标准物质数据处理及定值的MPDPS软件

本文详细介绍了由六个功能模块组成的多参数标准物质数据处理及定值软件MPDPS。软件除了对标准物质进行定值处理外,还能对测试数据进行多方面的统计处理。文中结合对GSR7-126个标样数据处理及定值的实例介绍了MPDPS的各项功能。用MPDPS对这6个标样定值的结果较为满意。     

离子吸附型稀土监控样定值研究

离子吸附型稀土矿是中国极其重要、世界罕见的矿床类型,是中国的优势矿产资源。目前现有的稀土标样全部为稀土矿石成分分析标样,稀土元素以氧化物形式稳定存在,无法淋滤浸取,不能对离子吸附型稀土淋滤过程进行监控。为进一步满足离子吸附型稀土资源勘查和评价需要,本文按照导则《标准物质定值的通用原则及统计学原理》（JJF 1343—2012）,制备了三种岩性共9个离子吸附型稀土监控样。样品采自南岭地区典型富轻稀土（HREE）离子吸附型稀土风化壳,经干燥、球磨至200目后混合机混匀,以硫酸铵淋滤、电感耦合等离子体质谱法（ICP-MS）测试离子相稀土元素含量,检验样品均匀性,结果表明F值小于临界值F_{0.05（19,20）},样品均匀性良好。在两年内对样品进行4次稳定性检验,在95%置信度时│β₁│<t_0.05×s（β₁）,未发现明显不稳定变化,表明稳定性良好。由8家技术权威的实验室进行协作定值,经过统计计算给出各离子相稀土元素含量的加权平均值和扩展不确定度,定值结果涵盖除Sc以外的15种稀土元素。该系列监控样的研制能够为离子吸附型稀土矿产资源评价和有效利用等工作提供计量支撑。     

试谈磁参数统计问题

磁测解释结果的正确与否,在很大程度上依赖于物性测定中磁参数统计值(K,Jr)的可靠性。对磁参数的统计有许多方法,例如,有求几何平均值、算术平均值、众数、绘制频率曲线取其极大值所对应的磁参数等等。采用何种计算方法取决于假定磁参数的测定值符合何种分布这一前提,如满足正态分布,则取算术平均值;满足对数正态分布,则取几何平均值。但是磁     

常量金标准物质定值中离群值的统计识别

离群值的剔除常用数理统计的方法,如格拉布斯检验法和迪克逊检验法等,但是这些统计方法用于常量金标准物质分析结果的统计检验,都存在着对离群值剔除明显不够的问题.本文建立了以常量金重复分析相对偏差允许限为依据的离群值统计识别方法,包括统计计算待定值样品中金的算术平均值x和相对偏差允许限YG,确定合格的测定结果的数据区间,从而识别出离群值并予以剔除;一次剔除后,按照新的统计量确定下一轮的离群值剔除范围,直到无离群值后,给出金的平均值及其波动范围.以15个人工组合的常量金标准物质为例,模拟金标准物质定值分析,以密码形式分派给不同单位和分析者,共收集10套独立分析结果,采用本法剔除离群值后,所得金算术平均值与金标准参考值更加接近,其相对偏差的质量分数为0.35,达到优秀;而格拉布斯法(或迪克逊法)和中位值法的质量分数分别为0.42和0.40,只能达到良好.应用本文建立的离群值统计识别方法,质量分数等级有了明显提高,增强了数据统计分析的有效性.     

河流沉积物多环芳烃标准样品的制备与定值

多环芳烃环境标准样品是保证多环芳烃环境监测和污染调查数据可靠性以及不同实验室间分析数据可比性必不可少的计量工具。美国NIST、欧盟IRMM、英国LGC等研究机构已有沉积物多环芳烃环境标准样品,但浓度水平大部分集中在mg/kg浓度水平,与我国当前的污染水平存在一定差距,难以满足痕量分析(μg/kg)质量保证与质量控制的要求。而我国受天然基体标准样品制备技术和定值分析技术的制约,尚没有研制沉积物多环芳烃环境标准样品。本文研制了适合我国多环芳烃环境监测和科学研究需要的河流沉积物标准样品。沉积物样品采集自松花江哈尔滨段流域,经过自然阴干、研磨、筛分、混匀和灭菌等加工处理,分层随机抽取15瓶样品对16种多环芳烃进行均匀性研究,结果表明样品均匀性良好。在30℃避光保存条件下,采用线性模型进行稳定性研究,标准样品在16个月稳定性检验期间未发现不稳定变化趋势。11家实验室对该河流沉积物标准样品采用气相色谱-质谱法和液相色谱法进行联合定值,经统计分析处理评定出14种多环芳烃的标准值和不确定度,并给出2种多环芳烃的参考值,特性量值在6.9~158 μg/kg之间,不确定度在1.5~25 μg/kg之间,可以满足多环芳烃痕量分析质量保证与质量控制的要求。该标准样品被批准为国家标准样品,填补了国内此类标准样品和标准物质空白,已成功应用于土壤和沉积物样品中多环芳烃的监测,可为进一步开展有机污染物环境基体标准样品研制提供参考和借鉴。     

标准物质的中子活化定值测试研究

用中子活化分析方法,定值测试了湖北岩矿测试中心研制中的GSS31~GSS35土壤底泥标准物质,测定了28个元素;定值测试捷克生物标准物质TY1~4,测定了12个元素,对谱干扰和U裂变干扰作了修正。这里列出了由国际地质分析协会(IAG)组织,有全球71个地球化学分析实验室参与测试的玄武岩BNV-1的平均值及本实验室的测试结果。     

X射线荧光光谱法在有色金属矿石标准物质定值分析中的应用

报告了X射线荧光光谱法在铜矿、铅矿、锌矿、钨矿和钼矿标准物质定值分析中的应用。样品用低倍稀释熔融法在热解石墨坩埚中熔融,用人工标样进行校准,计算了COLA方程的理论影响系数,所有分析元素全部用理论影响系数法校正吸收-增强效应。分析结果与不同实验室不同分析方法的结果相吻合。     

金标样定值中全金量的湿法分析

含金试样经王水溶解,溶液经活性炭吸附-碘量法测定溶液中的金;残渣经氢氟酸和王水再溶解,活性炭富集-火焰原子吸收测定残渣中的金;两项结果相加作为样品中金的总量。方法用于金的地质标样定值分析,结果与标准值吻合(E<3s)。     

地质标准参考样分析数据的处理

随着分析化学,特别是仪器分析的进展,迫切需要用标准参考样(即标样)来作为鉴定新分析方法的依据和分析测试的校准。国外从五十年代起就开始进行标样的制作和定值工作。目前国内也有不少单位在进     

标准物质研制信息管理系统与合作平台

标准物质研制信息管理系统实现了对标准物质研制全过程如均匀性检验、稳定性试验、正态性检验、数据处理、标准物质定值、检测方法统计等原始数据及文档进行全方位综合管理.设计开发了项目管理、标准物质管理、用户管理、定值实验室管理、日志管理、稳定性试验、均匀性检验数据采集与统计处理、定值数据的采集与导入、定值数据离群值检验与处理、...     

Five Robust Indicators of Central Value

The presence of outliers and the statistical noise that affects the data for reference materials have undesirable effects on the mean and on other indicators of the central value. Five robust indicators of the central value, which are resistant to obvious outliers and less obvious contamination (spurious data), were investigated: the dominant cluster mode, the modian, the Gastwirth median, the trimean, and the trimmed mean. The mean and the median were investigated for purposes of comparison.
The results confirm that the mean is very unreliable, and that the Gastwirth median and the dominant cluster mode are strong indicators of the central value.     

A Laterite Standard Reference Material: Venezuelan Laterite VL-1*

Details on the preparation of a Laterite Standard Reference Material (SRM) from Venezuela, on the homogeneity-testing and the round-robin testing scheme from 11 international laboratories are given. The proposed values (%) for the concentrations of the main constituents are: Al₂O₃: 37.38, SiO₂: 1.16, TiO₂: 3.15, Fe₂O₃: 35.77 and LOI, 22.44. The following statistical parameters are also reported: the arithmetic mean, the variance, the standard deviation, the coefficient of variation, the median, the skewness factor, the preferred mean, the geometric mean, the Gastwirth Median and the dominant cluster mode.     

Summary statistics in geochemistry: A study of the performance of robust estimates

Numerical data summaries in many geochemical papers rely on arithmetic means, with or without standard deviations. Yet the mean is the worst average (estimate of location) for those extremely common geochemical data sets which are non-normally distributed or include outliers. The widely used geometric mean, although allowing for skewed distributions, is equally susceptible to outliers. The superior performance of 19 robust estimates of location (simple median, plus various combined, adaptive, trimmed, and skipped,L, M, andW estimates) is illustrated using real geochemical data sets varying in sources of error (pure analytical error to multicomponent geological variability), modality (unimodal to polymodal), size (20 to >2000 data values), and continuity (continuous to truncated in either or both tails). The arithmetic mean tends to overestimate location of many geochemical data sets because of positive skew and large outliers; robust estimates yield consistent smaller averages, although some (e.g., Hampel's and Andrew's) do perform better than others (e.g., Shorth mean, dominant cluster mode). Recommended values for international standard rocks, and for such important geochemical concepts as average chondrite, can be reproduced far more simply via robust estimation on complete interlaboratory data sets than via the rather complicated and subjective methods (e.g., laboratory ratings) so far used in the literature. Robust estimates also seem generally less affected by truncation than the mean; for example, if values below machine detection limits are alternatively treated as missing values or as real values of zero, similar averages are obtained. The standard (and mean) deviations yield consistently larger values of scale for many geochemical data sets than the hinge width (interquartile range) or median absolute deviation from the median. Therefore, summaries of geochemical data should always include at least the simple median and hinge width, to complement the often misleading mean and standard deviation.     

A Study of Robust Estimators

Using the compiled analytical data for the constituents of four Canadian iron-formation reference samples, a comparison is made among nine different "robust" estimators of "true" value, including the median, median, Gastwirth median, trimean, quarter-trimmed and sixthtrimmed means and the three Hampel estimators. Individual estimators and combinations of them are compared by means of the summation and "iron-oxide compatibility" tests. Comparisons are also made with values derived by the "select laboratories" method.     

BLU Estimators and Compositional Data

One of the principal objections to the logratio approach for the statistical analysis of compositional data has been the absence of unbiasedness and minimum variance properties of some estimators: they seem not to be BLU estimator. Using a geometric approach, we introduce the concept of metric variance and of a compositional unbiased estimator, and we show that the closed geometric mean is a c-BLU estimator (compositional best linear unbiased estimator with respect to the geometry of the simplex) of the center of the distribution of a random composition. Thus, it satisfies analogous properties to the arithmetic mean as a BLU estimator of the expected value in real space. The geometric approach used gives real meaning to the concepts of measure of central tendency and measure of dispersion and opens up a new way of understanding the statistical analysis of compositional data.     

Spatial distribution of soil organic carbon concentrations in grassland of Ireland

Soil organic C (SOC) concentrations in topsoil samples taken at 678 sites in the grassland of Ireland were investigated using statistics and geostatistics. SOC concentrations (Walkley–Black method) follow a lognormal distribution, with a median and geometric mean of 5.0%, and an arithmetic mean of 5.3%. Statistically significant (P<0.01) positive correlation between SOC and silt-plus-clay, and negative correlation between SOC and sand were observed, with lower correlation (P=0.17) between SOC and pH. Lower SOC concentrations were associated with higher percentages of land in tillage. In order to obtain a robust measurement of spatial structure, spatial outliers were detected, and subsequently eliminated, using the local Moran's I index. The spatial distribution of SOC concentrations based on kriging interpolation showed coherent spatial patterns, with the highest values in the western coastal area, and relatively low values in the inland and southeastern coastal areas; soils at higher elevation were also found to contain higher SOC concentrations. These patterns are consistent with the distribution of rainfall within the country.     

属性数据与空间数据连接对土壤有机碳储量估算的影响

土壤有机碳(Soil Organic Carbon, SOC) 库在陆地生态系统中具有重要作用。在基于GIS利用土壤类型法估算SOC储量时,由于土壤图比例尺的限制,属性数据与空间数据的连接会导致对SOC储量的估算结果产生很大不确定性。利用中国滇黔桂地区（云南省、贵州省和广西壮族自治区）798个土壤剖面及1∶[KG-*2]50万土壤图在不同的制图单元水平上,分别采用算术平均值法、中值法和面积加权平均值法（以土种志中的面积为权重）估算了该地区1 m深度SOC储量,并与基于土壤学专业知识连接方法（Pedological Knowledge Based Method, PKB）的估算结果进行了比较。结果表明：面积加权平均值法比中值法和算术平均值法的估算结果更为准确,而且在土类、亚类和土属水平上应用面积加权平均值法对SOC储量的估算结果差异不大。在采用较大比例尺、较为详细的土壤图时,PKB法较理想;反之,则面积加权平均值法较好。     

基于粒子群算法的岩体结构面产状模糊C均值聚类分析

结构面产状分析是进行岩体力学分析及稳定性评价的基础,玫瑰花图、等密度图等传统的图形分析方法较为粗糙,无法对产状数据进行准确分析,采用模糊聚类方法则可以得到较为准确的优势产状,但需要事先指定分组数及初始聚心,且模糊聚类算法为局部寻优算法,初始划分对最终的数据分析结果影响较大。为了得到较为客观的分组结果及优势产状,同时能够针对大量结构面产状数据进行聚类分析,提出了基于粒子群算法的岩体结构面产状模糊C均值聚类算法。采用粒子群算法进行模糊C均值聚类算法初始聚心的求解,在计算过程中可同时确定最佳分组数,避免了人为指定的主观性,克服了模糊C均值聚类算法易陷入局部极小值和对初始划分敏感的不足。最后,通过工程实例中不同方法的聚类效果对比分析该算法的有效性,并将该方法应用于实测结构面产状数据的分析,得到较为符合实际的优势结构面分组。     

几何平均重力归一化总梯度在山东招远金矿采空区的应用

传统方法对重力异常的总梯度作归一化计算时,通常是对总梯度的模作算术平均。笔者对归一化函数的分母分别作了几何平均、调和平均、修匀平均和中值平均,计算归一化函数。通过模型试验对比这四种不同的统计平均,其中几何平均重力归一化总梯度在进行反演计算时,抗噪能力更强,反演结果更加集中、准确。将该方法应用于山东招远金矿采空区,改进后的方法可以更加准确地确定采空区的水平位置和中心埋深。     

Contribution to Evaluation of Analytical Data On Reference Samples

For evaluating the analytical data on reference samples, the following approach is proposed: 1. Analytical methods used are critically reviewed. 2. Outliers are eliminated. 3. Normality of the results is tested. 4. If normality of the results can be accepted, arithmetic mean, its confidence interval and coefficient of the precision are computed. 5. If the results are not normally distributed, median and its statistical characteristics are given. 6. For sets of data with anomalous skewness, logarithmic and lambda transformations are useful approaches.