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The cell surface hydrophobicity (CSH) is an assessable physicochemical property used to evaluate the microbial adhesion
to the surface of biomaterials, which is an essential step in the microbial biofilm formation and pathogenesis. For
the present in vitro fermentation experiment, the CSH of ruminal mixed microbes was considered, along with other data
records of pH, ammonia-nitrogen concentration, and neutral detergent fibre digestibility, conditions of surface tension
and specific surface area in two different time scales. A dataset of 170,707 perturbations of input variables, grouped into
two blocks of data, was constructed. Next, Expected Measurement Moving Average – Machine Learning (EMMA-ML)
models were developed in order to predict CSH after perturbations of all input variables. EMMA-ML is a Perturbation
Theory method that combines the ideas of Expected Measurement, Box-Jenkins Operators/Moving Average, and Time
Series Analysis. Seven regression methods have been tested: Multiple Linear regression, Generalized Linear Model with
Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, Elastic Net regression, Neural Networks
regression, and Random Forests (RF). The best regression performance has been obtained with RF (EMMA-RF model)
with an R-squared of 0.992. The model analysis has shown that CSH values were highly dependent on the in vitro fermentation
parameters of detergent fibre digestibility, ammonia – nitrogen concentration, and the expected values of cell
surface hydrophobicity in the first time scale
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.: SABIA :. Sistemas Adaptativos y Bioinspirados en Inteligencia Artificial