Antioxidant capacities of phenolic compounds and tocopherols from Tunisian pomegranate (Punica granatum) fruits

This article aims to determine the phenolic, tocopherol contents, and antioxidant capacities from fruits (juices, peels, and seed oils) of 6 Tunisian pomegranate ecotypes. Total anthocyanins were determined by a differential pH method. Hydrolyzable tannins were determined with potassium iodate. The tocopherol (α-tocopherol, γ-tocopherol, and δ-tocopherol) contents were, respectively, 165.77, 107.38, and 27.29 mg/100 g from dry seed. Four phenolic compounds were identified and quantified in pomegranate peel and pulp using the high-performance liquid chromatography/ultraviolet method: 2 hydroxybenzoic acids (gallic and ellagic acids) and 2 hydroxycinnamic acids (caffeic and p-coumaric acids). Juice, peel, and seed oil antioxidants were confirmed by ferric reducing antioxidant power (FRAP) and oxygen radical absorbance capacity (ORAC) methods. The highest values were recorded in peels with 25.63 mmol trolox equivalent/100 g and 22.08 mmol TE/100 g for FRAP and ORAC assay, respectively. Results showed that the antioxidant potency of pomegranate extracts was correlated with their phenolic compound content. In particular, the highest correlation was reported in peels. High correlations were also found between peel hydroxybenzoic acids and FRAP ORAC antioxidant capacities. Identified tocopherols seem to contribute in major part to the antioxidant activity of seed oil. The results implied that bioactive compounds from the peel might be potential resources for the development of antioxidant function dietary food.     

Preparation and characterization of In2Se3 thin films

The spray pyrolysis conditions required preparing In₂Se₃ films were optimised. The structural, optical and morphological properties of the films and their evolution are related with the variation of some preparation parameters, which are the substrate temperature and the Se/In molar concentration ratio in the solution.     

Mechanistic Studies on the [2 + 2] Photocycloaddition of Deuterium-Labeled Olefins

The mechanism of intramolecular [2 + 2] photocycloaddition of 1b has been investigated. Structure determination of deuterium-labeled photoadducts was accomplished by ²H NMR.     

Fluorescence spectroscopy coupled with factorial discriminant analysis technique to identify sheep milk from different feeding systems

Rapid measurements of milk properties and discrimination of milk origin are necessary techniques for quality control of milk products. The present study was undertaken to evaluate the potential of using front face fluorescence spectroscopy (FFFS) and synchronous fluorescence spectroscopy (SFS) for monitoring the quality of forty-five ewe’s milk samples originating from different feeding systems. Physico-chemical analyses and fluorescence spectra were conducted on samples during lactation periods (the first 11 weeks). The principal component analysis (PCA) separately applied to the physico-chemical and fluorescence spectral data showed only small discrimination between milk samples based on lactation periods and diet compositions. Similar results were obtained by separately applying factorial discriminant analysis (FDA) on each technique. In a second step, concatenation technique were applied to FFF spectra acquired after excitation set at 250, 290, 380 nm and emission set at 410 nm. Results obtained showed a good discrimination among milk samples with regard to feeding systems given to the ewes throughout the lactation periods. In addition, a better discrimination was observed with FFFS than with SFS.     

Siamese convolutional neural network and fusion of the best overlapping blocks for kinship verification

Multimedia Tools and Applications - Analysis of facial images decoding familial features has been attracting the attention of researchers to develop a computerized system interested in determining...     

A nonparametric Bayesian learning model using accelerated variational inference and feature selection

Pattern Analysis and Applications - Developing effective machine learning methods for multimedia data modeling continues to challenge computer vision scientists. The capability of providing...     

Online variational learning of finite inverted Beta-Liouville mixture model for biomedical analysis

Image segmentation is widely applied for biomedical image analysis. However, segmentation of medical images is challenging due to many image modalities, such as, CT, X-ray, MRI, microscopy among others. An additional challenge to this is the high variability, inconsistent regions with missing edges, absence of texture contrast, and high noise in the background of biomedical images. Thus, many segmentation approaches have been investigated to address these issues and to transform medical images into meaningful information. During the past decade, finite mixture models have been revealed to be one of the most flexible and popular approaches in data clustering. In this article, we propose a statistical framework for online variational learning of finite inverted Beta-Liouville mixture model for clustering medical images. The online variational learning framework is used to estimate the parameters and the number of mixture components simultaneously, thus decreasing the computational complexity of the model. To this end, we evaluated our proposed algorithm on five different biomedical image data sets including optic disc detection and localization in diabetic retinopathy, digital imaging in melanoma lesion detection and segmentation, brain tumor detection, colon cancer detection and computer aid detection (CAD) of Malaria. Furthermore, we compared the proposed algorithm with three other popular algorithms. In our results, we analyze that the proposed online variational learning of finite IBL mixture model algorithm performs accurately on multiple modalities of medical images. It detects the disease patterns with high confidence. Computational and statistical approaches like the one presented in this article hold a significant impact on medical image analysis and interpretation in both clinical applications and scientific research. We believe that the proposed algorithm has the capacity to address multi modal biomedical image data sets and can be further applied by researchers to analyze correct disease patterns.     

Physical investigations on Ni doping ZnO thin films along with ethanol response

Journal of Materials Science: Materials in Electronics - Undoped and Ni-doped ZnO thin films were grown on glass substrates at 460 °C using the spray pyrolysis method. All samples...     

Image and video denoising by combining unsupervised bounded generalized gaussian mixture modeling and spatial information

Multimedia Tools and Applications - In recent years, a great deal of effort has been expended on developing robust solutions for images quality degradation caused mainly by noise. In this paper, we...