Application of NIRS for predicting fatty acids in intramuscular fat of rabbit

The aim of this study was to evaluate the use of near infrared reflectance spectroscopy (NIRS) for predicting fatty acid content in intramuscular fat to be applied in rabbit selection programs. One hundred and forty three freeze-dried Longissimus muscles (LM) were scanned by NIRS (1100–2498 nm). Modified Partial Least Squares models were obtained. Equations were selected according to standard error of cross validation (SECV) and coefficient of determination of cross validation (R2CV). Residual predictive deviation of cross validation (RPDCV) was also studied. Accurate predictions were reported for IMF (R2CV = 0.98; RPDCV = 7.57), saturated (R2CV = 0.96; RPDCV = 5.08) and monounsaturated FA content (R2CV = 0.98; RPDCV = 6.68). Lower accuracy was obtained for polyunsaturated FA content (R2CV = 0.83; RPDCV = 2.40). Several individual FA were accurately predicted such as C14:0, C15:0, C16:0, C16:1, C17:0, C18:0, C18:1 n-9, C18:2 n-6 and C18:3 n-3 (R2CV = 0.91-0.97; RPDCV > 3). Long chain polyunsaturated FA and C18:1 n-7 presented less accurate prediction equations (R2CV = 0.12-0.82; RPDCV < 3).

► Accurate predictions were obtained for IMF content and the main individual FA.
► NIRS is a suitable alternative to chemical methods to predict IMF and FA content.
► The models developed in this study will be applied in rabbit selection programs.