ABSTRACT
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.
Subject(s)
Discriminant Analysis , Fragaria/chemistry , Fruit/chemistry , Mass Spectrometry/methods , Food Technology , Fragaria/genetics , Genotype , Quality ControlABSTRACT
Proton transfer reaction mass spectrometry is a relatively new field that has attracted a great deal of interest in the last few years. This technique uses H(3)O(+) as a chemical ionization (CI) reagent to measure volatile organic compounds (VOCs) in the parts per billion by volume (ppbv) to parts per trillion by volume (pptv) range. Mass spectra acquired with a proton transfer reaction mass spectrometer (PTR-MS) are simple because proton transfer chemical ionization is "soft" and results in little or no fragmentation. Unfortunately, peak identification can still be difficult due to isobaric interferences. A possible solution to this problem is to couple the PTR drift tube to an ion trap mass spectrometer (ITMS). The use of an ITMS is appealing because of its ability to perform MS/MS and possibly distinguish between isomers and other isobars. Additionally, the ITMS duty cycle is much higher than that of a linear quadrupole so faster data acquisition rates are possible that will allow for detection of multiple compounds. Here we present the first results from a proton transfer reaction ion trap mass spectrometer (PTR-ITMS). The aim of this study was to investigate ion injection and storage efficiency of a simple prototype instrument in order to estimate possible detection limits of a second-generation instrument. Using this prototype a detection limit of 100 ppbv was demonstrated. Modifications are suggested that will enable further reduction in detection limits to the low-ppbv to high-pptv range. Furthermore, the applicability of MS/MS in differentiating between isobaric species was determined. MS/MS spectra of the isobaric compounds methyl vinyl ketone (MVK) and methacrolein (MACR) are presented and show fragments of different mass making differentiation possible, even when a mixture of both species is present in the same sample. However, MS/MS spectra of acetone and propanal produce fragments with the same molecular masses but with different intensity ratios. This allows quantitative distinction only if one species is predominant. Fragmentation mechanisms are proposed to explain the results.
ABSTRACT
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.
