Bioaccessibility of phenolic compounds from Brazilian grape juices using a digestion model with intestinal barrier passage

Grape juices are rich in bioactive compounds;however,for these compounds to exert their functionality,they must be bioaccessible.Thus,the present study evaluated a simulated digestion process on the main bioactive compounds of monovarietal grape juices of five Brazilian hybrid cultivars(V.vinifera x V.labrusca).Characterization of the chemical profiles in liquid chromatography(HPLC-DAD-RID),behaviour of phenolics in the stages of digestion and bioaccessibility through the INFOGEST protocol plus intestinal barrier passage were carried out.Of the 24 polyphenols identified in the grape juice samples,11 were bioaccessible,with emphasis on the class of flavanols.Procyanidin B2(101-426%),(+)-catechin(169-370%)and gallic acid(61-230%)stood out in all juices,showing that these compounds are key to the functionality of these drinks.Particularities were observed to differ between juices,demonstrating that factors such as the cultivar should be explored more extensively in studies on functional foods.The study also suggests that quality components such as sugars and organic acids influence the bioaccessibility of beverages.

Fine-grained classification of grape leaves via a pyramid residual convolution neural network

The value of grape cultivars varies.The use of a mixture of cultivars can negate the benefits of improved cultivars and hamper the protection of genetic resources and the identification of new hybrid cultivars.Classifying cultivars based on their leaves is therefore highly practical.Transplanted grape seedlings take years to bear fruit,but leaves mature in months.Foliar morphology differs among cultivars,so identifying cultivars based on leaves is feasible.Different cultivars,however,can be bred from the same parents,so the leaves of some cultivars can have similar morphologies.In this work,a pyramid residual convolution neural network was developed to classify images of eleven grape cultivars.The model extracts multi-scale feature maps of the leaf images through the convolution layer and enters them into three residual convolution neural networks.Features are fused by adding the value of the convolution kernel feature matrix to enhance the attention on the edge and center regions of the leaves and classify the images.The results indicated that the average accuracy of the model was 92.26%for the proposed leaf dataset.The proposed model is superior to previous models and provides a reliable method for the fine-grained classification and identification of plant cultivars.