Browse
Submit Data
Databases
API
Help

Dataset Information

17 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Rapid geographical indication of peppercorn seeds using corona discharge mass spectrometry.

ABSTRACT: With increasing demands for more rapid and practical analyses, various techniques of ambient ionization mass spectrometry have gained significant interest due to the speed of analysis and abundance of information provided. Herein, an ambient ionization technique that utilizes corona discharge was applied, for the first time, to analyze and categorize whole seeds of black and white peppers from different origins. This setup requires no solvent application nor gas flow, thus resulting in a very simple and rapid analysis that can be applied directly to the sample without any prior workup or preparation. Combined with robust data pre-processing and subsequent chemometric analyses, this analytical method was capable of indicating the geographical origin of each pepper source with up to 98% accuracies in all sub-studies. The simplicity and speed of this approach open up the exciting opportunity for onsite analysis without the need for a highly trained operator. Furthermore, this methodology can be applied to a variety of spices and herbs, whose geographical indication or similar intellectual properties are economically important, hence it is capable of creating tremendous impact in the food and agricultural industries.

SUBMITTER: Charoensumran P

PROVIDER: S-EPMC8352875 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Rapid high mass resolution mass spectrometry using matrix-assisted ionization.

Project description:Matrix-assisted ionization (MAI) is demonstrated to be a robust and sensitive analytical method capable of analyzing proteins such as cholera toxin B-subunit and pertussis toxin mutant from conditions containing relatively high amounts of inorganic salts, buffers, and preservatives without the need for prior sample clean-up or concentration. By circumventing some of the sample preparation steps, MAI simplifies and accelerates the analytical workflow for biological samples in complex media. The benefits of multiply charged ions characteristic of electrospray ionization (ESI) and the robustness of matrix-assisted laser desorption/ionization (MALDI) can be obtained from a single method, making it well suited for analysis of proteins and other biomolecules at ultra-high resolution as demonstrated on an Orbitrap Fusion where protein subunits were resolved for which MALDI-time-of-flight failed. MAI results are compared with those obtained with ESI, MALDI, and laserspray ionization methods and fundamental commonalities discussed.

| S-EPMC4930895 | biostudies-literature

Rapid Determination of Procyanidins Using MALDI-ToF/ToF Mass Spectrometry.

Project description:Although procyanidins constitute a unique class of polymeric plant secondary metabolites with a variety of biological properties including potent antioxidant activity, structure determination has been challenging, and structures of many complex procyanidins remain uncertain. To expedite the characterization of procyanidins, negative ion matrix-assisted laser desorption ionization high-energy collision-induced dissociation tandem time-of-flight (MALDI-ToF/ToF) mass spectra of 20 isolated procyanidins containing catechin and epicatechin subunits with degrees of polymerization up to five were obtained and evaluated. Structurally significant fragmentation pathways of singly charged, deprotonated molecules were identified representing quinone methide, heterocyclic ring fission, and retro-Diels-Alder fragmentation. The interpretation of the tandem mass spectra for sequencing A-type, B-type, mixed-type, linear, and branched procyanidins is explained using specific examples of each.

| S-EPMC6296472 | biostudies-literature

Rapid Prediction of Electron-Ionization Mass Spectrometry Using Neural Networks.

Project description:When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In response, we propose to improve the library's coverage by augmenting it with synthetic spectra that are predicted from candidate molecules using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules, averaging 5 ms per molecule with a recall-at-10 accuracy of 91.8%. Achieving high-accuracy predictions requires a novel neural network architecture that is designed to capture typical fragmentation patterns from electron ionization. We analyze the effects of our modeling innovations on library matching performance and compare our models to prior machine-learning-based work on spectrum prediction.

| S-EPMC6487538 | biostudies-literature

Rapid Cyanobacteria Species Identification with High Sensitivity Using Native Mass Spectrometry.

Project description:Cyanobacteria have evolved over billions of years to adapt and survive in diverse climates. Environmentally, this presents a huge challenge because cyanobacteria can now rapidly form algae blooms that are detrimental to aquatic life. In addition, many cyanobacteria produce toxins, making them hazardous to animals and humans that they encounter. Rapid identification of cyanobacteria is essential to monitor and prevent toxic algae blooms. Here, we show for the first time how native mass spectrometry can quickly and precisely identify cyanobacteria from diverse aquatic environments. By monitoring phycobiliproteins, abundant protein complexes within cyanobacteria, simple, easy-to-understand mass spectral "fingerprints" were created that were unique to each species. Moreover, our method is 10-fold more sensitive than the current MALDI-TOF mass spectrometric methods, meaning that cyanobacteria can be monitored using this technology prior to bloom formation. Together, the data show great promise for the simultaneous detection and identification of co-existing cyanobacteria in situ.

| S-EPMC8552214 | biostudies-literature

Detailed chemical analysis of a fully formulated oil using dielectric barrier discharge ionisation-mass spectrometry.

Project description:RationaleFully formulated oils (FFOs) are integral to automotive lubrication; however, detailed compositional analysis is challenging due to high levels of chemical complexity. In particular, existing mass spectrometric approaches often target particular FFO components, leading to poor analytical coverage of the overall formulation, with increased overheads and analytical timescales.MethodsHerein we report the application of a commercially available SICRIT SC-20 dielectric barrier discharge ionisation (DBDI) source and Thermo Fisher Scientific LTQ Orbitrap XL to the analysis of an FFO. Nitrogen was used as a discharge gas for the DBDI source, and was modified using a range of commonplace solvents to tailor the experimental conditions for the analysis of various components.ResultsThe reported method allowed analysis of a range of FFO components of interest, encompassing a wide range of chemistries, in under 1 min. By modifying the discharge gas used for ionisation, experiments could be optimised for the analysis of particular FFO components across positive and negative ion modes. In particular, use of water vapour as a discharge gas modifier with positive ion mode mass spectrometry permitted concomitant analysis of antioxidants and base oil hydrocarbons. Furthermore, case studies of selected linear alkanes and alkenes profile the differences in the range of ions formed across these saturated and unsaturated aliphatic compounds, giving insight into the fate of base oil hydrocarbons in FFO analyses.ConclusionsA rapid method for analysis of FFO compositions has been developed and provides coverage of a range of components of interest. The results indicate that the method presented may be of utility in analysis of other FFOs or similarly challenging complex mixtures.

| S-EPMC9286547 | biostudies-literature

A rapid, simple method for the genetic discrimination of intact Arabidopsis thaliana mutant seeds using metabolic profiling by direct analysis in real-time mass spectrometry.

Project description:BackgroundEfficient high throughput screening systems of useful mutants are prerequisite for study of plant functional genomics and lots of application fields. Advance in such screening tools, thanks to the development of analytic instruments. Direct analysis in real-time (DART)-mass spectrometry (MS) by ionization of complex materials at atmospheric pressure is a rapid, simple, high-resolution analytical technique. Here we describe a rapid, simple method for the genetic discrimination of intact Arabidopsis thaliana mutant seeds using metabolic profiling by DART-MS.ResultsTo determine whether this DART-MS combined by multivariate analysis can perform genetic discrimination based on global metabolic profiling, intact Arabidopsis thaliana mutant seeds were subjected to DART-MS without any sample preparation. Partial least squares-discriminant analysis (PLS-DA) of DART-MS spectral data from intact seeds classified 14 different lines of seeds into two distinct groups: Columbia (Col-0) and Landsberg erecta (Ler) ecotype backgrounds. A hierarchical dendrogram based on partial least squares-discriminant analysis (PLS-DA) subdivided the Col-0 ecotype into two groups: mutant lines harboring defects in the phenylpropanoid biosynthetic pathway and mutants without these defects. These results indicated that metabolic profiling with DART-MS could discriminate intact Arabidopsis seeds at least ecotype level and metabolic pathway level within same ecotype.ConclusionThe described DART-MS combined by multivariate analysis allows for rapid screening and metabolic characterization of lots of Arabidopsis mutant seeds without complex metabolic preparation steps. Moreover, potential novel metabolic markers can be detected and used to clarify the genetic relationship between Arabidopsis cultivars. Furthermore this technique can be applied to predict the novel gene function of metabolic mutants regardless of morphological phenotypes.

| S-EPMC3138417 | biostudies-literature

Rapid Quantification of Low-Viscosity Acetyl-Triacylglycerols Using Electrospray Ionization Mass Spectrometry.

Project description:Acetyl-triacylglycerols (acetyl-TAG) possess an sn-3 acetate group, which confers useful chemical and physical properties to these unusual triacylglycerols (TAG). Current methods for quantification of acetyl-TAG are time consuming and do not provide any information on the molecular species profile. Electrospray ionization mass spectrometry (ESI-MS)-based methods can overcome these drawbacks. However, the ESI-MS signal intensity for TAG depends on the aliphatic chain length and unsaturation index of the molecule. Therefore response factors for different molecular species need to be determined before any quantification. The effects of the chain length and the number of double-bonds of the sn-1/2 acyl groups on the signal intensity for the neutral loss of short chain length sn-3 groups were quantified using a series of synthesized sn-3 specific structured TAG. The signal intensity for the neutral loss of the sn-3 acyl group was found to negatively correlated with the aliphatic chain length and unsaturation index of the sn-1/2 acyl groups. The signal intensity of the neutral loss of the sn-3 acyl group was also negatively correlated with the size of that chain. Further, the position of the group undergoing neutral loss was also important, with the signal from an sn-2 acyl group much lower than that from one located at sn-3. Response factors obtained from these analyses were used to develop a method for the absolute quantification of acetyl-TAG. The increased sensitivity of this ESI-MS-based approach allowed successful quantification of acetyl-TAG in various biological settings, including the products of in vitro enzyme activity assays.

| S-EPMC5890426 | biostudies-literature

Rapid diagnosis of periodontitis, a feasibility study using MALDI-TOF mass spectrometry.

Project description:AIM:The aim of the present study was to assess the feasibility and diagnostic contribution of protein profiling using MALDI-TOF mass spectrometry applied to saliva, gingival crevicular fluid (GCF) and dental plaque from periodontitis and healthy subjects. We hypothesized that rapid routine and blinded MALDI-TOF analysis could accurately classify these three types of samples according to periodontal state. MATERIALS AND METHODS:Unstimulated saliva, GCF and dental plaque, collected from periodontitis subjects and healthy controls, were analyzed by MALDI-TOF MS. Based on the differentially expressed peaks between the two groups, diagnostic decision trees were built for each sample. RESULTS:Among 141 patients (67 periodontitis and 74 healthy controls), the decision trees diagnosed periodontitis with a sensitivity = 70.3% (± 0.211) and a specificity = 77.8% (± 0.165) for saliva, a sensitivity = 79.6% (± 0.188) and a specificity = 75.7% (± 0.195) for GCF, and a sensitivity = 72.1% (± 0.202) and a specificity = 72.2% (± 0.195) for dental plaque. The sensitivity and specificity of the tests were improved to 100% (CI 95% = [0.91;1]) and 100% (CI 95% = [0.92;1]), respectively, when two samples were tested. CONCLUSION:We developed, for the first time, diagnostic tests based on protein profiles of saliva, GCF and dental plaque between periodontitis patients and healthy subjects. When at least 2 of these samples were tested, the best results were obtained.

| S-EPMC7069628 | biostudies-literature

MMP qualitative and quantitative mass spectrometry

Project description:Methanococcus maripaludis is a methanogenic archaeon. Within its genome, there are two operons for membrane associated hydrogenases, eha and ehb. To investigate the regulation of ehb on the cell, an S40 mutant was constructed in such a way that a portion of the ehb operon was replaced by pac cassette in the wild type parental strain S2 (done by Whitman's group at the University of Georgia). The S40 and S2 strains were grown in 14N and 15N media with acetate separately. A biological replicate was made by switching the media. Mass spectrometry based quantitative proteomics were done on the mixtures to investigate the differences in expression patterns between S40 and S2. Keywords: isotope labeling mass spectrometry, quantitative proteomics

2005-09-20 | GSE2744 | GEO

Methodology to analyse small silicon samples by glow discharge mass spectrometry using a thin wafer mask.

Project description:Glow discharge mass spectrometry (GDMS) is widely used for trace element analysis of bulk solid samples. The geometry of the GD source limits the minimum size of the sample, which for the instrument used in this work (ThermoElementGD) is 20 mm in diameter. From time to time, there is the need to analyse smaller samples with this technique, and we present here a methodology to analyse samples of 9-20 mm diameter through the use of thin masks. Thin masks have been previously used mostly as secondary cathode for the analysis of non-conducting materials, with hole size smaller than the area of the glow discharge. The use of masks in this work includes the following customization:•The choice of highly-pure Si as mask material, to decrease the chance of interferences with the Si samples.•The use of a hole in the mask of the same size as the discharge area. This implies that the mask material is not sputtered, thus decreasing chances for contamination from the mask itself.

| S-EPMC4644257 | 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