New bicompartmental model: an application for the production of gases using the in vitro technique / Novo modelo bicompartimental: uma aplicação para a produção de gases pela técnica in vitro

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 (Raj.), 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.(AU)

No presente trabalho, com silagem de milho, girassol e suas misturas, objetivou-se propor um modelo não linear bicompartimental e identificar entre o modelo proposto e Logístico Bicompartimental (LB), aquele que apresenta maior qualidade de ajuste à curva de cinética de produção cumulativa de gases (PCG). A leitura da produção de gás foi realizada nos tempos 2, 3, 4, 6, 8, 9, 10, 12, 15, 19, 24, 30, 36, 48, 72 e 96 horas, após o início do processo de fermentação in vitro. Os dados gerados foram utilizados para geração dos parâmetros de cada modelo testado com auxílio do pacote stats da ferramenta computacional R versão 4.0.4. Os modelos matemáticos foram submetidos aos seguintes critérios de seleção o coeficiente de determinação ajustado (Raj.), quadrado médio do resíduo (QMR), desvio médio absoluto (DMA) e o critério de informação de Akaike (AIC). Foi demonstrado que o modelo proposto teve melhor desempenho com altos Raj., e menores valores de QMR, AIC e DMA, por apresentar um ajustamento superior no conjunto dos critérios em comparação com o modelo logístico bicompartimental para a predição dos parâmetros de produção cumulativa de gases (PCG) de acordo com a metodologia e condições em que foi desenvolvido o presente estudo.(AU)

Proposal of a non-linear model to adjust in vitro gas production at different incubation times

This work aims to propose a new model named Gompertz-Von Bertalanffy bicompartmental (GVB), a combination of the models Gompertz and Von Bertalanffy. The GVB models is applied to fit the kinetic curve of cumulative gas production (CGP) of four foods (SS ­ sunflower silage; CS ­ corn silage; and the mixtures 340SS ­ 660 gkg-1 of corn silage and 340 gkg-1 of sunflower silage; and 660SS ­ 340 gkg-1 of corn silage and 660 gkg-1 of sunflower silage). The GVB fit is compared to models Logistic-Von Bertalanffy bicompartmental (LVB) and bicompartmental logistic (BL). All the process studied employed the semi-automatic "in vitro" technique of producing gases used in ruminant nutrition. The gas production readout was performed at times 2, 4, 6, 8, 10, 12, 15, 19, 24, 30, 48, 72, and 96 h. The data generated were used to estimate the models' parameters by the least squared method with the iterative Gauss-Newton process. The data fit quality of the models was verified using the adjusted coefficient of determination criterion (), mean residual square (MRS), Akaike information criterion (AIC), and mean absolute deviation (MAD). Among the analyzed models, the LVB model presented the best quality of fit evaluators for CS. In contrast, the GVB model showed better quality of fit to describe CGP over time for 340SS, 660SS, and SS, presenting the highest values of () and the lowest values of MSR, AIC, and MAD.

Generation of models from existing models composition: An application to agrarian sciences.

Mathematical models that describe gas production are widely used to estimate the rumen degradation digestibility and kinetics. The present study presents a method to generate models by combining existing models and to propose the von Bertalanffy-Gompertz two-compartment model based on this method. The proposed model was compared with the logistic two-compartment one to indicate which best describes the kinetic curve of gas production through the semi-automated in vitro technique from different pinto peanut cultivars. The data came from an experiment grown and harvested at the Far South Animal Sciences station (Essul) in Itabela, BA, Brazil and gas production was read at 2, 4, 6, 8, 10, 12, 14, 17, 20, 24, 28, 32, 48, 72, and 96 h after the start of the in vitro fermentation process. The parameters were estimated by the least squares method using the iterative Gauss-Newton process in the software R version 3.4.1. The best model to describe gas accumulation was based on the adjusted coefficient of determination, residual mean squares, mean absolute deviation, Akaike information criterion, and Bayesian information criterion. The von Bertalanffy-Gompertz two-compartment model had the best fit to describe the cumulative gas production over time according to the methodology and conditions of the present study.