火焰原子吸收光谱法测定肥料中重金属含量的不确定度评估

根据GB/T 23349—2009 《肥料中砷、镉、铅、铬、汞生态指标》中所规定的方法,采用火焰原子吸收光谱法测定肥料中重金属镉、铬和铅的含量,并建立了火焰原子吸收光谱法测定肥料中重金属含量的不确定度测量模型。测定肥料中重金属镉的含量为0. 003 6%(1±4. 2%),k=2;铬的含量为0. 006 3%(1±3. 8%),k=2;铅的含量为0. 005 0%(1±2. 1%),k=2。结果表明,对镉、铬和铅元素的测试而言,不确定度主要影响因素均来源于样品浓度测定中标准曲线的拟合及测量重复性引入的不确定度分量。     

丙烯酰胺改性壳聚糖处理含Co2+模拟放射性废水吸附工艺研究

为了提高丙烯酰胺改性壳聚糖(AM-Cts)对模拟放射性废水中Co~(2+)的吸附容量,对AM-Cts处理含Co~(2+)模拟放射性废水的吸附工艺进行了优化研究。考察了废水中Co~(2+)含量、pH、AM-Cts和废水质量比、吸附时间、搅拌速率和吸附温度对AM-Cts吸附容量的影响,并在此基础上开展了正交试验、验证实验和掺杂离子影响实验。结果表明:在pH=8. 0、温度40℃、改性壳聚糖:废水中Co~(2+)含量=2. 5g/g、吸附时间60min、搅拌速率200rpm的条件下,AM-Cts对模拟废水中Co~(2+)的吸附容量为22. 33mg/g,较未优化前提高了约40. 0%;在含有Fe~(2+)、Sr~(2+)、Cs+等杂质离子条件下,对Co~(2+)的吸附选择性较好,可达78. 96%。     

ICP-OES法测定硅锰、锰铁合金中磷含量

应用ICP—OES法测定硅锰、锰铁合金中磷的含量,对试样前处理及分析方法的各种参数进行了优化,包括溶样酸的选择,分析谱线的选择,试样基体、分析酸度和干扰元素对测定结果的影响。通过各种分析参数的优化选择实现了硅锰、锰铁中磷的测定,取得了满意的效果。线性相关系数：P=0．9998,检测限为：0．030％。该分析方法准确快速,试剂用量少,线性范围宽,适用于硅锰、锰铁合金中质量分数为0．03～0．50％的磷测定。     

同位素稀释质谱法测定α-淀粉酶的蛋白质含量

利用同位素稀释质谱法测定了盐溶液中α-淀粉酶的蛋白质含量.在含有α-淀粉酶的盐溶液中加入适量的苯丙氨酸标记物后进行酸水解,水解后的氨基酸经过高效液相色谱(HPLC)分离,质谱检测采用选择离子模式,分别监测苯丙氨酸(m/z=166)和标记苯丙氨酸(m/z=174)的离子,根据苯丙氨酸的含量计算α-淀粉酶的蛋白质含量,因此在测定中只需要氨基酸的标准物质就可以保证蛋白质含量测定结果的溯源性.实验中优化了酸水解的时间,采用HPLC-MS和MALDI-TOF质谱证明了淀粉酶已完全水解,并对测定结果的不确定度进行了评估.该方法可以准确测定溶液中蛋白质含量,并可用于蛋白质含量标准物质的定值.     

食品及食品包装中邻苯二甲酸酯类化合物的测定分析

采用优化的超声提取法提取市售食品和食品包装袋中的邻苯二甲酸酯类化合物(PAEs),并采用紫外-可见分光光度法(λmax=227nm)测定其含量,结果显示:被测定的食品和食品包装样品中普遍检出了PAEs,其含量范围在0～1 308.88mg/kg之间;塑料袋包装食品中PAEs含量高于非塑料包装的食品;油脂含量高的食品中PAEs含量也高。结果表明,食品中PAEs主要来源于包装材料,高油脂是导致食品中PAEs含量高的一个重要因素。     

0.01cc小腔体气密封器件内部气氛含量测试方法研究

根据气密封器件内部气氛含量的测试原理,从0.01cc小腔体气密封器件内部气氛含量测试的取样、校准及夹具等方面分析造成0.01cc(1 cc=1 cm3)小腔体气密封器件内部气氛含量测试结果误差的主要因素,其中夹具的结构设计是造成误差的最大影响因素。随后从缩小测试夹具腔体的密封范围、减少穿刺后的内部气氛向外扩散、提高0.01cc小腔体内部气氛进入设备分析室的浓度等方面对夹具优化设计。最后采取同批次型号的0.01cc小腔体器件进行新旧夹具分组比对分析,从测试数据的离散性、设备的端口气氛浓度数据分析可见,经过优化的夹具能够有效提高0.01cc小腔体气密封器件内部气氛含量测试的准确性。这一改进途径也可为提高其他气密封器件内部气氛含量检测的可行性及准确性提供思路。     

室温一步法制备PMMA-PS/PU浇注胶

研究了聚甲基丙烯酸甲酯 -聚苯乙烯 /聚氨酯 (PMMA- PS/ PU)聚合反应速度的影响因素 ,对影响PMMA- PS/ PU浇注体系主要力学性能的几个关键因素进行了分析讨论。研究结果表明 ,控制 - NCO/- OH比值、低分子二元醇与聚酯多元醇的 - OH摩尔比、催化引发体系用量等 ,可得到具有优良的反应性、工艺性及优异力学性能的改性体系。当 R=1.10、α=0 .5、BPO/ St=1.6 %、DMA/ St=0 .9%、β=0 .0 4 %、γ=30 %、ε=3/ 10 0、甲组分中 Si O2 含量为 30 %～ 35 %时体系的反应性和力学性能均较佳 :室温下的凝胶时间为 30 min左右 ,2 4 h完全固化 ;拉伸强度 2 6 .0 MPa,拉伸剪切强度 (ABS-乙丙橡胶 ) 6 .0MPa,断裂伸长率 30 0 % ,永久变形率 10 .0 % ,热空气老化强度变化率 - 1.0 % ,耐酸后强度变化率- 0 .0 5 % ,硬度 (邵 D) 88°     

利用酶学参考实验室网络建立IVD厂家γ-GT溯源性主校准品

中国酶学实验室网络按照γ-GT酶学一级参考方法对厂家γ-GT主校准品进行联合定值,由厂家使用互通性良好的主校准品将量值传递至常规检测系统商用校准品,并测定由参考方法定值的系列人血清样品以确认检测系统的溯源性.使用具有溯源性的商用校准品校准配套迈瑞常规检测系统γ-GT项目,对至少30份覆盖了医学决定水平的人血清样品的测量结果与参考方法测量结果可比,相关系数r=0.999 8,回归方程y=1.015 2x-0.12,回归斜率与1以及截距与0无显著性差异,迈瑞常规检测系统γ-GT项目检测结果具有溯源性.实验表明国内酶学参考实验室网络为建立厂家主校准品、实现常规系统检测结果的溯源性提供了关键保证.     

红外分光光度法测定污泥中矿物油的不确定度评定

分析探讨了红外红外分光光度法测定污泥中矿物油含量不确定度的影响因素,并对其不确定度分量进行了评定,计算得到相对标准不确定度urel(X)为0. 0187,污泥样品中矿物油含量的标准不确定度u(X)为1. 0450mg/L,扩展不确定度U为2. 0899mg/L(k=2),所测污泥样品中的矿物油的含量为X=55. 83±2. 0899mg/L(k=2)。扩展不确定度贡献较大的主要分量是样品重复测定和配置三波长校准溶液引入的标准不确定度分量。     

用双波长分光光度法同时测定锡镍合金镀液中的钴和镍

为研究新型环保锡镍合金镀液中钴和镍含量同时测定的方法,在pH=4.5的醋酸-醋酸钠介质中,采用1%tween-80增溶,经0.02%5-Br-PADAP显色,以双波长分光光度法对Co2 与Ni2 含量进行了同时测定.试验表明,Co2 、Ni2 含量在0～6μg/(25 mL)范围内符合比尔定律.该方法方便、快速、准确,结果令人满意.     

双基火药储存过程中安定剂含量的快速检测技术

为解决传统检测方法耗时长、操作复杂等问题,研究了近红外光谱法快速定量检测双芳 3双基火药中安定剂含量的可行性。通过分析安定剂的特征光谱区间,得到合适的建模波段。采用不同的光谱预处理方法和选取最佳主因子数优化模型,使用偏最小二乘法建立安定剂的定量校正模型,对模型进行了外部验证。结果表明:使用1 100~ 1 248 nm、1 323~ 1 515 nm波段,采用标准正态变量变化（SNV）预处理原始光谱,主因子数为7时建立的定量校正模型的预测准确性和稳定性较高。校正模型决定系数(R²_C)以及交互验证的决定系数(R²_CV)分别为0.991和0.987;校正标准偏差（R_MSEC）和交互验证的标准偏差（R_MSECV）分别为0.065和0.077。使用预测集样品对建立的最佳校正模型进行外部验证,安定剂含量预测值与参考值的平均误差为0.044%。该方法可用于双芳-3双基火药中安定剂含量的快速检测。维也里试验证明,近红外光谱法可以用于评估双芳-3双基火药安定性的好坏。     

Advanced process design and understanding of continuous twin-screw granulation via implementation of in-line process analytical technologies

Process analytical technologies (PAT) are identified as an essential element in the Quality by Design framework, providing the cornerstone to implement continuous pharmaceutical manufacturing. This study is concerned with employing three in-line PATs: Eyecon? 3D imaging system, Near-infrared spectroscopy (NIRS) and Raman spectroscopy (RS), in a continuous twin-screw granulation process to enable real-time monitoring and prediction of critical quality attributes of granules. The Thermo Scientific? Pharma 11 twin-screw granulator was used to manufacture granules from a low-dose formulation with caffeine anhydrous as the model drug. A 30-run full factorial design including three critical process parameters (liquid to solid ratio, barrel temperature and throughput) was conducted to evaluate the performance of each analytical tool. Eyecon? successfully captured the granule size and shape variation from different experimental conditions and demonstrated sufficient sensitivity to the fluctuation of size parameter D10 in the presence of process perturbations. The partial least square regression (PLSR) models developed using NIRS showed small relative standard error of prediction values (less than 5%) for most granule physical properties. In contrast, the RS-based PLSR models revealed higher prediction errors towards granule drug concentration, potentially due to the inhomogeneous premixing of raw materials during calibration model development.     

In-line reaction monitoring of a methyl methacrylate and N, N-dimethylacrylamide copolymerization reaction using near-infrared spectroscopy

Fast and accurate monitoring of monomer concentration during copolymerization reactions is of much interest. It is known that near-infrared spectroscopy (NIRS) can be used to monitor polymerization reactions. Here, a free radical solution copolymerization reaction between methyl methacrylate and N,N-dimethylacrylamide is considered. NIR spectra were measured in-line with a transflectance probe. The spectra of both involved monomers are very similar, making monitoring with NIRS challenging. It is shown that the NIRS calibration can be set up with only a few (5) off-line measured mixtures. Several validation methods for such a NIRS calibration model are discussed and tested. NIRS is used to follow conversion of the two monomers in a copolymerization reaction on-line.     

Use of artificial neural networks in near-infrared reflectance spectroscopy calibrations for predicting the inclusion percentages of wheat and sunflower meal in compound feedingstuffs

The use of near-infrared reflectance spectroscopy (NIRS) calibrations to predict the ingredient composition in compound feeds (i.e., inclusion percentage of each ingredient) is a complex task, regarding both the nature of the parameters to be predicted, since they are not well-defined chemical entities, and the heterogeneousness of the matrices/formulas in which each ingredient participates. The present paper evaluates the use of nonlinear regression methods, such as artificial neural networks (ANN), for developing NIRS calibrations to predict these parameters. Two of the most representative ingredients in the Spanish compound feed formulations (wheat and sunflower meal) were selected for evaluating ANN possibilities, using a large spectral library comprising a total of 7523 commercial compound feed samples; 7423 were used as training set and 100 as validation set. Three general models of networks were studied: multilayer perceptron with back-propagation training (BP), multilayer perceptron with Levenberg-Maquartd training (LM), and radial basis function nets (RBF); moreover, in accordance with a factorial design, more complex architectures were evaluated gradually, changing the number of hidden layers and hidden neurons, for the determination of the optimal network topology. For both ingredients, the best results were obtained using ANN with BP training, showing prediction error values (SEP) of 2.72% and 0.66% for wheat and sunflower meal, respectively. These SEP values showed a significant improvement (19%-49% for sunflower meal and wheat, respectively) in comparison with those obtained using calibrations developed with linear methods.     

The use of near-infrared spectroscopy for the quantitation of a drug in hot-melt extruded films

The objective of the study was to demonstrate the utility of near-infrared spectroscopy (NIRS) for quantitative analysis of a model drug in hot-melt extruded film formulations. Polyethylene oxide (PEO) films with clotrimazole (CT) as a model drug were prepared by hot-melt extrusion (HME) incorporating drug concentrations ranging from 0-20% and analyzed using a Fourier transform near-infrared (FT-NIR) spectrophotometer in the reflectance mode. High performance liquid chromatography (HPLC) was the reference method used for this study. The NIR calibration model derived for CT was composed of 21 frequency ranges that were correlated to the values quantified using the HPLC reference method. The NIR method developed resulted in an assayed CT amount in the film matrix to be within 3.5% of the quantity determined by the reference method. These studies clearly demonstrate that NIRS is a powerful method for the quantitation of active drug substances contained in films produced by HME and warrants further investigation.     

基于PSO-GRNN的直升机旋翼不平衡故障诊断

为了准确诊断直升机旋翼不平衡故障,提出了一种基于粒子群算法和广义回归神经网络模型(PSO-GRNN)的故障诊断方法。将交叉验证得到的平均均方误差作为粒子群的适应度函数,运用粒子群算法搜寻最优的GRNN光滑因子,建立最优的故障诊断模型。结果表明:采用PSO-GRNN模型可实现直升机旋翼不平衡的类型和程度的有效诊断,故障类型准确率高达94.29%,故障程度的诊断最大误差仅6.54%,满足工程需求。     

Implementation of LOCAL algorithm with near-infrared spectroscopy for compliance assurance in compound feedingstuffs

Seven thousand four hundred and twenty-three compound feed samples were used to develop near-infrared (NIR) calibrations for predicting the percentage of each ingredient used in the manufacture of a given compound feedingstuff. Spectra were collected at 2 nm increments using a FOSS NIRSystems 5000 monochromator. The reference data used for each ingredient percentage were those declared in the formula for each feedingstuff. Two chemometric tools for developing NIRS prediction models were compared: the so-called GLOBAL MPLS (modified partial least squares), traditionally used in developing NIRS applications, and the more recently developed calibration strategy known as LOCAL. The LOCAL procedure is designed to select, from a large database, samples with spectra resembling the sample being analyzed. Selected samples are used as calibration sets to develop specific MPLS equations for predicting each unknown sample. For all predicted ingredients, LOCAL calibrations resulted in a significant improvement in both standard error of prediction (SEP) and bias values compared with GLOBAL calibrations. Determination coefficient values (r(2)) also improved using the LOCAL strategy, exceeding 0.90 for most ingredients. Use of the LOCAL algorithm for calibration thus proved valuable in minimizing the errors in NIRS calibration equations for predicting a parameter as complex as the percentage of each ingredient in compound feedingstuffs.     

A predictive model for slip resistance using artificial neural networks

This paper describes the formulation, building and validation of an artificial neural network model of the dynamic coefficient of friction (DCOF) as measured by a slip resistance testing device. The model predicts the DCOF as a function of six independent variables over a wide range of conditions. A grouped cross validation method is used to show the consistent performance of the model in predicting the DCOF for new values of the independent variables.     

The use of near-infrared spectroscopy in understanding skeletal muscle physiology: recent developments

This article provides a snapshot of muscle near-infrared spectroscopy (NIRS) at the end of 2010 summarizing the recent literature, offering the present status and perspectives of the NIRS instrumentation and methods, describing the main NIRS studies on skeletal muscle physiology, posing open questions and outlining future directions. So far, different NIRS techniques (e.g. continuous-wave (CW) and spatially, time- and frequency-resolved spectroscopy) have been used for measuring muscle oxygenation during exercise. In the last four years, approximately 160 muscle NIRS articles have been published on different physiological aspects (primarily muscle oxygenation and haemodynamics) of several upper- and lower-limb muscle groups investigated by using mainly two-channel CW and spatially resolved spectroscopy commercial instruments. Unfortunately, in only 15 of these studies were the advantages of using multi-channel instruments exploited. There are still several open questions in the application of NIRS in muscle studies: (i)?whether NIRS can be used in subjects with a large fat layer; (ii) the contribution of myoglobin desaturation to the NIRS signal during exercise; (iii) the effect of scattering changes during exercise; and (iv) the effect of changes in skin perfusion, particularly during prolonged exercise. Recommendations for instrumentation advancements and future muscle NIRS studies are provided.