On-farm NIR sensor for milk analysis: Exploitation of bias monitoring and bias correction.

Long-term studies have shown a bias drift over time in the prediction performance of near-infrared spectroscopy measurement systems. This bias drift generally requires extra laboratory reference measurements to detect and correct for this bias. Since these reference measurements are expensive and time consuming, there is a need for advanced methodologies for bias drift monitoring and correction without the need for taking extra samples. In this study, we propose and validate a method to monitor the bias drift and two methods to tackle it. The first method requires no extra measurements and uses a modified version of Partial Least Squares Regression to estimate and correct the bias. This method is based on the assumption that the mean concentration of the predicted component remains constant over time. The second method uses regular bulk milk measurements as a reference for bias correction. This method compares the measured concentrations of the bulk milk to the volume-weighted average concentrations of individual milk samples predicted by the sensor. Any difference between the actual and calculated bulk milk composition is then used to perform a bias correction on the predictions by the sensor system. The effectiveness of these methods to improve the component prediction was evaluated on data originating from a custom-built sensor that automatically measures the NIR reflectance and transmittance spectra of raw milk on the farm. We evaluate the practical use case where models for predicting the milk composition are trained upon installation of the sensor at the farm, and later used to predict the composition of subsequent samples over a period of more than 6 months. The effectiveness of the fully unsupervised method was confirmed when the mean concentration of the milk samples remained constant, while the effectiveness reduced when this was not the case. The bulk milk correction method was effective when all relevant samples for the component were measured by the sensor and included in the analyzed bulk milk, but is less effective when samples included in the bulk which are not measured by the sensor system. When the necessary conditions are met, these methods can be used to extend the lifetime of deployed prediction models by significantly reducing the bias on the predicted values.

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.

Gadolinium Complexes of Highly Rigid, Open-Chain Ligands Containing a Cyclobutane Ring in the Backbone: Decreasing Ligand Denticity Might Enhance Kinetic Inertness.

In an effort to explore novel ligand scaffolds for stable and inert lanthanide complexation in magnetic resonance imaging contrast agent research, three chiral ligands containing a highly rigid (1S,2S)-1,2-cyclobutanediamine spacer and different number of acetate and picolinate groups were efficiently synthesized. Potentiometric studies show comparable thermodynamic stability for the Gd3+ complexes formed with either the octadentate (L3)4- bearing two acetate or two picolinate groups or the heptadentate (L2)4- analogue bearing one picolinate and three acetate groups (log KGdL = 17.41 and 18.00 for [Gd(L2)]- and [Gd(L3)]-, respectively). In contrast, their dissociation kinetics is revealed to be very different: the monohydrated [Gd(L3)]- is considerably more labile, as a result of the significant kinetic activity of the protonated picolinate function, as compared to the bishydrated [Gd(L2)]-. This constitutes an uncommon example in which lowering ligand denticity results in a remarkable increase in kinetic inertness. Another interesting observation is that the rigid ligand backbone induces an unusually strong contribution of the spontaneous dissociation to the overall decomplexation process. Thanks to the presence of two inner-sphere water molecules, [Gd(L2)]- is endowed with high relaxivity (r1 = 7.9 mM-1 s-1 at 20 MHz, 25 °C), which is retained in the presence of large excess of endogenous anions, excluding ternary complex formation. The water exchange rate is similar for [Gd(L3)]- and [Gd(L2)]-, while it is 1 order of magnitude higher for the trishydrated tetraacetate analogue [Gd(L1)]- (kex298 = 8.1, 10, and 127 × 106 s-1, respectively). A structural analysis via density functional theory calculations suggests that the large bite angle imposed by the rigid (1S,2S)-1,2-cyclobutanediamine spacer could allow the design of ligands based on this scaffold with suitable properties for the coordination of larger metal ions with biomedical applications.

P-Ergonomics Platform: Toward Precise, Pervasive, and Personalized Ergonomics using Wearable Sensors and Edge Computing.

Preventive healthcare has attracted much attention recently. Improving people's lifestyles and promoting a healthy diet and wellbeing are important, but the importance of work-related diseases should not be undermined. Musculoskeletal disorders (MSDs) are among the most common work-related health problems. Ergonomists already assess MSD risk factors and suggest changes in workplaces. However, existing methods are mainly based on visual observations, which have a relatively low reliability and cover only part of the workday. These suggestions concern the overall workplace and the organization of work, but rarely includes individuals' work techniques. In this work, we propose a precise and pervasive ergonomic platform for continuous risk assessment. The system collects data from wearable sensors, which are synchronized and processed by a mobile computing layer, from which exposure statistics and risk assessments may be drawn, and finally, are stored at the server layer for further analyses at both individual and group levels. The platform also enables continuous feedback to the worker to support behavioral changes. The deployed cloud platform in Amazon Web Services instances showed sufficient system flexibility to affordably fulfill requirements of small to medium enterprises, while it is expandable for larger corporations. The system usability scale of 76.6 indicates an acceptable grade of usability.

Quantitative spectroscopy analysis of prokaryotic cells: vegetative cells and spores.

Multiwavelength ultraviolet/visible (UV-Vis) spectra of microorganisms and cell suspensions contain quantitative information on properties such as number, size, shape, chemical composition, and internal structure of the suspended particles. These properties are essential for the identification and classification of microorganisms and cells. The complexity of microorganisms in terms of their chemical composition and internal structure make the interpretation of their spectral signature a difficult task. In this paper, a model is proposed for the quantitative interpretation of spectral patterns resulting from transmission measurements of prokaryotic microorganism suspensions. It is also demonstrated that different organisms give rise to spectral differences that may be used for their identification and classification. The proposed interpretation model is based on light scattering theory, spectral deconvolution techniques, and on the approximation of the frequency dependent optical properties of the basic constituents of living organisms. The quantitative deconvolution in terms of the interpretation model yields critical information necessary for the detection and identification of microorganisms, such as size, dry mass, dipicolinic acid concentration, nucleotide concentration, and an average representation of the internal scattering elements of the organisms. E. coli, P. agglomerans, B. subtilis spores, and vegetative cells and spores of Bacillus globigii are used as case studies. It is concluded that spectroscopy techniques coupled with effective interpretation models are applicable to a wide range of cell types found in diverse environments.

Application of wavelet transforms and an approximate deconvolution method for the resolution of noisy overlapped peaks in DNA capillary electrophoresis.

A new procedure for resolving noisy overlapped peaks in DNA separations by capillary electrophoresis (CE) is developed. The procedure combines both a wavelet-based denoising method that effectively denoises the signal and a novel approximate deconvolution technique that resolves the fragment peaks and improves the ability to separate highly overlapped peaks early in the electrophoresis process. Different kinds of overlapped peaks with and without noise simulated by computer as well as some DNA experimental electropherograms were submitted to the new procedure. A second order differential operator with variable coefficients is applied to the entire electrophoresis signal at any given time and approximate deconvolutions of the individual Gaussian peaks are performed. The operator incorporates the effect of the superposition and gives exact annihilation in the neighborhood of each peak. Overlapped peaks with a resolution higher than 0.46 can be resolved directly. Also, the method can determine the peak components of signals with a signal to noise ratio higher than 1.4

Liquid core waveguide for full imaging of electrophoretic separations.

We employed a liquid core waveguide to image both DNA electrophoresis separations and isoelectric focusing of proteins. The utility of the system is demonstrated for DNA fragment sizing and protein separations. The system utilizes the liquid-core waveguide as an efficient window for both the excitation of separated samples and the collection of light through total internal reflectance, with an ability to detect target molecules in the zeptomolar range. Scanning the excitation laser along the length of the electrophoresis capillary excites individually separated analyte bands, while the fluorescence is collected end-on by an optical fiber coupled to a photomultiplier, thus, creating an image of the separation along the length of the capillary.