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Partitioned partial least squares regression with application to a batch fermentation process
Stina W. Andersen,
George Runger
L. William Seidman Research Institute
Engineering, Ira A. Fulton Schools of (IAFSE)
Industrial, Systems and Operations Engineering
Health Information and Research, Center for (CHIR)
Computer Science and Engineering
Computing and Augmented Intelligence, School of (IAFSE-SCAI)
Research output
:
Contribution to journal
›
Article
›
peer-review
7
Scopus citations
Overview
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Mathematics
Fermentation
100%
Partial Least Squares Regression
96%
Partial Least Squares
89%
Batch
76%
Predictors
36%
Standardization
25%
Prediction
23%
Genetic Algorithm
21%
Subgroup
19%
Profile
16%
Near Infrared
15%
Least Square Algorithm
12%
Pharmaceuticals
12%
Performance
11%
Alignment
11%
Variable Selection
10%
Regression Analysis
10%
Predict
9%
Output
7%
Interpretation
6%
Engineering & Materials Science
Fermentation
77%
Standardization
44%
Genetic algorithms
32%
Drug products
25%
Regression analysis
23%
Infrared radiation
19%
Chemical Compounds
Fermentation
65%
Near-Infrared Spectrum
28%
Application
21%
Error
17%
Pharmaceutical
16%
Modification
12%
Reaction Yield
9%