[Biasioli2003] "Coupling proton transfer reaction-mass spectrometry with linear discriminant analysis: a case study.",
J Agric Food Chem
, vol. 51, no. 25: Istituto Agrario di S. Michele a/A, S. Michele, Via E. Mach 2, 38010, Italy. email@example.com, pp. 7227–7233, Dec, 2003.
Proton transfer reaction-mass spectrometry (PTR-MS) measurements on single intact strawberry fruits were combined with an appropriate data analysis based on compression of spectrometric data followed by class modeling. In a first experiment 8 of 9 different strawberry varieties measured on the third to fourth day after harvest could be successfully distinguished by linear discriminant analysis (LDA) on PTR-MS spectra compressed by discriminant partial least squares (dPLS). In a second experiment two varieties were investigated as to whether different growing conditions (open field, tunnel), location, and/or harvesting time can affect the proposed classification method. Internal cross-validation gives 27 successes of 28 tests for the 9 varieties experiment and 100% for the 2 clones experiment (30 samples). For one clone, present in both experiments, the models developed for one experiment were successfully tested with the homogeneous independent data of the other with success rates of 100% (3 of 3) and 93% (14 of 15), respectively. This is an indication that the proposed combination of PTR-MS with discriminant analysis and class modeling provides a new and valuable tool for product classification in agroindustrial applications.
[Biasioli2003a] "Fingerprinting mass spectrometry by PTR-MS: heat treatment vs. pressure treatment of red orange juice - a case study",
International journal of mass spectrometry
, vol. 223: Elsevier, pp. 343–353, 2003.
Proton transfer reaction mass spectrometry (PTR-MS) is more and more applied to rather different fields of research and applications showing interesting performances where high sensitivity and fast monitoring of volatile organic compounds (VOCs) are required. Based on this technique and aiming at the realisation of an automatic system for routine applications in food science and technology, we tested here a novel approach for fingerprinting mass spectrometric detection and analysis of complex mixtures of VOCs. In particular, we describe and discuss corresponding head space (HS) sampling methods and possible data analysis techniques. As a first test case we studied here the properties of four red orange juices processed by different stabilisation methods starting from the same industrial batch: untreated juice, thermal pasteurised (flash and standard) juice and high pressure stabilised juice. We demonstrate the possibility of a fast automatic discrimination/classification of the samples with the further advantage, compared to the use of electronic noses, of useful information on the mass of the discriminating compounds. Moreover, first comparisons with discriminative analysis by a sensory panel shows evidence that there is a correlation between the ability of the PTR-MS to distinguish different juice samples and that of a panel of trained judges with the obvious advantages of an instrumental approach.
[Boscaini2003] "Gas chromatography-olfactometry (GC-O) and proton transfer reaction-mass spectrometry (PTR-MS) analysis of the flavor profile of grana padano, parmigiano reggiano, and grana trentino cheeses.",
J Agric Food Chem
, vol. 51, no. 7: Institut fuer Ionenphysik, Universitaet Innsbruck, Technikerstrasse 25, A-6020 Innsbruck, Austria., pp. 1782–1790, Mar, 2003.
Gas chromatography-olfactometry (GC-O) and proton transfer reaction-mass spectrometry (PTR-MS) techniques were used to deduce the profile of odor-active and volatile compounds of three grana cheeses: Grana Padano (GP), Parmigiano Reggiano (PR), and Grana Trentino (GT). Samples for GC-O analysis were prepared by dynamic headspace extraction, while a direct analysis of the headspace formed over cheese was performed by PTR-MS. The major contributors to the odor profile were ethyl butanoate, 2-heptanone, and ethyl hexanoate, with fruity notes. A high concentration of mass 45, tentatively identified as acetaldehyde, was found by PTR-MS analysis. Low odor threshold compounds, e.g., methional and 1-octen-3-one, which contributed to the odor profile but were not detected by FID, were detected by PTR-MS. Principal component analysis on both GC-O and PTR-MS data separated the three cheese samples well and showed specific compounds related to each sample.