Isolation of peanut protein aggregates using aqueous extraction processing combined with membrane separation

A two-stage microfiltration and ultrafiltration (MF/UF) for peanut protein recovery and water recycling from aqueous extraction processing (AEP) were investigated. Peanut protein aggregates and alkaline water were obtained using flat and wound MF/UF and alkaline water recycled in AEP. With this approach, most of the wastewater could be recycled to produce reusable water to solve wastewater problems in aqueous oil extraction processing from oilseeds. The result showed that a little oil and protein were lost (1.21% and 4.35%, respectively) during the recycling of permeate in AEP, which was still acceptable. Peanut protein aggregations (PPAs) were characterised by analysis with Fourier-transform infrared spectroscopy (FTIR), sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE), high-performance gel filtration chromatography (HPGFC) and binding force analysis. Solubility analysis suggested that PPA had molecular structures with more hydrophobic groups on the surface than in the peanut protein isolate (PPI). In vitro, PPA was digested by both pepsin and trypsin more than PPI. FTIR profiles demonstrated there were more β-sheet and less α-helix in PPA, which means much more aggregation. In addition, binding force analysis showed hydrophobic interaction was the major force that restricted dissolution of PPA and disulfide bonds were of second importance, which provided a possible physical treatment opportunity for improving the solubility of PPA.     

Current technologies and new insights for the recovery of high valuable compounds from fruits by-products

The recovery of high valuable compounds from food waste is becoming a tighten issue in food processing. The large amount of non-edible residues produced by food industries causes pollution, difficulties in the management, and economic loss. The waste produced during the transformation of fruits includes a huge amount of materials such as peels, seeds, and bagasse, whose disposal usually represents a problem. Research over the past 20 years revealed that many food wastes could serve as a source of potentially valuable bioactive compounds, such as antioxidants and vitamins with increasing scientific interest thanks to their beneficial effects on human health. The challenge for the recovery of these compounds is to find the most appropriate and environment friendly extraction technique able to achieve the maximum extraction yield without compromising the stability of the extracted products. Based on this scenario, the aim of the current review is twofold. The first is to give a brief overview of the most important bioactive compounds occurring in fruit wastes. The second is to describe the pro and cons of the most up-to-dated innovative and environment friendly extraction technologies that can be an alternative to the classical solvent extraction procedures for the recovery of valuable compounds from fruit processing. Furthermore, a final section will take into account published findings on the combination of some of these technologies to increase the extracts yields of bioactives.     

SFEP文本因果关系提取及其与SFN转化研究

为将系统故障演化过程(system fault evolution process，SFEP)的文本描述转化为空间故障网络(space fault network，SFN)结构，用于故障分析，本文提出SFEP文本因果关系提取方法，及其与SFN基本结构的转化方法。首先给出SFEP中事件的几种典型因果关系。随后提出因果关系与SFN基本结构的转化流程。本文方法围绕着关键字和因果关系组模式展开，通过模型的不断学习补充和丰富关键字和组模式。最终使方法具备将SFEP文本转化为SFN结构的能力。以飞机起落架故障发生过程文本为例进行了应用，实验结果表明该方法可用于SFEP文本中的因果关系分析，并得到了理想的SFN。完善的关键字和组模式有利于使用计算机智能处理SFEP的SFN。     

Measurement of Consumed Energy Due to the Two-Phase Pressure Drop in a Pulsed Semi-Industrial Column: Effect of Geometry

This article deals with the evaluation of pressure drop and consumption of energy for a steady-state solvent extraction in a horizontal pulsed sieve-plate column, which are important for the design and optimization of the periodic-flow processes for industrial applications. In this study, the pressure drop and the position of loading points are investigated. Moreover, a mathematical evaluation on the energy consumption in the case of a pulsed flow is conducted, and besides the influence of pulsation intensity, the effect of geometrical parameters including the plate spacing and plate-free area is investigated as well. The results of this study are helpful for optimization of column geometry targeted to higher performance and lower energy consumption.     

利用非糖物质快速鉴别单花蜂蜜

本文利用非糖物质快速鉴别四种常见的单花蜂蜜(枸杞蜂蜜、荆条蜂蜜、枣花蜂蜜和洋槐蜂蜜)。利用固相萃取除糖提取蜂蜜非糖物质,并用高效液相色谱(HPLC)结合化学计量学进行蜂蜜花源的判定。发现了四种单花蜂蜜的非糖特征标志物。芦丁、丁香酸甲酯、阿魏酸的含量在四种蜂蜜中差异最大。对羟基苯甲酸和脱落酸在枸杞蜂蜜中平均含量分别为0.59μg/g和0.42μg/g,可作为枸杞蜂蜜的花源标志物;对香豆酸和肉桂酸在荆条蜂蜜中平均含量为0.08μg/g和0.05μg/g,可作为荆条蜂蜜的潜在花源标志物;阿魏酸、异阿魏酸是枣花蜂蜜的特征标志物,平均含量分别为0.40μg/g和0.76μg/g;洋槐蜂蜜的特征标志物是芦丁和丁香酸甲酯,平均含量为0.08μg/g和0.14μg/g。利用高效液相色谱指纹图谱结合化学计量学方法(主成分分析和判别分析),四种单花蜂蜜依据其花源被成功区分。本研究对蜂蜜真实性判定有重要参考价值,为后续蜂蜜真伪鉴别奠定基础。     

离子液体/低共熔溶剂在煤基液体分离中的应用

为了实现煤基液体各组分利用价值最大化，本文综述了离子液体和低共熔溶剂对组分组成复杂的煤基液体进行高效萃取分离的研究进展。首先介绍了离子液体和低共熔溶剂的性质及分类；其次根据分离目标的不同，将离子液体和低共熔溶剂对煤基液体典型组分的萃取分离分为四个方面进行阐述：煤基液体提酚、燃料油萃取脱硫、燃料油萃取脱氮、芳烃和脂肪烃的分离。分析表明，离子液体和低共熔溶剂对实际煤基液体的提酚效果较好，能分离出绝大多数的酚类化合物；燃料油萃取脱硫时，离子液体和低共熔溶剂对实际煤基液体的单次脱硫率均不高，需3～5次重复萃取后才能获得理想效果；燃料油中的碱性及非碱性含氮化合物很难被同一种离子液体或低共熔溶剂一次性分离出，导致实际油品的脱氮率较低；大多数离子液体和低共熔溶剂进行芳烃和脂肪烃的分离时不能获得理想的分配系数和选择性，尚无法用于实际芳烃和脂肪烃的分离。氢键、π-π、CH-π、范德华力等分子间相互作用的差异是实现离子液体或低共熔溶剂进行煤基液体典型组分分离的主要原因。依据分离对象，设计合适的离子液体和低共熔溶剂，提高实际煤基液体分离时的萃取率和选择性；分析并解决离子液体和低共熔溶剂用于实际煤基液体各组分分离时可能出现的问题，势必会推进煤基液体高值化分离的工业化进程。     

Diagnosis of disc bulge and disc desiccation in lumbar MRI using concatenated shape and texture features with random forest classifier

Disc bulge and disc desiccation are the most common abnormalities occurring in the spine, which leads to severe low back pain. Despite computer-aided automatic abnormality diagnostic imaging systems are available still there is a need for betterment in diagnostic accuracy and in processing time. Image processing with combined imaging features like shape and texture has given better diagnostic ability when compared with processing with individual features. In the present study, the desiccated and bulged Intervertebral Discs (IVDs) are diagnosed automatically by combining shape features extracted using Histogram of Oriented Gradients (HOG) and texture feature extracted using novel Local Sub-Rhombus Binary Relation Pattern (LS-RBRP) techniques with Random Forest (RF) classifier. The performance analysis projects that the RF with HOG+LS-RBRP has an overall better accuracy of 94.7% when compared with HOG (87%) and LS-RBRP (90.2%) with RF classifier separately in categorizing the normal IVD, disc bulge and disc desiccation in the lumbar spine MRI.     

特厚煤层综放工作面瓦斯抽采技术研究

为解决蒋家河煤矿采空区瓦斯涌出量大、上隅角瓦斯超限和回风平巷风排瓦斯量大等问题,提出了本煤层预抽、专用瓦斯抽放巷抽采和上隅角埋管抽采瓦斯相结合的方法对该矿ZF202综放工作面进行瓦斯治理,进行了现场实测和瓦斯抽采效果分析。结果表明,本煤层预抽后,瓦斯含量由7.92 m^3/t下降为4.21 m^3/t,瓦斯压力由0.72 MPa下降为0.38 MPa;上隅角瓦斯浓度由0.78%下降至0.4%左右。通过对比,专用瓦斯抽放巷的抽采纯量是高位钻孔的2.5倍,抽采效果好于高位钻孔,使工作面和上隅角瓦斯浓度保持较低水平,有效地解决了特厚煤层综放工作面瓦斯超限问题,为安全生产提供了重要保障。     

Arabic sign language intelligent translator

ABSTRACT

Arabic sign language (ArSL) is method of communication between deaf communities in Arab countries; therefore, the development of systemsthat can recognize the gestures provides a means for the Deaf to easily integrate into society. In this research we implemented a computational structurefor an intelligent interpreter that automatically recognizes the isolated dynamic gestures. The proposed system recognizes and translates gesturesperformed with one or both hands. It comprises five subsystems, building dataset, video processing, feature extraction, mapping between ArSL and Arabictext, and text generation. To apply the system, 100-signs of ArSL was used, which was applied on 1500 video files. It's were divided into five classes:alphabet, numbers, "prepositions, pronouns and question words", Arabic life expressions, and "nouns and verbs". The evaluation indicated that thesystem automatically recognizes and translates isolated dynamic ArSL gestures by highly accurate manner. The results showed that the system accuracy is 95.8%.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.