Proposals of non-linear models to adjust in vitro gas production at different incubation times in cassava genotypes

Santos, André Luiz Pinto dos; Gomes-Silva, Frank; Moreira, Guilherme Rocha; Brito, Cícero Carlos Ramos de; Costa, Maria Lindomárcia Leonardo da; Filho, Moacyr Cunha; Saliba, Eloísa de Oliveira Simões; Oliveira, Marianne Caron; Gonçalves, Nélio Cunha

doi:10.5902/2179460x39962

Cited by 3 publications

(4 citation statements)

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“…The curves of in vitro gas production generate a sigmoid curve, divided into three phases, namely: initial phase with slow gas production, exponential phase of rapid gas production, and lastly, asymptotic phase with a decrease in the gas production rate, reaching zero (Santos et al 2021).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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“…57 feedstuffs were categorized into three groups commonly used for ruminants. Among them, there were 20 types of energy feed, totaling 24 (2). The samples were dried in a 65°C air oven for 48 hours, followed by grinding through a 0.5-millimeter sieve, for chemical analysis and in vitro rumen gas production.…”

Section: Feed Samples Preparation and Nutrient Analysismentioning

confidence: 99%

“…Though previous studies have investigated the comparison of the goodness of t of different models, they may suffer from issues such as small sample sizes and undivided feed categories 1,11,13,[19][20][21][22][23][24] . To address these issues, this study aims to improve the reliability and accuracy of the research by increasing the sample size and categorizing the data according to feedstuff types.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of different mathematical models on fitting the in vitro gas production parameters in beef cattle

Wang,

Zhou,

Cheng

et al. 2024

Preprint

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In vitro rumen gas production experiment was conducted with 57 kinds of feedstuff, which were categorized into energy feed, protein feed, and roughage, collected within China. Eight mathematical models were employed to describe the kinetics of in vitro rumen gas production. The results found that for energy feeds, protein feeds, and roughages, respectively, MM or Logistic-Exponential with lag (LEL), MM, and Mitscherlich (MIT) exhibited the highest or shown no significant difference compared to the highest coefficient of determination (R2) (P< 0.05) for all categories of feed. Furthermore, regression estimation of intercept and slope for regression estimates of intercept and slope for Observed versus Predicted of aforementioned models shown no significant difference from 0 and 1, respectively (P< 0.05), except LEL for energy feed. Mean absolute error (MAE), root mean squared error of prediction (RMSEP), mean squared error of prediction (MSEP) of those models were relatively lower, with minimal systematic bias and regression bias. Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) rankings were higher compared with other models. Given these results, in studies where feedstuff categories are not distinguished or multiple feedstuffs categories are included, the MM model proves to be a good choice. MM or LEL was considered to better fit energy foodstuffs. The MM model was the optimal choice for fitting protein foodstuffs. MIT provided the best accuracy and moderate precision when fitting roughages.

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“…Recently, mathematical models have been proposed to better describe the cumulative gas production (CGP) data (Santos et al, 2019(Santos et al, , 2020. Thus, there is always the possibility that a new model may perform better in terms of biological convergence and interpretation of parameters (Santos et al, 2021). In addition, few studies have been conducted on the kinetics of ruminal degradation of corn or sunflower silage as the only roughage in the diet or its associations.…”

Section: Introductionmentioning

confidence: 99%

Novo modelo bicompartimental: uma aplicação para a produção de gases pela técnica in vitro

Santos,

Ferreira,

Brito

et al. 2023

Sem. Ci. Agr.

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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.

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Proposals of non-linear models to adjust in vitro gas production at different incubation times in cassava genotypes

Cited by 3 publications

References 0 publications

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

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

Evaluation of different mathematical models on fitting the in vitro gas production parameters in beef cattle

Novo modelo bicompartimental: uma aplicação para a produção de gases pela técnica in vitro

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