1H NMR Spectroscopy for Determination of the Geographical Origin of Hazelnuts.

A total of 262 authentic samples was analyzed by 1H NMR spectroscopy for the geographical discrimination of hazelnuts ( Corylus avellana L.) covering samples from five countries (Germany, France, Georgia, Italy, and Turkey) and the harvest years 2013-2016. This article describes method development starting with an extraction protocol suitable for separation of polar and nonpolar metabolites in addition to reduction of macromolecular components. Using the polar fraction for data analysis, principle component analysis was applied and used to monitor sample preparation and measurement. Several machine learning algorithms were tested to build a classification model. The best results were obtained by a linear discrimination analysis applying a random subspace algorithm. The division of the samples in a trainings set and a test set yielded a cross validation accuracy of 91% for the training set and an accuracy of 96% for the test set. The identification of key features was carried out by Kruskal-Wallis test and t test. A feature assigned to betaine exhibits a significant level for the classification of all five countries and is considered a possible candidate for the development of targeted approaches. Further, the results were compared to a previously published study based on LC-MS analysis of nonpolar metabolites. In summary, this study shows the robustness and high accuracy of a discrimination model based on NMR analysis of polar metabolites.

Food Targeting: Geographical Origin Determination of Hazelnuts (Corylus avellana) by LC-QqQ-MS/MS-Based Targeted Metabolomics Application.

A targeted metabolomics LC-ESI-QqQ-MS application for geographical origin discrimination based on 20 nonpolar key metabolites was developed, validated according to accepted guidelines and used for quantitation via stable isotope labeled internal standards in 202 raw authentic hazelnut samples from six countries (Turkey, Italy, Georgia, Spain, France, and Germany) of harvest years 2014 and 2015. Multivariate statistics were used for detection of significant variations in metabolite levels between countries and, moreover, a prediction model using support vector machine classification (SVM) was calculated yielding 100% training accuracy and 97% cross-validation accuracy, which was subsequently applied to 55 hazelnut samples for the confectionary industry gaining up to 80% correct classifications compared to declared origin. The present method demonstrates the great suitability for targeted metabolomics applications in the geographical origin determination of hazelnuts and their applicability in routine analytics.

Automated Enrichment of Sulfanilamide in Milk Matrices by Utilization of Aptamer-Linked Magnetic Particles.

The present work demonstrates the first automated enrichment approach for antibiotics in milk using specific DNA aptamers. First, aptamers toward the antibiotic sulfanilamide were selected and characterized regarding their dissociation constants and specificity toward relevant antibiotics via fluorescence assay and LC-MS/MS detection. The performed enrichment was automated using the KingFisherDuo and compared to a manual approach. Verifying the functionality, trapping was realized in different milk matrices: (i) 0.3% fat milk, (ii) 1.5% fat milk, (iii) 3.5% fat milk, and (iv) 0.3% fat cocoa milk drink. Enrichment factors up to 8-fold could be achieved. Furthermore, it could be shown that novel implementation of a magnetic separator increases the reproducibility and reduces the hands-on time from approximately half a day to 30 min.

Food Fingerprinting: Metabolomic Approaches for Geographical Origin Discrimination of Hazelnuts (Corylus avellana) by UPLC-QTOF-MS.

Ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) was used for geographical origin discrimination of hazelnuts (Corylus avellana L.). Four different LC-MS methods for polar and nonpolar metabolites were evaluated with regard to best discrimination abilities. The most suitable method was used for analysis of 196 authentic samples from harvest years 2014 and 2015 (Germany, France, Italy, Turkey, Georgia), selecting and identifying 20 key metabolites with significant differences in abundancy (5 phosphatidylcholines, 3 phosphatidylethanolamines, 4 diacylglycerols, 7 triacylglycerols, and γ-tocopherol). Classification models using soft independent modeling of class analogy (SIMCA), linear discriminant analysis based on principal component analysis (PCA-LDA), support vector machine classification (SVM), and a customized statistical model based on confidence intervals of selected metabolite levels were created, yielding 99.5% training accuracy at its best by combining SVM and SIMCA. Forty nonauthentic hazelnut samples were subsequently used to estimate as realistically as possible the prediction capacity of the models.

Aptamer-based trapping of phytosphingosine in urine samples.

Usually, small molecules like single metabolites used in clinical diagnostic can be quantified by instrumental approaches like LC-MS or bioanalytical techniques using antibodies or aptamers as selective receptors. The present work comprises the generation of aptamers with an affinity towards the medically relevant metabolite phytosphingosine via the previously reported just in time-Selection approach (Hünniger et al., 2014). The whole approach could be seen as a proof of concept to extend the existing just in time-Selection protocol for selection towards small molecules with dissociation constants in the low nanomolar range. Moreover it is conceivable that the shown methods could be quickly adapted to further scopes. Aptamers could be applied for clean-up or concentration processes prior to further analysis. As an example, we used the selected aptamers towards phytosphingosine bound to magnetic particles for affinity enrichment in both selection buffer and urine samples. As an outcome, enrichment factors of up to 9-fold (selection buffer)/4-fold (urine samples) were achieved by this approach.