The correct identification and authentication of medicinal plants material is a crucial task that ensures quality and prevent adulteration. The use of UV–Vis spectroscopy with principal component analysis (PCA) and discriminant analysis (DA) was proposed for identification/authentication of plant material form different genus and different geographical areas provenience. Hydroalcoholic extracts of samples from twelve genus collected from seven countries (Romania, North Macedonia, Germany, Italy, Serbia, Russia and Kazakhstan) were used. The UV–Vis spectra of the extracts were acquired in the 200–800 nm spectral range, and signal smoothing was used for pre-processing the spectral data. Hierarchical clustering analysis (HCA) with 1-Pearson r distance measurement was used to classify the samples based on the original spectra and different-order derivative spectra, respectively. Data from original spectra and from different-order derivative spectra were evaluated by PCA method. Using the PCA with varimax rotation approach, the spectral ranges with significant contribution for samples classification were revealed for the first time. When the PCA method coupled with DA was applied to the data obtained from the original spectra and the fourth-order derivative spectra, the samples were correctly classified to the respective groups with a 98.04% accuracy. The proposed method can be a useful tool for rapid authentication of plant material derived from different countries.

In the current study, multiwavelength detection combined with color scales HPTLC fingerprinting procedure and chemometric approach were applied for direct clustering of a set of medicinal plants with different geographical growing areas. The fingerprints profiles of the hydroalcoholic extracts obtained after single and double development and detection under 254 nm and 365 nm, before and after selective spraying with specific derivatization reagents were evaluated by chemometric approaches. Principal component analysis (PCA) with factor analysis (FA) methods were used to reveal the contribution of red (R), green (G), blue (B) and, respectively, gray (K) color scale fingerprints to HPTLC classification of the analyzed samples. Hierarchical cluster analysis (HCA) was used to classify the medicinal plants based on measure of similarity of color scale fingerprint patterns. The 1-Pearson distance measurement with Ward’s amalgamation procedure proved to be the most convenient approach for the correct clustering of samples. Data from color scale fingerprints obtained for double development procedure and multiple visualization modes combined with appropriate chemometric methods proved to detect the similar medicinal plant extracts even though they are from different geographical regions, have different storage conditions and no specific markers are individually extracted. This approach could be proposed as a promising tool for authentication and identification studies of plant materials based on HPTLC fingerprinting analysis.

Lipophilicity, a significant physicochemical parameter of bioactive molecules, along with absorption, distribution, metabolism, excretion parameters and toxicity risk, was investigated for 32 thiazolo[3,2-b][1,2,4]triazole and imidazo[2,1-b][1,3,4]thiadiazole derivatives with anti-inflammatory potential. The experimental lipophilicity study was carried out by reversed-phase thin-layer chromatography in a binary isopropanol-water mobile phase, and the obtained results were compared with the theoretical lipophilicity parameters estimated by various computational methods. Strong correlations were found between the experimental retention factors and calculated partition coefficients. A modified Petra/Osiris/Molinspiration analysis was performed on the previously synthesized compounds, using SwissADME, Osiris and Molinspiration web tools. The predicted in silico parameters highlighted the most promising compounds as potential drug candidates. The compounds showed good gastrointestinal absorption, moderate activity according to the bioactivity score (values situated between −1.25 and −0.06), and a safe toxicity profile. The results obtained in this study will contribute to lipophilicity studies and other future studies focused on modulating new drug candidates starting from thiazolo[3,2-b][1,2,4]triazole and imidazo[2,1-b][1,3,4]thiadiazole derivatives, which are important heterocycles in medicinal chemistry.

Nitrite content was determined in fresh meat of chicken and pork and in different processed meat products such as sausages, salami, ham and pate with declared and not declared nitrite addition. The spectrophotometric method based on diazo-coupling reaction was used. Under the optimum working conditions, the method showed a limit of detection of 0.4 mg kg-1 and a limit of quantification of 1.2 mg kg-1. The method proved to be accurate with nitrite recovery of 98±14% from spiked samples. In the analyzed samples the nitrite content was in the range 1.1±0.1 to 26.4±2.5 mg Kg-1, values that are below the maximum admitted level of nitrite in meat products (150 mg Kg-1). The lowest nitrite concentration was found in chicken meat products (between 1.1±0.2 and 1.8±0.1 mg kg-1) while in salami, ham and sausages products the content was high (26.4±2.5; 19.0±0.7 and 13.4±1.1 respectively). The health risk exposure parameters were between 4% and 94 % and between 2% and 50% from established Acceptable Daily Intake (ADI of 0.07 mg/kg body weight/day) in case of adults and children respectively. Higher values correspond to pork meat products as salami, sausages and ham. The risk exposure to nitrite estimated for occasional consumption of meat products proved to be much more informative than the nitrite concentration. For these products, consumption should be limited to 150-300 g per week. Non-carcinogenic health effects, evaluated based on Target Hazard Quotient (THQ) were revealed for the investigated meat products by occasional consuming of 3 serving/week.

The ultraviolet-visible (UV-Vis) spectroscopy coupled with cluster analysis (CA) was evaluated for the classification of some medicinal plants of different geographical growing area. To have a deeper view, the experiment was carried out on herbs belonging to different families. The UV-Vis spectra of hydroalcoholic extracts were acquired in the range of 200-800 nm. The hierarchical clustering analysis (HCA) was applied to the data matrix provided by unprocessed, normalized and standardized spectra respectively. Different types of distance measuring of (dis)similarity between the samples as well as different kinds of linkage or amalgamation rule were taken into account. The best results for the classification of the selected medicinal plants were obtained using Ward’s method as the amalgamation rule combined with 1-Pearson r clustering distance measurement. The obtained results reveal the ability of HCA with Ward and 1-Pearson r algorithm to identify plant species even when the raw material has different provenience areas and different pedoclimatic growing conditions. In addition, this methodology revealed a direct link between herbs from different families.