493 Predicting pellet quality using multiple linear regression with Principal Component Analysis (PCA)

Abstract Pellet quality is a crucial key performance indicator (KPI) for commercial feed manufacturing, which influences both the efficiency of the feed mill and downstream performance of animals fed these diets. However, due to the complexity of feed manufacturing and the large number of factors involved in the manufacturing process, controlling pellet quality is an ongoing challenge for the feed industry. Previous studies have mainly explored the impact of a few factors on pellet quality under experimental settings, and empirical equations have been seldomly developed to reflect the relationship between the factors and pellet quality under the commercial feed mill settings. This study aimed to establish a relationship between pellet quality and factors collected under the settings of a commercial feed mill. The data were collected from Trouw Nutrition Canada’s feed mill located in St. Marys, Ontario (Plant 2), between December 15, 2021, and December 6, 2022. During this period, 2,691 observations were collected, with each observation representing an individual batch of pelleted feed. A total of 75 factors were recorded, including 4 factors associated with the general information of each batch, 10 manufacturing parameters, 41 feed ingredients, 8 factors regarding the nutrient composition of each diet, and 12 environmental factors. Pellet Durability Index (PDI), which was the response variable, was determined for each batch using the Holmen method. The data were randomly split into an 80% subset for training and a 20% subset for testing. The training subset was used to construct the model via a 5-fold cross-validation, while the testing subset was withheld as an independent dataset to evaluate the generalization performance of the model. The response variable (PDI) was transformed (tPDI) using the Box-Cox method to meet a normal distribution assumption. To avoid multicollinearity, Principal Component Analysis (PCA) was used to reduce the dimensionality of the numeric factors before building the multiple linear regression model. The model prediction performance was evaluated on both the training subset (using 5-fold cross-validation) and the testing subset, and the prediction performance metrics were consistent between the two subsets (Mean Absolute Error = 1.94 ± 0.102 vs. 2.02; Root Mean Square Prediction Error = 2.47 ± 0.111 vs. 2.58; Mean Square Prediction Error = 6.12 ± 0.538 vs. 6.68; Concordance Correlation Coefficient = 0.538 ± 0.0231 vs. 0.490; Pearson Correlation Coefficient = 0.606 ± 0.0247 vs. 0.553, respectively). Most feed ingredients and nutrient compositions showed either positive or negative loadings on Component 1 (17.87% of total variance), and outdoor/indoor environmental factors were positively loaded on Component 2 (14.21% of total variance). The model developed in this study could help commercial feed mills better understand how various factors impact pellet quality and optimize the manufacturing processes of pelleted feeds.