The work represents a successful attempt to combine a gas sensors array with instrumentation (hardware), and machine learning methods as the basis for creating numerical codes (software), together constituting an electronic nose, to correct the classification of the various stages of the wastewater treatment process. To evaluate the multidimensional measurement derived from the gas sensors array, dimensionality reduction was performed using the t-SNE method, which (unlike the commonly used PCA method) preserves the local structure of the data by minimizing the Kullback-Leibler divergence between the two distributions with respect to the location of points on the map. The k-median method was used to evaluate the discretization potential of the collected multidimensional data. It showed that observations from different stages of the wastewater treatment process have varying chemical fingerprints. In the final stage of data analysis, a supervised machine learning method, in the form of a random forest, was used to classify observations based on the measurements from the sensors array. The quality of the resulting model was assessed based on several measures commonly used in classification tasks. All the measures used confirmed that the classification model perfectly assigned classes to the observations from the test set, which also confirmed the absence of model overfitting.

The article presents the potential application of the time domain reflectometry (TDR) technique to measure moisture transport in unsaturated porous materials. The research of the capillary uptake phenomenon in a sample of autoclaved aerated concrete (AAC) was conducted using a TDR sensor with the modified construction for non-invasive testing. In the paper the basic principles of the TDR method as a technique applied in metrology, and its potential for measurement of moisture in porous materials, including soils and porous building materials are presented. The second part of the article presents the experiment of capillary rise process in the AAC sample. Application of the custom sensor required its individual calibration, thus a unique model of regression between the readouts of apparent permittivity of the tested material and its moisture was developed. During the experiment moisture content was monitored in the sample exposed to water influence. Monitoring was conducted using the modified TDR sensor. The process was additionally measured using the standard frequency domain (FD) capacitive sensor in order to compare the readouts with traditional techniques of moisture detection. The uncertainty for testing AAC moisture, was expressed as RMSE (0.013 cm³/cm³) and expanded uncertainty (0.01–0.02 cm³/cm³ depending on moisture) was established along with calibration of the applied sensor. The obtained values are comparable to, or even better than, the features of the traditional invasive sensors utilizing universal calibration models. Both, the TDR and capacitive (FD) sensor enabled monitoring of capillary uptake phenomenon progress. It was noticed that at the end of the experiment the TDR readouts were 4.4% underestimated and the FD readouts were overestimated for 12.6% comparing to the reference gravimetric evaluation.

The aim of this research was to optimize the separation and quantitative determination of nitrites and nitrates in human saliva. HPLC with UV absorption (HPLC/DAD) using a phosphatidylcholine column (IAM.PC.DD2 Regis HPLC) was applied in this assay. Nitrates were detected directly by their absorbance at 210 nm, whereas nitrites were detected after oxidation to nitrates by potassium permanganate at acidic conditions. The kinetics of the permanganate–nitrite reaction was measured chromatographically. The calibration graph for nitrates was linear in the range of 0.5–35 µg mL⁻¹ with a correlation coefficient of 0.9999. The limit of detection was 4.56 ng mL⁻¹. The calibration graph for nitrites (after oxidation to nitrates) was linear in the range of 0.5–15 µg mL⁻¹ with a correlation coefficient of 0.9972. The limit of detection was 4.21 ng mL⁻¹. The nitrate concentrations in the saliva samples were found in the range of 8.98–18.52 μg mL⁻¹, whereas nitrite was in the range of 3.50–5.34 μg mL⁻¹.