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Chemometrics in analytical chemistry-part II: modeling, validation, and applications
Richard G Brereton1, Jeroen Jansen2, João Lopes3
1School of Chemistry, University of Bristol, Cantocks Close, Bristol, BS8 1TS, UK.
Analytical and Bioanalytical Chemistry
|August 4, 2018
Summary
This tutorial covers supervised modeling for multivariate chemical data, focusing on building calibration and discrimination models. It details quantitative validation and applications, highlighting chemometrics
Area of Science:
- Analytical Chemistry
- Chemometrics
Background:
- Chemometrics is crucial for experimental design, sampling, and exploratory data analysis.
- Data preprocessing and fusion are key components of the analytical process.
Purpose of the Study:
- To detail supervised modeling of multivariate chemical data.
- To explain the building, validation, and application of calibration and discrimination models.
Main Methods:
- Supervised modeling techniques for multivariate data.
- Quantitative validation of chemometric models.
- Application of models across diverse scientific fields.
Main Results:
- Demonstration of calibration and discrimination model development.
- Evaluation of model performance through quantitative validation.
- Successful application examples in various scientific disciplines.
Conclusions:
- Chemometrics is a popular and powerful tool in analytical chemistry.
- Supervised modeling is essential for extracting meaningful information from complex chemical data.
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