Analytica

In recent years, research and development in the field of green extraction of bioactive compounds from plants has intensified. This increased focus is driven by market trends, environmental concerns, and consumers’ growing interest in natural and healthy ingredients, as well as bioactive compounds. This development aligns with a global trend toward more sustainable use of natural resources. In this context, macroalgae have been recognized as valuable sources of bioactive compounds with various health benefits. These molecules include proteins, fatty acids, vitamins, and pigments. Phycobiliproteins (PBPs) are pigments and metabolites of particular interest that can be extracted from microalgae. This group of colored proteins, mainly present in cyanobacteria and red algae, is known to have a wide range of potential applications. However, conventional methods for extracting PBPs, such as homogenization, maceration, and freezing, are time-consuming and energy-intensive, often producing unsatisfactory yields. As a result, new extraction technologies have been developed, including ultrasound-assisted extraction, ionic liquid extraction methods, and the use of natural deep eutectic solvents. This review summarizes existing green processes for extracting and purifying PBPs, with the aim of enabling feasible and sustainable valorization of algae. Specifically, it covers various extraction and purification techniques of PBPs, as well as the effects of environmental growth conditions on the production of these metabolites. It also highlights the biological and pharmacological activities of PBPs and explores their potential applications in the food, cosmetic, and biomedical sectors.

Wine phenolics serve as robust chemical signatures correlated to grape variety, processing, and regional identity. This study explores the potential of machine learning algorithms, combined with the phenolic profiles of Albanian wines, to classify them according to grape variety. Geographic origin analysis was conducted as a preliminary exploration. The dataset of phenolic compounds included white and red wines, spanning the 2017 to 2021 vintages. Using five supervised algorithms—Support Vector Machine (SVM), Random Forest, XGBoost, Logistic Regression, and K-Nearest Neighbors—a high classification accuracy was achieved, with SVM reaching 100% under Leave-One-Out Cross-Validation (LOOCV). To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) and stratified cross-validation were applied. Random Forest feature importance consistently highlighted trans-Fertaric acid and Procyanidin B3 as dominant discriminants. Parallel coordinates plots demonstrated clear varietal patterns driven by phenolic differences, while PCA and hierarchical clustering confirmed unsupervised grouping consistent with wine type and maceration level. Permutation testing (1000 iterations) confirmed the non-randomness of model performance. These findings show that a small set of phenolic markers can offer high classification accuracy, supporting chemically based wine authentication. Although the dataset is relatively small, thorough cross-validation, non-redundant modeling, and chemical interpretability provide a solid foundation for scalable methods. Future work will expand the dataset and explore sensor-based phenolic measurement to enable rapid authentication in wine.

This study was designed for the analytical method development and validation for Calcium butyrate (CAB) using High-Performance Liquid Chromatography (HPLC). ¹H and ¹³C Nuclear Magnetic Resonance (NMR) and Fourier Transform Infrared (FTIR) spectroscopy analysis were carried out to examine the molecular structure. For HPLC analysis, a blend of acetonitrile and phosphoric acid solution (0.1%) (20:80) (v/v) was used as a mobile phase. A C18 column (5 µm, 250 × 4.6 mm) was used as a stationary phase. The eluent of CAB was monitored with a UV detector at a wavelength of 206 nm and a flow rate of 1 mL/min. According to the results, FTIR and NMR spectra were consistent with the structural characteristics of CAB, and the expected proton and carbon signals were observed. During the HPLC analysis, due to the ionization of CAB, three distinct retention times were observed in the chromatograms at 3.4, 4.5, and 7.4 min. The validation was performed according to ICH guidelines. The obtained results demonstrated that the analytical method was successfully validated, with LOD values of 1.211, 0.606, and 1.816 µg/mL, and LOQ values of 3.670, 1.835, and 3.676 µg/mL. The assay displayed a linear range of 5–1000 µg/L concentration and was found suitable for further formulation content analysis.