Aromatic Compounds and Soluble Carbohydrate Profiles of Different Varieties of Tunisian Raisin (Vitis ViniferaL.)

The comparison of polymorphism among Avena species revealed by retrotransposon-based DNA markers and soluble carbohydrates in seeds

Here, we compared the polymorphism among 13 Avena species revealed by the iPBS markers and soluble carbohydrate profiles in seeds. The application of seven iPBS markers generated 83 bands, out of which 20.5% were polymorphic. No species-specific bands were scored. Shannon's information index (I) and expected heterozygosity (H_e) revealed low genetic diversity, with the highest values observed for A. nuda (I = 0.099; He = 0.068). UPGMA clustering of studied Avena accessions and PCoA results showed that the polyploidy level is the main grouping criterion. High-resolution gas chromatography revealed that the studied Avena accessions share the same composition of soluble carbohydrates, but significant differences in the content of total (5.30-22.38 mg g^-1 of dry weight) and particular sugars among studied samples were observed. Sucrose appeared as the most abundant sugar (mean 61.52% of total soluble carbohydrates), followed by raffinose family oligosaccharides (31.23%), myo-inositol and its galactosides (6.16%), and monosaccharides (1.09%). The pattern of interspecific variation in soluble carbohydrates, showed by PCA, was convergent to that revealed by iPBS markers. Thus, both methods appeared as a source of valuable data useful in the characterization of Avena resources or in the discussion on the evolution of this genus.

Identification of multiple raisins by feature fusion combined with NIR spectroscopy

Varieties of raisins are diverse, and different varieties have different nutritional properties and commercial value. In this paper, we propose a method to identify different varieties of raisins by combining near-infrared (NIR) spectroscopy and machine learning algorithms. The direct averaging of the spectra taken for each sample may reduce the experimental data and affect the extraction of spectral features, thus limiting the classification results, due to the different substances of grape skins and flesh. Therefore, this experiment proposes a method to fuse the spectral features of pulp and peel. In this experiment, principal component analysis (PCA) was used to extract baseline corrected features, and linear models of k-nearest neighbor (KNN) and linear discriminant analysis (LDA) and nonlinear models of back propagation (BP), support vector machine with genetic algorithm (GA-SVM), grid search-support vector machine (GS-SVM) and particle swarm optimization with support vector machine (PSO- SVM) coupling were used to classify. This paper compared the results of four experiments using only skin spectrum, only flesh spectrum, average spectrum of skin and flesh, and their spectral feature fusion. The experimental results showed that the accuracy and Macro-F1 score after spectral feature fusion were higher than the other three experiments, and GS-SVM had the highest accuracy and Macro-F1 score of 94.44%. The results showed that feature fusion can improve the performance of both linear and nonlinear models. This may provide a new strategy for acquiring spectral data and improving model performance in the future. The code is available at https://github.com/L-ain/Source.

Nontargeted 1 H NMR fingerprinting and multivariate statistical analysis for traceability of Greek PDO Vostizza currants

In this study, non-targeted ¹ H NMR fingerprinting was used in combination with multivariate statistical analyses for the classification of Greek currants based on their geographical origins (Aeghion, Nemea, Kalamata, Zante, and Amaliada). As classification techniques, Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were carried out. To elucidate different components according to PDO (Protected Designation of Origin), products from Aeghion (Vostizza) were statistically compared with each one of the four other regions. PLS-DA plots ensure that currants from Kalamata, Nemea, Zante, and Amaliada are well classified with respect to the PDO currants, according to differences observed in metabolites. Results suggest that composition differences in carbohydrates, amino, and organic acids of currants are sufficient to discriminate them in correlation to their geographical origin. In conclusion, currants metabolites which mostly contribute to classification performance of such discriminant analysis model present a suitable alternative technique for currants traceability. The study results contribute information to the currants' metabolite fingerprinting by NMR spectroscopy and their geographical origin. PRACTICAL APPLICATION: This study presents an analytical approach for a high nutritional value Greek PDO product, Vostizza currant. A further research and implementation of this method in food industry, can be the key to food fraud incidents. Thus, application of this work opens up posibilities to "farm to table" mission.