In this way, the diffusion of the salt can be monitored during the ham's maturation process.
The salt (sodium chloride) content of the cured ham influences the product's sensory characteristics, texture and flavour. But salt is also a very important parameter from the viewpoint of chemistry, as it is impossible for the maturation process to occur without it. Salt reduces the activity of water, inhibits the proliferation of micro-organisms and favours the formation of a meat gel.
"Making ham with a very low salt content involves a high technological risk, as it may present significant defects, making it impossible to market in many cases", explained researcher, Teresa Antequera.
Up until now the meat industry only had destructive methods at its disposal to determine the salt content of ham. These methods are also longer and more expensive for producers, which is why the work of the UEx researchers is geared toward developing non-destructive measuring methods.
With this new methodology developed by the UEx and offered to companies through the Innovation Service for Products of Animal Origin (Servicio de Innovación de Productos de Origen Animal -- SIPA), the food industry now has an efficient, non-destructive method the results of which are available practically in real time.
"We believe that this technique could be extremely useful for the food industry as a form of quality control, because it enables one to determine the evolution of the salt during the maturation process of the ham without taking samples at different times. And it can also be applied for measuring the levels of fat, humidity, colour and other quality parameters in a meat product", said Antequera.
"After viewing the images and using the texture algorithms—which enable one to objectively define what the image is like—we extract the data and information that we process using data mining techniques in order to classify and predict the salt content", added Daniel Caballero, a member of the Food Technology Group.
In this study we have used two classification techniques: one based on association rules and another on decision trees, and two prediction techniques: isotonic regression and multiple linear regression. These techniques allow for statistical calculations that provide us with results quickly and effectively," Caballero added.
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