Literature DB >> 34230594

Phenotypic and genetic variation of ultraviolet-visible-infrared spectral wavelengths of bovine meat.

Giovanni Bittante1, Simone Savoia1,2,3, Alessio Cecchinato1, Sara Pegolo4, Andrea Albera2.   

Abstract

Spectroscopic predictions can be used for the genetic improvement of meat quality traits in cattle. No information is however available on the genetics of meat absorbance spectra. This research investigated the phenotypic variation and the heritability of meat absorbance spectra at individual wavelengths in the ultraviolet-visible and near-infrared region (UV-Vis-NIR) obtained with portable spectrometers. Five spectra per instrument were taken on the ribeye surface of 1185 Piemontese young bulls from 93 farms (13,182 Herd-Book pedigree relatives). Linear animal model analyses of 1481 single-wavelengths from UV-Vis-NIRS and 125 from Micro-NIRS were carried out separately. In the overlapping regions, the proportions of phenotypic variance explained by batch/date of slaughter (14 ± 6% and 17 ± 7%,), rearing farm (6 ± 2% and 5 ± 3%), and the residual variances (72 ± 10% and 72 ± 5%) were similar for the UV-Vis-NIRS and Micro-NIRS, but additive genetics (7 ± 2% and 4 ± 2%) and heritability (8.3 ± 2.3% vs 5.1 ± 0.6%) were greater with the Micro-NIRS. Heritability was much greater for the visible fraction (25.2 ± 11.4%), especially the violet, blue and green colors, than for the NIR fraction (5.0 ± 8.0%). These results allow a better understanding of the possibility of using the absorbance of visible and infrared wavelengths correlated with meat quality traits for the genetic improvement in beef cattle.

Entities:  

Year:  2021        PMID: 34230594     DOI: 10.1038/s41598-021-93457-5

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  36 in total

1.  Prediction of technological and organoleptic properties of beef Longissimus thoracis from near-infrared reflectance and transmission spectra.

Authors:  B Leroy; S Lambotte; O Dotreppe; H Lecocq; L Istasse; A Clinquart
Journal:  Meat Sci       Date:  2004-01       Impact factor: 5.209

2.  On-line prediction of chemical composition of semi-frozen ground beef by non-invasive NIR spectroscopy.

Authors:  G Tøgersen; J F Arnesen; B N Nilsen; K I Hildrum
Journal:  Meat Sci       Date:  2003-04       Impact factor: 5.209

3.  Genetic analysis of beef fatty acid composition predicted by near-infrared spectroscopy.

Authors:  A Cecchinato; M De Marchi; M Penasa; J Casellas; S Schiavon; G Bittante
Journal:  J Anim Sci       Date:  2011-09-23       Impact factor: 3.159

4.  The effect of the number of observations used for Fourier transform infrared model calibration for bovine milk fat composition on the estimated genetic parameters of the predicted data.

Authors:  M J M Rutten; H Bovenhuis; J A M van Arendonk
Journal:  J Dairy Sci       Date:  2010-10       Impact factor: 4.034

5.  Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS).

Authors:  D Alomar; C Gallo; M Castañeda; R Fuchslocher
Journal:  Meat Sci       Date:  2003-04       Impact factor: 5.209

6.  Near-infrared reflectance spectroscopy predictions as indicator traits in breeding programs for enhanced beef quality.

Authors:  A Cecchinato; M De Marchi; M Penasa; A Albera; G Bittante
Journal:  J Anim Sci       Date:  2011-03-31       Impact factor: 3.159

Review 7.  A Review of the Principles and Applications of Near-Infrared Spectroscopy to Characterize Meat, Fat, and Meat Products.

Authors:  Nuria Prieto; Olga Pawluczyk; Michael Edward Russell Dugan; Jennifer Lynn Aalhus
Journal:  Appl Spectrosc       Date:  2017-05-23       Impact factor: 2.388

Review 8.  Review: Beef-eating quality: a European journey.

Authors:  L J Farmer; D T Farrell
Journal:  Animal       Date:  2018-07-13       Impact factor: 3.240

9.  On-site evaluation of Wagyu beef carcasses based on the monounsaturated, oleic, and saturated fatty acid composition using a handheld fiber-optic near-infrared spectrometer.

Authors:  S Piao; T Okura; M Irie
Journal:  Meat Sci       Date:  2017-11-27       Impact factor: 5.209

10.  Prediction of milk fatty acid content with mid-infrared spectroscopy in Canadian dairy cattle using differently distributed model development sets.

Authors:  A Fleming; F S Schenkel; J Chen; F Malchiodi; V Bonfatti; R A Ali; B Mallard; M Corredig; F Miglior
Journal:  J Dairy Sci       Date:  2017-04-21       Impact factor: 4.034

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