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Raw beef for burgers

How much can we expect from X-ray fat analysis

Raw beef for Burgers
By Jens Borg, JEB@foss.dk
A new video equips meat producers to make the right choices when acquiring an X-ray solution, including an in-depth look at why accuracy is so important.

After its introduction to the meat industry in 2003, X-ray analysis has gradually gained acceptance as a profitable and practical way to control fat content in all sorts of meat products. To help meat producers make the right choice when acquiring this still relatively new-technology, In Focus magazine has made this video explaining how just a slight improvement in measurement accuracy can boost yield.

The difference a 0.2% improvement makes 
As discussed in the video, users of the MeatMaster in-line X-ray analyser have found it to be at least 0.2 to 0.3% more accurate than alternative in-line analytical solutions. In most cases the difference is even higher.

Now 0.2 to 0.3% may not sound like much of a difference, but it can result in a huge boost in value for the company using it. This can be explained using two different production plants as examples.

At the first, the production manager has a fat specification of maximum 20% fat in the products. He obviously wants to get as close to this as possible and to help him, he is using an analytical method with an error of 0.8%.
 
This margin of error gives him a certain variation over time (figure 1). 
Fat on target info graph
Figure 1

And it gives him a distribution curve like figure 2 where he has set the fat point to 18.4% to make sure he does not go over the target 20%

Fat on target info graph
Figure 2

The set point is calculated by multiplying the 0.8% error with a recommended standard deviation interval of two. 0.8% times two equals 1.6%, subtracted from 20% gives a set point of 18.4%.

The production manager is pretty happy with this. After all, the standard deviation needs to be there and the analytical error of 0.8% is pretty good – isn’t it? 

But what if the analytical error could be reduced even more?

Fat on target info graph
Figure 3

In our other example, the production manager has got his analytical error down to 0.6%. His fat content does not vary so much (figure 3).


Fat on target info graph
Figure 4


This gives him a distribution curve like this one in figure 4.

Here the calculation is 0.6% times two which equals 1.2%. Subtracted from 20% this gives a fat set point of 18.8% compared to the first one of 18.4%.

In this example, the production manager can use more fat in his burgers with confidence that he will still be on target. 
The price difference between the fat and the leaner raw material has been around 1.00 EUR per kilo and he produces 25 tons of burgers daily. He has therefore been saving around 1000 EUR per day on raw material costs. 

Over 300 working days he has made EUR 300,000 per year in extra income. 
 
 MeatMaster II After Grinder in use

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