Equações de predição de valores energéticos de alimentos obtidas utilizando meta-análise e componentes principais

Mariano, Flávia Cristina Martins Queiroz; Lima, Renato Ribeiro de Departamento de Ciências Exatas; Rodrigues, Paulo Borges; Alvarenga, Renata Ribeiro; Nascimento, Germano Augusto Jerônimo do

doi:10.1590/s0103-84782012005000061

Cited by 9 publications

(7 citation statements)

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“…The models selected in the present study -EqPred and committee neural network -were developed for the prediction of AMEn of both energetic and protein concentrate feedstuffs. The use of these models is still more appropriate to estimate the AMEn of feedstuffs such as maize, sorghum, soybean, soybean meal, wheat and wheat meal (Mariano et al, 2012(Mariano et al, , 2013. These models were indicated because these feedstuffs had greater representativeness in the dataset used in the development of the models.…”

Section: Resultsmentioning

confidence: 99%

“…Further information on the development of this committee can be obtained from Mariano et al (2014). Mariano et al (2012) proposed and evaluated some of the AMEn prediction equations of poultry feedstuffs based on their chemical composition. These prediction equations were adjusted by WMLR models, considering the principle of the meta-analysis, and the technique of principal components was used in the formation of homogeneous groups of experimental results.…”

Section: Methodsmentioning

confidence: 99%

“…These prediction equations were adjusted by WMLR models, considering the principle of the meta-analysis, and the technique of principal components was used in the formation of homogeneous groups of experimental results. In the present work, a weighted multiple linear regression equation was adjusted following the methodology of Mariano et al (2012), and using the set of 568 experimental results. Firstly, the TRAT variable was created by combining the levels of the categorical variables of the data set (feedstuff category, type of ingredient, and type of animal used in the bioassay).…”

Section: Methodsmentioning

confidence: 99%

“…In these equations, the energy values of the feedstuffs are established according to their chemical compositions. In the methods used to obtain these prediction equations two ways can be considered to develop a multiple linear regression, as follows: using a classical multiple regression analysis without any restriction or weighting (Rodrigues et al, 2002); or using the principle of meta-analysis, in which homogeneous groups are established as weighting factors (Nascimento et al, 2009;Mariano et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs

Mariano

Lima

Alvarenga

et al. 2020

Pesq. agropec. bras.

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How to citeMARIAno, F.C.M.Q.; LIMA, R.R. de; ALvAREnGA, R.R.; RoDRIGUES, P.B. Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs. Pesquisa Agropecuária Brasileira, v.55, e01199, 2020. Abstract -The objective of this work was to compare the committee neural network (CNN) and weighted multiple linear regression (WMLR) models, in order to estimate the nitrogen-corrected apparent metabolizable energy (AMEn) of poultry feedstuffs. The prediction equation was adjusted by using a WMLR model and the meta-analysis principle. The models were compared by considering the correct prediction percentages, based on the classic prediction intervals and on the highest-probability density intervals, and by using a comparison test for proportions. The accuracy of the models was evaluated based on the values of the mean squared error, coefficient of determination, mean absolute deviation, mean absolute percentage error, and bias. Data from metabolic trials were used to compare the selected models. The committee neural network is the model that showed the highest accuracy of prediction, being recommended as the most accurate model to predict AMEn values for energetic concentrate feedstuffs used by the poultry feed industry.Index terms: broilers, highest-probability density interval, meta-analysis, metabolizable energy, percentage of success. Comitê de redes neurais e regressão múltipla ponderada para a predição de valores energéticos de alimentos para aves de corteResumo -O objetivo deste trabalho foi comparar o modelo comitê de redes neurais e o modelo de regressão linear múltipla ponderada (RLMP), para estimar a energia metabolizável aparente corrigida por nitrogênio (EMAn) de alimentos para aves. A equação de predição foi ajustada por RLMP e pelo princípio da meta-análise. Os modelos foram comparados tendo-se considerando as percentagens de acerto de predição, com base em intervalos de predição clássicos e intervalos de credibilidade da máxima densidade de probabilidade, e utilizado um teste para comparação de proporções. A acurácia dos modelos foi avaliada com base nos valores de erro médio quadrático, coeficiente de determinação, desvio médio absoluto, erro percentual absoluto médio e viés. Dados provenientes de ensaios metabólicos foram utilizados na comparação dos modelos selecionados. O comitê de redes neurais é o modelo que forneceu predições mais acuradas, sendo recomendado como o de maior acurácia, para prever os valores de EMAn de alimentos concentrados utilizados na indústria alimentícia para aves.Termos para indexação: frangos de corte, intervalo de credibilidade da máxima probabilidade, meta-análise, energia metabolizável, percentagem de acerto. 2 F.C.M.Q. Mariano et al.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs

Mariano

Lima

Alvarenga

et al. 2020

Pesq. agropec. bras.

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“…Outro meio de se obter os valores de EMAn são as equações de predição estabelecidas em função da composição química dos alimentos (ALVARENGA et al, 2011;MARIANO et al, 2013), normalmente de fácil e rápida obtenção. Na literatura existem trabalhos que obtiveram as equações de predição por meio de regressão múltipla (RODRIGUES et al, 2001(RODRIGUES et al, , 2002BORGES et al, 2003;ZHAO et al, 2008), meta-análise (NASCIMENTO et al, 2009(NASCIMENTO et al, , 2011MARIANO et al, 2012) e redes neurais (PERAI et al, 2010;AHMADI et al, 2007AHMADI et al, , 2008MARIANO et al, 2013). No entanto, é necessário a inovação em termos de utilização metodológica (ALVARENGA et al, 2015), podendo assim, resultar em melhores estimativas dos valores energéticos.…”

Section: Introductionunclassified

Innovation in the prediction of the energy values of poultry feedstuffs

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The Thomson Reuters Web of Science is a database that enables to identify patterns and trends in scientific publications, thereby enabling a broad understanding of the publications in the area of interest. An area that has been arising attention to the statistical community is the Bayesian networks, mainly due to the flexibility of modeling the data, both discrete and continuous, in terms of regression and high accuracy in the obtained results, that is, they are probabilistically promising models. The objective of this study was to identify and describe the main categories of the Web of Science that contemplate studies on Bayesian networks, check the publications over the years, identify the types of documents published, as well as the main funding agencies, the main authors, countries and languages. For the accomplishment of this study, the data of the Thomson Reuters Web of Science database were collected from 1945 to 2018. By means of search it is possible to answer several questions of interest, among them, if there are publications of Bayesian networks mainly in the animal sciences, more specifically in the formulation of diets for broilers. The results confirm that this area of knowledge is still very recent. The first publications took place in 1990 and the main publications are concentrated in computer science and no study was found in the prediction of the metabolizable energy of broilers using this methodology.

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Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

Mariano

Paixão

Lima

et al. 2013

Animal

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Several researchers have developed prediction equations to estimate the metabolisable energy (ME) of energetic and protein concentrate feedstuffs used in diets for broilers. The ME is estimated by considering CP, ether extract, ash and fibre contents. However, the results obtained using traditional regression analysis methods have been inconsistent and new techniques can be used to obtain better estimate of the feedstuffs' energy value. The objective of this paper was to implement a multilayer perceptron network to estimate the nitrogen-corrected metabolisable energy (AMEn) values of the energetic and protein concentrate feeds, generally used by the poultry feed industry. The concentrate feeds were from plant origin. The dataset contains 568 experimental results, all from Brazil. This dataset was separated into two parts: one part with 454 data, which was used to train, and the other one with 114 data, which was used to evaluate the accuracy of each implemented network. The accuracy of the models was evaluated on the basis of their values of mean squared error, R 2 , mean absolute deviation, mean absolute percentage error and bias. The 7-5-3-1 model presented the highest accuracy of prediction. It was developed an Excel R AMEn calculator by using the best model, which provides a rapid and efficient way to predict the AMEn values of concentrate feedstuffs for broilers.Keywords: avian production, broilers, metabolisable energy, multilayer perceptron ImplicationsIt is difficult and expensive to estimate the nitrogencorrected metabolisable energy (AMEn) values of the feedstuffs used for broilers. The results of this study demonstrate that the implementation of multilayer perceptron networks (MLP) in a meta-analysis is suitable to estimate these energy values. Furthermore, a calculator was created on the basis of the results of the MLP, which allows an efficient way of predicting the AMEn values.

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Equações de predição de valores energéticos de alimentos obtidas utilizando meta-análise e componentes principais

Cited by 9 publications

References 13 publications

Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs

Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs

Innovation in the prediction of the energy values of poultry feedstuffs

Prediction of the energy values of feedstuffs for broilers using meta-analysis and neural networks

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