Quantification by SEM–EDS in uncoated non-conducting samples (original) (raw)
Related papers
The rank correlation coefficient: an additional aid in the interpretation of laboratory data
Clinica Chimica Acta, 1995
Correlation analysis is one of the most widely used-and most frequently misinterpreted-statistical techniques in the field of laboratory medicine. The authors of a recent opinion in this journal [1] are to be commended for pointing out several of the pitfalls in applying a special case of correlation analysis, i.e. linear correlation analysis (also termed 'Pearson's correlation'). We would like to extend their helpful hints by demonstrating the value of a non-parametric variant of correlation analysis, namely, Spearman's rank correlation method [2,3]. Pearson's correlation analysis is done under the assumption of a linear relationship between attributes, such as outcomes of a 'new' clinical chemical test and a standard method, or test results and other clinical findings. Thus, altering the first attribute by a certain proportion is expected to result in alteration of the second attribute by the same proportion. Clearly, not all relationships existing between attributes are linear ones, and in judging such non-linear relationships, linear techniques necessarily must fail to an extent which is determined by the degree of nonlinearity of the relationship under consideration. Speaxman's rank correlation technique works as follows: both attributes are * Corresponding author.
Precision and Detection Limits for EDS Analysis in the SEM
Microscopy Today, 2003
Microanalysis using an EDS on an SEM are sometimes asked whether two samples are of different composition, or if different regions in the same sample vary in composition, A best educated guess may be that differences of a few percent can be distinguished under optimized conditions that include favorable data collection times and count rates, stable instrument operation, and well prepared samples and standards that remain free of contamination. Another frequently asked question is whether it is possible to detect a given amount of a particular element in a sample.
This article was published in an Elsevier journal. The attached copy is furnished to the author for non-commercial research and education use, including for instruction at the author's institution, sharing with colleagues and providing to institution administration. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright
Chemometric methods in analytical research: A program of practical study
Journal of Chemometrics, 2005
Statistical methods should be a means of reasoning and decision making integrated into research design in analytical chemistry and not merely an instrument for analysing empirical data. Consequently, full and rigorous use of these methods should be encouraged from the earliest stages of postgraduate training. In this paper a selection of well tried, straightforward procedures is given in the form of practical studies that give reliable, consistent and meaningful results. KEY WORDS Statistical procedures Postgraduate practical studies in chemometrics Chemometrics has been accurately described as 'an interface between chemistry and mathematics'.' Such an interface will fulfil what is expected of it if the protocols for data transmission are concordant; otherwise, information cannot circulate through it freely. Obviously there are two facets to this question: the mathematical and the chemical. Our opinion on this matter is that the role of chemometrics is to strengthen the use of a basic knowledge of statistics not simply as a tool for interpreting empirical data but rather as a whole way of approaching experimental procedures and cause-effect relationships. Statistical methods are useful for analysing large amounts of data and for indicating reasonable decisions in cases of uncertainty. As a result, these methods constitute a theoretical tool to be used in close conjunction with experimentation. This point of view can and should be applied at various levels. The present work summarises our experience at the basic level, namely that at which an analytical chemist must start his statistical training with a view to designing empirical experiments. We shall not go into excessive detail but only give general guidelines as summarized in Figure 1. Any problem in analytical chemistry must be answered in the same chemical terms as which it was formulated. This is true not only in the case of choosing between alternative models but also when seeking an adequate description of a new situation produced by the results, which in turn are provided by a constantly evolving set of instruments. In both cases it can be foreseen that several successive formulations of a problem will be necessary so as to narrow it down in accordance with the experimental evidence.
Uncorrected proof 1 Analytical Data : Reliability and Presentation
2013
Chemical analysis whether it is used to determine the composition of a sample or to devise a procedure for testing or preparation of another sample requires systematic experiment design and implementation. In order to determine and verify the validity of results various methods are employed to evaluate the data obtained. This process enables the analyst not only to understand the results but to find possible reasons for differences and similarities between samples. A simple scheme for carrying out analysis in order to obtain valid and reliable results is outlined in this paper. Moreover the importance of using reference and quality control materials to obtain quantitative results is also highlighted. To evaluate the performance and capability of a laboratory or an analytical procedure, parameters such as relative bias, z-scores, u-test, tests for accuracy and precision etc can be used. The use and significance of these parameters is explained using examples in this manuscript. Uncer...
Quantitative Analytical Studies: 2 Metamers, 2 Error Models, and 3 Error Regimes
Inside Laboratory Management, 2019
This column is a series of articles concerning statistics-related issues relevant to the activities of AOAC INTERNATIONAL. Contributions are generally made by members of the AOAC Committee on Statistics, chaired by Sidney Sudberg of Alkemist Labs. The current column is authored by Robert LaBudde of Least Cost Formulations, Ltd., a fellow of AOAC and a longtime member of the AOAC Committee on Statistics. He is also a former professor of statistics at Old Dominion University. Unless specifically stated otherwise, the opinions expressed are solely those of the individual author(s) and any guidelines suggested may not yet be endorsed by either the Committee on Statistics or the AOAC Official Methods Board.