Objectives Computer-aided design (CAD)/computer-aided manufacturing (CAM) dentures are assumed to have more favourable material properties than conventionally fabricated dentures, among them a lower methacrylate monomer release. The aim of this study was to test this hypothesis. Materials and methods CAD/CAM dentures were generated from ten different master casts by using four different CAD/CAM systems. Conventional, heat-polymerised dentures served as control group. Denture weight and volume were measured; the density was calculated, and the denture surface area was assessed digitally. The monomer release after 7 days of water storage was measured by high-performance liquid chromatography. Results Whole You Nexteeth and Wieland Digital Dentures had significantly lower mean volume and weight than conventional dentures. Baltic Denture System and Whole You Nexteeth had a significantly increased density. Baltic Denture System had a significantly smaller surface area. None of the CAD/CAM dentures released significantly less monomer than the control group. Conclusions All tested dentures released very low amounts of methacrylate monomer, but not significantly less than conventional dentures. A statistically significant difference might nevertheless exist in comparison to other, less recommendable denture base materials, such as the frequently used autopolymerising resins. Clinical relevance CAD/CAM denture fabrication has numerous advantages. It enables the fabrication of dentures with lower resin volume and lower denture weight. Both could increase the patient comfort. Dentures with higher density might exhibit more favourable mechanical properties. The hypothesis that CAD/CAM dentures release less monomer than conventional dentures could, however, not be verified.

The performance of a newly developed pocket-sized near-infrared (NIR) spectrometer was investigated by analysing 46 cheese samples for their water and fat content, and comparing results with a benchtop NIR device. Additionally, the automated data analysis of the pocket-sized spectrometer and its cloud-based data analysis software, designed for laypeople, was put to the test by comparing performances to a highly sophisticated multivariate data analysis software. All developed partial least squares regression (PLS-R) models yield a coefficient of determination (R²) of over 0.9, indicating high correlation between spectra and reference data for both spectrometers and all data analysis routes taken. In general, the analysis of grated cheese yields better results than whole pieces of cheese. Additionally, the ratios of performance to deviation (RPDs) and standard errors of prediction (SEPs) suggest that the performance of the pocket-sized spectrometer is comparable to the benchtop device. Small improvements are observable, when using sophisticated data analysis software, instead of automated tools.

Background: Meat fraud generated a huge outrage amongst customers in 2013 in Europe due to the horsemeat scandal. Portable and hand-held optical near-infrared (NIR, 4,00012,500 cm-1/800-2,500 nm) spectroscopy sensors are traded as promising fast, non-invasive and easy analytical tools that might be applicable at any independent place of inspection. In order to embrace the on-going trend towards instrumental miniaturization, it was the aim of the present feasibility study to evaluate the application of Design of Experiment for frequently applied portable micro-electro-mechanical system (MEMS) based spectrometer by comparing its performance to a bench-top Fourier-Transform polarization near-infrared (FT-NIR) instrument. Methods: 63 samples of different meat types (beef: 9, chicken: 10, mutton: 10, turkey: 10, pork: 10, horse meat: 14) were measured in order to classify the meat-type using a portable micro-electromechanical system (MEMS) based spectrometer and a bench-top Fourier-Transform polarization nearinfrared (FT-NIR) instrument, in order to compare the performance of both systems. In a second step different meat types were minced together in order to investigate the level of adulteration which can be detected using MEMS and FT-NIR. Design of Experiment (DoE) was applied to enhance results. Results: The accuracy of MEMS versus FT-NIR for identifying whole / minced pieces of chicken, pork, turkey, beef and mutton meat (63 samples) against horse meat appeared to be 75.0-100.0% (MEMS) vs. 62.5%-100.0% (FT-NIR) for whole pieces and 75.0-100.0% (MEMS and FT-NIR) for minced meat. When mincing different types of meat together, a maximum of 4 and 1 factors were required for establishing a PLS-R model using again the spectra recorded with MEMS and FT-NIR, respectively. The resulting quality parameters for the MEMS device were: R2=0.06-0.62, Standard Error of Cross Valdiation (SECV)= 17.33-32.91, Ratio of Performance to Deviation (RPD) =0,54-1,70 and for the FTNIR system: R2=0.85-0.94, SECV=7.52-13.83%, RPD=2.2-5.7 (FT-NIR). The limit of detection was found at 10% for the MEMS and at 1% for the FT-NIR device. Conclusion: Meat classification can be performed using the bench-top FT-NIR as well as the hand-held MEMS-NIR. Mincing the meat samples does not necessarily improve classification accuracy as information about the surface structure is lost. NIRS prediction models for adulterations were established for the bench-top system. Prediction models for the hand-held device are inconclusive and have to be improved by a larger sample set and/or further progress in miniaturization technique. Low level adulteration (<10%) may also be predictable with NIRS, but continuative research is necessary.

Hemp and hemp seed press cake—a by-product of hemp oil production—are high-protein, gluten-free raw materials that are often used to enhance the nutritional value of breads. The addition of hemp materials, however, often negatively impacts the technological parameters of breads. Consequently, this study investigated whether and how much the addition of various by-products of hemp seed press cakes to wheat bread mixtures adversely affects the texture and colour profile. The texture profile and colour were determined using a texture analyser and tristimulus measurements. The particle size of raw materials was also measured. Principal component analysis was then used to visualise the correlation between all measured values as well as nutritional parameters. The results showed that the addition of only 1% of some hemp raw materials caused significant technological changes (p > 0.05). Hemp raw materials increased bread hardness and decreased elasticity. The colour of breads containing 1% hemp was also visibly darker than the reference bread. The addition of more hemp led to further darkening and the deterioration of the technological parameters of the products. Consequently, while various hemp materials have high nutritional value, a balance with sensory properties, e.g., textural and colour, has to be reached.