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Vision versus reflectance technology to estimate pork cuts composition

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Gérard Dumas et Mathieu Monziols, 63e International Congress of Meat Science and Technology, Cork, Irlande, 13-18 août 2017, poster

Sorting meat cuts is an ongoing challenge. Many pork plants use weighing and carcass grading information for pre-sorting. Automation of pig carcass classification entailed a change in the location of the measured variables. Since mid-2013 in France the thicknesses measured by reflectance (CGM) on the back, laterally to the splitline, were replaced by thicknesses measured by vision (CSB Image-Meater®) at the ham-loin junction on the splitline. The objective of this work was to compare the precision of these two classification methods to predict the proportion of muscle in the main cuts.

PDF icon Gérard Dumas et Mathieu Monziols, 63e ICOMST, Cork, Irlande, 13-18 août 2017
2017

Vision versus reflectance technology to estimate pork cuts composition

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Gérard Daumas et Mathieu Monziols, 63e International Congress of Meat Science and Technology, Cork, Irlande, 13-18 août 2017, p. 115-116

The objective of this article is to compare the precision of two carcass classification methods to assess the lean meat percentage (LM%) of the major pork cuts. One method is automatic by vision (CSB Image-Meater®, IM) and the other is semi-automatic by reflectance (CGM). A sample of 241 carcasses, comprising 50% of females and 50% of castrated males, was measured by both methods. The left sides were cut according to the EU procedure and the four main cuts were weighed and scanned by X-Ray tomography. Each LM% was regressed on the one hand on the two CGM thicknesses and, on the other hand, on the four IM thicknesses. The thickness M4 of IM was not significant. The hierarchy of the cuts has been preserved in therms of increasing residual standard deviation (RSD): ham, shoulder, loin and belly. The RSDrose about 15% for the limbs and 30-35% for the trunk. The help of carcass classification for directing cutting should turn out to be weakened.

PDF icon Gérard Daumas et Mathieu Monziols, 63e ICOMST, Cork, Irlande, 13-18 août 2017
2017

What kind of predictors for calibrating the pig classification methods ?

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Some countries use surrogate predictors for assessing pig classification methods. The french reference method (RMSE = 1.67) is more accurate than all the classification devices used today. But a slight improvement in accuracy and a change in the nature of the measurements on line are expected.
PDF icon What kind of predictors for calibrating the pig classification methods ?
2000

What kind of surrogate predictors can be used for assessing the pig classification methods ?

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2001

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