Quality Evaluation Based on Multivariate Statistical Methods
Joint Authors
Zhu, Xiangping
Yin, Shen
Karimi, Hamid Reza
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-10
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Quality prediction models are constructed based on multivariate statistical methods, including ordinary least squares regression (OLSR), principal component regression (PCR), partial least squares regression (PLSR), and modified partial least squares regression (MPLSR).
The prediction model constructed by MPLSR achieves superior results, compared with the other three methods from both aspects of fitting efficiency and prediction ability.
Based on it, further research is dedicated to selecting key variables to directly predict the product quality with satisfactory performance.
The prediction models presented are more efficient than tradition ones and can be useful to support human experts in the evaluation and classification of the product quality.
The effectiveness of the quality prediction models is finally illustrated and verified based on the practical data set of the red wine.
American Psychological Association (APA)
Yin, Shen& Zhu, Xiangping& Karimi, Hamid Reza. 2013. Quality Evaluation Based on Multivariate Statistical Methods. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1032049
Modern Language Association (MLA)
Yin, Shen…[et al.]. Quality Evaluation Based on Multivariate Statistical Methods. Mathematical Problems in Engineering No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-1032049
American Medical Association (AMA)
Yin, Shen& Zhu, Xiangping& Karimi, Hamid Reza. Quality Evaluation Based on Multivariate Statistical Methods. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1032049
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1032049