[Roberts2003] "Comparison of nosespace, headspace, and sensory intensity ratings for the evaluation of flavor absorption by fat.",
J Agric Food Chem
, vol. 51, no. 12: NestlÃ© Research Center, P.O. Box 44, Vers-Chez-les-Blanc, 1000 Lausanne 26, Switzerland. firstname.lastname@example.org, pp. 3636–3642, Jun, 2003.
The goal of this study was to better understand the correspondence between sensory perception and in-nose compound concentration. Five aroma compounds at three different concentrations increasing by factors of 4 were added to four matrixes (water, skim milk, 2.7% fat milk, and 3.8% fat milk). These were evaluated by nosespace analysis with detection by proton transfer reaction mass spectrometry (PTR-MS), using five panelists. These same panelists evaluated the perceived intensity of each compound in the matrixes at the three concentrations. PTR-MS quantification found that the percent released from an aqueous solution swallowed immediately was between 0.1 and 0.6%, depending on the compound. The nosespace and sensory results showed the expected effect of fat on release, where lipophilic compounds showed reductions in release as fat content increases. The effect is less than that observed in headspace studies. A general correlation between nosespace concentration and sensory intensity ratings was found. However, examples of perceptual masking were found where higher fat milks showed reductions in aroma compound intensity ratings, even if the nosespace concentrations were the same.
[Christian2003] "Comprehensive laboratory measurements of biomass-burning emissions: 1. Emissions from Indonesian, African, and other fuels",
J. Geophys. Res
, vol. 108, no. 4719, pp. 1–4719, 2003.
Trace gas and particle emissions were measured from 47 laboratory fires burning 16 regionally to globally significant fuel types. Instrumentation included the following: open-path Fourier transform infrared spectroscopy; proton transfer reaction mass spectrometry; filter sampling with subsequent analysis of particles with diameter <2.5 μm for organic and elemental carbon and other elements; and canister sampling with subsequent analysis by gas chromatography (GC)/flame ionization detector, GC/electron capture detector, and GC/mass spectrometry. The emissions of 26 compounds are reported by fuel type. The results include the first detailed measurements of the emissions from Indonesian fuels. Carbon dioxide, CO, CH4, NH3, HCN, methanol, and acetic acid were the seven most abundant emissions (in order) from burning Indonesian peat. Acetol (hydroxyacetone) was a major, previously unobserved emission from burning rice straw (21–34 g/kg). The emission factors for our simulated African fires are consistent with field data for African fires for compounds measured in both the laboratory and the field. However, the higher concentrations and more extensive instrumentation in this work allowed quantification of at least 10 species not previously quantified for African field fires (in order of abundance): acetaldehyde, phenol, acetol, glycolaldehyde, methylvinylether, furan, acetone, acetonitrile, propenenitrile, and propanenitrile. Most of these new compounds are oxygenated organic compounds, which further reinforces the importance of these reactive compounds as initial emissions from global biomass burning. A few high-combustion-efficiency fires emitted very high levels of elemental (black) carbon, suggesting that biomass burning may produce more elemental carbon than previously estimated.
[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.