Automatic On-Line Analysis Of Milk Constituents (Urea, Ketones, Enzymes And Hormones) Using Biosensors

Abstract

Traditional methods of monitoring health changes in animals are based entirely on the human senses. However, in modern dairy production systems humans are rarely present, this is particularly the case with the introduction of robotic milking. In these systems all the functions of milking are automated and cows visit at times of their own choosing. Systems of automatic health monitoring are therefore a priority for research to ensure that the health and reproductive status of the animals can be assessed for management purposes. These systems must be automatic, work in ¢eld conditions without technical support and cost a few pence per analysis. The ¢rst task is to obtain representative biological samples automatically and non-invasively. As milk is £owing into the milking machine from the cow this can be achieved with ease, except that milk is non-homogeneous with a changing lipid fraction during milking. Lipid soluble components such as progesterone and vitamin A are a¡ected by this change and a model has to be established to determine thresholds at di¡erent times during milking. Our main interests in dairy cows are in predicting ovulation, detecting metabolic imbalance and detecting preclinical mastitis in£am-matory response. Our team is developing a fully automated ovulation prediction system based on the screen-printed carbon electrode biosensor for progesterone demonstrated by Pemberton et al. (1998). In recent experiments the automated system was able to detect concentrations of progesterone between 2 and 30 ng/ml in stored milk samples (r 2 = 0.96). The results of ¢eld tests are presented showing a good correlation between ELISA and the biosensor (r 2 = 0.91) on samples of fresh milk. The results of the recent ¢eld tests show the ability of the biosensor to characterise ovulation cycles of cows and to detect pregnancy. We have identi¢ed a major lack of other biological models to detect disease with on-line sensors. Our next objective is to create an integrated system for biological research with sensor systems for urea, ketones, lipids and enzymes in milk. This will allow the development of diagnostic models based on analysing numerical sensor-derived data rather than human visual observations for signs of ill health in dairy cows.

Effect of changes in diet energy density on feed intake, milk yield and metabolic parameters in dairy cows in early lactation

“…Such metabolic imbalance is likely to be monitored in the future by means of in-line measurements of milk constituents (Mottram et al, 2002;Nielsen et al, 2005) or by small portable analytical devices for on-farm analyses of metabolites in blood (Andersen et al, 2002a;Perkins et al, 2005). It is therefore of interest to use the information from physiological parameters in blood and/or milk to make adjustments in the feeding on a group or individual level, in order to minimise the risk of metabolic diseases (Ingvartsen, 2006).…”

Development in Indirect Infra-red Determination of Milk Acetone

“…Milk sample investigation has advantage as non-invasive monitoring Mottram et al, 2002) while investigation of other body fl uids is invasive monitoring which can be associated with sure disadvantages and risks. Milk samples can be attend fast, regularly and cheaply.…”

“…Milk samples can be attend fast, regularly and cheaply. That is reason why already previously the various methods with diff erent efectiveness, advantages and disadvantages of ketone determination in milk has been developed (Mottram et al, 2002), from stable tests (relatively cheaper (Geishauser et al, 1997;Hanuš et al, 1999;Carrier et al, 2004)) to direct (colorimetric with salicylaldehyde, with vanillin, fl ow injection analysis with hydroxylamine and gas chromatography, relatively more expensive (O'Moore, 1949;Majewska and Rybczyňska, 1975;Vojtíšek, 1986;Hansen, 1999;Mottram et al, 2002;Heuer et al, 2001;Baticz et al, 2002;Roos et al, 2007;Beran et al, 2012)) and indirect (infrared spectrometry, relatively cheap (Hansen, 1999;Roos et al, 2007;Knegsel et al, 2010;Dri et al, 2012)) laboratory analytical methods. Whole raw of semiquantitative stable tests exists for ketones in urine (for instance Ketophan (Hanuš et al, 2001)) for quick diagnosis but there are only three (Carrier et al, 2004) good usable tests for work with milk.…”

“…Also genetic, lactation and milk yield impacts on milk acetone content and linked energy balance of dairy cows or other animals were evaluated (Miettinen, 1994;Heuer et al, 2001;Janů et al, 2007;Hanuš et al, 2011 b, c). The interpretation procedures of ketones in blood, urine, milk and/or cervical mucus were described in previous papers (Gustafsson and Emanuelson, 1996;Enjalbert et al, 2001;Heuer et al, 2001;Hanuš et al, 2001Mottram et al, 2002;Knegsel et al, 2010;Beran et al, 2012).…”

Cross validation of cut-off limit estimations for milk indicators of subclinical ketosis

“…Real time analyses of main milk components are attended in milking parlours today. Breeders can know milk composition regularly (Mottram et al, 2002). According to literature sources the milk indicators such as acetone content and fat/crude protein (F/CP) and fat/lactose (F/L) quotient could be successfuly used in the SK diagnosis (Duffi eld et al, 1997;Geishauser and Ziebell, 1995;Steen et al, 1996;Geishauser et al, 1997;Heuer et al, 2001;Reist et al, 2002;Van Knegsel et al, 2010;Siebert and Pallauf, 2010;Manzenreiter et al, 2013;Hanuš et al, 2013).…”