There is currently a limited understanding of the effect of food structure on physical breakdown and gastric emptying of solid starch-based foods during gastric digestion. Moisture uptake, pH, particle size,...

The current recommendation, when calculating a protein digestibility-corrected amino acid score, is to determine the digestibility of a dietary protein across the entire digestive tract, using the rat as a model animal for humans. This fecal digestibility value is subsequently corrected for endogenous contributions of protein using a metabolic nitrogen value determined by feeding rats a protein-free diet. The limitations inherent with this method are well recognized, however, and determining the digestibility of a dietary protein to the end of the small intestine is the preferred alternative. Unlike the fecal digestibility assay, which has only one basic methodology, ileal digestibility values can be determined in a number of ways. We discuss the various methods available for determining ileal digestibility values and compare results obtained for dietary proteins using both fecal and ileal digestibility assays. The relative value of using individual amino acid digestibility values as opposed to nitrogen digestibility values is reviewed. In addition, we address issues surrounding measurement of endogenous nitrogen flows, and in particular, the relative merits of determining "true" versus "real" digestibility values.

Cooking conditions affect the true ileal digestible amino acid content and DIAAS of beef, as determined with the use of the pig model.

Endogenous nitrogen and amino acid losses are associated with the digestion process. Different methods can be used to distinguish between gut endogenous and exogenous amino acid flows. These methods include feeding N-free diets, the regression approach, the use of enzyme hydrolysed proteins coupled with ultrafiltration and the use of markers (e.g., homoarginine, enzyme hydrolysed casein, 15N-labelled protein, 15N-leucine). All of the methods described have their limitations, but they have allowed major advances to be made in the measurement and understanding of endogenous amino acid secretion, reabsorption, flow and excretion.

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Near infrared reflectance spectroscopy (NIRS) was used to predict the nutritional value of dog foods sold in Chile. Fifty-nine dry foods for adult and growing dogs were collected, ground and scanned across the visible/NIR range and subsequently analysed for dry matter (DM), crude protein (CP), crude fibre (CF), total fat, linoleic acid, gross energy (GE), estimated metabolizable energy (ME) and several amino acids and minerals. Calibration equations were developed by modified partial least squares regression, and tested by cross-validation. Standard error of cross validation (SE(CV)) and coefficient of determination of cross validation (SE(CV)) were used to select best equations. Equations with good predicting accuracy were obtained for DM, CF, CP, GE and fat. Corresponding values for and SE(CV) were 0.96 and 1.7 g/kg, 0.91 and 3.1 g/kg, 0.99 and 5.0 g/kg, 0.93 and 0.26 MJ/kg, 0.89 and 12.4 g/kg. Several amino acids were also well predicted, such as arginine, leucine, isoleucine, phenylalanine-tyrosine (combined), threonine and valine, with values for and SE(CV) (g/kg) of 0.89 and 0.9, 0.94 and 1.3, 0.91 and 0.5, 0.95 and 0.9, 0.91 and 0.5, 0.93 and 0.5. Intermediate values, appropriate for ranking purposes, were obtained for ME, histidine, lysine and methionine-cysteine. Tryptophan, minerals or linoleic acid were not acceptably predicted, irrespective of the mathematical treatment applied. It is concluded that NIR can be successfully used to predict important nutritional characteristics of commercial dog foods.