Near-infrared spectra dataset of milk composition in transmittance mode.

Monitoring of milk composition can support several dimensions of dairy management such as identification of the health status of individual dairy cows and the safeguarding of dairy quality. The quantification of milk composition has been traditionally executed employing destructive chemical or laboratory Fourier-transform infrared (FTIR) spectroscopy analyses which can incur high costs and prolonged waiting times for continuous monitoring. Therefore, modern technology for milk composition quantification relies on non-destructive near-infrared (NIR) spectroscopy which is not invasive and can be performed on-farm, in real-time. The current dataset contains NIR spectral measurements in transmittance mode in the wavelength range from 960 nm to 1690 nm of 1224 individual raw milk samples, collected on-farm over an eight-week span in 2017, at the experimental dairy farm of the province of Antwerp, 'Hooibeekhoeve' (Geel, Belgium). For these spectral measurements, laboratory reference values corresponding to the three main components of raw milk (fat, protein and lactose), urea and somatic cell count (SCC) are included. This data has been used to build multivariate calibration models to predict the three milk compounds, as well as develop strategies to monitor the prediction performance of the calibration models.

Association between management practices and estimated mastitis incidence and milk losses on robotic dairy farms.

This study aims to describe the relation between farm-level management factors and estimated farm-level mastitis incidence and milk loss traits (MIMLT) at dairy farms with automated milking systems. In this observational study, 43 commercial dairy farms in Belgium and the Netherlands were included and 148 'management and udder health related variables' were obtained during a farm visit through a farm audit and survey. The MIMLT were estimated from milk yield data. Quarter-level milk yield perturbations that were caused by presumable mastitis cases (PMC) were selected based on quarter-level milk yield and electrical conductivity. On average, 57.6 ± 5.4% of the identified milk yield perturbations complied with our criteria. From these PMC, 3 farm-level MIMLT were calculated over a one-year period around the farm visit date: (1) the 'average number of PMC per cow per year', (2) the 'absolute milk loss per cow per day', calculated as the farm-level sum of all milk losses during PMC in one year, divided by the average number of lactating cows and the number of days, and (3) the 'relative milk loss', calculated as the farm-level sum of milk losses during PMC in one year, divided by the estimated total production in the absence of PMC. The 'average number of PMC per cow per year' was on average 1.81 ± 0.47. The PMC caused an average milk loss of 0.77 ± 0.26 kg per lactating cow per day, which corresponded to an average production loss of 2.38 ± 0.82% of the expected production in the absence of PMC. We performed a principal component regression (PCR) analysis to link the 3 MIMLT to the 'management and udder health related variables', whilst reducing the multicollinearity and the number of dimensions. The first principal component was mainly related to 'milking system brand, maintenance and settings'. The second component mainly linked to average productivity and somatic cell counts, whereas the third component mainly contained variables linked with mastitis management, treatment, and biosecurity. The 3 PCR models had R² ranging from 0.46 (for absolute milk loss per cow per day) to 0.57 (for relative milk loss). For all models, the second PC had the largest effect size. This analysis raises awareness of the impact of management factors on a factual basis and provides handles to take management actions to improve udder health.

Short communication: Predicting blood plasma non-esterified fatty acid and beta-hydroxybutyrate concentrations from cow milk-addressing systematic issues in modelling.

Negative energy status in early lactation is linked to a variety of metabolic disorders, reduced fertility, and decreased milk production. To improve the energy status of cows by breeding and management, the identification of negative energy status is crucial. While biomarkers such as non-esterified fatty acid (NEFA) concentration and beta-hydroxybutyrate (BHB) in blood plasma could be used to identify a negative energy state, measuring them directly from blood is both invasive and expensive. In this work, we developed prediction equations for blood plasma NEFA and BHB levels based on mid-IR spectral measurements of milk. The models were fitted using partial least squares regression and evaluated using both cross-validation and independent-herd validation. A total of 3 183 spectral records from 606 lactations originating from three different herds were utilised. R2 values of 0.53 (RMSE = 0.206 mmol/l, RMSE of cross-validation (RMSECV) 0.217 mmol/l) for NEFA and 0.63 (RMSE = 0.326 mmol/l, RMSECV = 0.353 mmol/l) for BHB were obtained. Furthermore, relatively similar prediction accuracies were found for BHB (RMSE of prediction (RMSEP) 0.411 mmol/l and 0.422 mmol/l) and NEFA (RMSEP = 0.186 mmol/l and 0.221 mmol/l) when model training was done using two herds and validated on the third herd. The results from the model fits confirm that it is possible to build blood plasma BHB and NEFA models based on mid-IR spectra that are sufficiently accurate for practical use.

Joint Models to Predict Dairy Cow Survival from Sensor Data Recorded during the First Lactation.

Early predictions of cows' probability of survival to different lactations would help farmers in making successful management and breeding decisions. For this purpose, this research explored the adoption of joint models for longitudinal and survival data in the dairy field. An algorithm jointly modelled daily first-lactation sensor data (milk yield, body weight, rumination time) and survival data (i.e., time to culling) from 6 Holstein dairy farms. The algorithm was set to predict survival to the beginning of the second and third lactations (i.e., second and third calving) from sensor observations of the first 60, 150, and 240 days in milk of cows' first lactation. Using 3-time-repeated 3-fold cross-validation, the performance was evaluated in terms of Area Under the Curve and expected error of prediction. Across the different scenarios and farms, the former varied between 45% and 76%, while the latter was between 3.5% and 26%. Significant results were obtained in terms of expected error of prediction, meaning that the method provided survival probabilities in line with the observed events in the datasets (i.e., culling). Furthermore, the performances were stable among farms. These features may justify further research on the use of joint models to predict the survival of dairy cattle.

Microbial profile during fermentation and aerobic stability of ensiled mixtures of maize stover and banana pseudostem in South Ethiopia.

AIMS: This study evaluated pH reduction and microbial growth during fermentation of maize stover (MS) mixed with banana pseudostem (BPS) under South Ethiopian conditions. MATERIALS AND RESULTS: The MS and BPS were chopped and mixed into six treatments (T): 80% BPS plus 20% DMS (T1), 70% BPS plus 30% DMS (T2), 40% BPS plus 60% FMS (fresh MS) (T3), 20% BPS plus 80% FMS (T4), 100% FMS (T5), and 95% BPS plus 5% molasses (T6). At 0, 7, 14, 30, 60, and 90 days, pH and dry matter were determined. Microbiological quality was assessed using plate counts and Illumina MiSeq sequencing. On day 60 and 90, aerobic stability was investigated. The results showed a significant reduction in pH in all mixtures, except in T1 and T2. Lactic acid bacteria counts reached a maximum in all treatments within 14 days. Sequencing showed marked changes in dominant bacteria, such as Buttiauxella and Acinetobacter to Lactobacillus and Bifidobacterium. CONCLUSIONS: The fresh MS and BPS mixtures and fresh maize showed significant pH reduction and dominance of desirable microbial groups. SIGNIFICANCE AND IMPACT OF THE STUDY: The study enables year-round livestock feed supplementation to boost milk and meat production in South Ethiopia.

Evaluation of MEMS NIR Spectrometers for On-Farm Analysis of Raw Milk Composition.

Today, measurement of raw milk quality and composition relies on Fourier transform infrared spectroscopy to monitor and improve dairy production and cow health. However, these laboratory analyzers are bulky, expensive and can only be used by experts. Moreover, the sample logistics and data transfer delay the information on product quality, and the measures taken to optimize the care and feeding of the cattle render them less suitable for real-time monitoring. An on-farm spectrometer with compact size and affordable cost could bring a solution for this discrepancy. This paper evaluates the performance of microelectromechanical system (MEMS)-based near-infrared (NIR) spectrometers as on-farm milk analyzers. These spectrometers use Fabry-Pérot interferometers for wavelength tuning, giving them the advantage of very compact size and affordable price. This study discusses the ability of MEMS spectrometers to reach the accuracy limits set by the International Committee for Animal Recording (ICAR) for at-line analyzers of the milk content regarding fat, protein and lactose. According to the achieved results, the transmission measurements with the NIRONE 2.5 spectrometer perform best, with an acceptable root mean squared error of prediction (RMSEP = 0.21% w/w) for the measurement of milk fat and excellent performance (RMSEP ≤ 0.11% w/w) for protein and lactose. In addition, the transmission measurements using the NIRONE 2.0 module give similar results for fat and lactose (RMSEP of 0.21 and 0.10% w/w respectively), while the prediction of protein is slightly deteriorated (RMSEP = 0.15% w/w). These results show that the MEMS spectrometers can reach sufficient prediction accuracy compared to ICAR standard values for at-line and in-line fat, protein and lactose prediction.

Milk losses linked to mastitis treatments at dairy farms with automatic milking systems.

Mastitis-associated milk losses in dairy cows have a massive impact on farm profitability and sustainability. In this study, we analyzed milk losses from 4 553 treated mastitis cases as recorded via treatment registers at 41 AMS dairy farms. Milk losses were estimated based on the difference between the expected and the actual production. To estimate the unperturbed lactation curve, we applied an iterative procedure using the Wood model and a variance-dependent threshold on the milk yield residuals. We calculated milk losses both in a fixed window around the first treatment day of each mastitis case and in the perturbations corresponding to this day, at the cow level as well as at the quarter level. In a fixed time window of day -5 to 30 around the first treatment, the absolute median milk losses per case were 101.5 kg, highly dependent on the parity and the lactation stage with absolute milk losses being highest in multiparous cows and at peak lactation. Relative milk losses expressed in percentage were highest on the first treatment day, and full recovery was often not reached within 30 days from treatment onset. In 62 % of the cases, we found a perturbation in milk yield at the cow level at the time of treatment. On average, perturbations started 8.7 days before the first treatment and median absolute milk losses increased to 128 kg of milk per perturbation. Mastitis is not expected to have equal effects on the four quarters so this study additionally investigated losses in the individual udder quarters. We used a data-based method leveraging milk yield and electrical conductivity to project the presumably inflamed quarter. Next, we compared losses with the average of presumably non-inflamed quarters. Median absolute losses in a fixed 36-day window around treatment varied between 50.2 kg for front and 59.3 kg for hind inflamed quarters compared to respectively 24.7 and 26.3 kg for the median losses in the non-inflamed quarters. Also here, these losses differed between lactation stages and parities. Expressed proportionally to expected yield, the relative median milk losses in inflamed quarters on the treatment day were 20 % higher in inflamed quarters with a higher variability and slower recovery. In 86 % of the treated mastitis cases, at least one perturbation was found at the quarter level. This analysis confirms the high impact of mastitis on milk production, and the large variation between quarter losses illustrates the potential of quarter analysis for on-farm monitoring at farms with an automated milking system.

Bridging the gap between measurement-based and simulation-based metamodels for deriving bulk optical properties from spatially-resolved reflectance profiles: effect of illumination and detection geometry.

Non-invasive determination of the optical properties is essential for understanding the light propagation in biological tissues and developing optical techniques for quality detection. Simulation-based models provide flexibility in designing the search space, while measurement-based models can incorporate the unknown system responses. However, the interoperability between these two types of models is typically poor. In this research, the mismatches between measurements and simulations were explored by studying the influences from light source and the incident and detection angle on the diffuse reflectance profiles. After reducing the mismatches caused by the factors mentioned above, the simulated diffuse reflectance profiles matched well with the measurements, with R2 values above 0.99. Successively, metamodels linking the optical properties with the diffuse reflectance profiles were respectively built based on the measured and simulated profiles. The prediction performance of these metamodels was comparable, both obtaining R2 values above 0.96. Proper correction for these sources of mismatches between measurements and simulations thus allows to build a simulation-based metamodel with a wide range of desired optical properties that is applicable to different measurement configurations.

Silage making of maize stover and banana pseudostem under South Ethiopian conditions: evolution of pH, dry matter and microbiological profile.

The study was conducted to evaluate the microbial dynamics during silage of maize stover and banana pseudostem in the environmental conditions of southern Ethiopia. To meet this objective, microsilos containing either maize stover or banana pseudostem, both with and without molasses, were prepared. Subsequently, samples were analysed on day 0, 7, 14, 30, 60 and 90 of the fermentation process. As a result, on day 7, all treatments except banana pseudostem without molasses showed a significant reduction in pH. It was also this silage type that supported the growth of Enterobacteriaceae longer than three other silage types, i.e. until 30 days. The yeasts and moulds and the Clostridum endospore counts also showed a reducing trend in early fermentation and afterwards remained constant until day 90. Illumina MiSeq sequencing revealed that Leuconostoc, Buttiauxella species and Enterobacteriaceae were the most abundant bacteria in the initial phases of the fermentation. Later on, Buttiauxella, Lactobacillus, Weissella and Bifidobacterium species were found to be dominant. In conclusion, silage of the two crop by-products is possible under South Ethiopian conditions. For banana pseudostem, the addition of molasses is crucial for a fast fermentation, in contrast to maize. Upscaling needs to be investigated for the two by-products.

Mid-infrared spectroscopic analysis of raw milk to predict the blood nonesterified fatty acid concentrations in dairy cows.

In high-yielding dairy cattle, severe postpartum negative energy balance is often associated with metabolic and infectious disorders that negatively affect production, fertility, and welfare. Mobilization of adipose tissue associated with negative energy balance is reflected through an increased level of nonesterified fatty acids (NEFA) in the blood plasma. Earlier, identification of negative energy balance through detection of increased blood plasma NEFA concentration required laborious and stressful blood sampling. More recently, attempts have been made to predict blood NEFA concentration from milk samples. In this study, we aimed to develop and validate a model to predict blood plasma NEFA concentration using the 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 wk 2, 3, and 20 postpartum for 192 lactations in 3 herds. The blood plasma samples were taken in the morning, and representative milk samples were collected during the morning and evening milk sessions on the same day. To predict 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 2 independent herds to explore their robustness and wide applicability. The final model could accurately predict blood plasma NEFA concentrations <0.6 mmol/L with a root mean square error of prediction of <0.143 mmol/L. However, for blood plasma with >1.2 mmol/L NEFA, the model clearly underestimated the true level. Additionally, we found that morning blood plasma NEFA levels were predicted with significantly higher accuracy using MIR spectra of evening milk samples compared with MIR spectra of morning samples, with root mean square error of prediction 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 negative energy status, indicated by 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 an opportunity for regular metabolic screening and improved resilience phenotyping.

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 fertility management. To properly evaluate and compare the performance of new and existing data-processing algorithms, a test data set 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 data set of well-controlled but highly variable and farm-representative profiles. Besides the progesterone profiles, information on (the timing of) luteolysis was extracted from the modeling approach and used as a reference for the evaluation and comparison of the algorithms. In this study, 2 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 2 different sampling schemes: one sample per cow every 8 h versus 1 sample per day. The alerts for luteolysis of the PMASC algorithm were on average 20 h earlier compared with the ones of the multiprocess Kalman filter, and their timing was less sensitive to missing values. This was shown by the fact that, when 1 sample per day was used, the Kalman filter gave its alerts on average 24 h later, and the variability in timing of the alerts compared with 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 with the current state of the art.

Estimation of Particle Size Distribution from Bulk Scattering Spectra: Validation on Monomodal Suspensions.

A particle size distribution (PSD) estimation method based on light-scattering properties was validated on experimental visible/near-infrared scattering spectra of polystyrene suspensions, with a nominal particle size ranging from 0.1 to 12 µm in diameter. On the basis of µs and g spectra extracted from double integrating sphere measurements, good PSD estimates were obtained for particles ≥1 µm. The particle volume fraction estimates in the case of µs were close to the target concentrations, although influenced by small baseline fluctuations on the spectra. For submicrometer particles, on the other hand, the non-oscillating µs spectra lack discriminating power, resulting in erroneous PSD estimates. The reduced scattering coefficient spectra (µs') were found less useful for particle size estimation as they lack a characteristic shape, causing an over- or underestimation of the distribution width. In summary, the estimation routine proved to deliver PSD estimates in line with the reference measurements for micrometer-sized or larger particles based on their µs and g scattering spectra. Additional validation on more polydisperse samples forms the next step before going to bimodal PSD estimates.

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

The progesterone (P4) monitoring algorithm using synergistic control (PMASC) uses luteal dynamics to identify fertility events in dairy cows. This algorithm employs a combination of mathematical functions describing the increasing and decreasing P4 concentrations during the development and regression of the corpus luteum and a statistical control chart that allows identification of luteolysis. The mathematical model combines sigmoidal functions from which the cycle characteristics can be calculated. Both the moment at which luteolysis is detected and confirmed by PMASC, as well as the model features themselves, can be used to inform the farmer on the fertility status of the cows.

Short communication: Sensitivity of estrus alerts and relationship with timing of the luteinizing hormone surge.

Both the sensitivity of an estrus detection system and the consistency of alarms relative to ovulation determine its value for a farmer. The objective of this study was to compare an activity-based system and a milk progesterone-based system for their ability to detect estrus reliably, and to investigate how their alerts are linked to the time of the LH surge preceding ovulation. The study was conducted on an experimental research farm in Flanders, Belgium. The activity alerts were generated by a commercial activity meter (ActoFIT, DeLaval, Tumba, Sweden), and milk progesterone was measured using a commercial ELISA kit. Sensitivity and positive predictive value of both systems were calculated based on 35 estrus periods over 43 d. Blood samples were taken for determination of the LH surge, and the intervals between timing of the alerts and the LH surge were investigated based on their range and standard deviation (SD). Activity alerts had a sensitivity of 80% and a positive predictive value of 65.9%. Alerts were detected from 39 h before until 8 h after the LH surge (range: 47 h, SD: 16 h). Alerts based on milk progesterone were obtained from a recently developed monitoring algorithm using a mathematical model and synergistic control. All estruses were correctly identified by this algorithm, and the LH surge followed, on average, 62 h later. Using the mathematical model, model-based indicators for the estimation of ovulation time can be calculated. Depending on which model-based indicator was used, ranges of 33 to 35 h and SD of about 11 h were obtained. Because detection of the LH surge was very labor intensive, only a limited number of potential estrus periods could be studied.

Method for short-term prediction of milk yield at the quarter level to improve udder health monitoring.

Udder health problems are often associated with milk losses. These losses are different between quarters, as infected quarters are affected both by systemic and pathogen-specific local effects, whereas noninfected quarters are only subject to systemic effects. To gain insight in these losses and the milk yield dynamics during disease, it is essential to have a reliable reference for quarter-level milk yield in an unperturbed state, mimicking its potential yield. We developed a novel methodology to predict this quarter milk yield per milking session, using an historical data set of 504 lactations collected on a test farm by an automated milking system from DeLaval (Tumba, Sweden). Using a linear mixed model framework in which covariates associated with the linearized Wood model and the milking interval are included, we were able to describe quarter-level yield per milking session with a proportional error below 10%. Applying this model enables us to predict the milk yield of individual quarters 1 to 50 d ahead with a mean prediction error ranging between 8 and 20%, depending on the amount of historical data available to estimate the random effect covariates for the predicted lactation. The developed methodology was illustrated using 2 examples for which quarter-level milk losses are calculated during clinical mastitis. These showed that the quarter-level mixed model allows us to gain insight in quarter lactation dynamics and enables to calculate milk losses in different situations.

Estimation of particle size distributions from bulk scattering spectra: sensitivity to distribution type and spectral noise.

A shape dependent method for particle size distribution (PSD) estimation based on bulk scattering properties was elaborated. This method estimates the parameters of a particle size distribution with predefined shape from the bulk scattering spectra. The estimation routine was validated on simulated data of polystyrene in water suspensions. To investigate the effect of measurement errors on PSD estimates, a sensitivity analysis was performed. The influence of spectral resolution and range was rather limited. Good PSD estimations were obtained on noise-free spectra, spectra with limited random noise and for estimations on µs or µs' in case of a multiplicative baseline. However, the PSD estimation deteriorated if an incorrect value for the refractive index of the particle relative to the medium was used as input parameter. Deviations caused by an incorrect distribution type were smaller for more narrow PSDs than for broader ones. Overall, this study showed the potential to estimate PSDs from bulk scattering spectra and indicated the factors affecting the accuracy.

A novel system for on-farm fertility monitoring based on milk progesterone.

Timely identification of a cow's reproduction status is essential to minimize fertility-related 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 can 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. The objective of this paper is to present a newly developed methodology for detecting luteolysis preceding estrus and give an indication of its on-farm use. The innovative monitoring system presented is based on milk P4 using the principles of synergistic control. Instead of using filtering techniques and fixed thresholds, the present system employs an individually on-line updated model to describe the P4 profile, combined with a statistical process control chart to identify the cow's fertility status. The inputs for the latter are the residuals of the on-line updated model, corrected for the concentration-dependent variability that is typical for milk P4 measurements. To show its possible use, 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 13 cows, no luteolysis was detected by the system within the 25 to 32 d 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, predicting the optimal insemination window, and establishing rules to detect problems based on deviating P4 patterns.

Evolution of the bulk optical properties of bovine muscles during wet aging.

The bulk optical properties (BOP) of two bovine muscles were studied in the 500nm to 1850nm wavelength range. Over a two-week period of wet aging, the BOP of the biceps femoris (BF) and longissimus lumborum (LL) were determined and related to moisture content, tenderness and cooking loss. The absorption by myoglobin and reduced scattering coefficient were higher in the BF compared to the LL. The scattering anisotropy factor was relatively high (>0.95 for LL), representing dominant forward scattering. Two-toning effects in the BF could be attributed to significant scattering differences, as no differences in absorption properties were observed. During wet aging, the anisotropy factor decreased, while tenderness increased. It was hypothesized that this might be related to proteolysis of cytoskeletal proteins. The results show the potential use of BOP to monitor tenderization and the cause of color differences in beef muscles. Moreover, this information could be used to develop and optimize optical sensors for non-destructive meat quality monitoring.

Anisotropic light propagation in bovine muscle tissue depends on the initial fiber orientation, muscle type and wavelength.

The effects of fiber orientation on vis/NIR light propagation were studied in three bovine muscles: biceps brachii, brachialis and soleus. Broadband light was focused onto the sample and the diffuse reflectance spot was captured using a hyperspectral camera (470-1620 nm), after which rhombuses were fitted to equi-intensity points. In samples with fibers running parallel to the measurement surface, the rhombus' major axis was oriented perpendicular to the fiber direction close to the point of illumination. However, at larger distances from the illumination spot, the major axis orientation aligned with the fiber direction. This phenomenon was found to be muscle dependent. Furthermore, the rhombus orientation was highly dependent on the sample positioning underneath the camera, especially when the muscle fibers ran parallel to the measurement surface. The bias parameter, indicating the deviation from a circular shape, was higher for samples with the fibers running parallel to the measurement surface. Moreover, clear effects of wavelength and distance from the illumination point on this parameter were observed. These results show the importance of fiber orientation when considering optical techniques for measurements on anisotropic, fibrous tissues. Moreover, the prediction of muscle fiber orientation seemed feasible, which can be of interest to the meat industry.

Mathematical characterization of the milk progesterone profile as a leg up to individualized monitoring of reproduction status in dairy cows.

Reproductive performance is an important factor affecting the profitability of dairy farms. Optimal fertility results are often confined by the time-consuming nature of classical heat detection, the fact that high-producing dairy cows show estrous symptoms shorter and less clearly, and the occurrence of ovarian problems. Today's commercially available solutions for automatic estrus detection include monitoring of activity, temperature and progesterone. The latter has the advantage that, besides estrus, it also allows to detect pregnancy and ovarian problems. Due to the large variation in progesterone profiles, even between cycles within the same cow, the use of general thresholds is suboptimal. To this end, an intelligent and individual interpretation of the progesterone measurements is required. Therefore, an alternative solution is proposed, which takes individual and complete cycle progesterone profiles into account for reproduction monitoring. In this way, profile characteristics can be translated into specific attentions for the farmers, based on individual rather than general guidelines. To enable the use of the profile and cycle characteristics, an appropriate model to describe the milk progesterone profile was developed. The proposed model describes the basal adrenal progesterone production and the growing and regressing cyclic corpus luteum. To identify the most appropriate way to describe the increasing and decreasing part of each cycle, three mathematical candidate functions were evaluated on the increasing and decreasing parts of the progesterone cycle separately: the Hill function, the logistic growth curve and the Gompertz growth curve. These functions differ in the way they describe the sigmoidal shape of each profile. The increasing and decreasing parts of the P4 cycles were described best by the model based on respectively the Hill and Gompertz function. Combining these two functions, a full mathematical model to characterize the progesterone cycle was obtained. It was shown that this approach retains the flexibility to deal with both varying baseline and luteal progesterone values, as well as prolonged or delayed cycles.