Proficiency Testing in Chemical Analysis of Iron Ore: Comparison of Statistical Methods for Outlier Rejection

被引:7
作者
Chakravarty, Sanchita [1 ]
Mohanty, Ashok [1 ]
Ghosh, Barun [1 ]
Tarafdar, Mita [1 ]
Aggarwal, Shankar G. [2 ]
Gupta, Prabhat K. [2 ]
机构
[1] CSIR Natl Met Lab, Jamshedpur, Jharkhand, India
[2] CSIR Natl Phys Lab, New Delhi, India
来源
MAPAN-JOURNAL OF METROLOGY SOCIETY OF INDIA | 2014年 / 29卷 / 02期
关键词
Proficiency testing; Outliers; Iron ore analysis; Statistical methods; Inter laboratory comparison; PERFORMANCE;
D O I
10.1007/s12647-013-0059-8
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
Participation in proficiency testing (PT) is an important task to meet the requirements of ISO/IEC 17025 in the area of quality assurance of laboratory results. A PT program in the field of chemical analysis of iron ore was organized by CSIR-National Metallurgical Laboratory, Jamshedpur (nodal laboratory) and CSIR-National Physical Laboratory, New Delhi (PT Coordinator) during November 2011-January 2012. Twenty-two (22) laboratories in India participated in the PT program. The results of participating laboratories were first analyzed to identify the distribution patterns and the presence of outliers. Several parametric and robust statistical methods were used to identify the outliers. Correct outlier rejection is of utmost importance because the choice of the outlier test method influence the consensus value and standard deviation which in turn determine the Z-score of a laboratory result in a PT program. In the present study, five parametric outlier tests were compared: Dixon's Q test, Grubbs single test, double test, t test, and Z-scores. In addition three robust tests as alternative to parametric tests were chosen: box plot, Huber test and MAD-based test. It was observed that multiple outlier test methods should be used to identify the outliers in a PT program especially when the number of participating laboratories is less. They complement each other and helps give diverse information and better overview of the data set. Among the 22 participating laboratories, Z-scores of 4 laboratories for analysis of total iron fall outside the acceptable limit of +/- 2. Similarly, for analysis of alumina and silica, five laboratories had unacceptable Z-scores.
引用
收藏
页码:87 / 95
页数:9
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