Estimation of apparent dietary nutrient digestibility in Yellowtail Kingfish Seriola lalandi by Near-Infrared Spectroscopy (NIRS)

Abstract

The apparent digestibility (AD) of dietary nutrients is an essential criterion used to improve nutrition in aquaculture. We assessed whether Near-Infrared Spectroscopy (NIRS) can assist in streamlining the process by predicting directly diet AD or the composition of diets and/or faecal nutrients and digestibility marker (yttrium oxide) concentrations collected in Yellowtail Kingfish (YTK) in vivo experiments. NIRS models were developed using up to 121 diets and 185 faecal samples, and assessed as part of a case-study investigating the accuracy of AD results for 7 validation diets using a range of prediction scenarios (S2-S5) against the benchmark analytical method (S1; all samples analysed). A large range in AD of diets was noted and results from the case-study suggested diet nutrient digestibility was significantly affected by the type of alternative ingredients used (AD dry matter ranged 45–73%; AD protein 75–85%; AD lipid 66–92%; AD energy 65–87%; AD amino acids 78–86%; AD methionine 81–90%; AD lysine 77–91%; AD taurine 70–79%). NIRS models had cross-validation R2 values ranging 0.63–0.89 and residual predictive deviation (RPD) values ranging 0.7–3.3 for diet nutrients; R2 values ranging 0.63–0.94 and RPD values ranging 1.8–4.0 for faeces. Successful NIRS models were developed for diet dry matter, ash, crude protein, total lipid, gross energy, and total amino acids; successful faeces models also included methionine and lysine but not dry matter and ash; taurine and yttrium oxide models were deemed unsuccessful for both diets and faeces. Dry matter AD based on either yttrium concentration predictions (S4) or direct prediction of diet AD (S5) were relatively inaccurate (±20% error in dry matter AD). Nonetheless S4 was relatively successful to predict lipid AD and S5 the protein AD of the seven diets with an average error of ±6.7% AD and ± 5% AD respectively compared to the benchmark. When the digestibility marker concentrations were analysed, the S2 (diet composition predicted) and S3 (diet and faeces composition predicted) scenarios could predict nutrient AD with a degree of error of ±1–3%, with the exception of lipid AD (±6–8% error), and taurine which was modelled poorly in this study. NIRS prediction of diet and faecal composition, combined with digestibility marker analysis, appears to be a cost-effective and reasonably accurate rapid method to assess diet AD in fish, reducing sample volume requirements, welfare impact and opening a range of new opportunities for fish nutrition and the development of more efficient phenotypes.