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Scientific Articles - PTR-MS Bibliography

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Publications

Found 6 results
Title [ Year(Asc)]
Filters: Author is Koot, Alex  [Clear All Filters]
2014
[1611] Granato, D., A. Koot, and S. M. { van Ruth}, "Geographical provenancing of purple grape juices from different farming systems by proton transfer reaction mass spectrometry using supervised statistical techniques.", J Sci Food Agric, Nov, 2014.
Link: http://dx.doi.org/10.1002/jsfa.7001
Abstract
<p>Organic, biodynamic and conventional purple grape juices (PGJ; n = 79) produced in Brazil and Europe were characterized by volatile organic compounds (m/z 20-160) measured by proton transfer reaction mass spectrometry (PTR-MS), and classification models were built using supervised statistical techniques.k-Nearest neighbours and soft independent modelling of class analogy (SIMCA) models discriminated adequately the Brazilian from European PGJ (overall efficiency of 81% and 87%, respectively). Partial least squares discriminant analysis (PLSDA) classified 100% European and 96% Brazilian PGJ. Similarly, when samples were grouped as either conventional or organic/biodynamic, the PLSDA model classified 81% conventional and 83% organic/biodynamic juices. Intraregional PLSDA models (juices produced in the same region&nbsp;-&nbsp;either Europe or Brazil) were developed and were deemed accurate in discriminating Brazilian organic from conventional PGJ (81% efficiency), as well as European conventional from organic/biodynamic PGJ (94% efficiency).PGJ from Brazil and Europe, as well as conventional and organic/biodynamic PGJ, were distinguished with high efficiency, but no statistical model was able to differentiate organic and biodynamic grape juices. These data support the hypothesis that no clear distinction between organic and biodynamic grape juices can be made with respect to volatile organic compounds. &copy; 2014 Society of Chemical Industry.</p>
2013
[Oezdestan2013] Özdestan, Ö., S. M. van Ruth, M. Alewijn, A. Koot, A. Romano, L. Cappellin, and F. Biasioli, "Differentiation of specialty coffees by proton transfer reaction-mass spectrometry", Food Research International: Elsevier, 2013.
Link: http://www.sciencedirect.com/science/article/pii/S0963996913003025
Abstract
In the coffee sector a diversity of certifications is available, with the most well-known being organic and fair trade. Intrinsic markers of products may help to assure the authenticity of food products and complement administrative controls. In the present study 110 market coffees with special production traits were characterized by high sensitivity proton transfer reaction mass spectrometry (HS PTR-MS) and volatiles were tentatively identified by PTR-time of flight MS. Espresso coffees, Kopi Luwak coffee and organic coffees could be distinguished by their profiles of volatile compounds with the help of chemometrics. A PLS-DA classification model was estimated to classify the organic and regular coffees by their HS PTR-MS mass spectra. Cross validation showed correct prediction of 42 out of the 43 (98%) organic coffee samples and 63 out of the 67 (95%) regular coffee samples. Therefore, the presented strategy is a promising approach to rapid organic coffee authentication.
2012
[Ruiz-Samblas2012] Ruiz-Samblàs, C., A. Tres, A. Koot, S. M. van Ruth, A. González-Casado, and L. Cuadros-Rodríguez, "Proton transfer reaction-mass spectrometry volatile organic compound fingerprinting for monovarietal extra virgin olive oil identification", Food Chemistry, vol. -: Elsevier, pp. -, 2012.
Link: http://www.sciencedirect.com/science/article/pii/S0308814612003500
Abstract
Proton transfer reaction-mass spectrometry (PTR-MS) is a relatively new technique that allows the fast and accurate qualification of the volatile organic compound (VOC) fingerprint. This paper describes the analysis of thirty samples of extra virgin olive oil, of five different varieties of olive fruit (Arbequina, Cornicabra, Frantoio, Hojiblanca, and Picual) by PTR-MS. A multivariate pattern recognition method (partial least square-discriminant analysis, PLS-DA) was applied on the full spectra fingerprint of the PTR-MS measurements. The multivariate model was doubly validated: firstly by means of internal validation (cross-validation) and secondly with an external validation data set. The results showed that the five varieties could be successfully distinguished within them. The proposed method provides a new valuable tool for extra virgin olive oil classification according to variety, and it could serve as a screening technique for the authentication of monovarietal extra-virgin olive oil and as a methodology to confirm that a variety is in agreement with claimed identity.
2011
[Galle2011] Galle, S. A., A. Koot, C. Soukoulis, L. Cappellin, F. Biasioli, M. Alewijn, and S. M. { van Ruth}, "Typicality and geographical origin markers of protected origin cheese from The Netherlands revealed by PTR-MS.", J Agric Food Chem, vol. 59, no. 6: RIKILT-Institute of Food Safety, Wageningen University and Research Centre , P.O. Box 230, 6700 EV Wageningen, The Netherlands., pp. 2554–2563, Mar, 2011.
Link: http://dx.doi.org/10.1021/jf104170r
Abstract
Volatile fingerprints of 30 cumin cheese samples of artisanal farmers' cheese of Leiden with EU Protected Designation of Origin (PDO) and 29 cumin cheese samples of varying commercial Dutch brands without PDO protection were used to develop authentication models. The headspace concentrations of the volatiles, as measured with high sensitivity proton-transfer mass spectrometry, were subsequently subjected to partial least-squares discriminant analysis (PLS-DA). Farmers' cheese of Leiden showed a distinct volatile profile with 27 and 9 out of the 60 predominant ions showing respectively significantly higher and lower concentrations in the headspace of the cheese in comparison to the other cumin cheeses. The PLS-DA prediction models developed classified in cross-validation 96% of the samples of PDO protected, artisanal farmers' cheese of Leiden correctly, against 100% of commercial cumin cheese samples. The characteristic volatile compounds were tentatively identified by PTR-time-of-flight-MS. A consumer test indicated differences in appreciation, overall flavor intensity, creaminess, and firmness between the two cheese groups. The consumers' appreciation of the cumin cheese tested was not influenced by the presence of a name label or PDO trademark.
2009
[Macatelli2009] Maçatelli, M., W. Akkermans, A. Koot, M. Buchgraber, A. Paterson, and S. van Ruth, "Verification of the geographical origin of European butters using PTR-MS", Journal of Food Composition and Analysis, vol. 22, no. 2: Elsevier, pp. 169–175, 2009.
Link: http://www.sciencedirect.com/science/article/pii/S0889157508001890
Abstract
In the present study, proton transfer reaction-mass spectrometry (PTR-MS) in combination with partial least square-discriminant analysis (PLS-DA) was evaluated as a method for the prediction of the origin of European butters. Eighty-three commercial butters from three European regions were subjected to headspace analysis using PTR-MS. Data were collected for the mass range m/z 20–150 using a dwell time of 0.2 s mass−1, resulting in a cycle time just under 30 s. The log transformed headspace concentrations of the masses were subjected to PLS-DA in order to estimate classification models for the butter samples. One model predicted the region of origin; a second set of models predicted dichotomously whether or not a butter originated from a particular EU country. The performance of each model was evaluated by means of a 10-fold double cross validation procedure. For 76% of the butters the region of origin was predicted correctly in the cross validation. The success rate of the countries, averaged over all dichotomous models, was 88% but large differences between countries were observed. Additional work is required to study the underlying factors that determine the geographical differences in butter volatile compositions.
2008
[Araghipour2008] Araghipour, N., J. Colineau, A. Koot, W. Akkermans, J. Manuel Mor Rojas, J. Beauchamp, A. Wisthaler, T. D. Märk, G. Downey, C. Guillou, et al., "Geographical origin classification of olive oils by PTR-MS", Food Chemistry, vol. 108, no. 1: Elsevier, pp. 374–383, 2008.
Link: http://www.sciencedirect.com/science/article/pii/S0308814607010965
Abstract
The volatile compositions of 192 olive oil samples from five different European countries were investigated by PTR-MS sample headspace analysis. The mass spectra of all samples showed many masses with high abundances, indicating the complex VOC composition of olive oil. Three different PLS-DA models were fitted to the data to classify samples into ‘country’, ‘region’ and ‘district’ of origin, respectively. Correct classification rates were assessed by cross-validation. The first fitted model produced an 86% success rate in classifying the samples into their country of origin. The second model, which was fitted to the Italian oils only, also demonstrated satisfactory results, with 74% of samples successfully classified into region of origin. The third model, classifying the Italian samples into district of origin, yielded a success rate of only 52%. This lower success rate might be due to either the small class set, or to genuine similarities between olive oil VOC compositions on this tight scale.

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Selected PTR-MS related Reviews

F. Biasioli, C. Yeretzian, F. Gasperi, T. D. Märk: PTR-MS monitoring of VOCs and BVOCs in food science and technology, Trends in Analytical Chemistry 30 (7) (2011).
Link

J. de Gouw, C. Warneke, T. Karl, G. Eerdekens, C. van der Veen, R. Fall: Measurement of Volatile Organic Compounds in the Earth's Atmosphere using Proton-Transfer-Reaction Mass Spectrometry. Mass Spectrometry Reviews, 26 (2007), 223-257.
Link

W. Lindinger, A. Hansel, A. Jordan: Proton-transfer-reaction mass spectrometry (PTR–MS): on-line monitoring of volatile organic compounds at pptv levels, Chem. Soc. Rev. 27 (1998), 347-375.
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Lists with PTR-MS relevant publications of the University of Innsbruck can be found here: Atmospheric and indoor air chemistry, IMR, Environmental Physics and Nano-Bio-Physics

 

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