[Mayr2003b] "Breath-by-breath analysis of banana aroma by proton transfer reaction mass spectrometry",
International Journal of Mass Spectrometry
, vol. 223: Elsevier, pp. 743–756, 2003.
We report on the in vivo breath-by-breath analysis of volatiles released in the mouth during eating of ripe and unripe banana. The air exhaled through the nose, nosespace (NS), is directly introduced into a proton transfer reaction mass spectrometer and the time-intensity profiles of a series of volatiles are monitored on-line. These include isopentyl and isobutyl acetate, two characteristic odour compounds of ripe banana, and 2E-hexenal and hexanal, compounds typical of unripe banana. Comparing the NS with the headspace (HS) profile, two differences are outlined. First, NS concentrations of some compounds are increased, compared to the HS, while others are decreased. This indicates that the in-mouth situation has characteristics of its own—mastication, mixing/dilution with saliva, temperature and pH—which modify the aroma relative to an HS aroma. Second, we discuss the temporal evolution of the NS. While 2E-hexenal and hexanal steadily increase in the NS during mastication of unripe banana, no such evolution is observed in volatile organic compounds (VOCs) while eating ripe banana. Furthermore, ripe banana shows high VOC concentrations in the swallow breath in contrast to unripe banana.
[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.