This chapter provides a basic theoretical background on chemometrics and chemometric methods for the analysis of multivariate data. Multivariate data analysis is essential for both product and process development and optimization. Depending on the problem studied, classification and/or regression multivariate methods are applied for data analysis. Different supervised and unsupervised methods for classification and regression are presented, followed by examples of their application in pharmaceutical technology. Some of the methods described include principal component analysis, various supervised classification methods, multiple linear regression, principal component regression, partial least squares regression, support vector machines, etc.
Keywords:
Chemometrics / Classification / Multiple linear regression / Partial least squares regression / Principal component analysis / Principal component regression / Regression / Support vector machines / Unsu
Source:
Computer-Aided Applications in Pharmaceutical Technology, 2013, 57-90
TY - CHAP
AU - Đuriš, Jelena
AU - Ibrić, Svetlana
AU - Đurić, Zorica
PY - 2013
UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2023
AB - This chapter provides a basic theoretical background on chemometrics and chemometric methods for the analysis of multivariate data. Multivariate data analysis is essential for both product and process development and optimization. Depending on the problem studied, classification and/or regression multivariate methods are applied for data analysis. Different supervised and unsupervised methods for classification and regression are presented, followed by examples of their application in pharmaceutical technology. Some of the methods described include principal component analysis, various supervised classification methods, multiple linear regression, principal component regression, partial least squares regression, support vector machines, etc.
PB - Elsevier Inc.
T2 - Computer-Aided Applications in Pharmaceutical Technology
T1 - Chemometric methods application in pharmaceutical products processes analysis control
SP - 57
EP - 90
DO - 10.1016/B978-1-907568-27-5.50004-4
ER -
@inbook{
author = "Đuriš, Jelena and Ibrić, Svetlana and Đurić, Zorica",
year = "2013",
abstract = "This chapter provides a basic theoretical background on chemometrics and chemometric methods for the analysis of multivariate data. Multivariate data analysis is essential for both product and process development and optimization. Depending on the problem studied, classification and/or regression multivariate methods are applied for data analysis. Different supervised and unsupervised methods for classification and regression are presented, followed by examples of their application in pharmaceutical technology. Some of the methods described include principal component analysis, various supervised classification methods, multiple linear regression, principal component regression, partial least squares regression, support vector machines, etc.",
publisher = "Elsevier Inc.",
journal = "Computer-Aided Applications in Pharmaceutical Technology",
booktitle = "Chemometric methods application in pharmaceutical products processes analysis control",
pages = "57-90",
doi = "10.1016/B978-1-907568-27-5.50004-4"
}
Đuriš, J., Ibrić, S.,& Đurić, Z.. (2013). Chemometric methods application in pharmaceutical products processes analysis control. in Computer-Aided Applications in Pharmaceutical Technology
Elsevier Inc.., 57-90.
https://doi.org/10.1016/B978-1-907568-27-5.50004-4
Đuriš J, Ibrić S, Đurić Z. Chemometric methods application in pharmaceutical products processes analysis control. in Computer-Aided Applications in Pharmaceutical Technology. 2013;:57-90.
doi:10.1016/B978-1-907568-27-5.50004-4 .
Đuriš, Jelena, Ibrić, Svetlana, Đurić, Zorica, "Chemometric methods application in pharmaceutical products processes analysis control" in Computer-Aided Applications in Pharmaceutical Technology (2013):57-90,
https://doi.org/10.1016/B978-1-907568-27-5.50004-4 . .
