The technique, which is used throughout the world to analyse milk samples, involves shining a light (within the mid-infrared range) through a small sample of milk using a MIR instrument (see Image 1).
Some of the light is absorbed by the molecules in the milk and some is reflected. This process produces a ‘spectra’. Using these spectra we are able to predict the fat, protein, lactose and urea content of milk with a high degree of accuracy using a series of calibration equations. For example, Figure 1 shows the spectra for two different cows, and while the lines are similar, there are subtle differences, which indicate different milk compositions. More recently, MIR has been used to predict the type of fat in milk, allowing processors to identify how much of the fat in milk is unsaturated and how much is saturated. Research is also underway using MIR to identify the diet a cow is being offered (i.e. grass vs. silage). This could be used to validate the ‘providence’ of milk from a farm, i.e. ‘grass fed’ milk.
However, over the last decade research has increasingly examined what MIR can tell us about the cow that is producing the milk (and not just about the milk). This was examined in a major EU project called GplusE, in which AFBI was a partner, and more recently as part of a DAERA and AgriSearch co-funded project. Within these and other projects, MIR has been used to predict a number of ‘difficult-to-measure’ traits in cows, including the energy balance of individual cows. In addition, MIR has been used to identify cows in a herd that are ‘metabolically at risk’, and which may need special attention. MIR also offers potential to help reduce the environmental footprint of dairying. For example, MIR can be used to predict the methane production of individual cows, and to identify cows which are using nitrogen efficiently (or inefficiently). While some of these predictions equations are already being adopted, most are still in the development stage, with a number of research groups throughout the world, including those at AFBI, currently working in this area.
MIR has a number of benefits, including that it is ‘non-invasive’ (unlike a blood sample) and that milk samples are readily available from farms on both a bulk-tank and individual cow basis. It looks likely that in the near future information obtained from MIR analysis of milk samples will become increasing useful and important in helping farmers manage the nutrition of their herds, and indeed individual cows within the herd, and to help farmers reduce the environmental impact of dairy farming.