Wastewater is major source of contaminants originating from the production, usage, and disposal of plastic materials. Due to their poor biodegradability of these contaminants in municipal wastewater treatment plants, additional advanced oxidation processes such as electrochemical treatments have been developed to improve the standard biological treatment. Here we review the applications of electrochemical treatments of wastewater for the removal of the following plastic contaminants: bisphenol A, phthalic acid esters, and benzotriazoles. We present the effectiveness of treatment in terms of contaminant removal and mineralization; the identification of transformation products; toxicity assessment; and process energy requirements. In the present review, we have focused on the applications of electrochemical treatments of wastewater for the removal of three important groups of contaminants originating mainly from plastics: bisphenol A, phthalic acid esters, and benzotriazoles. The review focuses on the research of electrochemical treatments for these contaminants from the last five years. The papers are assessed from the point of i) effectiveness of treatment in terms of contaminant removal and mineralization; ii) identification of transformation products; iii) toxicity assessment; iv) processes’ energy requirements. Electrochemical treatments were confirmed to be a viable option for the removal of selected contaminants from wastewater.

In this study, a method was developed for the determination of five neonicotinoid pesticides (acetamiprid, clothianidin, imidacloprid, thiacloprid, and thiamethoxam) in propolis. Two sample preparation methods were tested: solid-phase extraction and the quick, easy, cheap, effective, rugged, and safe (QuEChERS) method. The identities of analytes were confirmed using liquid chromatography–tandem mass spectrometry (LC-MS/MS) in the selected reaction monitoring mode. Solid-phase extraction resulted in cleaner extracts; therefore, the SPE-LC-MS/MS method was validated according to the SANTE protocol in triplicate at two spiking levels (10 ng/g and 50 ng/g). The average recoveries of analytes ranged from 61% to 101%, except for clothianidin (10–20%). The LOD ranged from 0.2 ng/g to 4.4 ng/g, whereas the LOQ was in the range of 0.8 ng/g–14.7 ng/g. In order to compensate for the matrix effect, matrix-matched calibration was used. Good accuracy (relative error: 1.9–10.4%) and good linearity (R² > 0.991) were obtained for all compounds. The optimised method was applied to 30 samples: 18 raw propolis and 12 ethanol tinctures. Acetamiprid, imidacloprid, and thiacloprid were detectable in seven samples but were still below the LOQ. This study is the first to report the determination of several neonicotinoid residues in propolis.

Non-destructive, fast, and accurate methods of dating are highly desirable for many heritage objects. Here, we present and critically evaluate the use of near-infrared (NIR) spectroscopic data combined with three supervised machine learning methods to predict the publication year of paper books dated between 1851 and 2000. These methods provide different accuracies; however, we demonstrate that the underlying processes refer to common spectral features. Regardless of the machine learning method used, the most informative wavelength ranges can be associated with C-H and O-H stretching first overtone, typical of the cellulose structure, and N-H stretching first overtone from amide/protein structures. We find that the expected influence of degradation on the accuracy of prediction is not meaningful. The variance-bias decomposition of the reducible error reveals some differences among the three machine learning methods. Our results show that two out of the three methods allow predictions of publication dates in the period 1851-2000 from NIR spectroscopic data with an unprecedented accuracy of up to 2 years, better than any other non-destructive method applied to a real heritage collection.

The aim of this study was to evaluate biomarkers for the differentiation and classification of cinnamon species and geographical origin. A reverse phase high performance liquid chromatography method with UV-Vis detection was used to quantify the following biomarkers: coumarin, cinnamic acid, cinnamaldehyde and eugenol. A total of 23 cinnamon samples were analysed and processed using chemometric methods. Besides commonly used principal component analysis, uniform manifold approximation and projection was also used for exploratory analysis. Next, combination of dimensions reduction and classification algorithms were used for the development of 16 classification models. The models were validated using the bootstrapping resampling method. The best classification was achieved with the combination of principal component analysis - regularised discriminant analysis model with an overall accuracy of 84.0% and 89.2% in discriminating cinnamon species and their geographical origin, respectively. In addition, the samples were successfully classified according to their shape and whether the cinnamon samples were certified as “bio” organic.