822 research outputs found
Characterization of strawberry genotypes by PTR-MS spectral finger printing
No full textElsewhere we have shown the possibility to follow post harvest evolution of strawberry by Proton Transfer Reaction-Mass Spectrometry (PTR-MS) and we indicated how this technique can produce a rapid and non-destructive fingerprint of the head-space of different agroindustrial products (Boschetti et al., 1999, Biasioli et al. 2003a). In particular we recently showed that the coupling of PTR-MS with proper data mining techniques unambiguously identifies the variety of single intact strawberry fruits (Biasioli et al. 2003b). In this latter work we considered only a few samples for 9 varieties harvested in 2002. Here we show that i) different data mining techniques can be effectively applied to PTR-MS data and ii) variety discrimination is evident even if we increase the number of fruits and extend the sampling on two different years. Data refer to two commercial strawberry cultivars: ‘Miss’ and its daughter ‘Queen’. Two supervised chemometric techniques (Discriminant Analysis – both Linear and Quadratic – and Artificial Neural Networks) have been used to authenticate the cultivar of 63 strawberry fruits, collected in different places and at different times, on the basis of their PTRMS spectra. The optimized models, built using only the 2 most discriminating variables, have been able to correctly predict 100 % of the samples as evaluated by a leave-n-out cross-validation procedure, with n ranging from 1 to
Characterization of strawberry genotypes by PTR-MS spectral fingerprinting
No full textElsewhere we have shown the possibility to follow post harvest evolution of strawberry by Proton Transfer Reaction-Mass Spectrometry (PTR-MS) and we indicated how this technique can produce a rapid and non-destructive fingerprint of the head-space of different agroindustrial products (Boschetti et al., 1999, Biasioli et al. 2003a). In particular we recently showed that the coupling of PTR-MS with proper data mining techniques unambiguously identifies the variety of single intact strawberry fruits (Biasioli et al. 2003b). In this latter work we considered only a few samples for 9 varieties harvested in 2002. Here we show that i) different data mining techniques can be effectively applied to PTR-MS data and ii) variety discrimination is evident even if we increase the number of fruits and extend the sampling on two different years. Data refer to two commercial strawberry cultivars: 'Miss' and its daughter 'Queen'.
Two supervised chemometric techniques (Discriminant Analysis - both Linear and Quadratic - and Artificial Neural Networks) have been used to authenticate the cultivar of 63 strawberry fruits, collected in different places and at different times, on the basis of their PTRMS spectra. The optimized models, built using only the 2 most discriminating variables, have been able to correctly predict 100% of the samples as evaluated by a leave-n-out cross-validation procedure, with n ranging from 1 to 6
Effect of the pig rearing system on the final volatile profile of Iberian dry-cured ham as detected by PTR-ToF-MS
No full textThe volatile compound profile of dry-cured Iberian ham lean and subcutaneous fat from pigs fattened outdoors on acorn and pasture (Montanera) or on high-oleic concentrated feed (Campo) was investigated by proton transfer reaction time-of-flight mass spectrometry. In addition to the usual proton transfer ionization we implemented the novel switchable reagent ions system which allows the use of different precursor ions (H3O+, NO+ and O2+). The analysis of the lean and subcutaneous fat volatile compounds allowed good sample discrimination according to the diet. Differences were evident for several classes of compounds: in particular, Montanera hams showed higher concentrations of aldehydes and ketones and lower concentrations of sulfur-containing compounds compared to Campo hams. The use of NO+ as precursor ion confirmed the results obtained with H3O+ in terms of classification capability and provides additional analytical insights.Fil: Sanchez del Pulgar, J.. Fondazione Edmund Mach. Research and Innovation Centre; Italia. Universidad de Extremadura. Facultad de Veterinaria; ArgentinaFil: Soukoulis, S.. Fondazione Edmund Mach. Research and Innovation Centre; ItaliaFil: Carrapiso, A. I.. Universidad de Extremadura. Escuela de Ingenierías Agrarias; EspañaFil: Cappellin, L.. Fondazione Edmund Mach. Research and Innovation Centre; ItaliaFil: Granitto, Pablo Miguel. Universidad Nacional de Rosario; ArgentinaFil: Aprea, E.. Fondazione Edmund Mach. Research and Innovation Centre; ItaliaFil: Romano, A. Fondazione Edmund Mach. Research and Innovation Centre; ItaliaFil: Gasperini, F.. Fondazione Edmund Mach. Research and Innovation Centre; ItaliaFil: Biasioli, F.. Fondazione Edmund Mach. Research and Innovation Centre; Itali
Volatilomics by direct injection mass spectrometry
No full textVolatile metabolites play a relevant role in food science and technology in most, if not all, steps of the production chain: they are, e.g., important for plant ecology and physiology (plant response and signaling upon biotic or abiotic stress), they are drivers and products of fruit changes during ripening and storage and they control to a large extent the way we perceive food before (odor), during (flavour, aroma) and after (aftertaste) consumption. Moreover, being spontaneously and continuously released, volatile compounds provide a non-invasive and rapid tool for the control of food samples and the real-time monitoring of biological and technological processes. For these reasons, the analysis of food volatolome is of interest if, mostly in an omic approach, it can provide i) high sensitivity and large dynamic range because volatile compounds can produce biological or sensory effects at different, possibly very low, concentrations and ii) fast and non-invasive measurements both to allow the screening of large sample sets and the monitoring of rapid processes. These issues can be efficiently addressed by different Direct Injection Mass Spectrometry (DIMS) methods developed for volatile compound analysis, Proton Transfer Reaction Mass Spectrometry (PTR-MS) in particular. The lack of specificity of these techniques, as compared with chromatographic ones, is compensated by other features: they are very fast, non-invasive and provide high sensitivity even without sample pretreatment. This contribution, after a short description of a prototypical DIMS set-up based on PTR-MS developed for agroindustrial applications, aims at pointing out DIMS pros and cons in food volatolomics by describing few selected applications investigated at the Volatile Compound Facility at FEM. Firstly, PTR-MS profiling of berry fruit, apple and dairy products has been used for sample sets exploration and to set classification or calibration models that link food volatolome with sensory or genomics allowing, for instance, i) the efficient identification of quantitative trait loci related to fruit volatile compounds, ii) the setting of instrumental models of sensory quality which should make “sensomic” studies realistic and iii) the identification of typicality markers. Secondly, a fully automated system for the monitoring of volatile compounds released during biological or technological processes has been developed and used to investigate microbiological processes as bread leavening, lactic and alcoholic fermentation and spoilage during storage.
Finally, DIMS allows the investigation of the interaction of food with humans or animal models, both on a sensory and health perspective, by measuring metabolites released during food consumption (nose-space analysis) or in exhaled breath (breath analysis). Some recent developments which should increase specificity of PTR-MS based methods without compromising its positive features are also described
Food related VOCs analysis by direct injection mass spectrometry: a tradeoff between sensitivity/time resolution and specificity
No full textThe good, the bad and the aged: quality control of anhydrous milk fat by Proton Transfer Reaction Mass Spectrometry
No full textPTR-MS (Proton Transfer Reaction Mass Spectrometry) is an accurate, high sensitivity, direct-injection technique that allows for the rapid characterization of food products and for the monitoring of processes in food science and agro-industry, without any pre-treatment [1]. In the last years, the importance of this technology in food research has increased quickly and it has been applied to address different issues as the monitoring of volatile organic compound (VOCs) emission during time (e.g. shelf life or processing) and the rapid classification of food samples according, e.g., to quality or geographical origin [2].
This study aims at verifying whether rapid and direct headspace PTR-MS analysis can correctly classify anhydrous milk fat (AMF) samples according to classes defined by sensory analysis or accelerated shelf-life. 39 samples were divided in three classes (OK, BAD, AGED) according to sensory evaluation by 7 to 12 trained panelists (OK, BAD) or thermal treatment (AGED). Five replicates of each sample were then measured through PTR-ToF-MS for a total of about 200 measurements. Measurements were performed in an automated way using a multipurpose GC automatic sampler (Gerstel GmbH, Mulheim am Ruhr, Germany) connected to the inlet of a commercial PTR-ToF-MS 8000 instrument (Ionicon Analytik GmbH, Innsbruck, Austria). PTR-MS data were then extracted [3, 4]. Principal component analysis (PCA) was performed on the dataset of mass peaks belonging to AMF samples significantly different from blank samples (1-way ANOVA with Boneferroni correction, p.value <0.01). The analysis lead to a clear separation of the three different classes of AMF samples. PLS-DA was then performed to build a discrimination model. The correct classification with an exception of only one sample was obtained from the dataset contained all mass peaks.
This pilot study indicate that PTR-ToF-MS can be implemented as a rapid (less than 60 s per measurement) and efficient tool for anhydrous milk fat quality control in agroindustry.
References:
1. Biasioli, F., et al., PTR-MS monitoring of VOCs and BVOCs in food science and technology. Trac-Trends in Analytical Chemistry, 2011. 30(7): p. 968-977.
2. Ellis, A.M. and C.A. Mayhew, PTR-MS in the Food Sciences, in Proton Transfer Reaction Mass Spectrometry. 2014, John Wiley & Sons, Ltd. p. 221-265.
3. Lindinger, W., A. Hansel, and A. Jordan, On-line monitoring of volatile organic compounds at pptv levels by means of proton-transfer-reaction mass spectrometry (PTR-MS) - Medical applications, food control and environmental research. International Journal of Mass Spectrometry, 1998. 173(3): p. 191-241
Application of a new sensory-instrumental tool for the evaluation of promising apple genotypes
No full textMost frequently, breeding programs adopt instrumental evaluation methods to assess the quality of the selections of interest. Sensory methods, when applied, are employed only at a later stage on fruit of selection chosen on the basis of previous instrumental screenings. Instrumental measurements, however, cannot replace sensory evaluation when a description of perceivable quality is needed. In this work, a detailed protocol, combining sensory and instrumental methods, is applied to define the sensory properties of 11 new apple breeding selections (Malus × domestica Borkh.) and to characterise them from their original parental genotypes. The final aim is to provide to breeders a reliable sensory profile of new selections, and a useful tool to predict sensory properties by instrumental characterisation. Descriptive sensory analysis was performed by a trained sensory panel of 14 judges through a sensory vocabulary composed of 11 attributes related to texture, taste and overall odour and flavour [1]. Simultaneously on the same samples the basic chemical composition and texture properties were assessed by a set of standard and innovative instrumental measurements [2], to allow the interpretation of the sensory description in terms of fruit chemical and physical properties. The collected data permitted to study affinities and specificities of the new selections in comparison to their parental genotypes. Instrumental measurements confirmed the existence of differences between the samples and reflected their sensory description, helping to interpret the perceivable quality in terms of chemical and physical properties. Moreover, the combined analyses allowed the development of effective prediction models, with very good results especially for the texture attributes. This is an important first step in the achievement of a reliable complete sensotyping of apples by rapid instrumental characterisation that can be applied on the large data sets usually needed in the initial phase of breeding programs.
References
[1] Corollaro ML, Endrizzi I, Bertolini A, Aprea E, Demattè ML, Costa F, Biasioli F, Gasperi F. Sensory profiling of apple: Methodological aspects, cultivar characterisation and postharvest changes. Postharvest Biol Technol. 2013;77:111-120.
[2] Costa F, Cappellin L, Longhi S, Guerra W, Magnago P, Porro D, Soukoulis C, Salvi S, Velasco R, Biasioli F, Gasperi F. Assessment of apple (Malus × domestica Borkh.) fruit texture by a combined acoustic–mechanical profiling strategy. Postharvest Biol Technol. 2011;61:21-28
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