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Scientific Articles - PTR-MS Bibliography

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Found 3 results
Title [ Year(Asc)]
Filters: Author is Granitto, Pablo M  [Clear All Filters]
[Cappellin2011a] Cappellin, L., F. Biasioli, P. M. Granitto, E. Schuhfried, C. Soukoulis, F. Costa, T. D. Maerk, and F. Gasperi, "On data analysis in PTR-TOF-MS: From raw spectra to data mining", Sensors and actuators B: Chemical, vol. 155, no. 1: Elsevier, pp. 183–190, 2011.
Recently the coupling of proton transfer reaction ionization with a time-of-flight mass analyser (PTR-TOF-MS) has been proposed to realise a volatile organic compound (VOC) detector that overcomes the limitations in terms of time and mass resolution of the previous instrument based on a quadrupole mass analysers (PTR-Quad-MS). This opens new horizons for research and allows for new applications in fields where the rapid and sensitive monitoring and quantification of volatile organic compounds (VOCs) is crucial as, for instance, environmental sciences, food sciences and medicine. In particular, if coupled with appropriate data mining methods, it can provide a fast MS-nose system with rich analytical information. The main, perhaps even the only, drawback of this new technique in comparison to its precursor is related to the increased size and complexity of the data sets obtained. It appears that this is the main limitation to its full use and widespread application. Here we present and discuss a complete computer-based strategy for the data analysis of PTR-TOF-MS data from basic mass spectra handling, to the application of up-to date data mining methods. As a case study we apply the whole procedure to the classification of apple cultivars and clones, which was based on the distinctive profiles of volatile organic compound emissions.
[Granitto2007] Granitto, P. M., F. Biasioli, E. Aprea, D. Mott, C. Furlanello, T. D. Maerk, and F. Gasperi, "Rapid and non-destructive identification of strawberry cultivars by direct PTR-MS headspace analysis and data mining techniques", Sensors and actuators B: Chemical, vol. 121, no. 2: Elsevier, pp. 379–385, 2007.
Proton transfer reaction-mass spectrometry (PTR-MS) is a spectrometric technique that allows direct injection and analysis of mixtures of volatile compounds. Its coupling with data mining techniques provides a reliable and fast method for the automatic characterization of agroindustrial products. We test the validity of this approach to identify samples of strawberry cultivars by measurements of single intact fruits. The samples used were collected over 3 years and harvested in different locations. Three data mining techniques (random forests, penalized discriminant analysis and discriminant partial least squares) have been applied to the full PTR-MS spectra without any preliminary projection or feature selection. We tested the classification models in three different ways (leave-one-out and leave-group-out internal cross validation, and leaving a full year aside), thereby demonstrating that strawberry cultivars can be identified by rapid non-destructive measurements of single fruits. Performances of the different classification methods are compared.
[Granitto2006] Granitto, P. M., C. Furlanello, F. Biasioli, and F. Gasperi, "Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products", Chemometrics and Intelligent Laboratory Systems, vol. 83, no. 2: Elsevier, pp. 83–90, 2006.
In this paper we apply the recently introduced Random Forest-Recursive Feature Elimination (RF-RFE) algorithm to the identification of relevant features in the spectra produced by Proton Transfer Reaction-Mass Spectrometry (PTR-MS) analysis of agroindustrial products. The method is compared with the more traditional Support Vector Machine-Recursive Feature Elimination (SVM-RFE), extended to allow multiclass problems, and with a baseline method based on the Kruskal–Wallis statistic (KWS). In particular, we apply all selection methods to the discrimination of nine varieties of strawberries and six varieties of typical cheeses from Trentino Province, North Italy. Using replicated experiments we estimate unbiased generalization errors. Our results show that RF-RFE outperforms SVM-RFE and KWS on the task of finding small subsets of features with high discrimination levels on PTR-MS data sets. We also show how selection probabilities and features co-occurrence can be used to highlight the most relevant features for discrimination.

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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).

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.

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.


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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