Biology subjects

Saeys, W.

Publications and source records attributed to Saeys, W..

6 recordsLinked to original sources

bioRxiv2020

Online milk composition analysis with an on-farm near-infrared sensor

On-farm monitoring of milk composition can support close control of the udder and metabolic health of individual dairy cows. In previous studies, near-infrared (NIR) spectroscopy applied to milk analysis has proven useful for predicting the main components of raw milk (fat, protein, and lactose). In this contribution, we present and evaluate a precise tool for online milk composition analysis on the farm. For each milking, the online analyzer automatically collects and analyses a representative milk sample. The system acquires the NIR transmission spectra of the milk samples in the wavelength range from 960 to 1690 nm and performs a milk composition prediction afterward. Over a testing period of 8 weeks, the sensor collected 1165 NIR transmittance spectra of raw milk samples originating from 36 cows for which reference chemical analyses were performed for fat, protein, and lactose. For the same online sensor system, two calibration scenarios were evaluated: training post-hoc prediction models based on a representative set of calibration samples (n = 319) acquired over the entire testing period, and training real-time prediction models exclusively on the samples acquired in the first week of the testing period (n = 308). The obtained prediction models were thoroughly tested on all the remaining samples not included in the calibration sets (n respectively 846 and 857). For the post-hoc prediction models, this resulted in an overall prediction error (root-mean-squared error of prediction, RMSEP) smaller than 0.08% (all % are in w/w) for milk fat (range 1.5-6.3%), protein (2.6-4.3%) and lactose (4-5.1%), while for the real-time prediction models the RMSEP was smaller than 0.09% for milk fat and lactose, and smaller than 0.11% for protein. The milk lactose predictions could be further improved by taking into account a cow-specific bias. The presented online sensor system using the real-time prediction approach can thus be used for detailed and autonomous on-farm monitoring of milk composition after each individual milking, as its accuracy is well within the ICAR requirements for on-farm milk analyzers and even meet the ICAR standards for laboratory analysis systems for fat and lactose. For this real-time prediction approach, a drift was observed in the predictions, especially for protein. Therefore, further research on the development of online calibration maintenance techniques is required to correct for this model drift and further improve the performance of this sensor system.

bioengineering↗

bioRxiv2019

Mid-Infrared Spectroscopic Analysis of Raw Milk to Predict the Blood Plasma Non-Esterified Fatty Acid Concentration in Dairy Cows

In high yielding dairy cattle, severe postpartum negative energy status is often associated with metabolic and infectious disorders that negatively affect production, fertility and welfare. Mobilization of adipose tissue associated with a negative energy status is reflected through an increased level of non-esterified fatty acids (NEFA) in the blood plasma. Earlier, identification of a negative energy status through the detection of increased blood plasma NEFA concentration required laborious and stressful blood sampling. More recently there have been attempts to predict blood NEFA concentration from milk samples. This study aimed to develop and validate a model to predict the blood plasma NEFA concentration using milk mid-infrared (MIR) spectra that are routinely measured in the context of milk recording. To this end, blood plasma and milk samples were collected in weeks 2, 3 and 20 post-partum for 192 lactations in 3 different herds. The blood plasma samples were taken in the morning, while representative milk samples were collected during the morning and evening milk session on the same day. To predict the blood plasma NEFA concentration from the milk MIR spectra, partial least squares regression models were trained on part of the observations from the first herd. The models were then thoroughly validated on all other observations of the first herd and on the observations of the two independent herds to explore their robustness and wide applicability. The final model can accurately predict blood plasma NEFA concentrations below 0.6 mmol/L with a root mean square error of prediction (RMSE) of less than 0.143 mmol/L. However, for blood plasma with more than 1.2 mmol/L NEFA, the model clearly underestimates the true level. Additionally, it was found that morning blood plasma NEFA levels were predicted with a significantly higher accuracy (p = 0.009) using MIR spectra of evening milk samples compared to morning samples, with RMSEP values of respectively 0.182 and 0.197 mmol/L and R2 values of 0.613 and 0.502. These results suggest a time delay between variations in blood plasma NEFA and related milk biomarkers. Based on the MIR spectra of evening milk samples, cows at risk for a negative energy status, indicated with detrimental morning blood plasma NEFA levels (> 0.6 mmol/L), could be identified with a sensitivity and specificity of respectively 0.831 and 0.800. As this model can be applied to millions of historical and future milk MIR spectra, it opens opportunities for regular metabolic screening and improved resilience phenotyping.

bioengineering↗

bioRxiv2019

SHORT COMMUNICATION: Validation of a novel milk progesterone based tool to monitor luteolysis in dairy cows. Performance on cost-effective, on-farm measured data

INTERPRETATIVE SUMMARYValidation of luteolysis monitoring tool Adriaens. Recently, a novel progesterone-based monitoring algorithm using synergistic control, PMASC, was developed. This algorithm employs the known luteal dynamics of the progesterone profile to estimate the moment of luteolysis with minimal dependence on the absolute measurement values. As its value and implementability strongly dependends on the number of samples it requires to obtain the desired accuracy, its performance on cost-effective, smart sampled and on-farm measured data is demonstrated. SHORT COMMUNICATIONValidation of a novel milk progesterone based tool to monitor luteolysis in dairy cows. Performance on cost-effective, on-farm measured data

bioengineering↗

bioRxiv2019

Validation of a novel milk progesterone-based tool to monitor luteolysis in dairy cows. Timing of the alerts and robustness against missing values

Automated monitoring of fertility in dairy cows using milk progesterone is based on the accurate and timely identification of luteolysis. In this way, well-adapted insemination advice can be provided to the farmer to further optimize the fertility management. To properly evaluate and compare the performance of new and existing data-processing algorithms, a test dataset of progesterone time-series that fully covers the desired variability in progesterone profiles is needed. Further, the data should be measured with a high frequency to allow rapid onset events, such as luteolysis, to be precisely determined. Collecting this type of data would require a lot of time, effort and budget. In the absence of such data, an alternative was developed using simulated progesterone profiles for multiple cows and lactations, in which the different fertility statuses were represented. To these, relevant variability in terms of cycle characteristics and measurement error was added, resulting in a large cost-efficient dataset of well-controlled but highly variable and farm-representative profiles. Besides the progesterone profiles, information on (the timing of) luteolysis was extracted from the modelling approach and used as a reference for the evaluation and comparison of the algorithms. In this study, two progesterone monitoring tools were compared: a multiprocess Kalman filter combined with a fixed threshold on the smoothed progesterone values to detect luteolysis, and a progesterone monitoring algorithm using synergistic control PMASC, which uses a mathematical model based on the luteal dynamics and a statistical control chart to detect luteolysis. The timing of the alerts and the robustness against missing values of both algorithms were investigated using two different sampling schemes: one sample per cow every eight hours versus one sample per day. The alerts for luteolysis of the PMASC algorithm were on average 20 hours earlier compared to the ones of the multiprocess Kalman filter, and their timing was less sensitive to missing values. This was shown by the fact that, when one sample per day was used, the Kalman filter gave its alerts on average 24 hours later, and the variability in timing of the alerts compared to simulated luteolysis increased with 22%. Accordingly, we postulate that implementation of the PMASC system could improve the consistency of luteolysis detection on farm and lower the analysis costs compared to the current state of the art.\n\nInterpretative SummaryValidation of luteolysis monitoring tool for dairy cows.\n\nAdriaensIn this study, the performance of two monitoring algorithms to detect luteolysis using milk progesterone measurements was validated on a simulated dataset of realistic milk progesterone profiles. The synergistic control-based algorithm, PMASC, was able to identify luteolysis almost simultaneously with its occurrence. It was found to be more robust against missing samples and less dependent on the absolute milk progesterone values compared to a multiprocess Kalman filter combined with a fixed threshold. This research showed that implementation of PMASC could improve progesterone-based fertility monitoring on farm.

bioengineering↗

bioRxiv2018

A novel system for online fertility monitoring based on milk progesterone

Timely identification of a cows reproduction status is essential to minimize fertilityrelated losses on dairy farms. This includes optimal estrus detection, pregnancy diagnosis and the timely recognition of early embryonic death and ovarian problems. On farm milk progesterone (P4) analysis could indicate all of these fertility events simultaneously. However, milk P4 measurements are subject to a large variability both in terms of measurement errors and absolute values between cycles. In this study, an innovative monitoring system based on milk P4 using the principles of synergistic control is presented. Instead of using filtering techniques and fixed thresholds, the present system employs an individually updated online model to describe the P4 profile, combined with a statistical process control chart to identify the cows fertility status. The inputs for the latter are the residuals of the online model, corrected for the concentration-dependent variability which is typical for milk P4 measurements. The objective of this paper is to present the developed methodology and give an indication of its use on farm. To this end, the system was validated on the P4 profiles of 38 dairy cows. The positive predictive value for luteolysis followed by estrus was 100%, meaning that the monitoring system picked up all estrous periods identified by the experts. Pregnancy or embryonic mortality was characterized by the absence or detection of luteolysis following an insemination, respectively. For thirteen cows, no luteolysis was detected by the system within the 25-32 days after insemination, indicating pregnancy, which was confirmed later by rectal palpation. It was also shown that the system is able to cope with deviating P4 profiles having prolonged follicular or luteal phases, which may suggest the occurrence of cysts. Future research is recommended for optimizing sampling frequency, prediction of the optimal insemination window and the establishment of rules to detect problems based on deviating P4 patterns.\n\nINTERPRETIVE SUMMARY. Progestrone-based online fertility monitoring. Adriaens.Milk progesterone based monitoring systems can be valuable for indicating fertility events in dairy cows, as the evolution of the milk progesterone levels can be related to acyclicity, estrus, pregnancy, and ovarian problems. We developed a system to translate raw progesterone measurements into clear information for the farmer, based on a mathematical model and a statistical control chart. In this way, a detailed image of a cows reproduction status on-farm can be obtained and fertility-related losses minimized.

bioengineering↗

bioRxiv2018

Linking heat attentions and the moment of LH surge in dairy cows - how close can we get?

Both estrus detection and timely insemination are important factors in optimizing fertility management. The latter is dependent on ovulation time, which is preceded by the LH surge. The performance of an estrus detection system based on activity and based on milk progesterone was evaluated and the timing of the alerts was contrasted against the moment of the LH surge. Activity alerts had a sensitivity of 83% and a positive predictive value of 66%; the LH surge followed average 9.4 {+/-} 16.1 hours later. Using milk progesterone, one can reliably detect luteolysis, which is followed by the LH surge after 62 {+/-} 12 hours.

bioengineering↗