Abstract
Pellet quality, measured as the Pellet Durability Index (PDI), is an important key performance indicator for commercial feed manufacturing, as it can impact both mill efficiency and downstream performance of animals fed the manufactured diets. However, it is an ongoing challenge for the feed industry to control pellet quality, due to the complexity of feed manufacturing and the large number of variables influencing the process. Previous studies have explored the prediction of pellet quality using either simple empirical models with a few variables or machine learning models with many variables. The objective of the current study was to develop statistical regression models to predict PDI and to describe the relationship between pellet quality and 55 available variables based on a dataset with 2691 observations collected from a commercial feed mill. In the current study, the response variable (PDI) was transformed using the Box-Cox approach into the transformed response variable (tPDI), which was more normally distributed. Three multiple regression models were developed based on subsets of variables processed by variable selection and dimensionality reduction methods: Forward Selection, Principal Component Analysis, and Partial Least Squares. The results indicated that Model 1 (Forward Selection with manual removal of sparse variables), built on 9 variables, performed better than the other 2 models. It exhibited consistent model prediction performance on the training data and testing data, in terms of mean absolute error (1.93 ± 0.063 vs. 1.96), RMSPE (2.45 ± 0.079 vs. 2.45), and concordance correlation coefficient (0.549 ± 0.0273 vs. 0.550), with a better prediction precision based on the fit plot. Expanding Temperature (°C), Fat Content (%), ADF Content (%), and Indoor Humidity (Pelletizer) (%) were identified as more influential than other variables on the transformed response variable (tPDI) in Model 1, based on a behavior analysis. The models developed in the current study can be helpful to feed mills for predicting and comprehending the effect of a number of commonly measured variables on pellet quality in the commercial setting.