离子色谱法检测泡菜中亚硝酸盐含量的校准曲线线性性能评价

目的 评价校准曲线线性性能。方法 以离子色谱法检测泡菜中亚硝酸盐的校准曲线制作为实验内容,研究线性范围、工作范围,幵迚行校准曲线的质量检验。结果 检验方法的工作范围决定于校准曲线线性范围和样品中目标组分的含量范围;相关系数不能全面反映校准曲线的质量;在使用校准曲线前,还应迚行曲线是否通过原点(有要求时)和拟合误差检验;校准曲线上各浓度点应均匀布置,并且第一个非零浓度点最好高于定量限。结论 根据检测工作的实际需要,校准曲线的工作范围应在线性范围内灵活确定,还应迚行校准曲线质量检验。     

浅谈一元线性加权回归在分析化学领域中的研究应用现状

本文主要阐述了一元线性回归中的普通最小二乘法和加权最小二乘法的原理、差异和选用原则，介绍了异方差性检验、最佳权函数选择、回归方程检验、拟合质量比较等4个方面的应用方法，剖析了加权最小二乘法在国内外的应用现状及其存在的问题，并针对如何加快分析化学领域回归技术的应用研究提出建议，以期为分析化学和相关领域的研究人员提供借鉴和思考。     

ICP-MS检验茶叶中稀土元素不确定度的评定

对ICP-MS检验茶叶中稀土元素不确定度的效果评价,采用ICP-MS法测定茶叶中稀土元素的含量,对样本进行称量、消解、定容、配制溶液,对不确定的分量进行统计和分析。结果表明,标准溶液的配置、样品浓度、样本消解和仪器测量重复性对测量结果有较大影响,样本称量及定容对测量结果无影响。因此,在测量时应严格控制对测量结果有较大影响的因素,提高测量结果的准确性。     

HVI大容量纤维测试仪比对试验中统计方法的运用及Excel的使用

实验室间的比对试验中,对数据的统计分析是必不可少的重要内容。HVI型大容量纤维测试仪(简称HVI)比对试验是用单因子方差分析法对样品的均匀性进行评价,用稳健统计方法对检验结果进行分析,并用Excel表格对上述统计方法进行操作。     

真空校准系统测量过程统计控制的设计与实施

为确定真空校准系统测量过程是否稳定受控,文章对其设计并实施了统计控制,这也是我院首次将统计控制方法应用在测量过程的控制。在采集测量控制数据基础上,建立了控制模型,应用t检验和F检验两种统计检验法,计算得到统计量,对测量过程是否受控进行判定。实施结果表明,真空校准系统测量过程处于稳定受控状态,测量结果真实可靠。     

不确定度分析在检验工作中的应用

GB/T 15481—2000《检测和校准实验室能力的通用要求》5.4.6.1规定:校准实验室或进行自校准的检测实验室,对所有的校准和各种校准类型都应具有并应用评定测量不确定度的程序。 作为纤维、纺织品的法定检验机构,我们应依据该条款从检验工作中寻找影响试验不确定度因素,用以评定检验结果可靠性以及对整个实验室检验水平评定。本篇列举三例对不同检验项目不确定度的计算,介绍不确定度的评定方法。     

生丝线密度检验精密度的评价与测定

为准确评价采用生丝线密度仪进行线密度检验的精密度(即重复性与重现性误差),掌握样本的非均质性对检验结果精密度的影响程度,利用抽样数量为200绞100回(112.5 m)时不同规格生丝线密度检验的精密度数据,设计了2种试验方案进行精密度的测定。一是采用同一样本进行5个实验室间的循环比对试验;二是由5个实验室分别对同一批丝的不同样本进行检验。试验结果表明,采用同一样本进行检验时精密度水平很高,重复性与重现性平均误差变异小于3%,而采用不同样本时,平均误差变异达到了10%左右,表明生丝样本的非均质性对生丝线密度检验精密度的影响较大,应用该试验方法进行精密度评价和测定时应考虑样本的非均质性,以便真实地反映实际检验的精密度水平。     

两种不同的处理方法在钠含量检测中的对比分析

目的通过运用微波消解和干灰化法两种检测方法,对饼干、酱油以及花生的钠含量进行检测,对比两种检测方法的测定结果;方法从市面上采集火腿肠、酱油以及乳饮料三种食品样本,对其进行相应处理,并选择用于调整单色器的标液浓度,用于拟合标准曲线的浓度范围,合适的稀释倍数以及干灰化法和微波消解两种处理方法在三种食品钠含量的检测结果进行对比;结果选择1mg/L作为标准溶液来进行单色器的调整;以(0-2mg/L)浓度范围来进行标准曲线的二次曲线拟合;选取适当的稀释倍数,最佳的浓度应当是更加接近调整单色器的中间浓度;微波消解处理后,其回收率达到了96.87%,相较于干灰化法87.17%的回收率,明显微波消解更为理想。而两种方法在估算中,其结果显示其绝对差值与算术平均值之比均控制在20%以内,故两种方法均不存在明显差异,其均满足相关检验要求;结论在食品钠含量的测定中,微波消解和干灰化法两种方法均较为理想。     

影响食品检验准确性的因素分析

在食品检验过程中,应严格从检验流程的各个环节加以控制,从样品抽取、制备、校准仪器设备、配制试剂、选择检验方法、控制实验室环境、操作人员的熟练程度及对相关检验项目的检查等几方面,确保获得准确的检验结果。     

主成分分析研究白酒基酒香气成分

以485个优质基酒和普通基酒的主要香气成分数据为基础,采用R语言软件,分别对两类基酒进行4次随机抽样,每次抽取60个样品,基酒品质分析数据经标准化处理后进行主成分分析。结果表明,优质基酒4次抽样主成分分析结果比较稳定,普通基酒分析结果差异较大。碎石图检验表明,优质基酒在第一、二、三主成分后断崖明显,普通白酒在第一主成分后下降平缓,没有明显断崖。分布分析表明,优质基酒密度分布图的尖峰厚尾现象明显,方差为0.560,样本间的差异小,香气成分含量范围相对集中;普通基酒分布较为分散,方差为0.925,样本间差异较大,香气成分含量范围不稳定。在白酒风味分析时,应考虑样本的数据特征,以保证分析结果的准确性和可靠性,进而有效指导白酒生产。     

原子荧光光度法测定卤味香膏中砷含量的不确定度评定

评价氢化物原子荧光光度法测定卤味香膏中砷含量的不确定度。方法 根据实验流程,分析实验过程中不确定度来源,评定不确定度分量,最后计算合成不确定度和扩展不确定度。结果 当卤味香膏样品中砷的含量为0.46 mg/kg 时, 扩展不确定度U=0.04 mg/kg,(k=2)。结论 氢化物原子荧光光度法测定卤味香膏样品中砷的含量时,对不确定度影响较大的是标准曲线拟合和标准溶液配制, 这两项分量占总不确定度的68.77 %;测定重复性和仪器校准次之,样品称量和试样定容影响较小。实验中要准确配制标准溶液,绘制合理标准曲线,关注线性范围,线性相关系数应大于0.997,保证检测结果的准确性。     

Multiresidue analysis of pesticides in agricultural products by GC/MS coupled with database software

We evaluated a multiresidue method for determination of pesticides in agricultural products by SCAN mode GC/MS coupled with three kinds of database for 253 pesticides: relative retention time, mass spectra and calibration curve (SCAN method). Twenty-six pesticides, a total of 131 pesticides were detected in samples by the SCAN method. The detection results agreed closely with those of the SIM mode GC/MS method using calibration standards (SIM method). The ratios of the SCAN method to the SIM method ranged from 0.3 to 3.1 with SD values of 0.63. It was judged that the SCAN method could be applied to the screening analysis of pesticide residues in agricultural products, provided that the sample preparation method makes it possible to effectively remove sample matrixes with minimal loss of analytes.     

纤维轴向压缩性能测试及其刺痒感属性判断

为探讨纤维材料的轴向压缩性能及其刺痒感属性,改进了测试装置与测试方法,对测试过程中纤维试样与测试头接触条件的转变及对应压缩曲线特征进行分析,提出了应用纤维轴向压缩性能判断其刺痒感属性的方法。结果表明:在进行纤维材料轴向压缩测试时,试样与测试头的接触条件会发生由"点接触"向"线接触"的转变,压缩性能测试应以"点接触"的结束为终结点;以纤维轴向压缩性能为依据判断其刺痒感属性时,纤维端与皮肤的接触条件应为"点接触",且应具有不少于0.2 mm的长度,并能承受不少于0.75 m N的轴向载荷。     

Semi-quantitative estimation of the walnut content in fillings of bakery products using real-time polymerase chain reaction with internal standard material

A method for absolute quantification of walnuts in fillings of bakery products was developed. Macadamia nuts were used as an internal standard material. A duplex real-time polymerase chain reaction (PCR) with 5′-nuclease (TaqMan) probes labelled with FAM and JOE for walnuts and the internal standard, respectively, was used. Difference between threshold cycle values (Δc _T) for the analyte and the internal standard, plotted against logarithm of contents, was used to construct the calibration line. A level of 5?% (w/w) of the internal standard material was found to be suitable for quantification of walnuts in nut fillings, the calibration line being linear. The developed method was applied to bakery products from the market, and crucial factors for its routine applicability have been identified in sampling and sample preparation. The present study demonstrates that quantification of walnuts in the fillings of bakery products should be achievable by real-time PCR with an internal standard material when the reference filling, which is used for calibration, is comparable to the fillings of the samples, a calibration line of low variability is obtained, and the sample material is properly homogenized before weighing the analytical sample.     

Methods for fitting a parametric probability distribution to most probable number data

Every year hundreds of thousands, if not millions, of samples are collected and analyzed to assess microbial contamination in food and water. The concentration of pathogenic organisms at the end of the production process is low for most commodities, so a highly sensitive screening test is used to determine whether the organism of interest is present in a sample. In some applications, samples that test positive are subjected to quantitation. The most probable number (MPN) technique is a common method to quantify the level of contamination in a sample because it is able to provide estimates at low concentrations. This technique uses a series of dilution count experiments to derive estimates of the concentration of the microorganism of interest. An application for these data is food-safety risk assessment, where the MPN concentration estimates can be fitted to a parametric distribution to summarize the range of potential exposures to the contaminant. Many different methods (e.g., substitution methods, maximum likelihood and regression on order statistics) have been proposed to fit microbial contamination data to a distribution, but the development of these methods rarely considers how the MPN technique influences the choice of distribution function and fitting method. An often overlooked aspect when applying these methods is whether the data represent actual measurements of the average concentration of microorganism per milliliter or the data are real-valued estimates of the average concentration, as is the case with MPN data. In this study, we propose two methods for fitting MPN data to a probability distribution. The first method uses a maximum likelihood estimator that takes average concentration values as the data inputs. The second is a Bayesian latent variable method that uses the counts of the number of positive tubes at each dilution to estimate the parameters of the contamination distribution. The performance of the two fitting methods is compared for two data sets that represent Salmonella and Campylobacter concentrations on chicken carcasses. The results demonstrate a bias in the maximum likelihood estimator that increases with reductions in average concentration. The Bayesian method provided unbiased estimates of the concentration distribution parameters for all data sets. We provide computer code for the Bayesian fitting method.     

气相色谱法测定蔬菜中16种有机磷农药残留量的测量不确定度评定

目的:评定气相色谱法测定蔬菜中有机磷农药残留量的测量不确定度,为客观分析蔬菜中多组分农药残留检测结果的可靠性提供参考。方法:以NY/T 761-2008第一部分方法二的标准方法为研究对象,分析有机磷农药残留量测定的全过程,从标准溶液、样品制备、重复性、标准曲线拟合和回收率等五个方面对检测方法的测量不确定度进行了评定,确认不确定度的来源和大小,合成标准不确定度,计算扩展不确定度并报出结果。结果:蔬菜中16种有机磷农药的含量范围为0.100~0.339 mg/kg,扩展不确定度范围在0.016~0.052 mg/kg之间(P=95%,k=2)。结论:气相色谱法测定蔬菜中有机磷农药残留量的测量不确定度主要来源为标准曲线拟合,其次为回收率,可通过选择合适的标准溶液浓度制作标准曲线、优化前处理方法、质控样品等措施减少不确定度,保证检测结果的准确、可靠。     

Statistical Calibration of instruments Using Water Activity Determination As An Example

An example is given of a method for establishing the confidence limits for the calibration of an electronic instrument utilized in measuring water activity. The method consists of the inverse use of a regression line with associated Working-Hotelling-Scheffe bounds for the true line and the construction of limits suitable for repeated use of the derived calibration. Interval estimates are given for the Kaymont-Rotronics Hydroskop DT, calibrated with vapor pressure manometer measurements of equilibrated salt solutions as standards. The statistical approach has general applicability and should be applied routinely for all calibration problems.     

全自动葡萄酒成分分析仪测定葡萄酒中柠檬酸

目的建立全自动葡萄酒成分分析仪测定葡萄酒中柠檬酸的方法。方法仪器自动完成校准曲线的绘制、样品与试剂的混合、孵育、反应及340 nm波长处的比色检测。结果在0～0.5 g/L标准溶液浓度范围内,体系吸光度(Y)与溶液浓度(X)良好的函数关系,二阶拟合校准方程为:Y=0.6232X~2-0.6804X-0.00896,相关系数为0.9999,样品的加标回收率为94%～104%,同时与GB/T 15038-2006《葡萄酒、果酒通用分析方法》的高效液相色谱法进行了对比,相对标准偏差小于3%。结论该法灵敏度高、测定速度快、试剂使用量少,适用于不同类型葡萄酒中柠檬酸的大批量分析。     

Modelling uncertainty estimation for the determination of aflatoxin M(1) in milk by visual and densitometric thin-layer chromatography with immunoaffinity column clean-up

The uncertainty of aflatoxin M(1) concentration in milk, determined by thin-layer chromatography (TLC) with visual and densitometric quantification of the fluorescence intensities of the spots, was estimated using the cause-and-effect approach proposed by ISO GUM (Guide to the expression of uncertainty in measurement) following its main four steps. The sources of uncertainties due to volume measurements, visual and densitometric TLC calibration curve, allowed range for recovery variation and intermediary precision to be taken into account in the uncertainty budget. For volume measurements the sources of uncertainties due to calibration, resolution, laboratory temperature variation and repeatability were considered. For the quantification by visual readings of the intensity of the aflatoxin M(1) in the TLC the uncertainty arising from resolution calibration curves was modelled based on the intervals of concentrations between pairs of the calibration standard solutions. The uncertainty of the densitometric TLC quantification arising from the calibration curve was obtained by weighted least square (WLS) regression. Finally, the repeatability uncertainty of the densitometric peak areas or of the visual readings for the test sample solutions was considered. For the test samples with aflatoxin M(1) concentration between 0.02 and 0.5 μg l(-1), the relative expanded uncertainties, with approximately 95% of coverage probability, obtained for visual TLC readings were between 60% and 130% of the values predicted by the Horwitz model. For the densitometric TLC determination they were about 20% lower. The main sources of uncertainties in both visual and densitometric TLC quantification were the intermediary precision, calibration curve and recovery. The main source of uncertainty in the calibration curve in the visual TLC analysis was due to the resolution of the visual readings, whereas in the densitometric analysis it was due to the peak areas of test sample solutions followed by the intercept and slope uncertainties of the calibration line.