The presence of benzene in food and in particular in soft drinks has been reported in several studies and should be considered in fundamental investigations about formation of this carcinogen compound as well as in quality control. Proton transfer reaction-mass spectrometry (PTR-MS) has been used here for rapid, direct quantification of benzene and to monitor its formation in model systems related to the use of benzoate, a common preservative, in presence of ascorbic acid: a widespread situation that yields benzene in, e.g., soft drinks and fruit juices. Firstly, we demonstrate here that PTR-MS allows a rapid determination of benzene that is in quantitative agreement with independent solid phase micro-extraction/gas chromatography (SPME/GC) analysis. Secondly, as a case study, the effect of different sugars (sucrose, fructose and glucose) on benzene formation is investigated indicating that they inhibit its formation and that this effect is enhanced for reducing sugars. The sugar-induced inhibition of benzene formation depends on several parameters (type and concentration of sugar, temperature, time) but can be more than 80% in situations that can be expected in the storage of commercial soft drinks. This is consistent with the reported observations of higher benzene concentrations in sugar-free soft drinks.

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.

Esters strongly influence the perceived aroma of alcoholic beverages and their rapid monitoring can play an important role in the quality control of these products. Proton transfer reaction mass spectrometry (PTR-MS) allows the rapid and non invasive monitoring of foodstuff but there is still a lack of information about the proton transfer induced fragmentation and on the effect of high ethanol concentration. PTR-MS fragmentation patterns of 21 esters are reported, most of them for the first time. For linear methyl and ethyl esters the fragmentation dependence on E/N was also evaluated. Acetate esters, with exception of methyl acetate, show as main peaks the characteristic fragment ions at m/z 61 and m/z 43, whereas propanoate esters, but methyl propanoate, exhibit as main peaks the typical signals at m/z 75 and m/z 57. For all the other esters, here reported, the spectra are dominated by the protonated molecular ion. For methyl and ethyl esters we also report, in many cases for the first time, the water-solution/air partition coefficients (Henry's law constant) and the ethanol-solution/air partition coefficients at different ethanol concentrations. The information provided in this work may be useful as a basis for further studies for the identification and quantification of esters in the headspace of alcoholic beverages extending the application field of PTR-MS.

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.

The gastronomic relevance and high price of white truffle are related mainly to its unique aroma. Here we evaluate, for the first time, the possibility of characterizing in a rapid and non-destructive way the aroma of white truffles based on proton transfer reaction mass spectrometry (PTR-MS). We indicate that anonymous PTR-MS fingerprinting allows sample classification and we also compare qualitatively and quantitatively PTR-MS data with measurements made by solid-phase microextraction gas chromatography (SPME-GC) of the same samples under the same conditions. PTR-MS fragmentation data of truffle-relevant compounds are also published here for the first time. Most of the sulfur-containing compounds detected by GC and relevant for white truffle aroma have a high positive correlation with single PTR-MS peaks. Our work indicates that, after preliminary comparison with GC data, PTR-MS is a new tool for the rapid, quantitative and non-invasive characterization of white truffle by direct headspace injection without any pre-concentration.

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 interaction of oral processing protocols and food texture on in vivo flavour release was evaluated by nose-space analysis. Nose-space analysis was carried out by proton transfer reaction mass spectrometry, and strawberry-flavoured custards were prepared with 0.1% (w/w) and 1.0% (w/w) carboxymethyl cellulose to modify the texture. Two oral processing protocols were adopted during the study; a free-chewing protocol and an imposed protocol. Twenty-one subjects participated in the study. Significant effects on in-nose flavour release were observed for the type of compound, the custard's texture, the oral processing protocol and the subjects. When people were allowed to eat as they normally do, individuals could be divided into three groups on the basis of swallowing time: first group, swallowing time <4 s; second group, swallowing time >6 s; intermediate group, t(swallow) varying (4–6 s). Within each group, different effects of the texture of the custards on in-nose flavour concentrations were observed, indicating that individual behaviour plays a considerable role in determining texture effects on flavour perception.

Olive oil has been characterized by rapid proton transfer reaction-mass spectrometry (PTR-MS) headspace analysis without any concentration of the volatiles or pretreatment of the samples. Comparison of extra virgin and defective (rancid) samples, as described by a panel of sensory judges, and the monitoring of thermo-oxidation processes are discussed. Multivariate analysis of PTR-MS data has been carried out and cross-validated, providing (i) reliable classification models for extra virgin oil as opposed to defective oil and (ii) calibration models able to predict independently thermo-oxidative degradation and the corresponding peroxide value. PTR-MS fragmentation patterns of volatiles considered in this study are also reported.

In this paper we apply the recently introduced Random Forest-Recursive Feature Elimination (RF-RFE) algorithm to the identification of relevant features in the spectra produced by Proton Transfer Reaction-Mass Spectrometry (PTR-MS) analysis of agroindustrial products. The method is compared with the more traditional Support Vector Machine-Recursive Feature Elimination (SVM-RFE), extended to allow multiclass problems, and with a baseline method based on the Kruskal–Wallis statistic (KWS). In particular, we apply all selection methods to the discrimination of nine varieties of strawberries and six varieties of typical cheeses from Trentino Province, North Italy. Using replicated experiments we estimate unbiased generalization errors. Our results show that RF-RFE outperforms SVM-RFE and KWS on the task of finding small subsets of features with high discrimination levels on PTR-MS data sets. We also show how selection probabilities and features co-occurrence can be used to highlight the most relevant features for discrimination.

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.

Heterobasidion annosum represents one of the most dangerous fungi in the conifer forest of the boreal hemisphere. This fungus was differentiated into three biological species (Intersterile groups; ISGs) characterized by different host specificity, patogenicity and distribution. A fast and easy identification of the ISG has important consequences on the silvicultural decition making. However, the determination of the ISG from the morphological traits of fruit bodies or, least of all, from mycelium is usually not easy. Proton Transfer Reaction – Mass Spectrometry (PTR-MS) is a new technique proposed and realised by the University of Innsbruck, and now issued also to the market. This technique allows high performance on-line measurements for a large number of VOCs. The aim of this work was to verify the possibility to discriminate the H. annosum ISGs based on their volatile compound emission and to identify putative characteristic masses that may play an important role in the host specificity and in the specific antagonistic fungi among each ISGs. Twelve strains belong to the three ISGs (here idicated by F, P and S) were analysed by PTRMS fingerprinting. P ISG was definitely separated from F and S ISGs. In addition, several masses show to be significantly different among the three ISGs. ANOVA on PTR-MS peak values identified 16 significant masses out of 230. Applications and limitations of this approach are discussed.

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.

Proton transfer reaction mass spectrometry (PTR-MS) is more and more applied to rather different fields of research and applications showing interesting performances where high sensitivity and fast monitoring of volatile organic compounds (VOCs) are required. Based on this technique and aiming at the realisation of an automatic system for routine applications in food science and technology, we tested here a novel approach for fingerprinting mass spectrometric detection and analysis of complex mixtures of VOCs. In particular, we describe and discuss corresponding head space (HS) sampling methods and possible data analysis techniques. As a first test case we studied here the properties of four red orange juices processed by different stabilisation methods starting from the same industrial batch: untreated juice, thermal pasteurised (flash and standard) juice and high pressure stabilised juice. We demonstrate the possibility of a fast automatic discrimination/classification of the samples with the further advantage, compared to the use of electronic noses, of useful information on the mass of the discriminating compounds. Moreover, first comparisons with discriminative analysis by a sensory panel shows evidence that there is a correlation between the ability of the PTR-MS to distinguish different juice samples and that of a panel of trained judges with the obvious advantages of an instrumental approach.

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.