Routine detection of hyperketonemia in dairy cows using Fourier transform infrared spectroscopy analysis of β-hydroxybutyrate and acetone in milk in combination with test-day information

Abstract

The objective of this study was to assess the quality of a diagnostic model for the detection of hyperketonemia in early lactation dairy cows at test days. This diagnostic model comprised acetone and β-hydroxybutyrate (BHBA) concentrations in milk, as determined by Fourier transform infrared (FTIR) spectroscopy, in addition to other available test-day information. Plasma BHBA concentration was determined at a regular test day in 1,678 cows between 5 and 60 d in milk, originating from 118 randomly selected farms in the Netherlands. The observed prevalence of hyperketonemia (defined as plasma BHBA ≥1,200 µmol/L) was 11.2%. The value of FTIR predictions of milk acetone and milk BHBA concentrations as single tests for hyperketonemia were found limited, given the relatively large number of false positive test-day results. Therefore, a multivariate logistic regression model with a random herd effect was constructed, using parity, season, milk fat-to-protein ratio, and FTIR predictions of milk acetone and milk BHBA as predictive variables. This diagnostic model had 82.4% sensitivity and 83.8% specificity at the optimal cutoff value (defined as maximum sum of sensitivity and specificity) for the detection of hyperketonemia at test days. Increasing the cutoff value of the model to obtain a specificity of 95% increased the predicted value of a positive test result to 56.5%. Confirmation of test-positive samples with wet chemistry analysis of milk acetone or milk BHBA concentrations (serial testing) improved the diagnostic performance of the test procedure. The presented model was considered not suitable for individual detection of cows with ketosis due to the length of the test-day interval and the low positive predictive values of the investigated test procedures. The diagnostic model is, in our opinion, valuable for herd-level monitoring of hyperketonemia, especially when the model is combined with wet chemistry analysis of milk acetone or milk BHBA concentrations. By using the diagnostic model in combination with wet chemistry milk BHBA analysis, 84% of herds were correctly classified at a 10% alarm-level prevalence. As misclassification of herds may particularly occur when only a limited number of fresh cows are sampled, we suggest using prevalence estimates over several consecutive test days to evaluate feeding and management practices in smaller dairy farms.

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle

“…The disorder is closely related to the negative energy balance occurring in early lactation. Prevalence of SCK can vary between farms; reported prevalence rates range from 8.9 to 43% in the first 2 mo of lactation (McArt et al, 2012;van der Drift et al, 2012;Suthar et al, 2013).…”

“…Milk sampling is performed monthly in the milk recording procedures. More routinely, measurements of milk BHB can be done by Fourier transform mid-infrared (FT-MIR) spectroscopy analysis in milk samples at test days (de Roos et al, 2007;van der Drift et al, 2012). Blood or milk BHB predicted from milk spectra could be used for detection of SCK in farm management for dairy cows.…”

“…Blood BHB concentration has been used as a gold standard for detection of SCK and several studies have used a threshold of 1.2 (Duffield et al, 1997;van der Drift et al, 2012) to 1.4 mmol/L (Geishauser et al, 2000;Oetzel, 2004;Denis-Robichaud et al, 2014) to discriminate between cows with and without SCK. However, 6313 the gold standard method does not allow for routine testing of all animals at risk at regular intervals due to some practical limitations, such as difficulty in blood sampling, especially for farmers or testing many blood samples at a time.…”

Infrared Spectrophotometry, Infrared Spectroscopy and Infrared Spectrometry in Diary Spectral Qualimetry: Contemporary Ideas and Retro-Engineering

“…Следует отметить и другую сторону энергетического подхода -спектральный анализ молока в среднем инфракрасном диапазоне позволяет определять и прогнозировать энергетический статус самих коров [19]. В связи с этим, так как энергетический статус регулируется АТФ и клеточными редокс-процессами, а метаболический алкалоз и ацидоз в организме являются крайними фазами данного редокс-состояния, необходимо указать на тот факт, что как кетоацидоз, сопряженный с образованием кетоновых тел (гиперкетонемия), так и кетоз молочных коров (его возможно определить как состояние, при котором источником энергии становится переработка кетоновых тел, и он часто сопряжен с гиперкетонемией), могут быть диагностированы с использованием инфракрасного спектрального анализа молока [20][21][22][23].…”

The utility of sensor technology to support reproductive management on dairy farms

“…Each testing dataset was used for an independent validation of the model. Similar to the methodology of van der Drift et al (2012), the models were fitted with a random herd effect to correct for clustering of cows within a farm. During the validation, only the fitted values for the fixed effects were used to estimate a prognosis as the random effects would be unknown.…”