From a glimpse into the key aspects of calibration and correlation to their practical considerations in chemical analysis.
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| Title: | From a glimpse into the key aspects of calibration and correlation to their practical considerations in chemical analysis. |
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| Authors: | Stalikas, Constantine1 (AUTHOR) cstalika@uoi.gr, Sakkas, Vasilios1 (AUTHOR) |
| Source: | Microchimica Acta. Feb2024, Vol. 191 Issue 2, p1-15. 15p. |
| Subjects: | Microsoft Corp., Electronic spreadsheets, Analytical chemistry, Calibration, Education software, Nonlinear systems, Detection limit |
| Abstract: | In this tutorial review, we provide a guiding reference on the good practice in building calibration and correlation experiments, and we explain how the results should be evaluated and interpreted. The review centers on calibration experiments where the relationship between response and concentration is expected to be linear, although certain of the described principles of good practice can be applied to non-linear systems, as well. Furthermore, it gives prominence to the meaning and correct interpretation of some of the statistical terms commonly associated with calibration and regression. To reach a mutual understanding in this significant field, we present, through a practical example, a step-by-step procedure, which deals with typical challenges related to linearity and outlier assessment, calculation of the associated error of the predicted concentration, and limits of detection. The utilization of regression lines to compare analytical methods is also elaborated. The results of regression and correlation data are acquired by implementing the Excel spreadsheet of Microsoft, being perhaps one of the most widely used user-friendly software in education and research. [ABSTRACT FROM AUTHOR] |
| Copyright of Microchimica Acta is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | In this tutorial review, we provide a guiding reference on the good practice in building calibration and correlation experiments, and we explain how the results should be evaluated and interpreted. The review centers on calibration experiments where the relationship between response and concentration is expected to be linear, although certain of the described principles of good practice can be applied to non-linear systems, as well. Furthermore, it gives prominence to the meaning and correct interpretation of some of the statistical terms commonly associated with calibration and regression. To reach a mutual understanding in this significant field, we present, through a practical example, a step-by-step procedure, which deals with typical challenges related to linearity and outlier assessment, calculation of the associated error of the predicted concentration, and limits of detection. The utilization of regression lines to compare analytical methods is also elaborated. The results of regression and correlation data are acquired by implementing the Excel spreadsheet of Microsoft, being perhaps one of the most widely used user-friendly software in education and research. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00263672 |
| DOI: | 10.1007/s00604-023-06157-4 |
