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.

Isotope ratio mass spectrometry in combination with chemometrics for characterization of geographical origin and agronomic practices of table grape

BACKGROUND

Although table grape is one of the most cultivated and consumed fruits worldwide, no study has been reported on its geographical origin or agronomic practice based on stable isotope ratios. This study aimed to evaluate the usefulness of isotopic ratios (i.e. ² H/¹ H, ¹³ C/¹² C, ¹⁵ N/¹⁴ N and ¹⁸ O/¹⁶ O) as possible markers to discriminate the agronomic practice (conventional versus organic farming) and provenance of table grape.

RESULTS

In order to quantitatively evaluate which of the isotopic variables were more discriminating, a t test was carried out, in light of which only δ¹³ C and δ¹⁸ O provided statistically significant differences (P ≤ 0.05) for the discrimination of geographical origin and farming method. Principal component analysis (PCA) showed no good separation of samples differing in geographical area and agronomic practice; thus, for classification purposes, supervised approaches were carried out. In particular, general discriminant analysis (GDA) was used, resulting in prediction abilities of 75.0 and 92.2% for the discrimination of farming method and origin respectively.

CONCLUSION

The present findings suggest that stable isotopes (i.e. δ¹⁸ O, δ² H and δ¹³ C) combined with chemometrics can be successfully applied to discriminate the provenance of table grape. However, the use of bulk nitrogen isotopes was not effective for farming method discrimination. © 2016 Society of Chemical Industry.

Electronic nose and isotope ratio mass spectrometry in combination with chemometrics for the characterization of the geographical origin of Italian sweet cherries

Sweet cherries from two Italian regions, Apulia and Emilia Romagna, were analysed using electronic nose (EN) and isotope ratio mass spectrometry (IRMS), with the aim of distinguishing them according to their geographic origin. The data were elaborated by statistical techniques, examining the EN and IRMS datasets both separately and in combination. Preliminary exploratory overviews were performed and then linear discriminant analyses (LDA) were used for classification. Regarding EN, different approaches for variable selection were tested, and the most suitable strategies were highlighted. The LDA classification results were expressed in terms of recognition and prediction abilities and it was found that both EN and IRMS performed well, with IRMS showing better cross-validated prediction ability (91.0%); the EN-IRMS combination gave slightly better results (92.3%). In order to validate the final results, the models were tested using an external set of samples with excellent results.

Studying ancient crop provenance: implications from δ(13)C and δ(15)N values of charred barley in a Middle Bronze Age silo at Ebla(NW Syria)

The discovery of a storeroom full of barley and other cereals (L.9512) in the proto-historic site of Ebla has provided a unique opportunity to study the centralized storage system of the early city-state from a different perspective. Epigraphic evidence available within the site reveals a complex system of taxation which included gathering grain tributes from satellite sites and redistributing semi-finished products such as flour. In this paper, we intend to explore the possibilities of a combined approach to studying the storage system, based on estimated barley grain volumes and δ(13)C-δ(15)N analyses. This approach is used to distinguish between grain from different harvesting sites and to identify any grain cultivated using special agricultural practices (e.g. manuring or irrigation). The basic assumption for this kind of analysis is that the growth-site conditions, natural or anthropogenic, of harvested cereals are reflected in their grain size and δ(13)C-δ(15)N values. Since the remains found in the storeroom were charred, the first task was to evaluate the effect of carbonization on the δ(13)C-δ(15)N and the size of the grains. Thus, the effect of charring was tested on modern samples of Syrian barley landraces. Once it had been ascertained that fresh grains reduced to charred remains retain their original biometric and isotopic traits, the ancient material was examined. Thirteen groups were identified, each characterized by a specific average volume and specific carbon and nitrogen values. The analysis revealed that what had first appeared to be a homogeneous concentration of grain was in fact an assemblage of barley harvested from different sites.

Instrumental and multivariate statistical analyses for the characterisation of the geographical origin of Apulian virgin olive oils

In this paper, virgin olive oils (VOOs) coming from three different geographic origins of Apulia, were analysed for free acidity, peroxide value, spectrophotometric indexes, chlorophyll content, sterol, fatty acid, and triacylglycerol compositions. In order to predict the geographical origin of VOOs, different multivariate approaches were applied. By performing principal component analysis (PCA) a modest natural grouping of the VOOs was observed on the basis of their origin, and consequently three supervised techniques, i.e., general discriminant analysis (GDA), partial least squares-discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA) were used and the results were compared. In particular, the best prediction ability was produced by applying GDA (average prediction ability of 82.5%), even if interesting results were obtained also by applying the other two classification techniques, i.e., 77.2% and 75.5% for PLS-DA and SIMCA, respectively.