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.
A dual-laser Raman spectrometer was developed, in which the two lasers image two different parts of the spectrum alternately on the same spectrograph. This solves the resolution/bandwidth trade-off problem of CCD Raman spectrometers, previously solved with a dual-grating technique. The dual-laser technique has, however, an additional advantage of alleviating the contradiction between sample fluorescence and detector response, which is illustrated here by applying the technique to the measurement of paper coating paste.
Efficient recovery of minerals from bedrock ore requires precise knowledge of the mineral levels during enrichment processes. Froth flotation is a commonly used method for efficient separation of different minerals from finely grinded sulfide ore. The mineral level information is an important tool for the optimization of flotation process parameters as it is uneconomical to make mineral products with unacceptably low concentrations and to lose a share of the valuable minerals of the ore to the tailings. Online mineral control is commonly executed with X‐ray fluorescence detection or laser‐induced breakdown spectroscopy, which detect the elements of the minerals during flotation. Unfortunately, in case of sulfide ores, the method suffers from inadequate detection of lightweight materials such as sulfur and the common nature of iron, as iron is constantly present in several different minerals found in sulfide ores. Raman spectroscopy can be used to detect the minerals instead of mere elements of ore. This paper presents the study of Raman spectroscopy for online detection of enriched sulfide ore minerals from froth flotation. The Raman instrument with a custom‐made probe connector allowed for the recording of good quality Raman spectra during froth flotation and for the identification and analysis of the valuable minerals levels. The comparison of Raman analysis to online X‐ray fluorescence and offline mineral liberation analysis show that Raman spectroscopy is a suitable method for the online analysis of sulfide ores.
The alignment of optical components is one of the fundamental tasks faced in the optical manufacturing industry. If the task is extended to miniaturized aspheric glass lenses, or multi-lens systems, the complexity is further increased. Wavefront sensors are commonly used in the precision alignment of optical components, and they provide an elegant and efficient tool for systems with high complexity. We have implemented an alignment simulation environment which is based on Zemax optics studio (ZOS) and related application interface (API). This tool is used to create a set of linear equations or a neural network which are used to solve the relation between wavefront and optical element alignment.In our first alignment approach, the optical system description is linearized by simulating a set of linear dependencies connecting the known perturbation values and corresponding aberrations in the resulting wavefront described as a set of Zernike coefficients. Based on the simulated perturbation and the resulting Zernike coefficients, the alignment correction action can be determined by solving the resulting set of linear equations in MATLAB. To get more equations to describe shallow dependencies on the on-axis solution, we added options for additional fields and an iterative solution which mimics the real alignment system by minimizing the perturbations through successive corrections in lens position estimation. As a non-linear alternative to computationally expensive equation solving approach, we have also evaluated AI-based neural networks for which the training data was automatically generated in the optical simulator via the ZOS-API. In preliminary evaluations the neural network approach has shown promising performance when compared to the approach with linearized equations.
The dependent scattering problem in dense attering media has been studied theoretically and experimentally for concentraLions up to 50%, and for spherical scatters having the values ofka in the Mie scattering range. The agreement between theory and experiment is good.
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