Evaluation of methodologies to determine vegetable oil species present in oil mixtures: Proposition of an approach to meet the EU legislation demands for correct vegetable oils labelling

Osório, Maria Teresa Moreira; Haughey, Simon A.; Elliott, Christopher T.; Koidis, Anastasios

doi:10.1016/j.foodres.2013.12.013

Cited by 47 publications

(19 citation statements)

References 80 publications

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“…The method of calculation (TDDFT/PBE0/6-31+G*), used in this paper, has been successful, and it reproduced very well the experimental values, so we can say that, our work will permit to identify the other antioxidants present in natural oils [57], based on the experimental fluorescence spectra of different oils [23,[58][59][60], and applying the TDDFT on these molecules to predict their emission wavelengths.…”

Section: In Solventsmentioning

confidence: 58%

TDDFT prediction of UV–vis absorption and emission spectra of tocopherols in different media

et al. 2015

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We use the TDDFT/PBE0/6-31+G* method to determine the electronic absorption and emission energies, in different media, of the four forms of tocopherol, which differ by the number and the position of methyl groups on the chromanol. Geometries of the ground state S0 and the first singlet excited state S1 were optimized in the gas phase, and various solvents. The solvent effect is evaluated using an implicit solvation model (IEF-PCM). Our results are compared to the experimental ones obtained for the vitamin E content in several vegetable oils. For all forms of tocopherols, the HOMO-LUMO first vertical excitation is a π-π* transition. Gas phase and non-polar solvents (benzene and toluene) give higher absorption wavelengths than polar solvents (acetone, ethanol, methanol, DMSO, and water); this can be interpreted by a coplanarity between the O-H group and the chroman, allowing a better electronic resonance of the oxygen lone pairs and the aromatic ring, and therefore giving an important absorption wavelength, whereas the polar solvents give high emission wavelengths comparatively to gas phase and non-polar solvents. Fluorescence spectra permit the determination, the separation, and the identification of the four forms of tocopherols by a large difference in emission wavelength values. Graphical Abstract Scheme from process methodological to obtain the absorption and emission spectra for tocopherols.

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Section: In Solventsmentioning

confidence: 58%

TDDFT prediction of UV–vis absorption and emission spectra of tocopherols in different media

et al. 2015

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“…Up to now, it has been common practice to label the oil with the generic term ''vegetable oil''. Consequently, there is a growing need to develop methodologies for industry and regulators to verify the type of oil used (Osorio, Haughey, Elliott, & Koidis, 2014). Few studies assessing the genuineness of the liquid medium in production of in-oil canned fish have been reported.…”

Section: Introductionmentioning

confidence: 99%

Authentication of canned fish packing oils by means of Fourier transform infrared spectroscopy

et al. 2016

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“…Recalibrating or recreating similar 21 models to work in those situations may be an extremely arduous task, with a 22 similar time and effort scale to the design, and tuning of the first model. 23 To avoid a full recalibration, model updating and calibration transfer techni- 24 ques have been proposed to cover the transfer of multivariate classification mo-25 dels between different spectrometers [2,3], temperatures [3,4], harvesting sea- 26 sons [4] and even different geographical regions [5]. Calibration transfer techni- 27 ques [2] allow mapping the new spectra to the primary model spectra domain 28 by calculating a transformation matrix from one domain to the other.…”

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confidence: 99%

“…Among the variety of incremental learning 79 techniques, we have chosen subspace based learning as the family of machine 80 learning to apply due to their proved ability to evolve online [13], the ability 81 to generate efficient models using a reduced number of calibration samples, analogies (SIMCA)-in food science [20,21], both for exploratory analysis [22] 85 and classification [23,24,25] and it is frequent in spectroscopic and chromatographic application, where the 113 number of variables per sample is in the order of thousands while the total 114 number of samples used for calibration rarely goes above the hundreds [22].…”

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confidence: 99%

Incremental model learning for spectroscopy-based food analysis

Diaz-Chito

Georgouli

Koidis

et al. 2017

Chemometrics and Intelligent Laboratory Systems

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In this paper we propose the use of incremental learning for creating and improving multivariate analysis models in the field of chemometrics of spectral data.As main advantages, our proposed incremental subspace-based learning allows creating models faster, progressively improving previously created models and sharing them between laboratories and institutions without requiring transferring or disclosing individual spectra samples. In particular, our approach allows to improve the generalization and adaptability of previously generated models with a few new spectral samples to be applicable to real-world situations. The potential of our approach is demonstrated using vegetable oil type identification based on spectroscopic data as case study. Results show how incremental models maintain the accuracy of batch learning methodologies while reducing their computational cost and handicaps.

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Evaluation of methodologies to determine vegetable oil species present in oil mixtures: Proposition of an approach to meet the EU legislation demands for correct vegetable oils labelling

Cited by 47 publications

References 80 publications

TDDFT prediction of UV–vis absorption and emission spectra of tocopherols in different media

TDDFT prediction of UV–vis absorption and emission spectra of tocopherols in different media

Authentication of canned fish packing oils by means of Fourier transform infrared spectroscopy

Incremental model learning for spectroscopy-based food analysis

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