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

• Autor(a) principal: Santos, André Luiz Pinto dos
• Data de Publicação: 2023
• Outros Autores: Ferreira, Tiago Alessandro Espínola, Brito, Cícero Carlos Ramos de, Moreira, Guilherme Rocha, Gomes-Silva, Frank, Jale, Jader Silva, Reis, Ronaldo Braga, Leite, Leonardo Andrade, Pimentel, Patrícia Guimarães
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Bioscience journal (Online)
• Texto Completo: https://seer.ufu.br/index.php/biosciencejournal/article/view/63017
• topic: Alternative foods; Degradation kinetics; Non-linear models; Silage; Suggested model.; Agricultural Sciences

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.