To evaluate the suitability of an analytical instrument, essential figures of merit such as the limit of detection (LOD) and the limit of quantification (LOQ) can be employed. However, as the definitions k nown in the literature are mostly applicable to one signal per sample, estimating the LOD for substances with instruments yielding multidimensional results like electronic noses (eNoses) is still challenging. In this paper, we will compare and present different approaches to estimate the LOD for eNoses by employing commonly used multivariate data analysis and regression techniques, including principal component analysis (PCA), principal component regression (PCR), as well as partial least squares regression (PLSR). These methods could subsequently be used to assess the suitability of eNoses to help control and steer processes where volatiles are key process parameters. As a use case, we determined the LODs for key compounds involved in beer maturation, namely acetaldehyde, diacetyl, dimethyl sulfide, ethyl acetate, isobutanol, and 2-phenylethanol, and discussed the suitability of our eNose for that dertermination process. The results of the methods performed demonstrated differences of up to a factor of eight. For diacetyl, the LOD and the LOQ were sufficiently low to suggest potential for monitoring via eNose.

In this contribution, the effect of the presence of a presumed inert gas like ${\mathrm{N}}_2$ in the feed gas on the biological methanation of hydrogen and carbon dioxide with Methanothermobacter marburgensis was investigated. ${\mathrm{N}}_2$ can be found as a component besides ${\mathrm{CO}}_2$ in possible feed gases like mine gas, weak gas, or steel mill gas. To determine whether there is an effect on the biological methanation of ${\mathrm{CO}}_2$ and ${\mathrm{H}}_2$ from renewable sources or not, the process was investigated using feed gases containing ${\mathrm{CO}}_2$ , ${\mathrm{H}}_2$ , and ${\mathrm{N}}_2$ in different ratios, depending on the ${\mathrm{CO}}_2$ content. A possible effect can be a lowered conversion rate of ${\mathrm{CO}}_2$ and ${\mathrm{H}}_2$ to ${\mathrm{CH}}_4$ . Feed gases containing up to 47% ${\mathrm{N}}_2$ were investigated. The conversion of hydrogen and carbon dioxide was possible with a conversion rate of up to 91% but was limited by the amount of ${\mathrm{H}}_2$ when feeding a stoichiometric ratio of 4:1 and not by adding ${\mathrm{N}}_2$ to the feed gas.

Thermal preservation is widely used for the stabilization of fruit and vegetable juices. However, this method can potentially impair product quality and it is necessary to categorize and quantify these changes in order to optimize the treatment process. Here, carrot juice was treated with different time temperature combinations selected to achieve equivalent microbiological lethality. Different temperatures were selected - low temperature long time (LTLT) with 90 and 95 °C and high temperature short time (HTST) with 120 and 125 °C. The thermally treated juice exhibited significant differences in physical, chemical and sensory parameters in contrast to untreated juice. HTST treatment had less of a negative effect on the juice. A significant deterioration in odor, appearance and taste was observed after the LTLT treatment compared to an untreated reference juice. The juice exhibited elevated acid taste, a shift in olfactory profile from white to red vegetables and diminished homogeneity. Significant changes in sugar composition, pH, carotenoids and color were also observed during this treatment. The juice that had been treated with LTLT exhibited a higher amount of monosaccharides, a lower pH value, a reduced quantity of carotenoids and a perceptible difference in color in comparison to the untreated and fresh juice. Therefore, it can be concluded that varying temperatures show different effects on juice quality, despite the same microbiological lethal effect. This must be taken into account when designing the pasteurization process.

With the implementation of EU Directive 2019/904, which mandates a minimum of 25% recycled polyethylene terephthalate (rPET) in single-use beverage bottles by 2025, there is an urgent need for reliable analytical methods to verify regulatory compliance. This study evaluates a data-driven approach to quantify and classify recycled PET based on near infrared (NIR) and mid infrared (MIR) spectral data.Investigations were conducted on two sample systems: industrial preforms containing various recyclate types and electrospun nanofiber tissues with controlled rPET concentrations (0–100%). Data analysis utilized Random Forest models for classification and Lasso regression for quantitative prediction.The results demonstrate that NIRS data allows for robust classification of recyclate types in preforms, achieving precision and recall values exceeding 0.8. For quantitative determination in the fiber samples, the multimodal data fusion of NIRS and MIRS spectra yielded the highest accuracy, with a coefficient of determination of R2 = 0.93 and a Root Mean Square Error of Prediction (RMSEP) of 8.24%. However, leave-one-out validation indicated a decrease in performance for unknown material compositions, where the average prediction error rose to 21.97%. Overall, these findings highlight the potential of spectroscopic techniques for monitoring recycled content within a data-driven framework. To ensure practical application in heterogeneous material streams, future research must focus on improving model generalization through larger, more diverse calibration datasets and advanced transfer learning to bridge the gap between controlled laboratory settings and variable industrial waste.