New bicompartmental model: an application for the production of gases using the in vitro technique

The purpose of this study was to propose a bicompartmental nonlinear model and to identify the best-performing model between the proposed model and the bicompartmental logistic (BL) mode regarding the quality of fit to the curve of cumulative gas production (CGP) using corn silage, sunflower, and their mixtures. Gas production was measured 2, 3, 4, 6, 8, 9, 10, 12, 15, 19, 24, 30, 36, 48, 72, and 96 h after beginning the in vitro fermentation process. The generated data were used to generate the parameters of each model tested using the stats package of the R computational tool version 4.0.4. The mathematical models were subjected to the following selection criteria: the adjusted coefficient of determination (R-aj(2)), residual mean square (RMS), mean absolute deviation (MAD), and Akaike information criterion (AIC). It was demonstrated that the proposed model had better performance with a high Raj., and lower values of RMS, AIC, and MAD than the bicompartmental logistic model for the prediction of the parameters of cumulative gas production (CGP), per to present a superior fit in the set of criteria according to the methodology and conditions in which the present study was developed.