Using artificial neural networks to model the urinary excretion of total and purine derivative nitrogen fractions in cows

Using artificial neural networks to predict pH, ammonia, and volatile fatty acid concentrations in the rumen

The objectives of this study were (1) to predict ruminal pH and ruminal ammonia and volatile fatty acid (VFA) concentrations by developing artificial neural networks (ANN) using dietary nutrient compositions, dry matter intake, and body weight as input variables; and (2) to compare accuracy and precision of ANN model predictions with that of a multiple linear regression model (MLR). Data were collected from 229 published papers with 938 treatment means. The data set was randomly split into a training data set containing 70% of the observations and a test data set with the remaining observations. A series of ANN with a range of 1 to 9 artificial neurons in 1 hidden layer were examined, and the best one was selected to compare with the best-fitted MLR model. The performance of model predictions was evaluated by root mean square errors (RMSE) and concordance correlation coefficients (CCC) using cross-evaluations with 100 iterations. When using the ANN to predict ruminal pH and concentrations of ammonia, total VFA, acetate, propionate, and butyrate, the RMSE were 4.2, 41.4, 20.9, 22.3, 32.9, and 29.7% of observed means, respectively. The RMSE for the MLR were 4.2, 37.8, 18.3, 19.9, 29.8, and 26.6% of the observed means. The CCC for ruminal pH, ruminal concentrations of ammonia, total VFA, acetate, propionate, and butyrate were 0.57, 0.49, 0.45, 0.40, 0.52, and 0.40, using the ANN, and 0.37, 0.48, 0.40, 0.29, 0.43, and 0.35, using the MLR. Evaluations of the MLR and the ANN indicated that these 2 model forms exhibited similar prediction errors, with 4.2, 39.6, 19.6, 21.1, 31.3, and 28.1% of observed means for pH, ammonia, total VFA, acetate, propionate, and butyrate. Although the ANN increased the precision of predictions related to ruminal metabolism, it failed to improve the accuracy compared with the linear regression model.

Predicting in vitro rumen VFA production using CNCPS carbohydrate fractions with multiple linear models and artificial neural networks

The objectives of this trial were to develop multiple linear regression (MLR) models and three-layer Levenberg-Marquardt back propagation (BP3) neural network models using the Cornell Net Carbohydrate and Protein System (CNCPS) carbohydrate fractions as dietary variables for predicting in vitro rumen volatile fatty acid (VFA) production and further compare MLR and BP3 models. Two datasets were established for the trial, of which the first dataset containing 45 feed mixtures with concentrate/roughage ratios of 10∶90, 20∶80, 30∶70, 40∶60, and 50∶50 were used for establishing the models and the second dataset containing 10 feed mixtures with the same concentrate/roughage ratios with the first dataset were used for testing the models. The VFA production of feed samples was determined using an in vitro incubation technique. The CNCPS carbohydrate fractions (g), i.e. CA (sugars), CB1 (starch and pectin), CB2 (available cell wall) of feed samples were calculated based on chemical analysis. The performance of MLR models and BP3 models were compared using a paired t-test, the determination coefficient (R2) and the root mean square prediction error (RMSPE) between observed and predicted values. Statistical analysis indicated that VFA production (mmol) was significantly correlated with CNCPS carbohydrate fractions (g) CA, CB1, and CB2 in a multiple linear pattern. Compared with MLR models, BP3 models were more accurate in predicting acetate, propionate, and total VFA production while similar in predicting butyrate production. The trial indicated that both MLR and BP3 models were suitable for predicting in vitro rumen VFA production of feed mixtures using CNCPS carbohydrate fractions CA, CB1, and CB2 as input dietary variables while BP3 models showed greater accuracy for prediction.