Evaluation of analytical and statistical approaches for predicting in vitro nitrogen solubility and in vivo pre-caecal crude protein digestibility of cereal grains in growing pigs.

Different analytical (enzyme system and near-infrared spectroscopy (NIRS)) and statistical (single and multiple regressions) approaches were used to predict in vivo standardized pre-caecal digestibility (PCD) of crude protein (CP) and amino acids (AA) in cereal grains for growing pigs as well as in vitro nitrogen (N) solubility. Furthermore, different chemical and physical characteristics were categorized (e.g. crude nutrients, AA, minerals, fibre components or combinations of these) and used for generating prediction equations. There were strong linear relationships (p < .05) between in vivo PCD of CP and essential AA and in vitro N solubility when grain species was considered as covariate in the model. Predicting in vivo PCD values using various chemical and physical characteristics produced inconsistent results among different grain species and AA and could therefore not be used for predicting PCD. It is possible to predict in vitro N solubility from chemical and physical characteristics for some grain species. However, the relationships between some of these categories and the in vitro N solubility were not consistent and not always causative or physiologically explainable. The R2 of NIRS for predicting in vitro N solubility was at a relatively high level (up to R2  = 0.80). This level of R2 indicates that a classification of the grain samples in, for example, high, medium and low in vitro N solubility levels is possible, but it does not allow for a quantitative prediction of the in vitro N solubility. In conclusion, the present database can be used for establishing a ranking of different cereal grain species for PCD of CP and essential AA values. However, it was not possible to create clear prediction equations for in vivo or in vitro digestibility values. Therefore, greater variation within grain species, for example due to different growing and harvesting conditions, is warranted for predicting PCD values of individual grain samples.