platforms where 2D-GC/TOF-MS enabled quantification of a total 384 metabolites (118 HMDB IDs) and 1D-GC/Orbitrap-MS analysis quantification of a total 200 metabolites (47 HMDB IDs). Thus, roughly 30-70% of the peaks remain unidentified or un-annotated across both platforms.ConclusionsOur study provides insights into the benefits and limitations of the use of a higher mass resolution and mass accuracy instrument for untargeted GC/MS-based metabolomics with multi-dimensional chromatography in future studies addressing clinical conditions or exposome studies.

| S-EPMC6395519 | biostudies-literature

Thermal degradation assessment of canola and olive oil using ultra-fast gas chromatography coupled with chemometrics.

Project description:Oil blending is often used to enhance the properties of vegetable oils. The admixture of a more thermally stable oil makes the resulting blend more suitable for use in frying. A new method of quality assessment of vegetable oils used in frying is presented in this paper. In this method, ultra-fast gas chromatography coupled with flame ionization detector and chemometrics is employed. Principal component analysis was used for data processing. The results obtained with this method were compared with the results of the Rancimat test and sensory evaluation. It is demonstrated that the addition of olive oil improves the stability of rapeseed oil, and also changes its flavour and aroma profile. In addition, it was found that ultra-fast GC coupled with chemometrics is an effective tool for the assessment of the quality of edible oils. The proposed method does not require sample preparation, and the total time of analysis is less than 2 min.

| S-EPMC5541118 | biostudies-other

Calibration-Curve-Locking Database for Semi-Quantitative Metabolomics by Gas Chromatography/Mass Spectrometry.

Project description:Calibration-Curve-Locking Databases (CCLDs) have been constructed for automatic compound search and semi-quantitative screening by gas chromatography/mass spectrometry (GC/MS) in several fields. CCLD felicitates the semi-quantification of target compounds without calibration curve preparation because it contains the retention time (RT), calibration curves, and electron ionization (EI) mass spectra, which are obtained under stable apparatus conditions. Despite its usefulness, there is no CCLD for metabolomics. Herein, we developed a novel CCLD and semi-quantification framework for GC/MS-based metabolomics. All analytes were subjected to GC/MS after derivatization under stable apparatus conditions using (1) target tuning, (2) RT locking technique, and (3) automatic derivatization and injection by a robotic platform. The RTs and EI mass spectra were obtained from an existing authorized database. A quantifier ion and one or two qualifier ions were selected for each target metabolite. The calibration curves were obtained as plots of the peak area ratio of the target compounds to an internal standard versus the target compound concentration. These data were registered in a database as a novel CCLD. We examined the applicability of CCLD for analyzing human plasma, resulting in time-saving and labor-saving semi-qualitative screening without the need for standard substances.

| S-EPMC8065573 | biostudies-literature

Three cases of xanthinuria identified by gas chromatography/mass spectrometry-based urine metabolomics.

Project description:IntroductionEarly diagnosis of patients with urolithiasis or hypouricemia owing to inborn errors of hypoxanthine metabolism is important in preventing renal failure or drug-induced toxicity.Case presentationWe identified three patients with xanthinuria using gas chromatography/mass spectrometry-based urine metabolomics: a 72-year-old male with bladder stone, a severe hypouricemic 59-year-old female with type 2 diabetes mellitus, and an 8-year and 9-month-old female who was first discovered to harbor a mutation in the xanthine dehydrogenase gene using whole-exome sequencing, but had a normal molybdenum cofactor sulfurase gene. Hydantoin-5-propionate was detected in the first and third patients but not in the second, suggesting that the first and second patients had type I and II xanthinuria, respectively.ConclusionGas chromatography/mass spectrometry-based metabolomics can be used for undiagnosed patients with xanthinuria, identification of the type of xanthinuria without allopurinol loading, and the quick functional evaluation of mutations in the xanthinuria-related genes.

| S-EPMC10622199 | biostudies-literature

Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography-Mass Spectrometry.

Project description:Large-scale metabolomics assays are widely used in epidemiology for biomarker discovery and risk assessments. However, systematic errors introduced by instrumental signal drifting pose a big challenge in large-scale assays, especially for derivatization-based gas chromatography-mass spectrometry (GC-MS). Here, we compare the results of different normalization methods for a study with more than 4000 human plasma samples involved in a type 2 diabetes cohort study, in addition to 413 pooled quality control (QC) samples, 413 commercial pooled plasma samples, and a set of 25 stable isotope-labeled internal standards used for every sample. Data acquisition was conducted across 1.2 years, including seven column changes. In total, 413 pooled QC (training) and 413 BioIVT samples (validation) were used for normalization comparisons. Surprisingly, neither internal standards nor sum-based normalizations yielded median precision of less than 30% across all 563 metabolite annotations. While the machine-learning-based SERRF algorithm gave 19% median precision based on the pooled quality control samples, external cross-validation with BioIVT plasma pools yielded a median 34% relative standard deviation (RSD). We developed a new method: systematic error reduction by denoising autoencoder (SERDA). SERDA lowered the median standard deviations of the training QC samples down to 16% RSD, yielding an overall error of 19% RSD when applied to the independent BioIVT validation QC samples. This is the largest study on GC-MS metabolomics ever reported, demonstrating that technical errors can be normalized and handled effectively for this assay. SERDA was further validated on two additional large-scale GC-MS-based human plasma metabolomics studies, confirming the superior performance of SERDA over SERRF or sum normalizations.

| S-EPMC10456436 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data