Minimizing end-effects in the coaxial cylinders viscometer: Viscoplastic flow inside the ConTec BML Viscometer 3

The ConTec BML Viscometer 3 is designed to measure the rheological properties of fresh concrete. The measuring unit of this device is composed of two parts, where the rheological properties are only registered by the part least affected by end-effects. Thus it is assumed that the measured data is not influenced by end-effects, allowing for the use of an algebraically derived equation to convert the data to fundamental physical quantities. In this work, this important assumption is supported by numerical analysis. This has been done by comparing values calculated by numerical means, where end-effects are included in the analysis, with values calculated by analytical means, where end-effects are excluded.     

凯氏定氮仪-分光光度法测定鹅肠中甲醛

建立鹅肠中甲醛测定的新方法,采用凯氏定氮蒸馏装置代替蒸汽蒸馏装置从鹅肠中提取甲醛,并对水浴温度和时间进行改进。新建立的方法RSD为2.36%—4.23%,加标回收率为92.7%—102.3%,具有良好的线性范围,R~2=0.9999。方法精密度好,准确度高,适用于鹅肠中甲醛的测定。     

Entry of online presale of fresh produce: A competitive analysis

The fresh produce market is one of the last unexplored spaces for e-commerce and has attracted the entrance of many e-commerce firms in recent years; these firms are likely to influence the traditional fresh produce sales channels. The online presale of fresh produce can lower the circulation loss rate while traditional sales channels suffer from a huge waste in the circulation. In this paper, we study the online presale model of fresh produce from a competitive perspective. We examine the pricing and order decisions of one online grocery and one physical store. By examining the physical store with and without the online grocery, we investigate the impact of the entry of an online grocery on the physical store. We first identify two pricing strategies, penetration-pricing and skimming-pricing, for the physical store and two pricing strategies for the online grocery, and then identify the conditions under which pricing equilibrium will occur. We find that the store does not necessarily alter its pricing strategy after the online grocery enters the market. When the physical store uses the penetration-pricing strategy to compete with the online grocery for Internet shoppers, it may fail to achieve that goal if the online grocery's delivery cost is low and if the Internet shoppers account for a relatively low proportion of shoppers. Alternatively, the physical store may fail if the online grocery's delivery cost is not high and if the circulation loss rate of fresh produce is high.     

Physicochemical properties,antioxidant action and practical application in fresh cheese of the solid inclusion compound γ-cyclodextrin·quercetin,in comparison with β-cyclodextrin·quercetin

Quercetin is an antioxidant flavonol very sensible to light and oxidants that can benefit from stabilisation by encapsulation into cyclodextrins. In this work we study the solid inclusion compounds of quercetin with β- and γ-cyclodextrins (β-CD and γ-CD) obtained by freeze-drying. Combined results from microanalysis, FT-IR, powder X-ray diffraction, ¹³C{¹H} CP/MAS NMR spectroscopy and thermogravimetry demonstrating that γ-CD, having a larger cavity, is the most adequate host to form a stable inclusion complex with quercetin. The anti-peroxidation capacity of the compounds was determined and followed the order β-CD·quercetin ≫ quercetin > γ-CD·quercetin. Both γ-CD·quercetin and β-CD·quercetin are able to inhibit DPPH radicals at a rate three times faster than pure quercetin, but their EC₅₀ is higher (50 μM vs 17 μM for quercetin). The practical applicability of the two CD·quercetin adducts as nutraceutical additives in fresh cheese was established. Fortified fresh cheese had a firmer texture and yellowish colour, but no significant changes in the overall sensorial qualities were found by a panel of non-trained tasters when compared with the control (non-treated fresh cheese).     

Performance of Ex Situ Bismuth Film Rotating Disk Electrode in Trace Metal Analysis by Stripping Chronopotentiometry: Definition of the Depletion Regime and Optimization of Experimental Parameters

The potentiality of the ex situ deposited bismuth film electrode, allied to the rotation of a glassy carbon disk electrode (BiFE‐RDE), was exploited in trace metal analyses of lead(II) and cadmium(II) by stripping chronopotentiometry (SCP). A single BiFE (6.2 nm film thickness) can be used for a 1‐day term with no significant variation in the analytical signal. The limit of detection (3σ) for a deposition time of 40 s and an oxidation current of 15×10^?9 A was 1.5×10^?8 M for Pb(II) and 3.0×10^?8 M for Cd(II). BiFE‐RDE was successfully applied to the direct SCP determination of lead(II) in a fresh water certified material.     

光学扩散特征的生鲜肉细菌总数的无损检测方法

细菌总数是生鲜肉的最主要安全参数之一,肉品的光学扩散特征反映其细菌总数。对高光谱扩散数据利用不同的拟合算法处理与分析,并比对生鲜猪肉细菌总数建模的相关性差异,确定了用于最终建模的最佳扩散特征参数,为后续的装置开发提供建模依据。冰箱调至4℃恒温,在里面放置63个分割好的猪肉样品,每天间隔固定时间从里面随机取出4~5块样品,获取猪肉表面的400~1 100nm波长范围内高光谱散射图像,从高光谱图像中提取猪肉的扩散光谱曲线,利用Lorentz函数和Gompertz函数以及修正后的函数,拟合处理与分析扩散数据,拟合后的不同参数可以代表样品的特征光谱,细菌总数的标准值用平板计数法获得,然后单独用各单个参数、多个参数结合的方式,多元线性回归的统计方法,与细菌总数分别建立模型。实验结果表明,Lorentz三参数结合,Lorentz四参数结合,Gompertz四参数拟合的模型相关系数较高,其校正集和预测集相关系数分别为0.93,0.96,0.96和0.90,0.90,0.92,标准偏差分别为0.47,0.44,0.39和0.56,0.46,0.42,其中,相关性最好的是Gompertz四参数结合,在装置的开发中可以优选相关性和稳定性最好的模型导入装置系统中。     

GWLS-SVR模型的红枣树叶片叶绿素含量估算

叶绿素含量是红枣树光合作用能力、生长状况、营养状况的指示剂,不同地理位置种植的红枣树受到自然、人为等因素的影响,叶绿素含量分布有所不同,该研究实地测定了若羌县枣树叶片高光谱反射率及表征叶绿素含量的枣树叶片SPAD（soil plant analysis development)值。为了高效无损地估算红枣树叶片SPAD值,计算了红枣树叶片SPAD值全局莫兰指数,以SPAD值和高光谱波段之间的相关性为基础,通过C_P统计量计算重要程度高的特征波段,运用地理加权最小二乘支持向量回归GWLS-SVR（geographically weighted least squares-support vector regression）模型对红枣树叶片SPAD值进行预测,与多元线性回归（MLR）、支持向量机回归（SVR）模型比较并探讨GWLS-SVR模型估算红枣树叶片SPAD值的能力。结果表明：（1）光谱一阶导数可以有效去除噪声并突出光谱信息尤其是492~510,542~543,642~652,657~670和682~692 nm区间内显著的提高了与SPAD值的相关性。（2）C_P统计量方法能够有效的选择敏感区间的特征波段,进而提高模型估算精度,由统计量方法计算出原始光谱重要程度最高的两个变量为595与696 nm,光谱一阶导数的特征波段为688 nm。其中对于同一个敏感波段区间的波段组合总有单个波段的统计量低于多个波段组合的统计量,这可能是相近波段间的较强共线性导致的。（3）若羌县红枣树叶片SPAD值存在显著的空间聚集性,全局莫兰指数为0.125 8（p<0.1）,适合建立考虑空间位置的GWLS-SVR模型。（4）结合Bootstrap再抽样与t检验模型检验得到结合地理位置信息的GWLS-SVR模型总体上估算能力优于SVR和MLR模型,且结果高度显著（p<0.001）,其中基于光谱一阶导数的GWLS-SVR模型为最优的红枣树叶片SPAD值估算模型（R2为0.975,MSE为1.082）,能够为高光谱定量反演红枣树SPAD值进而快速无损的监测红枣生长状况提供一定参考。     

可见近红外高光谱成像对灵武长枣定量损伤等级判别

利用可见近红外（Vis-NIR）高光谱成像技术对完好和损伤等级灵武长枣进行快速识别检测。采用定量损伤装置得到损伤Ⅰ,Ⅱ,Ⅲ,Ⅳ和Ⅴ级的灵武长枣,借助高光谱成像系统采集完好长枣和损伤长枣样本高光谱图像。提取感兴趣区域（region of interest,ROI）并计算样本平均光谱值。利用光谱-理化值共生距离算法（SPXY）将420个长枣样本按3∶1的比例划分校正集315个和预测集105个。灵武长枣原始光谱建立偏最小二乘判别分析（PLS-DA）分类模型,得到校正集和预测集准确率分别为72.70%和86.67%;灵武长枣原始光谱数据采用移动平均（MA）、卷积平滑（SG）、多元散射校正（MSC）、正交信号修正（OSC）、基线校准（baseline）和去趋势（de-trending）等方法进行光谱预处理并建立PLS-DA分类判别模型。通过分析比较,得到MSC-PLS-DA为最优分类判别模型,校正集准确率为76.19%,预测集准确率为86.67%,其中校正集比原始光谱建模准确率提高了3.49%,预测集准确率较原始光谱建模结果未提高;为了提高建模效果,对灵武长枣原始光谱和预处理后的光谱分别采用连续投影算法（SPA）、无信息变量消除（UVE）、竞争性自适应加权抽样（CARS）和区间变量迭代空间收缩法（iVISSA）等算法提取特征波长,建立PLS-DA分类判别模型,结果表明,MSC-CARS-PLS-DA为最优模型组合,校正集准确率为77.14%,预测集准确率为89.52%,建模准确率较原始光谱建模准确率分别提高了4.44%和2.85%。结果表明,Vis-NIR高光谱成像技术结合MSC-CARS-PLS-DA模型可实现灵武长枣损伤等级的快速识别。     

鲜肉中不饱和脂肪酸近红外检测方法优化

不饱和脂肪酸是鲜肉脂肪的基本组成成分，其种类和含量直接影响鲜肉的风味和品质。不同于用时长，破坏样品的气相色谱法，近红外分析可快速，无损地对鲜肉中不饱和脂肪酸进行定量检测。选取了驴肉，牛肉，羊肉和猪肉鲜肉样品共63个，在4 000～12 500 cm-1波段上，分别采集了肉块样品和粉碎口径3 mm的肉糜样品在5，10，15，20，25，30，35 ℃下的近红外漫反射光谱，并使用气相色谱法作为参考检测了样品的不饱和脂肪酸含量。为优化模型性能，比较了不同温度下肉块和肉糜样品全波段光谱的棕榈油酸、亚油酸、油酸、二十四碳一烯酸和总不饱和脂肪酸的偏最小二乘模型参数，发现棕榈油酸和总不饱和脂肪酸的5 ℃肉糜光谱模型，亚油酸的35 ℃和油酸的25 ℃肉糜光谱模型，以及二十四碳一烯酸的15 ℃肉块光谱模型较佳，且温度对模型的影响不具有明显的规律性。在较佳模型的基础上，分别以220，440，881和1 762个变量为窗口区间，使用前向和反向区间偏最小二乘法对波长进行了优选，发现棕榈油酸、亚油酸、油酸和总不饱和脂肪酸以220个变量作为窗口的反向区间偏最小二乘模型，以及二十四碳一烯酸以440个变量为窗口的前向区间偏最小二乘模型较佳，且预测性能均优于全波段PLS模型。其中棕榈油酸的建模波段为：4 425～4 636，4 849～5 272，5 486～5 696.7，7 398.6～7 818，8 031.1～8 666.5，9 947～10 363.6，12 495.5～12 498.4 cm-1；亚油酸的建模波段为：4 000.6～4 423.9，5 273.4～5 698.6，7 398.6～9 090.8，10 576.7～10 787.8，12 495.5～12 498.4 cm-1；油酸的建模波段为：4 000.6～4 423.9，4 637～4 848.2，7 398.6～8 242.3，8 455.4～9 090.8，9 947～10 787.8，12 495.5～12 498.4 cm-1；二十四碳一烯酸的建模波段为：4 849.1～5 272.4 cm-1；总不饱和脂肪酸的建模波段为：4 000.6～4 423.9，4 637～5 698.6，9 097.5～9 515.1，9 940.3～10 575.7，11 646～12 060.6，12 273.7～12 498.4 cm-1。使用偏最小二乘法对优选波长光谱数据进行降维，利用得到的潜在变量作为各指标最小二乘-支持向量机模型的输入，并和各指标的区间偏最小二乘模型进行了性能的比较，发现最小二乘-支持向量机模型的各指标定量结果最优。棕榈油酸、亚油酸、油酸、二十四碳一烯酸和总不饱和脂肪酸最优模型的校正集相关系数和均方根误差，以及留一交叉验证相关系数，均方根误差及相对预测误差分别为：0.974，1.403 mg·(100 g)-1，0.973，1.428 mg·(100 g)-1，4.31；0.99，2.233 mg·(100 g)-1，0.99，2.263 mg·(100 g)-1，7.21；0.982，8.194 mg·(100 g)-1，0.982，8.223 mg·(100 g)-1，5.19；0.921，0.224 mg·(100 g)-1，0.92，0.225 mg·(100 g)-1，2.52；0.996，24.21 mg·(100 g)-1，0.995，26.045 mg·(100 g)-1，10.01。其中，亚油酸、油酸和总不饱和脂肪酸最优模型的交叉验证相对预测误差超过5，棕榈油酸最优模型的交叉验证相对预测误差接近5，二十四碳一烯酸的交叉验证相对预测误差接近3，各指标最优模型的预测性能较为令人满意。研究结果表明，波段优选，偏最小二乘降维以及最小二乘-支持向量机算法的结合可对鲜肉中单个不饱和脂肪酸及总不饱和脂肪酸指标的近红外光谱定量结果进行有效的优化。     

基于ELM和可见/近红外光谱的鲜枣动态分类检测

枣营养丰富且品种繁多,不同品种的内外部品质与市场价格均存在差异,为了实现鲜枣品种分类的快速无损检测,以产自同一地区的板枣、鸡心枣和相枣为研究对象,动态采集光谱数据。采用移动平滑(moving smoothing)与多元散射校正(multiplicative scatter correction, MSC)相结合的方法预处理光谱数据,对预处理后的光谱数据采用连续投影算法(successive projections algorithm, SPA)提取出11个特征波长分别为：980, 1 860, 1 341, 1 386, 2 096, 1 831, 1 910, 1 628, 441, 768, 601 nm,其重要程度依次递减。以所提取的特征波长作为输入变量,建立极限学习机(extreme learning machine,ELM)分类模型,进行预测判别,并与偏最小二乘判别分析(partial least squares discriminant analysis, PLS-DA)和最小二乘支持向量机(least squares support vector machines, LS-SVM)方法进行比较。结果表明：SPA-ELM方法所建校正模型的决定系数R2=0.972 38,校正均方根误差RMESC=0.018 724,SPA-ELM方法与SPA-PLS-DA和SPA-LS-SVM方法判别准确率均为100%,说明ELM是一种有效的分类判别方法。该研究为鲜枣品种分类检测提供了新的理论基础。