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A comparison of quantitative structure‐activity relationships for the effect of benzoic and cinnamic acids on Listeria monocytogenes using multiple linear regression, artificial neural network and fuzzy systems
Author(s) -
RamosNino M.E.,
RamirezRodriguez C.A.,
Clifford M.N.,
Adams M.R.
Publication year - 1997
Publication title -
journal of applied microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.889
H-Index - 156
eISSN - 1365-2672
pISSN - 1364-5072
DOI - 10.1111/j.1365-2672.1997.tb03569.x
Subject(s) - linear regression , artificial neural network , correlation coefficient , biological system , cinnamic acid , regression , mathematics , listeria monocytogenes , selection (genetic algorithm) , coefficient of determination , chemistry , statistics , artificial intelligence , computer science , biology , biochemistry , bacteria , genetics
M.E. RAMOS‐NINO, C.A. RAMIREZ‐RODRIGUEZ, M.N. CLIFFORD AND M.R. ADAMS, 1997. The ability of artificial neural networks (ANN), fuzzy systems (FS) and multiple linear regression (MLR) to fit the biological activity surface describing the inhibition of Listeria monocytogenes by benzoic and cinnamic acid derivatives was compared. MLR and ANN were also compared for their ability to select the properties that best describe the biological activity of the compounds. The criteria used for comparing surface fits of all models were the coefficient of determination r 2 and the standard deviation of the error, s e . The ANN method gave a better correlation, r 2 = 0.96, compared with either MLR, r 2 = 0.81, or FS, r 2 = 0.92, and also a lower standard error, possibly indicating non‐linearity in the data. The ANN was shown to generalize better than MLR using the leave‐one‐out method. The ANN selection algorithm for the selection of the parameters that contributed most to the biological activity of the phenols (log K and p K a ) agreed with the selected parameters of the MLR system.