Wavelet Based Feature Extraction in Near Infrared Spectra for Compositional Analysis of Food

Near infrared spectroscopy is a common method for analysis of food, soil and pharmaceutical products. New developments in sensor technology, like hyperspectral camera systems and mobile spectrometers, allow broad applications of spectroscopy with devices out of specialized laboratories. Therefore, it is necessary to develop robust algorithms for classification and regression, regardless of the device. The key to robust analysis lies in data preparation to get standardized spectral information from each device. Wavelet based feature extraction could be a possible method to compress spectral data to its material specific absorption information. A method for wavelet based feature extraction, which also reduces the influence from elastic scattering effects is proposed in this report

A Theoretical Model for Measuring and Sensor Characterization in Optical Spectroscopy

The optical and digital resolution, aswell as the signal-to-noise ratio are important characteristics of optical spectrometers and available in data sheets. But how can an optical spectrometer system be selected for a specific application? The article shall serve as an aid to characterize optical spectrometers and hyperspectral cameras by introducing a benchmark calculation which indicates the measurement uncertainty of absorption bands

Simulation-Based Evaluation of Wavelet Coefficients for Robust Analysis of Near Infrared Spectra

Near infrared spectroscopy is a common method for analysis of food, soil and pharmaceutical products. New developments in sensor technology, like hyperspectral camera systems and mobile spectrometers, allow broad applications of spectroscopy with devices out of specialized laboratories. Wavelet coefficients are a promising approach for the detection and estimation of spectral absorption bands. The robustness of wavelet based features against typical measuring influences and calibration errors will be analyzed in the following by using simulations

Complex temperature dependence of coupling and dissipation of cavity-magnon polaritons from milliKelvin to room temperature

Hybridized magnonic-photonic systems are key components for future information processing technologies such as storage, manipulation or conversion of data both in the classical (mostly at room temperature) and quantum (cryogenic) regime. In this work, we investigate a YIG sphere coupled strongly to a microwave cavity over the full temperature range from $290\,\mathrm{K}$ down to $30\,\mathrm{mK}$. The cavity-magnon polaritons are studied from the classical to the quantum regime where the thermal energy is less than one resonant microwave quanta, i.e. at temperatures below $1\,\mathrm{K}$. We compare the temperature dependence of the coupling strength $g_{\rm{eff}}(T)$, describing the strength of coherent energy exchange between spin ensemble and cavity photon, to the temperature behavior of the saturation magnetization evolution $M_{\rm{s}}(T)$ and find strong deviations at low temperatures. The temperature dependence of magnonic disspation is governed at intermediate temperatures by rare earth impurity scattering leading to a strong peak at $40\,$K. The linewidth $\kappa_{\rm{m}}$ decreases to $1.2\,$MHz at $30\,$mK, making this system suitable as a building block for quantum electrodynamics experiments. We achieve an electromagnonic cooperativity in excess of $20$ over the entire temperature range, with values beyond $100$ in the milliKelvin regime as well as at room temperature. With our measurements, spectroscopy on strongly coupled magnon-photon systems is demonstrated as versatile tool for spin material studies over large temperature ranges. Key parameters are provided in a single measurement, thus simplifying investigations significantly.Comment: 10 pages , 9 figures in tota

Determination of tomato quality attributes using portable NIR-sensors

As part of a research project a multidisciplinary approach of different research institutes is followed to investigate the possibility of using a commercially available miniaturized NIR-sensor for the determination of tomato fruit quality parameters in postharvest. Correlation of spectra and tomato reference values of firmness, dry matter and total soluble solids showed good prediction accuracy. Additionally the decline of firmness over storage time with respect to storage temperature of tomatoes could be modelled. Therefore, the decline of firmness as an indicator for shelf-life can be predicted using this portable NIR-Sensor

Consistent lattice Boltzmann methods for the volume averaged Navier-Stokes equations

We derive a novel lattice Boltzmann scheme, which uses a pressure correction forcing term for approximating the volume averaged Navier-Stokes equations (VANSE) in up to three dimensions. With a new definition of the zeroth moment of the Lattice Boltzmann equation, spatially and temporally varying local volume fractions are taken into account. A Chapman-Enskog analysis, respecting the variations in local volume, formally proves the consistency towards the VANSE limit up to higher order terms. The numerical validation of the scheme via steady state and non-stationary examples approves the second order convergence with respect to velocity and pressure. The here proposed lattice Boltzmann method is the first to correctly recover the pressure with second order for space-time varying volume fractions

Optimization of a Micromixer with Automatic Differentiation

As micromixers offer the cheap and simple mixing of fluids and suspensions, they have become a key device in microfluidics. Their mixing performance can be significantly increased by periodically varying the inlet pressure, which leads to a non-static flow and improved mixing process. In this work, a micromixer with a T-junction and a meandering channel is considered. A periodic pulse function for the inlet pressure is numerically optimized with regard to frequency, amplitude and shape. Thereunto, fluid flow and adsorptive concentration are simulated three-dimensionally with a lattice Boltzmann method (LBM) in OpenLB. Its implementation is then combined with forward automatic differentiation (AD), which allows for the generic application of fast gradient-based optimization schemes. The mixing quality is shown to be increased by 21.4% in comparison to the static, passive regime. Methodically, the results confirm the suitability of the combination of LBM and AD to solve process-scale optimization problems and the improved accuracy of AD over difference quotient approaches in this context

Potential of UV and SWIR hyperspectral imaging for determination of levels of phenolic flavour compounds in peated barley malt

In this study, ultra-violet (UV) and short-wave infra-red (SWIR) hyperspectral imaging (HSI) was used to measure the concentration of phenolic flavour compounds on malted barley that are responsible for smoky aroma of Scotch whisky. UV-HSI is a relatively unexplored technique that has the potential to detect specific absorptions of phenols. SWIR-HSI has proven to detect phenols in previous applications. Support Vector Machine Classification and Regression was applied to classify malts with ten different concentration levels of the compounds of interest, and to estimate the concentration respectively. Results reveal that UV-HSI is at its current development stage unsuitable for this task whereas SWIR-HSI is able to produce robust results with a classification accuracy of 99.8% and a squared correlation coefficient of 0.98 with a Root Mean Squared Error of 0.32 ppm for regression. The results indicate that with further testing and development, HSI may potentially be exploited in an industrial production environment