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Modeling nutrient impacts on microalgae cells via image analyses
Author(s) -
McConico Morgan B.,
Vogt Frank
Publication year - 2013
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2510
Subject(s) - multivariate statistics , biological system , chemometrics , image (mathematics) , computer science , biochemical engineering , nonlinear regression , regression , regression analysis , nonlinear system , nutrient , bayesian multivariate linear regression , multivariate analysis , environmental science , artificial intelligence , process engineering , machine learning , data mining , mathematics , chemistry , statistics , engineering , biology , physics , quantum mechanics , organic chemistry
A nonlinear multivariate regression methodology is presented for modeling the responses of microalgae cells regarding their physical appearance (size, shape) to shifts in their chemical environment. This chemometric approach builds on a novel image analysis method recently published in this Journal and augments it such that: (i) the measured effect is expressed mathematically rather than described empirically and (ii) incorporates effects of multiple ambient parameters so that their interactions can be investigated. Copyright © 2013 John Wiley & Sons, Ltd.