Mass spectrometry-based electronic nose to authenticate 100% Italian durum wheat pasta and characterization of volatile compounds

Headspace solid-phase microextraction (HS-SPME) coupled with mass spectrometry-based electronic nose (MS-eNose), in combination with multivariate statistical analysis was used as untargeted method for the rapid authentication of 100% Italian durum wheat pasta. Among the tested classification models, i.e. PCA-LDA, PLS-DA and SVMc, SVMc provided the highest accuracy results in both calibration (90%) and validation (92%) processes. Potential markers discriminating pasta samples were identified by HS-SPME/GC-MS analysis. Specifically, the content of a pattern of 8 out of 59 volatile organic compounds (VOCs) was significantly different between samples of 100% Italian durum wheat pasta and pasta produced with durum wheat of different origins, most of which were related to different lipidic oxidation in the two classes of pasta. The proposed MS-eNose method is a rapid and reliable tool to be used for authenticating Italian pasta useful to promote its typicity and preserving consumers from fraudulent practices.

Discrimination of geographical origin of oranges (Citrus sinensis L. Osbeck) by mass spectrometry-based electronic nose and characterization of volatile compounds

An untargeted method using headspace solid-phase microextraction coupled to electronic nose based on mass spectrometry (HS-SPME/MS-eNose) in combination with chemometrics was developed for the discrimination of oranges of three geographical origins (Italy, South Africa and Spain). Three multivariate statistical models, i.e. PCA/LDA, SELECT/LDA and PLS-DA, were built and relevant performances were compared. Among the tested models, SELECT/LDA provided the highest prediction abilities in cross-validation and external validation with mean values of 97.8% and 95.7%, respectively. Moreover, HS-SPME/GC-MS analysis was used to identify potential markers to distinguish the geographical origin of oranges. Although 28 out of 65 identified VOCs showed a different content in samples belonging to different classes, a pattern of analytes able to discriminate simultaneously samples of three origins was not found. These results indicate that the proposed MS-eNose method in combination with multivariate statistical analysis provided an effective and rapid tool for authentication of the orange's geographical origin.

Volatile aroma components and MS-based electronic nose profiles of dogfruit (Pithecellobium jiringa) and stink bean (Parkia speciosa)

Dogfruit (Pithecellobium jiringa) and stink bean (Parkia speciosa) are two typical smelly legumes from Southeast Asia that are widely used in the cuisines of this region. Headspace/gas chromatography/flame ionization detection analysis and mass spectrometry (MS)-based electronic nose techniques were applied to monitor ripening changes in the volatile flavor profiles of dogfruit and stink bean. Compositional analysis showed that the ripening process greatly influenced the composition and content of the volatile aroma profiles of these two smelly food materials, particularly their alcohol, aldehyde, and sulfur components. The quantity of predominant hexanal in stink bean significantly declined (P < 0.05) during the ripening process, whereas the major volatile components of dogfruit changed from 3-methylbutanal and methanol in the unripe state to acetaldehyde and ethanol in the ripe bean. Moreover, the amount of the typical volatile flavor compound 1,2,4-trithiolane significantly increased (P < 0.05) in both ripened dogfruit and stink bean from 1.70 and 0.93%, to relative amounts of 19.97 and 13.66%, respectively. MS-based nose profiling gave further detailed differentiation of the volatile profiles of dogfruit and stink bean of various ripening stages through multivariate statistical analysis, and provided discriminant ion masses, such as m/z 41, 43, 58, 78, and 124, as valuable “digital fingerprint” dataset that can be used for fast flavor monitoring of smelly food resources.