Proton transfer reaction-mass spectrometry (PTR-MS) data have been analysed by chemometric techniques to monitor cheese ageing by means of on-line direct head-space gas analysis. Twenty cheese loaves of Trentingrana, a trademarked cheese produced in northern Italy, of different origin and ripening degree, were sampled over the whole Trentingrana production area. An increase of the spectral intensity with ripening has been observed for most of the PTR-MS peaks and a univariate analysis identified 16 mass peaks that were significantly different for ripened and young cheeses, respectively. Moreover, the usefulness of different discriminant analyses and class modelling techniques have been investigated. Discriminant Partial Least Squares analysis, while indicating average behaviour and possible outliers, was not able to correctly classify all samples. Soft class modelling performed better and allowed a 100% correct classification. Partial least square calibration predicted the ageing time of each loaf with reasonable accuracy with a maximum cross-validation error of 3.5 months.

Proton transfer reaction-mass spectrometry (PTR-MS) is a spectrometric technique that allows direct injection and analysis of mixtures of volatile compounds. Its coupling with data mining techniques provides a reliable and fast method for the automatic characterization of agroindustrial products. We test the validity of this approach to identify samples of strawberry cultivars by measurements of single intact fruits. The samples used were collected over 3 years and harvested in different locations. Three data mining techniques (random forests, penalized discriminant analysis and discriminant partial least squares) have been applied to the full PTR-MS spectra without any preliminary projection or feature selection. We tested the classification models in three different ways (leave-one-out and leave-group-out internal cross validation, and leaving a full year aside), thereby demonstrating that strawberry cultivars can be identified by rapid non-destructive measurements of single fruits. Performances of the different classification methods are compared.

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.

The availability of genetic linkage maps enables the detection and analysis of QTLs contributing to quality traits of the genotype. Proton Transfer Reaction Mass Spectrometry (PTR-MS), a relatively novel spectrometric technique, has been applied to measure the headspace composition of the Volatile Organic Compounds (VOCs) emitted by apple fruit genotypes of the progeny ‘Fiesta’ × ‘Discovery’. Fruit samples were characterised by their PTR-MS spectra normalised to total area. QTL analysis for all PTR-MS peaks was carried out and 10 genomic regions associated with the peaks at m/z = 28, 43, 57, 61, 103, 115 and 145 were identified (LOD > 2.5). We show that it is possible to find quantitative trait loci (QTLs) related to PTR-MS characterisation of the headspace composition of single whole apple fruits indicating the presence of a link between molecular characterisation and PTR-MS data. We provide tentative information on the metabolites related to the detected QTLs based on available chemical information. A relation between apple skin colour and peaks related to carbonyl compounds was established.

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.

We studied the possibility of monitoring with proton transfer reaction-mass spectrometry (PTR-MS) odours emitted in various situations related to composting plants of municipal solid waste (MSW), i.e., waste storage, waste management, and biofilters. Comparison of PTR-MS volatile profiles of the gaseous mixtures entering and exiting a biofilter suggests the possibility of fast and reliable monitoring biofilter efficiency. Moreover, we investigated the relationships between the olfactometric assessment of odour concentration and PTR-MS spectral line intensity finding a positive correlation between the former and several masses and their overall intensity. The application of multivariate calibration methods allows to determine odour concentrations based only on PTR-MS instrumental data. The possibility of avoiding the use of time consuming and expensive olfactometric methods and applications in monitoring waste treatments plants and, in particular, of biofilters is suggested.

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.