Two hundreds rabbits were obtained from 3 different maternal lines and 5 paternal lines, for a total of 11 combinations. After slaughtering the fresh hind legs (HL) and Longissimus dorsi muscles (LD) were scanned in the near infrared region by using a Foss NIRSystem 5000 (gimel=1100-2498 nm). The WINISI software (v 1.50) was used for the spectra analysis and samples selection (49 HL and 11 LD). Selected samples were analyzed chemically for dry matter (DM), protein, lipid, ash and fatty acid profile (FA). The obtained results were used to expand and improve the existing calibration equations for fresh rabbit's meat. Afterwards these equations were used to predict meat composition of the unselected samples. Discriminant analysis didn't segregate genetic lines. The calibration results for the 400 meat samples were accurate in predicting DM, protein, lipid and some FA (R(2)>0.80). Poor results were obtained for ash and for physical properties of meat. It was demonstrated that NIRS is a reliable and affordable technology to predict fresh rabbit meat composition, but because of the small differences between genotypes, NIRS wasn't able to discriminate samples according to their genetic belonging.

Near infrared spectroscopy (NIRS) as a tool to predict meat chemical composition and fatty acid profile in different rabbit genotypes

RIOVANTO, ROBERTO;MIRISOLA, MASSIMO;BERZAGHI, PAOLO;DALLE ZOTTE, ANTONELLA
2009

Abstract

Two hundreds rabbits were obtained from 3 different maternal lines and 5 paternal lines, for a total of 11 combinations. After slaughtering the fresh hind legs (HL) and Longissimus dorsi muscles (LD) were scanned in the near infrared region by using a Foss NIRSystem 5000 (gimel=1100-2498 nm). The WINISI software (v 1.50) was used for the spectra analysis and samples selection (49 HL and 11 LD). Selected samples were analyzed chemically for dry matter (DM), protein, lipid, ash and fatty acid profile (FA). The obtained results were used to expand and improve the existing calibration equations for fresh rabbit's meat. Afterwards these equations were used to predict meat composition of the unselected samples. Discriminant analysis didn't segregate genetic lines. The calibration results for the 400 meat samples were accurate in predicting DM, protein, lipid and some FA (R(2)>0.80). Poor results were obtained for ash and for physical properties of meat. It was demonstrated that NIRS is a reliable and affordable technology to predict fresh rabbit meat composition, but because of the small differences between genotypes, NIRS wasn't able to discriminate samples according to their genetic belonging.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/2438909
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