This study was conducted to evaluate the potential of near-infrared (NIR) spectroscopy (NIRS) technology for prediction of the chemical composition (moisture content and fatty acid composition) of fat from fast-growing, lean slaughter pig samples coming from breeding programmes. NIRS method I: a total of 77 samples of intact subcutaneous fat from pigs were analysed with the FOSS FoodScan NIR spectrophotometer (850 to 1050 nm) and then used to predict the moisture content by using partial least squares (PLS) regression methods. The best equation obtained has a coefficient of determination for cross-validation (CV; R2cv) and a root mean square error of a CV (RMSECV) of 0.88 and 1.18%, respectively. The equation was further validated with (n = 15) providing values of 0.83 and 0.42% for the coefficient of determination for validation (R2val) and root mean square error of prediction (RMSEP), respectively. NIRS method II: in this case, samples of melted subcutaneous fat were analysed in an FOSS XDS NIR rapid content analyser (400 to 2500 nm). Equations based on modified PLS regression methods showed that NIRS technology could predict the fatty acid groups, the main fatty acids and the iodine value accurately with R2cv, RMSECV, R2val and RMSEP of 0.98, 0.38%, 0.95 and 0.49%, respectively (saturated fatty acids), 0.94, 0.45%, 0.97 and 0.65%, respectively (monounsaturated fatty acids), 0.97, 0.28%, 0.99 and 0.34%, respectively (polyunsaturated fatty acids), 0.76, 0.61%, 0.84 and 0.87%, respectively (palmitic acid, C16:0), 0.75, 0.16%, 0.89 and 0.10%, respectively (palmitoleic acid, C16:1n-7), 0.93, 0.41%, 0.96 and 0.64%, respectively (steric acid, C18:0), 0.90, 0.51%, 0.94 and 0.44%, respectively (oleic acid, C18:1n-9), 0.97, 0.25%, 0.98 and 0.29% (linoleic acid, C18:2n-6), 0.68, 0.09%, 0.57 and 0.16% (α-linolenic acid, C18:3n-3) and 0.97, 0.57, 0.97 and 1.22, respectively (iodine value, calculated). The magnitude of this error showed quite good accuracy using these rapid methods in prediction of the moisture and fatty acid composition of fat from pigs involved in breeding schemes.

Subcutaneous fat from Norwegian Landrace (n = 3230) and Duroc (n = 1769) pigs was sampled to investigate the sources of variation and genetic parameters of various fatty acids, fat moisture percentage and fat colour, with the lean meat percentage (LMP) also included as a trait representing the leanness of the pig. The pigs were from half-sib groups of station-tested boars included in the Norwegian pig breeding scheme. They were fed ad libitum to obtain an average of 113 kg live weight. Near-infrared spectroscopy (NIRS) was applied for prediction of the fatty acids and fat moisture percentage, and Minolta was used for the fat colour measurements. Heritabilities and genetic correlations were estimated with a multi-trait animal model using average information-restricted maximum likelihood (AI-REML) methodology. Fat from Landrace pigs had considerably more monounsaturated fatty acids, polyunsaturated fatty acids (PUFAs) and fat moisture, as well as less saturated fatty acids (SFAs) than fat from Duroc pigs. The heritability estimates (s.e. 0.03 to 0.08) for the various fatty acids were as follows: Palmitic, C16:0 (0.39 and 0.51 for Landrace and Duroc pigs, respectively); Palmitoleic, C16:1n-7 (0.41 and 0.50); Steric, C18:0 (0.46 and 0.54); Oleic, C18:1n-9 (0.67 and 0.57); Linoleic, C18:2n-6 (0.44 and 0.46); α-linolenic, C18:3n-3 (0.37 and 0.25) and n-6/n-3 ratio (0.06 and 0.01). The other fat quality traits revealed the following heritabilities: fat moisture (0.28 and 0.33), colour values in subcutaneous fat: L* (whiteness; 0.22 and 0.21), a* (redness; 0.13 and 0.24) and b* (yellowness; 0.07 and 0.17) and LMP (0.46 and 0.47). LMP showed high positive genetic correlations to PUFA (C18:2n-6 and C18:3n-3), which implies that selecting leaner pigs changes the fatty acid composition and deteriorates the quality of fat. Higher concentrations of PUFA are not beneficial as the ratio of n-6 and n-3 fatty acids becomes unfavourably high. Owing to the high genetic correlation between C18:2n-6 and C18:3n-3 and a low heritability for this ratio, the latter is difficult to change through selection. However, a small reduction in the ratio should be expected if selection aims at reducing the level of C18:2n-6. Selection for more C18:1n-9 is possible in view of the genetic parameters, which are favourable for eating quality, technological quality and human nutrition. The NIRS technology and the high heritabilities found in this study make it possible to implement fat quality traits to achieve the breeding goal in the selection of a lean pig with better fat quality.