Artículos de revistas
PTR-TOF-MS and data-mining methods for rapid characterisation of agro-industrial samples: influence of milk storage conditions on the volatile compounds profile of Trentingrana cheese
Fecha
2010-09Registro en:
Fabris, Alessandra; Biasioli, Franco; Granitto, Pablo Miguel; Aprea, Eugenio; Cappellin, Luca; et al.; PTR-TOF-MS and data-mining methods for rapid characterisation of agro-industrial samples: influence of milk storage conditions on the volatile compounds profile of Trentingrana cheese; Wiley; Journal Of Mass Spectrometry; 45; 9; 9-2010; 1065-1074
1076-5174
Autor
Fabris, Alessandra
Biasioli, Franco
Granitto, Pablo Miguel
Aprea, Eugenio
Cappellin, Luca
Schuhfried, Erna
Soukoulis, Christos
Märk, Tilmann D.
Gasperi, Flavia
Endrizzi, Isabella
Resumen
Proton transfer reaction-mass spectrometry (PTR-MS), a direct injection mass spectrometric technique based on an efficient implementation of chemical ionisation, allows for fast and high-sensitivity monitoring of volatile organic compounds (VOCs). The first implementations of PTR-MS, based on quadrupole mass analyzers (PTR-Quad-MS), provided only the nominal mass of the ions measured and thus little chemical information. To partially overcome these limitations and improve the analytical capability of this technique, the coupling of proton transfer reaction ionisation with a time-of-flight mass analyser has been recently realised and commercialised (PTR-TOF-MS). Here we discuss the very first application of this new instrument to agroindustrial problems and dairy science in particular. As a case study, we show here that the rapid PTR-TOF-MS fingerprinting coupled with data-miningmethods can quickly verify whether the storage condition of themilk affects the final quality of cheese and we provide relevant examples of better compound identification in comparison with the previous PTR-MS implementations. In particular, ‘Trentingrana’ cheese produced by four different procedures for milk storage are compared both in the case of winter and summer production. It is indeed possible to set classification models with low prediction errors and to identify the chemical formula of the ion peaks used for classification, providing evidence of the role that this novel spectrometric technique can play for fundamental and applied agro-industrial themes.