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Lentil samples coming from two different countries, i.e. Italy and Canada, were analysed using untargeted 1 H NMR fingerprinting in combination with chemometrics in order to build models able to classify them according to their geographical origin. For such aim, Soft Independent Modelling of Class Analogy (SIMCA), k-Nearest Neighbor (k-NN), Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applied to the NMR data and the results were compared. The best combination of average recognition (100%) and cross-validation prediction abilities (96.7%) was obtained for the PCA-LDA. All the statistical models were validated both by using a test set and by carrying out a Monte Carlo Cross Validation: the obtained performances were found to be satisfying for all the models, with prediction abilities higher than 95% demonstrating the suitability of the developed methods. Finally, the metabolites that mostly contributed to the lentil discrimination were indicated. [ABSTRACT FROM AUTHOR]

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. 2H/ 1H, 13C/ 12C, 15N/ 14N and 18O/ 16O) 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 δ 13C and δ 18O 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. δ 18O, δ 2H and δ 13C) 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 [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the impact of botanical origin and harvesting period on carbon stable isotope ratio (13C/12C), colour intensity ( CI), radical scavenging activity (% RSA), P and Sn content of Greek unifloral honeys. Thus, twenty-four honey samples were collected during harvesting periods 2011-2012 and 2012-2013, from four different regions in Greece. 13C/12C ratios and minerals were determined using isotope ratio mass spectrometry ( IRMS) and inductively coupled plasma optical-emission spectroscopy ( ICP- OES), respectively. CI and % RSA were measured using spectrophotometric assays. Results showed that only 13C/12C values and % RSA were affected by both botanical origin and harvesting period ( P < 0.05). Applying then chemometric analyses to the collected data set, honeys were correctly classified according to honey type (correct classification rate 87.5% and 79.2% using the original and cross-validation method, respectively). The use of different origin parameters has the potential to aid in honey authentication. [ABSTRACT FROM AUTHOR]

Mentha piperita essential oil (EO) has high economic importance because of its wide usage area and health-beneficial properties. Besides health-beneficial properties, Mentha piperita EO has great importance in the flavor and food industries because of its unique sensory and quality properties. High-valued essential oils are prone to being adulterated with economic motivations. This kind of adulteration deteriorates the quality of authentic essential oil, injures the consumers, and causes negative effects on the whole supply chain from producer to the consumer. The current research used fast, economic, robust, reliable, and effective ATR-FTIR spectroscopy coupled chemometrics of hierarchical cluster analysis(HCA), principal component analysis (PCA), partial least squares regression (PLSR) and principal component regression (PCR) for monitoring of Mentha spicata EO and L-menthol adulteration in Mentha piperita EOs. Adulterant contents (Mentha spicata and L-menthol) were successfully calculated using PLSR and PCR models. Standard error of the cross-validation SECV values changed between 0.06 and 2.14. Additionally, bias and press values showed alteration between 0.06 and1.43 and 0.03 and 41.15, respectively. Authentic Mentha piperita was successfully distinguished from adulterated samples, Mentha spicata and L-menthol, by HCA and PCA analysis. The results showed that attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometrics could be effectively used for monitoring various adulterants in essential oils. [ABSTRACT FROM AUTHOR]

• Table grape quality was evaluated by three analytical methods and chemometrics. • 1H NMR, LC-HRMS, and HS-SPME/MS-eNose were used in an untargeted approach. • Two types of discriminations were tested by PCA-LDA and PLS-DA. • MS-eNose gave models showing the best performances for both discriminations. • Good models were also obtained with NMR and HRMS in the marketability discrimination. Three non-targeted methods, i.e. 1H NMR, LC-HRMS, and HS-SPME/MS-eNose, combined with chemometrics, were used to classify two table grape cultivars (Italia and Victoria) based on five quality levels (5, 4, 3, 2, 1). Grapes at marketable quality levels (5, 4, 3) were also discriminated from non-marketable quality levels (2 and 1). PCA-LDA and PLS-DA were applied, and results showed that, the MS-eNose provided the best results. Specifically, with the Italia table grapes , mean prediction abilities ranging from 87% to 88% and from 98% to 99% were obtained for discrimination amongst the five quality levels and of marketability/non-marketability, respectively. For the cultivar Victoria, mean predictive abilities higher than 99% were achieved for both classifications. Good models were also obtained for both cultivars using NMR and HRMS data, but only for classification by marketability. Satisfying models were further validated by MCCV. Finally, the compounds that contributed the most to the discriminations were identified. [ABSTRACT FROM AUTHOR]

Highlights • The use of FT-NIR or FT-MIR spectroscopy was evaluated for OTA analysis in wheat. • PLS-DA and PC-LDA chemometric models were compared for samples classification. • FT-MIR classification models showed the best predictive performances. Abstract The use of infrared spectroscopy for the screening of 229 unprocessed durum wheat samples naturally contaminated with OTA has been investigated. Samples were analysed by both Fourier Transform near- and mid-infrared spectroscopy (FT-NIR, FT-MIR). Partial-Least Squares-Discriminant Analysis (PLS-DA) and Principal Component-Linear Discriminant Analysis (PC-LDA) classification models were used to differentiate highly contaminated durum wheat samples from low contaminated ones and the performances of the resulting models were compared. The overall discrimination rates were higher than 94% for both FT-NIR and FT-MIR range by using a cut-off limit set at 2 µg/kg OTA, independently from the classification model used thus confirming the reliability of the two statistical approaches used. False compliant rates of 6% were obtained for both spectral ranges and both classification models. These findings indicate that FT-NIR, as well as FT-MIR analysis, might be a promising, inexpensive and easy-to-use screening tool to rapidly discriminate unprocessed wheat samples for OTA content. [ABSTRACT FROM AUTHOR]

Highlights • Discrimination of geographical origin of oranges was performed by MS-based e-nose. • Three supervised statistical models were tested. • Classification by SELECT/LDA provided best prediction abilities in validation. • Twenty-eight VOCs had a different content in oranges from different geographical origins. Abstract 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. [ABSTRACT FROM AUTHOR]