Rapid prediction of chemical composition and degree of starch cook of multi-species aquafeeds by near infrared spectroscopy

Ensuring aquafeeds meet the expected nutritional and physical specifications for a species is paramount in research and for the industry. This study aimed to examine the feasibility of predicting the proximate composition and starch gelatinisation (or cook) of aquaculture feeds (aquafeeds) regardless of their intended target species by near infrared (NIR) spectroscopy. Aquafeed samples used for nutrition experiments on various aquatic species with different nutritional requirements, as well as aquafeeds manufactured under varying extrusion conditions and steaming time to generate variable starch cook were used in this study. The various size pellets were ground before scanning by NIR spectroscopy, then models were developed to estimate dry matter, ash, total lipid, crude protein, and gross energy as well as starch cook. Proximate prediction models were successfully produced for diets with R-2 values between 0.88 and 0.97 (standard error of cross-validation (SECV) 0.43 to 1.46, residual predictive deviation (RPD) 4.6 to 15.6), while starch cook models were produced with R-2 values between 0.91 and 0.97 (SECV 3.60 to 5.76, RPD 1.2 to 1.9). The developed NIR models allow rapid monitoring of the nutritional composition, as well as starch cook, one of the major physical properties of aquafeeds. Models that provide rapid quality control assessment of diet characteristics is highly desirable in aquaculture research and the aquafeed industry.

Automated summary

This study successfully developed models using near infrared spectroscopy technology to rapidly predict the nutritional composition and starch gelatinization of aquafeeds, providing important quality control tools for aquaculture research and industry.