Predicting the metabolizable energy content of corn for ducks: a comparison of support vector regression with other methods

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) of corn were crude protein, ether extract, crude fibre, and ash (g kg). Goodness of fit of the models was examined using R, 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 R) 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. Additional key words: maize; poultry; nutritive value; chemical composition; artificial neural network; multiple linear regression. * Corresponding author: [email protected] Received: 20-03-13. Accepted: 12-11-13 Abbreviations used: AME (apparent metabolizable energy); AMEn (apparent metabolizable energy corrected for nitrogen); ANN (artificial neural network); CF (crude fibre); CP (crude protein); EE (ether extract); ME (metabolizable energy); MLR (multiple linear regression); SVM (support vector machine); SVR (support vector regression); TME (true metabolizable energy); TMEn (true metabolizable energy corrected for nitrogen); VIF (variance inflation factor). Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Spanish Journal of Agricultural Research 2013 11(4): 1036-1043 Available online at www.inia.es/sjar ISSN: 1695-971-X http://dx.doi.org/10.5424/sjar/2013114-4220 eISSN: 2171-9292 ME prediction models for ducks 1037 composition. However, despite the ability of ANN models to handle complex nonlinear problems (Faridi et al., 2012a), this approach is not necessarily simple and may provide an apparently good fit to the data-set from which predictive equations are derived, but a poor predictive performance on newly introduced data. Support vector machines (SVM), i.e. supervised learning models with associated learning algorithms, can be used for classification, regression or other tasks (Cortes & Vapnik, 1995; Vapnik et al., 1997). In recent years, they have been introduced as a new technique for solving a variety of learning, classif ication and prediction problems (Cristianini & Shawe-Taylor, 2000). Support vector regression (SVR), the regression version of SVM, was developed to estimate regression functions (Drucker et al., 1997) and similar to SVM, it is capable of solving non-linear problems (Nandi et al., 2004). SVR models have been successfully applied across a broad range of areas in engineering, science and economics (e.g. Kara et al., 2011) but, to our knowledge, application to animal nutrition studies has not been investigated. Therefore, the objectives of this study were 1) to test the ability of SVR models to estimate apparent 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 ANN and multiple linear regression (MLR) models. Material and methods