Can we detect welfare status in a milk sample?

This blog post was written by Mazen Bahadi, Dr.

During my Ph.D. studies, I developed a new methodology for infrared spectral analysis that will help the dairy farming industry in detecting the effects of housing on dairy cow welfare status using milk samples. Currently, the welfare status of dairy cows is evaluated through on-farm visits, while milk samples are routinely collected from individual cows and analyzed by infrared-based milk analyzers. What is infrared analysis? It is a chemical analysis technic that uses the infrared region of light to determine the chemical composition of the investigated sample, in this case, the milk sample. The advantage of this method is that it captures signals from all molecules in milk that absorb the infrared energy and that signal has a direct relationship with the concentration of the absorbing molecule. Hence, the output of this measurement, which is called a spectrum represents a snapshot of the chemical composition of the milk samples (Figure 1). Currently, this technique is used to determine the major components of milk (i.e., fat, protein, and lactose) and some minor ones (i.e., urea and β-hydroxybutyrate) for dairy herd improvement (DHI) programs.

Figure 1. Example of a loading spectrum isolated from milk spectral data that revealed significant tie-rail configuration effect. Shaded regions can be assigned to 1) lactose 1200-1000 cm-1, 2) acetone ~1237 cm-1, 3) citrate, BHB and acetone 1390-1250 cm-1, 4) BHB ~1404 cm-1, 5) fatty acids and carboxylate ion in citrate, BHB, free fatty acids and acetate 1618-1424 cm-1, 6) Carboxylic group of free fatty acids ~ 1716 cm-1, 7) CH stretching of fatty acids 3000-2800 cm-1, 8) =C-H stretching of fatty acids ~3008 cm-1.

We believe that the welfare status in cows might lead to some physiological changes, which in turn is reflected in the chemical composition of milk. However, the affected milk components are not necessarily those that are currently determined in DHI programs. For this reason, we think that by developing the appropriate methods, milk infrared spectra can be mined, like any other type of data, to help us determine which milk components are affected by specific treatments related to herd management practices. The methodology was applied to multiple animal trials that were designed to improve the welfare of dairy cows in tie-stall barns. We found that tie-rail configurations, as well as chain length, influenced the concentration of different milk components such as lactose, BHB, acetone, citrate as well as non-protein nitrogen and histamine. In turn, the concentration of these components has been shown to be associated with animal welfare statuses such as access to feed and acidosis. I invite you to check out our scientific paper published at the Journal Foods. The results indicated that milk spectral data can be mined to study the effects of herd management practices on animal welfare and that additional milk components should be analyzed to allow the development of welfare monitoring programs.

Lastly, Dr. Vasseur and I were then invited (!!!) to present our novel work at the 72nd Annual Meeting of the European Federation of Animal Science (Figure 2), which was held in Davos, Switzerland from August 30th to September 3rd, 2021. It was an excellent opportunity to meet experts from all over the world who work on milk infrared analysis and to learn about the latest advancement in this field. The reception of my work was positive, and we had the chance for a constructive conversation on future prospects related to the detection of animal welfare from milk samples.