Chemometrics in Pharmaceutical Research
A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Pharmaceutical Technology".
Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 2131
Special Issue Editor
2. Shanghai Key Laboratory for Pharmaceutical Metabolite Research, School of Pharmacy, Second Military Medical University, Shanghai 200433, China
Interests: similarity evaluation (SA); principal components analysis (PCA); hierarchical clustering analysis (HCA); fingerprint; quality evaluation; gut–liver axis; gut–brain axis; metabolism
Special Issue Information
Dear Colleagues,
Chemometrics is based on computer technology and establishes a relationship between the measured value and the state of a chemical system through statistical or mathematical methods. It can be used to achieve data dimension reduction, identification and classification for complex measurement data, such that multivariate information can be fully integrated and the differences in substances may be reflected correctly, truly and completely. Therefore, it is often combined with metabolomics and fingerprinting methods to extract valuable information.
Chemometrics can be divided into two types: unsupervised pattern and supervised pattern. Unsupervised pattern is a classification method with unknown sample categories and no training process. It only projects the similarity and difference in the data structure to a two-dimensional or three-dimensional space through dimensionality reduction, which is convenient for directly observing the classification of samples. PCA is arguably one of the most useful and extensive unsupervised methods used in chemometrics for exploratory data analyses. Supervised pattern needs to use computer algorithms to learn the classified training samples to build a mathematical model, and use the established model to classify and predict the validation samples. The degree of conformity between the predicted results and the actual classification results is used as an index of model prediction accuracy. The methods commonly used in supervised pattern include linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA) and artificial neural networks (ANNs).
In this Special Issue, we invite authors to contribute articles focusing on chemometrics in pharmaceutical research. The collected articles in this Special Issue will further bring new ideas and new directions to the development of the field of pharmaceutical analytical chemistry.
Dr. Tingting Zhou
Guest Editor
Manuscript Submission Information
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Keywords
- chemometrics
- metabolomics
- fingerprint
- principle component analysis (PCA)
- linear discriminant analysis (LDA)
- partial least squares discriminant analysis (PLS-DA)
- orthogonal partial least squares discriminant analysis (OPLS-DA)
- artificial neural networks (ANNs)
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