关键词：
ionic liquids;Hartree-Fock ab initio method;multiple linear regression;quantitative structure tribo-ability relationship;antiwear;hydrogen bond
摘要：
The antiwear properties of ionic liquids (ILs) as lubricant additives were studied with polyethylene glycol (PEG) used as the lubricant base oil. The quantum parameters of the ILs were calculated using a Hartree–Fock ab initio method. Correlation between the scale of the wear scar diameter and quantum parameters of the ILs was studied by multiple linear regression (MLR) analysis. A quantitative structure tribo-ability relationship (QSTR) model was built with a good fitting effect and predictive ability. The results show that the entropy of the ILs is the main descriptor affecting the antiwear performance of the lubricant system. To improve the antiwear performance of the lubricants, the entropy of the system should be decreased, reducing the system randomness and increasing the system regularity. A major influencing factor on the entropy of a system is the intra- and intermolecular hydrogen bonds present. Therefore, enhanced antiwear properties of lubricants could be achieved with a three-dimensional netlike structure of lubricant formed by hydrogen bonding.
作者机构：
[欧阳何一; 韩千慧; 侯温甫; 周敏; 吴忌] College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023, China;[王宏勋] School of Biological and Pharmaceutical Engineering, Wuhan Polytechnic University, Wuhan, 430023, China;[侯温甫; 王宏勋; 周敏] Fresh Food Engineering and Technology Research Center of Hubei Province, Wuhan, 430023, China
摘要：
Solar energy has recently attracted the attention of both industry and academia, to be used as a source of clean energy for green production of various products such as food-related commodities. This review aims to explore the applicability of this green energy source for extraction of valuable components (e.g., bioactive compounds and essential oils) from plant materials and waste biomass, dehydration of plant materials, water recovery through desalination, decontamination, cooking, and baking of agri-food products. According to the literature, concentrated solar power systems (CSP) have been successfully employed for bioactive compounds (e.g. essential oils) extraction, drying process, water desalination and decontamination, baking, and cooking. While CSP systems provide several benefits, such as low greenhouse gases emission and reduced production cost, their application is associated with several limitations which are taken into account in this review. Further considerations for improving the performance and applicability of CSP in the food industry are also discussed in the present study. (C) 2019 Elsevier Ltd. All rights reserved.
关键词：
Bio-nanocomposites;Chitin nanofibers;Natural rubber;Strength and toughness
摘要：
Rigid chitin nanofibers (ChNFs) self-assembled from dilute alpha-chitin/KOH/urea aqueous solution were utilized as 1D filler to reinforce soft natural rubber (NR). The prepared ChNFs suspension has good compatibility with natural rubber latex (NRL) and thus showing favorable dispersibility in NR matrix at nanoscale. The bio-nanocomposites were fabricated by casting and evaporating the pre-mixed NRL/ChNFs suspensions with different ChNFs loadings. Gratifyingly, the NR/ChNFs bio-nanocomposite with only 0.3 wt% ChNFs content presented distinct improvement in both the strength and toughness due to the large aspect ratio of ChNFs and its homogeneous dispersion in NRL matrix. Moreover, the introduction of ChNFs can promote the proliferation of mBMSCs effectively and endow NR/ChNFs bio-nanocomposites with good biocompatibility, enabling expanded applications of NR in biomedical field, such as artificial blood vessel, cosmetology prosthesis and human diaphragm materials.
作者机构：
[伍金娥; 童雅琪; 余雅琦; 郭文燕; 常超; 周胜男] College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023, China;[伍金娥] Key Laboratory of Intensive Processing of Staple Grain and Oil, Key Laboratory for Processing and Transformation of Agricultural Products, Wuhan Polytechnic University, Wuhan, 430023, China
通讯机构：
College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, China
摘要：
Purple sweet potato anthocyanins are common natural pigments widely used in food industry, while they are often thermally processed in application. Degradation of anthocyanins, formation of polymers and color changes of purple sweet potato extract (PSPE) were investigated at 90 degrees C in the range of pH 3.0-pH 7.0. Data analysis indicated a first-order reaction for anthocyanins degradation in solutions with pH 3.0, 5.0 and 7.0 have half-lives of 10.27, 12.42 and 4.66h, respectively. The polymeric color formation followed zero-order kinetics, progressively increasing with pH values. The color of PSPE were changed with heating time and pH value through visual observation and colorimetric characterization. Analysis by UV-Vis spectrophotometry and HPLC indicated that anthocyanins in solution with pH 3.0 changed from monomeric anthocyanin into new polymers during heat treatment. Degradation of anthocyanins was accompanied by an increase in polymeric color index, due to the formation of melanoidin pigments and condensation reactions.
摘要：
3-Monochloropropane-1,2-diol (3-MCPD) is a common food processing contaminant and a simple, rapid, sensitive and low cost monitoring technology is needed due to its potential carcinogenic nature. Carbon dots directly intercepted on filter paper provide high fluorescence intensity and can be adapted for use as a sensor. We synthesized a carbon dot-filter paper in combination with a molecularly imprinted polymeric film to extract 3-MCPD from samples. This grafted paper-based sensor exhibited a high adsorption capacity (68.97mgg(-1)), an excellent selectivity (imprinting factor=4.5) and a low detection limit (0.6ngmL(-1)). Recoveries ranged from 97.2% to 105.3% with relative standard deviations <5.6%. The results obtained using this method were linearly correlated to those of the classic GC-MS method (r=0.998). Based on the Chinese National Standard, this study provides a novel and powerful platform for the simple, rapid, sensitive and on-site analysis of 3-MCPD in soy sauce.
作者机构：
[石秀清; 张芬; 何守魁; 史贤明; 周秀娟] School of Agriculture and Biology, Shanghai Jiao Tong University, MOST-USDA Joint Research Center for Food Safety, Shanghai, 200240, China;[周敏] School of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023, China;[宋启发; 徐景野] Ningbo Center for Disease Control and Prevention, Ningbo, Zhejiang, 315010, China
通讯机构：
School of Agriculture and Biology, Shanghai Jiao Tong University, MOST-USDA Joint Research Center for Food Safety, Shanghai, China
作者机构：
[夏文水; Shan, Jinhui; 刘言; 王海滨; 陈季旺; 熊幼翎] College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023, China;[夏文水] School of Food Science and Technology, Jiangnan University, Wuxi, 214122, China;[陈季旺; 刘言; 王海滨; 熊幼翎] Hubei Collaborative Innovation Center for Processing of Agricultural Products, Wuhan, 430023, China
作者机构：
[Cao Yang; Yang Si-cheng] Acad State Adm Grain, Beijing 100037, Peoples R China.;[Shu Zai-xi; Yang Si-cheng] Wuhan Polytech Univ, Coll Food Sci & Engn, Wuhan 430023, Hubei, Peoples R China.
通讯机构：
[Cao Yang] A;Acad State Adm Grain, Beijing 100037, Peoples R China.
关键词：
高光谱;稻谷品种;鉴别;Fisher判别分析;偏最小二乘回归;人工神经网络
摘要：
许多不同的稻谷品种看起来很相似,但它们的化学成分和最终产品质量却有很大差别,每年因品种混淆而造成巨大的经济损失,对稻谷品种的鉴别是发展优质粮食工程的现实需要,为此提出了一种采用高光谱成像技术实现稻谷品种无损快速鉴别的方法。主要研究内容和结果如下:(1)在全波段388~1 000 nm范围内采集5个品种共150粒的稻谷高光谱反射率数据,筛选出差异明显的波段(600~800nm),将此波段内每个品种的反射率进行Stacked计算和curve-smoothing平滑处理以增加其区分度。(2)对5种稻谷经平滑处理后的反射率数据做主成分分析,找到权值系数最大的波长位于680nm,将其作为特征波长。加载特征波长下的纹理图像,计算每粒稻谷样品的纹理特征参数:均值(Mean)、方差(Variance)、信息熵(Entropy)和偏差(Skewness)。利用阈值分割的方法将目标与背景区分开,计算每粒稻谷形态特征参数:面积像素数/pixels2、边界的周长/pixels、长轴长度/pixels、短轴长度/pixels。结合稻谷的纹理特征参数和形态特征参数,比较Fisher判别分析模型、偏最小二乘回归模型(PLSR)和人工神经网络模型(ANN)对稻谷品种鉴别的效果。(3)结果显示,Fisher判别分析中函数1和函数2的累计方差贡献率达到93%,能够较好地解释稻谷的品种信息。将样本的函数值与组质心的平方马氏距离(Mahalanobis)做比较,值相近的作为同一分组类别,对稻谷品种的整体识别正确率能达到95.3%;偏最小二成回归模型:Y品种=0.03 X均值-0.36 X方差-0.24 X信息熵+0.37 X偏差+0.31 X面积-0.32 X周长-0.39 X长轴长度+0.45 X短轴长度,该回归模型相关系数r=0.98,校正均方根RMESS=0.29,交叉验证均方根PMESSCV=0.32,对稻谷的品种鉴别正确率能达到95%;构建的ANN模型为具有sigmoid隐含和softmax输出神经元的双层前馈网络,对150个样品按70%∶15%∶15%的比例随机划分训练集、测试集、验证集,选择共轭梯度法(scaled conjugate gradient)作为训练算法,以交叉熵(cross-entropy)作为模型的评价指标,对稻谷品种鉴别的正确率可达到98%。稻谷品种鉴别的ANN模型在分类精度上优于Fisher判别和PLSR,选择特征波长下的图像信息建立稻谷品种识别的ANN模型,对稻谷品种的无损快速鉴别具有重要指导意义。 <&wdkj&>Many different varieties of rice look very similar,but their chemical composition and final product quality vary greatly, which causes huge economic losses each year as a result of variety confusion.Identification of rice varieties is the practical requirement for developing high quality grain engineering.In this paper,a fast and non-destructive method for rice variety identification using hyperspectral imaging technology was proposed.The main research contents and results were as follows:(1)Average spectrawere extracted from the region of total 150samples with wavelength from 388~1 000nm.In the full band,the reflectance was most obvious at 600~800nm,which was calculated by Stacked stacking and curve-smoothing for increasing its differences.(2)Principal component analysis(PCA)was used to analyze the reflectance data smoothed.It was found that the wavelength with the largest weight coefficient was located at 680nm and used as the characteristic wavelength.Loading the texture image of the characteristic wavelengths,the texture characteristic parameters of each rice sample were calculated as follows: Mean,Variance,Entropy and Skewness.Meanwhile,the thresholding method was used to separate the target from the background, and the morphological parameters of each grain werecalculated as follows:areas/pixels2,perimeter/pixels,length of long axis/pixels,length of short axis/pixels.Based on the texture characteristics and morphological characteristics,the Fisher discriminant analysis model,partial least squares regression(PLSR)mode and Artificial neural network model(ANN)were established respectively for rice variety identification.(3)The results showed that the cumulative variance contribution rate of function 1and function 2established by Fisher discriminant analysis reached 93%,which could better explain the rice variety information. Comparing the function value of the sample with the square Mahalanobis distance of the group centroid,the individuals with similar values were taken as the same category.The overall recognition accuracy of the five rice varieties could reach 95.3%.The PLSR model:Yvarieties =0.03 Xmeans-0.36 Xvarious-0.24 Xentropy +0.37 Xskewness +0.31 Xarea-0.32 Xperimeter-0.39 Xlength of long axis+0.45 Xlength of short axis,with correlation coefficient(r)=0.98,corrected root mean square(RMESS)=0.29, cross validation root mean square(RMESSCV)=0.32,the accuracy of rice varieties identification could reach 95%.The neural network model is a two-layer feedforward network with sigmoid hidden and soft max output neurons,which randomly divides 150samples into training samples,validation sets and test sets according to the ratio of 70%∶15%∶15%.With training algorithm of conjugate gradient method and evaluation index of Cross-Entropy method,the accuracy of rice variety identification can reach 98%.The overall results show that the neural network model of rice variety identification is superior to Fisher discriminant and PLSR in classification accuracy,which has an important guiding significance for rapid and non-destructive identification of rice varieties.
