Effects of breed and age at slaughter on degradation of muscle lipids during processing of dry‐cured hams

Hams from Landrace, Duroc and Hampshire pigs slaughtered at ages 6, 7.5 and 9 months were processed to generate Norwegian Parma‐style hams. Lipid contents and the compositions of fatty acid classes (ΣSFA, ΣMUFA, ΣPUFA) within neutral lipids, phospholipids and free fatty acids were determined. Small differences in lipid degradation and composition of the classes were revealed. However, significant sensory differences related to lipids were observed. Breed was more important than age. Dry‐cured Hampshire hams gave a more intense mature odour that may be associated with higher overall lipid degradation. Unexpectedly, these hams also demonstrated high juiciness and tenderness, which could be related to the melting characteristics of the fat. Dry‐cured Duroc hams showed a higher susceptibility towards rancidity, presumably associated with preferential oxidation of n‐6 fatty acids relative to C18:1 n‐9. Dry‐cured Landrace hams showed the lowest juiciness and tenderness, likely due to their lower fat content (marbling).     

Decoder side information generation techniques in Wyner-Ziv video coding: a review

Multimedia Tools and Applications - In Wyner-Ziv (WZ) video coding, decoder side information (SI) takes a key role in a WZ video codec among other building blocks. In this paper, we review the...     

文献计量视角下健康人力资本研究动态分析

通过文献计量方法从样本选取、研究动态分析、主题分布三方面对健康人力资本发展现状及热点问题进行总结.研究结果表明,自2004年开始该领域所属学科种类和期刊源日益丰富,并形成合著度较高的核心作者群体,健康人力资本研究成为人力资本领域的研究热点,健康人力资本与经济增长或收入的关系、健康人力资本指标体系的构建和居民健康人力资本收入效应的测算等问题都成为当前和未来的研究重点.     

Effect of stoichiometry and Cu-substitution on the phase structure and hydrogen storage properties of Ml-Mg-Ni-based alloys

To improve the electrochemical properties of rare-earth–Mg–Ni-based hydrogen storage alloys, the effects of stoichiometry and Cu-substitution on the phase structure and thermodynamic properties of the ...     

Physical Stability of Octenyl Succinate–Modified Polysaccharides and Whey Proteins for Potential Use as Bioactive Carriers in Food Systems

The high cost and potential toxicity of biodegradable polymers like poly(lactic‐co‐glycolic)acid (PLGA) has increased the interest in natural and modified biopolymers as bioactive carriers. This study characterized the physical stability (water sorption and state transition behavior) of selected starch and proteins: octenyl succinate–modified depolymerized waxy corn starch (DWxCn), waxy rice starch (DWxRc), phytoglycogen, whey protein concentrate (80%, WPC), whey protein isolate (WPI), and α‐lactalbumin (α‐L) to determine their potential as carriers of bioactive compounds under different environmental conditions. After enzyme modification and particle size characterization, glass transition temperature and moisture isotherms were used to characterize the systems. DWxCn and DWxRc had increased water sorption compared to native starch. The level of octenyl succinate anhydrate (OSA) modification (3% and 7%) did not reduce the water sorption of the DWxCn and phytoglycogen samples. The Guggenheim–Andersen–de Boer model indicated that native waxy corn had significantly (P < 0.05) higher water monolayer capacity followed by 3%‐OSA‐modified DWxCn, WPI, 3%‐OSA‐modified DWxRc, α‐L, and native phytoglycogen. WPC had significantly lower water monolayer capacity. All Tg values matched with the solid‐like appearance of the biopolymers. Native polysaccharides and whey proteins had higher glass transition temperature (Tg) values. On the other hand, depolymerized waxy starches at 7%‐OSA modification had a “melted” appearance when exposed to environments with high relative humidity (above 70%) after 10 days at 23 °C. The use of depolymerized and OSA‐modified polysaccharides blended with proteins created more stable blends of biopolymers. Hence, this biopolymer would be suitable for materials exposed to high humidity environments in food applications.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.