A comparative study on hygroscopic and physiochemical properties of chicken powders obtained by different drying methods

Abstract

Different drying methods (spray drying (SD), vacuum drying (VD), microwave vacuum drying (MVD), and infrared vacuum drying (IFVD)) were applied in order to compare the hygroscopicity behavior of chicken powders. The hygroscopicity curves and glass transition temperature were used to evaluate the influence of ambient humidity and temperature on moisture absorption of powders. The results showed that the chicken powder dried by MVD had the lowest moisture absorption, followed by IFVD, VD, and SD. The hygroscopicity of SD chicken powders was different from other three kinds of chicken powders due to the physical properties of particles and the changes of protein secondary structure as detected by the Fourier transform-infrared spectrometer. For the three vacuum drying methods, the difference of protein secondary structure was the main reason of differences in hygroscopicity. Although MVD chicken powders were slightly inferior to SD chicken powders in taste, MVD chicken powders were the best in terms of smell and color as suggested by instrumental sensory parameter evaluations. It was found that MVD had a positive effect on reducing moisture absorption and maintaining sensory quality of chicken powders.     

河南省灵宝市董家埝银矿原生晕特征及地质意义

董家埝银矿是豫西小秦岭矿集区南成矿带目前发现的唯一大型矿床。为了矿床深部勘查工作的顺利进行,了解有用元素在垂向上的变化规律,选用原生晕地球化学方法,通过典型矿床剖面的原生晕特征研究,实现对深部矿体变化趋势的判断。经计算,确定As、Sb、Ag、Au、Cu、Pb、Zn、Co和Mo等9种元素可作为指示元素,其中As和Sb为前缘晕指示元素,Ag、Au、Cu、Pb和Zn为近矿晕指示元素,Co和Mo为尾晕指示元素。分带指数和浓集指数2种方法计算得出的元素在分带序列上的位置总体趋势一致。用分带指数法确定的主矿体自上而下的原生晕轴向分带序列为Co-Mo-Cu-Au-Sb-Zn-Pb-Ag-As,出现"反分带"现象。结合原生晕地球化学参数变化特征、矿体剥蚀程度研究和理想模型,预测M1-I主矿体向深部仍有较大的延伸。研究成果可以为区域同类型矿化地质体深部的趋势判断和决策提供参考。     

基于物联网的智慧林业可持续发展策略研究

随着我国科技的不断进步,在“互联网+”大数据的背景下,林业的智慧化发展是必经之路。虽然我国智慧林业的发展已经取得了一定的突破,但是仍然存在着许多问题。基于此,文章在物联网技术的基础上,对智慧林业的可持续发展策略进行探讨。     

iRGD-reinforced,photo-transformable nanoclusters toward cooperative enhancement of intratumoral penetration and antitumor efficacy

Nano Research - Insufficient intratumoral penetration greatly hurdles the anticancer performance of nanomedicine. To realize highly efficient tumor penetration in a precisely and spatiotemporally...     

Reconstructing gene regulatory networks with a memetic-neural hybrid based on fuzzy cognitive maps

Reconstructing gene regulatory networks (GRNs) plays an important role in identifying the complicated regulatory relationships, uncovering regulatory patterns in cells, and gaining a systematic view for biological processes. In order to reconstruct large-scale GRNs accurately, in this paper, we first use fuzzy cognitive maps (FCMs), which are a kind of cognition fuzzy influence graphs based on fuzzy logic and neural networks, to model GRNs. Then, a novel hybrid method is proposed to reconstruct GRNs from time series expression profiles using memetic algorithm (MA) combined with neural network (NN), which is labeled as MANN_FCM-GRN. In MANN_FCM-GRN, the MA is used to determine regulatory connections in GRNs and the NN is used to determine the interaction strength of the regulatory connections. In the experiments, the performance of MANN_FCM-GRN is validated on both synthetic data and the benchmark dataset DREAM3 and DREAM4. The experimental results demonstrate the efficacy of MANN_FCM-GRN and show that MANN_FCM-GRN can reconstruct GRNs with high accuracy without expert knowledge. The comparison with existing algorithms also shows that MANN_FCM-GRN outperforms ant colony optimization, non-linear Hebbian learning, and real-coded genetic algorithms.

     

MPE: a mobility pattern embedding model for predicting next locations

World Wide Web - The wide spread use of positioning and photographing devices gives rise to a deluge of traffic trajectory data (e.g., vehicle passage records and taxi trajectory data), with each...