Effects of almond gum as texture and sensory quality improver in wheat bread

The aim of this work was to investigate the effect of almond gum as dietary fibre source in enhancing the wheat bread quality. Different amounts of almond gum (2%, 5% and 10% (w/w)) were used in bread formulation. The volume, texture, crust and crumb colour, as well as the sensorial properties, were evaluated and compared to control (without almond gum). The obtained results showed that almond gum addition enhanced significantly the volume of bread. The highest volume was obtained using 2% almond gum concentration with 23.6% increase, compared to control. Using almond gum in bread formulation improved considerably its texture with a notable decrease in hardness by 61.7% and 42.5% when using 2% and 5% almond gum, respectively. The sensory analysis scores showed that the better overall acceptability was found for breads supplemented with 2% almond gum, as compared to control and breads supplemented with 5% or 10% almond gum.     

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.     

An Overview of the Approaches for Automotive Safety Integrity Levels Allocation

Journal of Failure Analysis and Prevention - ISO 26262, titled Road Vehicles–Functional Safety, is the new automotive functional safety standard for passenger vehicle industry. In order to...     

Ant colony algorithm for automotive safety integrity level allocation

ISO 26262, the new automotive functional safety standard, aims to foster the design and development of safe products by ensuring that the risks posed by hazardous components are reduced to a residual level. Therefore, the standard defines and uses the concept of Automotive Safety Integrity Levels (ASILs) that classify the strictness of safety requirements to be assigned to the failure modes of the system based on the hazard they may cause. ASIL allocation can be described as a hard optimization problem focused on finding the optimal ASIL allocation that maximizes the safety requirements and minimizes cost. However, finding this optimal allocation among a set of possible allocations can represent a difficult task in large systems that contain a large number of components, which subsequently increases the search space. In this paper, we introduce a novel approach that uses the nature-inspired meta-heuristic Ant Colony Optimization (ACO) algorithm to solve the ASIL allocation problem and makes use of strategies that reduce the solution space. The problem was formulated as a construction graph, which the ants use to construct possible ASIL allocations. The search space reduction is accelerated considerably by both the effective performance of the ACO and the convergence of the algorithm on the optimal solution. This approach has been evaluated by applying it to a hybrid braking system and a steer-by-wire system. The results show a significant improvement over genetic-based, penguins search-based and tabu search-based approaches.     

Synchronised interrelationship between lipid/protein oxidation analysis and sensory attributes in refrigerated minced beef meat formulated with Punica granatum peel extract

Microbiological, chemical and sensory effects of pomegranate peel ethanol extract (EE) at 0.1, 0.5 and 1% (w w⁻¹) on refrigerated minced beef meat were undertaken. Chemical analyses revealed that the treated meat underwent significant decrease (P < 0.05) of primary and secondary lipid oxidation. By the end of storage, EE at 1% reduced the MetMb, carbonyl group accumulation and the disappearance of sulphydryl proteins by 56.68, 65.71 and 59.69% respectively, compared with control samples. EE at 1% showed also the highest scores for colour, appearance, odour and overall acceptability (OA) attributes using a 9-point hedonic scale. Chemometric approaches showed that lipid/protein oxidation analysis, sensory attributes and storage time allowed the samples discrimination. Oxidative stability parameters were equally used to predict OA by linear regression analysis. The reported data provide an approach to strengthen the interpretation of sensory quality and how they are affected by chemical changes in newly formulated meat products.     

An optimization of a non-volatile latch using memristors for sequential circuit applications

Analog Integrated Circuits and Signal Processing - The next generation of non-volatile memory elements have been attracting significant attention for future emerging memory applications in recent...     

An evaluation and ranking of evolutionary algorithms in segmenting abnormal masses in digital mammograms

Breast cancer is the most common cancer in women worldwide and the second main cause of cancer mortality after lung cancer. Up to now, there still no prevention nor early symptoms of breast cancer. Early detection can decrease significantly the mortality rate as the disease can be treated at an early stage. X-Ray is the current screening method that helps in detecting the most two common abnormalities of the breast, masses and micro-calcifications. However, interpreting mammograms is challenging in dense breasts as the abnormal masses and the normal glandular tissue of the breast have similar characteristics. Recently, the evolutionary algorithms have been widely used in image segmentation. In this paper, we evaluate and compare the performance of six most used evolutionary algorithms, invasive weed optimization (IWO), genetic algorithm (GA), particle swarm optimization (PSO), electromagnetism-like optimization (EMO), ant colony optimization (ACO), and artificial bee colony (ABC) in terms of clustering abnormal masses in the breast, particularly dense and extremely dense breasts. This evaluation is conducted based on quantitative metrics including Cohen’s Kappa, correlation, and false positive and false negative rates. The evolutionary algorithms are then ranked based on two multi-criteria decision analysis methods, the Preference Ranking Organization Method for the Enrichment of Evaluations (PROMETHEE) and the Graphical Analysis for Interactive Aid (GAIA).

     

Detection of breast abnormalities in digital mammograms using the electromagnetism-like algorithm

Multimedia Tools and Applications - The detection of abnormalities in the breast at an early stage can be so helpful for breast cancer treatment. Currently, mammography is the cheapest and the most...