[Biasioli2004b] "Characterization of Strawberry Genotypes by PTR-MS Spectral Fingerprinting: a Three Year Study",
V International Strawberry Symposium 708
, pp. 497–500, 2004.
Proton Transfer Reaction Mass Spectrometry (PTR-MS) fingerprinting has been used to accurately and rapidly identify the cultivar of single intact strawberry fruits. The technique has been applied in a 3-cultivar experiment with 70 fruits harvested in 2002, 2003 and 2004. The proposed models correctly predicted the cultivar. Cross-validation tests verified 100% correct classification. The data indicated the possibility of correctly characterizing single fruit by fast non-invasive measurements without any pre-treatment and/or concentration of the headspace gas mixture. This is a necessary preliminary step in view of correlation studies of PTR-MS data with genetics and other characterization of fruits, in particular, sensory analysis. Extension to more cultivars is envisaged.
[Biasioli2006] "Correlation of PTR-MS spectral fingerprints with sensory characterisation of flavour and odour profile of "Trentingrana" cheese",
Food quality and preference
, vol. 17, no. 1: Elsevier, pp. 63–75, 2006.
Proton transfer reaction-mass spectrometry (PTR-MS) is a relatively new technique that allows the fast and accurate detection of volatile organic compounds. The paper discusses the possibility of correlating the PTR-MS spectral fingerprint of the mixture of volatile compounds present in the head-space of 20 samples of “Trentingrana”, the variety of Grana Padano produced in Trentino (Northern Italy), with the sensory evaluation (Quantitative Descriptive Analysis) of the same samples obtained by a panel of trained judges. Only attributes related to odours (six attributes) and flavours (six attributes) are considered. Results of descriptive statistics are shown and the performances of different multivariate calibration methods (Partial Least Squares, both PLS1 and PLS2) are compared by evaluating the errors in the cross-validated estimation of the sensory attributes. PLS2 seems to give a good average description providing an overall insight of the problem but does not provide an accurate prediction of the individual sensory attributes. PLS1 analysis is more accurate and performs well in most cases but it uses several latent variables, so that the interpretation of the loadings is not straightforward. The preliminary application of Orthogonal Signal Correction filtering on PTR-MS spectra followed by PLS1 analysis results in a good estimation for most of the attributes and has the advantage to use only one or two latent variables. Comparison with other works and a tentative indication of the compounds correlated with sensory description are reported.
[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. firstname.lastname@example.org, 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.