Abstract
Support vector regression (SVR) is used in this study to develop models to estimate apparent metabolizable energy (AME), AME corrected for nitrogen (AMEn), true metabolizable energy (TME), and TME corrected for nitrogen (TMEn) contents of corn fed to ducks based on its chemical composition. Performance of the SVR models was assessed by comparing their results with those of artificial neural network (ANN) and multiple linear regression (MLR) models. The input variables to estimate metabolizable energy content (MJ kg-1) of corn were crude protein, ether extract, crude fibre, and ash (g kg-1). Goodness of fit of the models was examined using R2, mean square error, and bias. Based on these indices, the predictive performance of the SVR, ANN, and MLR models was acceptable. Comparison of models indicated that performance of SVR (in terms of R2) on the full data set (0.937 for AME, 0.954 for AMEn, 0.860 for TME, and 0.937 for TMEn) was better than that of ANN (0.907 for AME, 0.922 for AMEn, 0.744 for TME, and 0.920 for TMEn) and MLR (0.887 for AME, 0.903 for AMEn, 0.704 for TME, and 0.902 for TMEn). Similar findings were observed with the calibration and testing data sets. These results suggest SVR models are a promising tool for modelling the relationship between chemical composition and metabolizable energy of feedstuffs for poultry. Although from the present results the application of SVR models seems encouraging, the use of such models in other areas of animal nutrition needs to be evaluated.
Highlights
Corn (Zea mays L.) is the main energy source in diets for intensively reared avian species, accurate information on its effective energy content is of importance to nutritionists
The objectives of this study were 1) to test the ability of Support vector regression (SVR) models to estimate apparent metabolizable energy (ME) (AME), apparent ME corrected for nitrogen (AMEn), true ME (TME), and true ME corrected for nitrogen (TMEn) of corn for ducks based on its chemical composition, and 2) to compare the predictive performance of SVR to that of artificial neural network (ANN) and multiple linear regression (MLR) models
Data used to develop the SVR and ANN models for apparent metabolizable energy (AME), AME corrected for nitrogen (AMEn), and TME corrected for nitrogen (TMEn) were taken from Zhao et al (2008), and information reported by Zhao et al (2008) and Zhou et al (2010) was used to develop the true metabolizable energy (TME) prediction models
Summary
Corn (Zea mays L.) is the main energy source in diets for intensively reared avian species (broilers and ducks), accurate information on its effective energy content is of importance to nutritionists. A number of studies have been conducted to estimate the metabolizable energy (ME) content of corn based on its physical characteristics and chemical composition Leeson et al, 1993; Zhao et al, 2008). The energy content of feedstuffs depends strongly on their chemical composition. Nutritionists are interested in using models that predict the nutritive value of poultry feedstuffs accurately. Artif icial neural network (ANN) models have received attention among poultry nutritionists, e.g. for estimating the ME of poultry offal meal (Ahmadi et al, 2008) and sorghum grain (Sedghi et al, 2011) based on their chemical
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