Reliable prediction of Norwegian lamb carcass composition - Kongsro, Jørgen
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The main objective of this work was to study prediction and determination of Norwegian lamb carcass composition with different techniques spanning from subjective appraisal to computer-intensive methods. There is an increasing demand, both from farmers and processors of meats, for a more objective and reliable system for prediction of muscle (lean meat), fat, bone and value of a lamb carcass. When introducing new technologies for determination of lamb carcass composition, the reference method used for calibration must be precise and reliable. The precision and reliability of the current…mehr

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Produktbeschreibung
The main objective of this work was to study prediction and determination of Norwegian lamb carcass composition with different techniques spanning from subjective appraisal to computer-intensive methods. There is an increasing demand, both from farmers and processors of meats, for a more objective and reliable system for prediction of muscle (lean meat), fat, bone and value of a lamb carcass. When introducing new technologies for determination of lamb carcass composition, the reference method used for calibration must be precise and reliable. The precision and reliability of the current dissection reference for lamb carcass classification and grading has never been quantified. A poor reference method will not benefit even the most optimal system for prediction and determination of lamb carcasses. To help achieve reliable systems, the uncertainty or errors in the reference method and measuring systems needs to be quantified. Using proper calibration methods for the measuring systems, the uncertainty and modeling power can be determined for lamb carcasses.
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Autorenporträt
I enjoy working at the intersection between biology, agriculture, engineering and computers. Finding practical solutions based on science to improve the production of food from farm to fork. Improving animal and farmer welfare based on engineering and clever solutions.