Quality classification of Spanish olive oils by untargeted gas chromatography coupled to hybrid quadrupole-time of flight mass spectrometry with atmospheric pressure chemical ionization and metabolomics-based statistical approach

Sales, Carlos; Cervera, M. I.; Gil, Rubén; Portolés, Tania; Pitarch, Elena; Beltrán, J.

doi:10.1016/j.foodchem.2016.08.033

Cited by 74 publications

(66 citation statements)

References 30 publications

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“…26,27 The replicates of S. aureus are clustered at the largest distance from the rest of the bacterial strains; S. haemolyticus are distant from S. aureus with highest distinction; S. hominis and S. epidermidis are close to each other but the boundaries are clear (detailed in Fig. 5).…”

Section: Resultsmentioning

confidence: 99%

Fast detection of volatile organic compounds from Staphylococcal blood cultures by CDI-MS

Hang

Chingin²,

Liang³

et al. 2017

RSC Adv.

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Rapid recognition of Staphylococcal bacteremia in humans is a serious challenge in clinical research. Here we report on direct analysis of molecular volatile organic compounds (VOCs) of blood cultures emitted by 35 patients with a suspicion of Staphylococcal bacteremia and 31 patients positively diagnosed with Staphylococcal bacteremia were fingerprinted by atmospheric corona discharge ionization mass spectrometry (CDI-MS) for the presence of four common Staphylococci (Staphylococcus aureus, S.hominis, S. haemolyticus, and S. epidermidis) which statistically account for ca. 90% of cases of Staphylococcal bacteremia in humans. All the infected blood cultures revealed characteristic and clearly distinct MS patterns specific to the presence of one of the four Staphylococci after an incubation time of 3-16 h. Technical replicates were incubated over 2-3 days for reference diagnosis using current conventional bacteremia detection. The results of our 6 month hospital study confirm peaks at m/z 87 (isobutyric acid) and m/z 101 (isovaleric acid) signals were specifically present in Staphylococci, and indicate that high suitability of CDI-MS for rapid, reliable and cost-efficient diagnosis of Staphylococcal bacteremia in humans.

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Section: Resultsmentioning

confidence: 99%

Fast detection of volatile organic compounds from Staphylococcal blood cultures by CDI-MS

Hang

Chingin²,

Liang³

et al. 2017

RSC Adv.

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“…A recent review that focused on the use of GC/MS with TOF mass analyzers describes their use for the analysis of a large number of organic contaminants and residues present at trace levels for environmental, food safety and biological applications . A recent example of the use of FS/HR GC/MS in the food analysis field combined atmospheric pressure chemical ionization (APCI) with the use of a Q‐TOF mass analyzer for the analysis of the volatile components of olive oil …”

Section: Introductionmentioning

confidence: 99%

Comparison of gas chromatography/quadrupole time‐of‐flight and quadrupole Orbitrap mass spectrometry in anti‐doping analysis: I. Detection of anabolic‐androgenic steroids

Abushareeda

Tienstra

Lommen

et al. 2018

Rapid Comm Mass Spectrometry

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“…Few studies (Borràs et al, 2015;Borràs et al, 2016;Garrido-Delgado et al, 2015;Sales et al, 2017;Contreras et al, 2019b) can be found to demonstrate the potential of analytical instruments in order to complement the sensorial analysis to classify olive oil samples as EVOO, VOO, and LOO. To demonstrate the usefulness of these methods, the amount of analyzed samples of different harvests should be high in order to obtain representative conclusions.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Techniques to Improve the Performance of Olive Oil Classification

et al. 2020

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The olive oil assessment involves the use of a standardized sensory analysis according to the "panel test" method. However, there is an important interest to design novel strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry (MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment for olive oil classification. It is an essential task in an attempt to get the most robust model over time and, both to avoid fraud in the price and to know whether it is suitable for consumption or not. The aim of this paper is to combine chemical techniques and Deep Learning approaches to automatically classify olive oil samples from two different harvests in their three corresponding classes: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). Our Deep Learning model is built with 701 samples, which were obtained from two olive oil campaigns (2014-2015 and 2015-2016). The data from the two harvests are built from the selection of specific olive oil markers from the whole spectral fingerprint obtained with GC-IMS method. In order to obtain the best results we have configured the parameters of our model according to the nature of the data. The results obtained show that a deep learning approach applied to data obtained from chemical instrumental techniques is a good method when classifying oil samples in their corresponding categories, with higher success rates than those obtained in previous works.

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Quality classification of Spanish olive oils by untargeted gas chromatography coupled to hybrid quadrupole-time of flight mass spectrometry with atmospheric pressure chemical ionization and metabolomics-based statistical approach

Cited by 74 publications

References 30 publications

Fast detection of volatile organic compounds from Staphylococcal blood cultures by CDI-MS

Fast detection of volatile organic compounds from Staphylococcal blood cultures by CDI-MS

Comparison of gas chromatography/quadrupole time‐of‐flight and quadrupole Orbitrap mass spectrometry in anti‐doping analysis: I. Detection of anabolic‐androgenic steroids

Deep Learning Techniques to Improve the Performance of Olive Oil Classification

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