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Food, fibre and pharmaceuticals from animals
RESEARCH ARTICLE

The value of objective online measurement technology: Australian red meat processor perspective

E. S. Toohey A D , R. van de Ven B and D. L. Hopkins C
+ Author Affiliations
- Author Affiliations

A NSW Department of Primary Industries, PO Box 865 Dubbo, NSW 2830, Australia.

B NSW Department of Primary Industries, Orange Agricultural Institute, Forest Road, Orange, NSW 2800, Australia.

C NSW Department of Primary Industries, Centre for Red Meat and Sheep Development, PO Box 129, Cowra, NSW 2794, Australia.

D Corresponding author. Email: edwina.toohey@dpi.nsw.gov.au

Animal Production Science 58(8) 1559-1565 https://doi.org/10.1071/AN17775
Submitted: 8 November 2017  Accepted: 19 March 2018   Published: 21 May 2018

Abstract

In the past, the adoption of online measurement technologies for measuring carcass and meat quality traits objectively has been low among Australian red meat processors. The aim of the present work was to obtain a greater understanding of Australian processor views on the value of objective online measurement technologies. This was achieved through consultation with 65 Australian processors, to understand which carcass and meat quality traits they considered important to objectively measure and what they thought of current and future technologies. It was shown that beef processors ranked meat colour and tenderness as the most important traits (P < 0.001) to objectively measure online. Sheep processors ranked tenderness, pH, age, meat colour, total tissue depth at the 12th rib 110 mm from the midline (GR) and saleable meat yield percentage as the most important traits (P < 0.001) to objectively measure online. The overall processor responses indicated that there is support for online measurement technologies, with 80% of processors stating that online objective grading systems have a role in the Australian meat processing sector now and 88% considered these to have a role in the future. Much can be learned from the implementation of previous online objective measurement technologies by processors in terms of commercialisation and adoption strategies. The development and adoption of objective online measurement technologies is challenging and complex. However, increased adoption of online measurement technologies has the potential to achieve benefits to the whole of industry and needs continued support, coupled with new approaches to enhance adoption.

Additional keywords: beef, objective measurement, sheep, technology.


References

Anonymous (2005) ‘Handbook of Australian meat.’ 7th edn. (International red meat manual). (AUS-MEAT Ltd)

Coleman G (2013) Issues relevant to the adoption of technology in the meat processing sector. MLA final report A.TEC.0105. pp. 1–41.

Craigie CR, Ross DW, Maltin CA, Purchas RW, Bünger L (2013) The relationship between video image analysis (VIA), visual classification, and saleable meat yield of sirloin and fillet cuts of beef carcasses differing in breed and gender. Livestock Science 158, 169–178.
The relationship between video image analysis (VIA), visual classification, and saleable meat yield of sirloin and fillet cuts of beef carcasses differing in breed and gender.Crossref | GoogleScholarGoogle Scholar |

Hopkins DL (1989) An evaluation of the Hennessy grading probe for measuring fat depth in beef carcasses. Australian Journal of Experimental Agriculture 29, 781–784.
An evaluation of the Hennessy grading probe for measuring fat depth in beef carcasses.Crossref | GoogleScholarGoogle Scholar |

Hopkins DL (2011) Processing technology changes in the Australian sheep meat industry: an overview. Animal Production Science 51, 399–405.
Processing technology changes in the Australian sheep meat industry: an overview.Crossref | GoogleScholarGoogle Scholar |

Hopkins DL, Anderson MA, Morgan JE, Hall DG (1995) A probe to measure GR in lamb carcasses at chain speed. Meat Science 39, 159–165.
A probe to measure GR in lamb carcasses at chain speed.Crossref | GoogleScholarGoogle Scholar |

Hopkins DL, Safari E, Thompson JM, Smith CR (2004) Video image analysis in the Australian meat industry: precision and accuracy of predicting lean meat yield in lamb carcasses. Meat Science 67, 269–274.
Video image analysis in the Australian meat industry: precision and accuracy of predicting lean meat yield in lamb carcasses.Crossref | GoogleScholarGoogle Scholar |

Hopkins DL, Toohey ES, Boyce M, van de Ven RJ (2013) Evaluation of the Hennessy grading probe for use in lamb carcases. Meat Science 93, 752–756.
Evaluation of the Hennessy grading probe for use in lamb carcases.Crossref | GoogleScholarGoogle Scholar |

Jay NP, van de Ven RJ, Hopkins DL (2014) Comparison of rankings for lean meat based on results from a CT scanner and a video image analysis system. Meat Science 98, 316–320.
Comparison of rankings for lean meat based on results from a CT scanner and a video image analysis system.Crossref | GoogleScholarGoogle Scholar |

Kirton AH, Feist CL, Duganzich DM, Jordan RB, O’Donnell KP, Woods EG (1987) The use of the Hennessy grading probe for predicting the meat, fat and bone yields of beef carcasses. Meat Science 20, 51–63.
The use of the Hennessy grading probe for predicting the meat, fat and bone yields of beef carcasses.Crossref | GoogleScholarGoogle Scholar |

Kongsro J, Røe M, Kvaal K, Aastveit AH, Egelandsdal B (2009) Prediction of fat, muscle and value in Norwegian lamb carcasses using EUROP classification, carcass shape and length measurements, visible light reflectance and computer tomography (CT). Meat Science 81, 102–107.
Prediction of fat, muscle and value in Norwegian lamb carcasses using EUROP classification, carcass shape and length measurements, visible light reflectance and computer tomography (CT).Crossref | GoogleScholarGoogle Scholar |

Lambe NR, Navajas EA, Bünger L, Fisher AV, Roehe R, Simm G (2009) Prediction of lamb carcass composition and meat quality using combinations of post-mortem measurements. Meat Science 81, 711–719.
Prediction of lamb carcass composition and meat quality using combinations of post-mortem measurements.Crossref | GoogleScholarGoogle Scholar |

MLA (2014) ‘Selling options.’ Available at https://www.mla.com.au/research-and-development/preparing-for-market/selling-options/ [Verified 20 May 2014]

Pabiou T, Fikse WF, Cromie AR, Keane MG, Nasholm A, Berry DP (2011) Use of digital images to predict carcass cut yields in cattle. Livestock Science 137, 130–140.
Use of digital images to predict carcass cut yields in cattle.Crossref | GoogleScholarGoogle Scholar |

R Development Core Team (2010) ‘I: a language and environment for statistical computing.’ (R Foundation for Statistical Computing: Vienna). Available at http://www.R-project.org [Verified 27 April 2018]

Rius-Vilarrasa E, Bunger L, Maltin C, Matthews KR, Roehe R (2009) Evaluation of video image analysis (VIA) technology to predict meat yield of sheep carcasses on-line under UK abattoir conditions. Meat Science 82, 94–100.
Evaluation of video image analysis (VIA) technology to predict meat yield of sheep carcasses on-line under UK abattoir conditions.Crossref | GoogleScholarGoogle Scholar |

Siddell J, McLeod BM, Toohey ES, van de Ven R, Hopkins DL (2012) The prediction of meat yield in lamb carcasses using primal cut weights, carcass measures and the Hennessy grading probe. Animal Production Science 52, 584–590.

Stanford K, Richmond RJ, Jones SDM, Robertson WM, Price MA, Gordon AJ (1998) Video image analysis for on-line classification of lamb carcases. Animal Science 67, 311–316.
Video image analysis for on-line classification of lamb carcases.Crossref | GoogleScholarGoogle Scholar |

Steiner R, Wyle AM, Vote DJ, Belk KE, Scanga JA, Wise JW, Tatum JD, Smith GC (2003) Real-time augmentation of USDA yield grade application to beef carcasses using video image analysis. Journal of Animal Science 81, 2239–2246.
Real-time augmentation of USDA yield grade application to beef carcasses using video image analysis.Crossref | GoogleScholarGoogle Scholar |

Vote DJ, Bowlin MB, Cunha BCN, Belk KE, Tatum JD, Montossi F, Smith GC (2009) Video image analysis as a potential grading system for Uruguayan beef carcasses. Journal of Animal Science 87, 2376–2390.
Video image analysis as a potential grading system for Uruguayan beef carcasses.Crossref | GoogleScholarGoogle Scholar |