The implementation of optical sensor technology to monitor the milk quality on dairy farms and milk processing plants would support the early detection of altering production processes. Basic visible ...and near-infrared spectroscopy is already widely used to measure the composition of agricultural and food products. However, to obtain maximal performance, the design of such optical sensors should be optimized with regard to the optical properties of the samples to be measured. Therefore, the aim of this study was to determine the visible and near-infrared bulk absorption coefficient, bulk scattering coefficient, and scattering anisotropy spectra for a diverse set of raw milk samples originating from individual cow milkings, representing the milk variability present on dairy farms. Accordingly, this database of bulk optical properties can be used in future simulation studies to efficiently optimize and validate the design of an optical milk quality sensor. In a next step of the current study, the relation between the obtained bulk optical properties and milk quality properties was analyzed in detail. The bulk absorption coefficient spectra were found to mainly contain information on the water, fat, and casein content, whereas the bulk scattering coefficient spectra were found to be primarily influenced by the quantity and the size of the fat globules. Moreover, a strong positive correlation (r≥0.975) was found between the fat content in raw milk and the measured bulk scattering coefficients in the 1,300 to 1,400nm wavelength range. Relative to the bulk scattering coefficient, the variability on the scattering anisotropy factor was found to be limited. This is because the milk scattering anisotropy is nearly independent of the fat globule and casein micelle quantity, while it is mainly determined by the size of the fat globules. As this study shows high correlations between the sample’s bulk optical properties and the milk composition and fat globule size, a sensor that allows for robust separation between the absorption and scattering properties would enable accurate prediction of the raw milk quality parameters.
The composition of produced milk has great value for the dairy farmer. It determines the economic value of the milk and provides valuable information about the metabolism of the corresponding cow. ...Therefore, online measurement of milk components during milking 2 or more times per day would provide knowledge about the current health and nutritional status of each cow individually. This information provides a solid basis for optimizing cow management. The potential of visible and near-infrared (Vis/NIR) spectroscopy for predicting the fat, crude protein, lactose, and urea content of raw milk online during milking was, therefore, investigated in this study. Two measurement modes (reflectance and transmittance) and different wavelength ranges for Vis/NIR spectroscopy were evaluated and their ability to measure the milk composition online was compared. The Vis/NIR reflectance measurements allowed for very accurate monitoring of the fat and crude protein content in raw milk (R2>0.95), but resulted in poor lactose predictions (R2<0.75). In contrast, Vis/NIR transmittance spectra of the milk samples gave accurate fat and crude protein predictions (R2>0.90) and useful lactose predictions (R2=0.88). Neither Vis/NIR reflectance nor transmittance spectroscopy lead to an acceptable prediction of the milk urea content. Transmittance spectroscopy can thus be used to predict the 3 major milk components, but with lower accuracy for fat and crude protein than the reflectance mode. Moreover, the small sample thickness (1mm) required for NIR transmittance measurement considerably complicates its online use.
Pre-clinical evidence suggests a period early after stroke during which the brain is most receptive to rehabilitation, if it is provided as high-dose motor training.
To evaluate the feasibility of ...repetitive gait training within the first 3 months post-stroke and the effects on gait-specific outcomes.
PubMed, Web of Science, Cochrane Library, Rehab Data and PEDro databases were searched systematically. Randomized controlled trials were included to descriptively analyse the feasibility and quantitatively investigate the effectiveness of repetitive gait training compared with conventional therapy.
Fifteen randomized controlled trials were included. Repetitive training can safely be provided through body weight support and locomotor assistance from therapists or a robotic device. No difference in drop-out rates was reported despite the demanding nature of the intervention. The meta-analysis yielded significant, but small, effects on walking independence and endurance. Training with end-effector robots appears most effective.
Robots enable a substantial, yet feasible, increase in the quantity of walking practice early post-stroke, which might enhance functional recovery. However, the mechanisms underlying these effects remain poorly understood.
In this research, the feasibility of a mobile spectroscopy instrument (Zeiss Corona 45 visnir fibre remote) in the visible (VIS) and near infrared (NIR) wavebands for onsite and online analysis of ...pig manure was investigated. The sensor was calibrated using the one-out cross-validation technique on a set of pig manure samples collected in the spring of 2004 and validated for its ‘true’ prediction accuracy on a set of samples collected in the spring of 2003 from different Flemish farms. Based on the values of coefficient of determination
r
2 and the ratio of standard deviation of validation set to root mean square error of cross-validation, the prediction results were evaluated as excellent for dry matter content, good for organic matter content and total nitrogen and approximate for ammonium nitrogen, phosphorus and magnesium. The calibrations for potassium and calcium were only able to discriminate between high and low values. These are encouraging results to recommend the VIS–NIR spectroscopy instrument for onsite measurement of manure composition. This sensor could promote a better manure management based on onsite or even online analysis during haul-out or soil application stages.
Changes in the drinking behaviour of pigs may indicate health, welfare or productivity problems. Automated monitoring and analysis of drinking behaviour could allow problems to be detected, thus ...improving farm productivity. A high frequency radio frequency identification (HF RFID) system was designed to register the drinking behaviour of individual pigs. HF RFID antennas were placed around four nipple drinkers and connected to a reader via a multiplexer. A total of 55 growing-finishing pigs were fitted with radio frequency identification (RFID) ear tags, one in each ear. RFID-based drinking visits were created from the RFID registrations using a bout criterion and a minimum and maximum duration criterion. The HF RFID system was successfully validated by comparing RFID-based visits with visual observations and flow meter measurements based on visit overlap. Sensitivity was at least 92%, specificity 93%, precision 90% and accuracy 93%. RFID-based drinking duration had a high correlation with observed drinking duration (R 2=0.88) and water usage (R 2=0.71). The number of registrations after applying the visit criteria had an even higher correlation with the same two variables (R 2=0.90 and 0.75, respectively). There was also a correlation between number of RFID visits and number of observed visits (R 2=0.84). The system provides good quality information about the drinking behaviour of individual pigs. As health or other problems affect the pigs' drinking behaviour, analysis of the RFID data could allow problems to be detected and signalled to the farmer. This information can help to improve the productivity and economics of the farm as well as the health and welfare of the pigs.
Analytical methods that are often used for the quantification of progesterone in bovine milk include immunoassays and chromatographic techniques. Depending on the selected method, the main ...disadvantages are the cost, time-to-result, labor intensity and usability as an automated at-line device. This paper reports for the first time on a robust and practical method to quantify small molecules, such as progesterone, in complex biological samples using an automated fiber optic surface plasmon resonance (FO-SPR) biosensor. A FO-SPR competitive inhibition assay was developed to determine biologically relevant concentrations of progesterone in bovine milk (1–10 ng/mL), after optimizing the immobilization of progesterone-bovine serum albumin (P4-BSA) conjugate, the specific detection with anti-progesterone antibody and the signal amplification with goat anti-mouse gold nanoparticles (GAM-Au NPs). The progesterone was detected in a bovine milk sample with minimal sample preparation, namely ½ dilution of the sample. Furthermore, the developed bioassay was benchmarked against a commercially available ELISA, showing excellent agreement (R2 = 0.95). Therefore, it is concluded that the automated FO-SPR platform can combine the advantages of the different existing methods for quantification of progesterone: sensitivity, accuracy, cost, time-to-result and ease-of-use.
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•FO-SPR based competitive inhibition assay for detecting progesterone is described.•A limit of detection of 0.5 ng mL−1 was achieved in 2-fold diluted bovine milk.•Capacity for reducing the time-to-result was proven.•The assay was evaluated with raw milk samples, showing excellent correlation with ELISA.
As lameness is a major health problem in dairy herds, a lot of attention goes to the development of automated lameness-detection systems. Few systems have made it to the market, as most are currently ...still in development. To get these systems ready for practice, developers need to define which system characteristics are important for the farmers as end users. In this study, farmers' preferences for the different characteristics of proposed lameness-detection systems were investigated. In addition, the influence of sociodemographic and farm characteristics on farmers' preferences was assessed. The third aim was to find out if preferences change after the farmer receives extra information on lameness and its consequences. Therefore, a discrete choice experiment was designed with 3 alternative lameness-detection systems: a system attached to the cow, a walkover system, and a camera system. Each system was defined by 4 characteristics: the percentage missed lame cows, the percentage false alarms, the system cost, and the ability to indicate which leg is lame. The choice experiment was embedded in an online survey. After answering general questions and choosing their preferred option in 4 choice sets, extra information on lameness was provided. Consecutively, farmers were shown a second block of 4 choice sets. Results from 135 responses showed that farmers' preferences were influenced by the 4 system characteristics. The importance a farmer attaches to lameness, the interval between calving and first insemination, and the presence of an estrus-detection system contributed significantly to the value a farmer attaches to lameness-detection systems. Farmers who already use an estrus detection system were more willing to use automatic detection systems instead of visual lameness detection. Similarly, farmers who achieve shorter intervals between calving and first insemination and farmers who find lameness highly important had a higher tendency to choose for automatic lameness detection. A sensor attached to the cow was preferred, followed by a walkover system and a camera system. In general, visual lameness detection was preferred over automatic detection systems, but this preference changed after informing farmers about the consequences of lameness. To conclude, the system cost and performance were important features, but dairy farmers should be sensitized on the consequences of lameness and its effect on farm profitability.
•A method to measure the colour in an objective and contactless way is presented.•Two methods to calculate colour information from reflectance spectra are compared.•Accurate results were achieved to ...measure the colour of vine tomatoes.•An R2 over 0.9 was achieved to predict a* and hue.•Visualization of the colour variability of individual tomatoes is made possible.
As consumers buy with their eyes, colour is considered one of the most important quality parameters of food products. Traditionally, this is defined by human inspection, or measured using a colorimeter or a spectrophotometer. As the first is subjective and prone to factors like fatigue, this is not ideal for industrial use. The second only measures a small area of the food product, making it difficult to get a clear overview of the colour of the whole sample. To overcome these limitations, hyperspectral imaging has been used in this research to measure the postharvest colour of vine tomatoes. Two methods to calculate the colour based on hyperspectral images are compared. The first is the use of a direct method to calculate the colour from the spectra in terms of CIELab-values, while the second method is a soft modelling approach involving multivariate statistics. The soft modelling method was found to achieve the best results (R2L*=0.86; R2a*=0.93; R2b*=0.42, R2Hue=0.95, R2Chroma=0.51), but its applicability is limited to the range of products on which the models have been trained. The direct method is more generally applicable, but was found to lack robustness against intensity variations due to the curvature and glossiness of the tomatoes.
Site-specific nitrogen management has been proposed as a tool to increase crop yield while decreasing nutrient losses to the environment. Many reports can be found on sensing technologies to quantify ...the variability within a field and the definition of management zones based on the observed variability. However, fewer studies have been dedicated to the selection of the most suitable N fertilizer management scenario: should more or less nutrients be applied in the zones with a lower crop productivity potential? To address this knowledge gap, nine Flemish maize fields were selected as potential candidates for precision fertilization based on the soil maps and historical vegetation index patterns. Within each field, two management zones were identified based on historical vegetation index patterns and electrical conductivity maps, and different fertilization strategies were tested in each zone. The field trial results in terms of yield and soil residual nitrate showed that site-specific N management outperforms the conventional practice only in the fields with temporally stable management zones. In the fields having differences in the physical soil properties (e.g. presence of stones or clay particles), affecting water availability, lower fertilization in zones with a poor soil productivity potential could be recommended. In the fields where the performance of the management zones changes from year to year mainly due to annual variation in precipitation, a risk of incorrect implementation of the precision fertilization concept was identified. Historical NDVI time series serve a good basis to delineate the temporally stable management zones.
Controllers working in uncertain environments are often required to adapt themselves continuously to changing conditions to avoid steady-state errors, oscillations at the output or even instability ...of the closed loop system. The moving horizon estimation (MHE)–nonlinear model predictive control (NMPC) framework being proposed combines these two optimization-based methods to control field vehicles utilizing an adaptive nonlinear kinematic model. The full system state, including two unknown slip parameters and the unmeasurable vehicle orientation, is estimated by the MHE after each new measurement and fed afterwards to the NMPC routine which provides a wheel velocity and a steering rate to follow arbitrary time-based reference trajectories in difficult environmental conditions. This control problem occurs in modern agriculture e.g. in planting or mechanical weeding while slippery conditions make these operation difficult and off-track navigation results in plant damage. The experimental results show accurate reference tracking performance of the MHE–NMPC framework on a wet and bumpy grass field. The feedback times lie in the range of 0.6–1.6ms when the ACADO Code Generation tool is used, which is part of the open-source software toolkit ACADO.
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