Recently, there has been a growing interest in protein-rich foods and functional foods, as well as plants characterized by high levels of drought resistance. Vigna unguiculata L. Walp. from the Fabaceae family, also known as cowpea, was chosen as the subject of the present study. The antioxidant potentials of 15 genotypes cultivated in Bulgaria, Southern Europe, were determined. The research focused on the green mass used as feed and on the seeds used as an “ancient” traditional crop. The total phenolic content (TPC), total flavonoid content (TFC), radical scavenging capacity, determined via the DPPH method, and crude protein content were measured. The seed coat extracts showed higher contents of phenolics (291.0 ± 4.6 mgGAE/g) and flavonoids (83.5 ± 1.1 mgCE/g) and a stronger radical scavenging capacity (50.4 ± 0.7 µmolTE/g) than those obtained from the leaves (22.0 ± 0.5 mgGAE/g, 13.7 ± 0.5 mgCE/g, and 19.7 ± 0.6 µmolTE/g, respectively). The protein content in the seeds ranged from 20.5% to 27.0%. According to the obtained results, the cowpea genotypes with dark-colored seed coats showed greater antioxidant potentials but lower protein contents. Due to its high antioxidant content, strong radical scavenging capacity, and high protein content, V. unguiculata L. shows great potential as a functional food for humans and animals.

Galium verum (Yellow Bedstraw) is a rhizomatous perennial herb belonging to the Rubiaceae family. It is native to Eurasia and Africa but has also been introduced to southern Canada and the northern U.S. Widely used in traditional medicine, G. verum has been recognized for its diuretic, anti-inflammatory, antimicrobial, analgesic, and anticancer properties. Phytochemical studies have shown that the plant is rich in significant bioactive compounds, such as flavonoids, phenolic acids, iridoids, anthraquinones, phytosterols, coumarins, and tannins. Research suggests that G. verum exhibits strong antioxidant activity, protecting cells from oxidative stress and inflammation. Its antimicrobial potential has been demonstrated against various bacterial and fungal pathogens, supporting its traditional use in wound healing and infection treatment. Moreover, modern studies indicate its cytotoxic effects on cancer cells, suggesting potential applications in oncology. Additionally, its hepatoprotective and neuroprotective properties highlight its promise for treating metabolic and neurodegenerative disorders. Despite its well-known therapeutic potential, further studies are required to fully clarify its mechanisms of action and ensure its safety for medicinal use. Given the variety of bioactive compounds found in G. verum and their pharmacological benefits, this review emphasizes the importance of this species as a valuable medicinal plant, encouraging further scientific research for its application in pharmacology.

Alkanna tinctoria L. Tausch (“alkanet” or “alkanna”) is a medicinal plant. Extracts from alkanet roots have applications as natural food coloring agents. In addition, they have proven antioxidant effects. Three classical solvents (ethanol and acidified ethanol/water) and four natural eutectic solvents (NADES)—choline chloride/urea; choline chloride/citric acid; choline chloride/lactic acid; and sodium acetate/formic acid—were compared for their effectiveness as “green” solvents for the extraction of the alkanet pigment. Notably, this study is the first to apply choline chloride-based NADESs for alkanet extraction, providing a comprehensive profile of key bioactive compounds and their contributions to antioxidant activity using UV/Vis and FT-IR spectrometry, GC-MS, and HPLC-PDA-MS. Among the classical solvents, 50% acidified ethanol showed the highest extraction capacity, as indicated by its total flavonoid (708 ± 32 mgCE/L) and total phenolic (1318 ± 63 mgGAE/L) content. However, this extract exhibited the highest total alkaloid content (256 ± 15 µg/L) compared to the other classical extraction solvents. Consequently, absolute ethanol was identified as a more suitable alternative. Among the NADES, the sodium acetate/formic acid (1:2 mol/mol, NADES4) extract was the only one to show the presence of alkannins. This extract also contained high levels of phenols (355 ± 21 mg GAE/L) and tannins (163 ± 10 mg CE/L), exhibited strong antioxidant potential (DPPH: 131 µmol TE/g dw, FRAP: 7.49 mg Fe(II)/mg dw), and contained significantly lower alkaloid levels (7.0 ± 0.5 µg/L). Comparative analyses indicated that the sodium acetate/formic acid extract outperformed those prepared with ethanolic solvents and other NADES.

Increasing prices and market demand for organic products are stimulants of organic farming. However, this sector is a challenge for producers and further improvements are still necessary. The present study case was conducted to compare the effects of organic (OF) and conventional (CF) farming on lavender (Lavandula angustifolia Mill.) oil yield, plant pigments and essential oil composition. The study was conducted for two years in the period 2019–2020. Six private farms were included in the experiment with conventional and organic agriculture systems. They are located in Kazanlak Valley, Southern Bulgaria. Organic lavender inflorescences were determined to have chlorophyll a and total chlorophyll within a narrow range between 251.3 and 275.7 μg·g⁻¹ and between 375.5 and 487.0 μg·g⁻¹ compared to conventional ones—between 245.9 and 377.5 μg·g⁻¹ and 385.3 and 595.4 μg·g⁻¹ respectively. However, carotenoids and anthocyanins were in a wide range in organic lavender between 36.9 and 72.2 μg·g⁻¹ and 410 and 1240 µg cyn-3-gly.g⁻¹ compared to conventional ones—between 55.5 and 77.3 μg·g⁻¹ and 200 and 780 µg cyn-3-gly.g⁻¹, respectively, for both studied years. The key constituents in essential oil were linalyl acetate (28.42–38.23%), linalool (20.01–31.04%) and β-caryophyllene (7.95–14.97%). The composition was compared with the parameters set out in the international standard for lavender oil. The influence of the type of agricultural system on essential oil yield and its composition was not found. According to the obtained results, levels of chlorophyll a, chlorophyll b and total chlorophyll were higher in conventional farming than in organic farming for the second year of the study.

This study conducted a systematic analysis and investigation of near-infrared (NIR) spectroscopy for assessing tomato fruits’ quality. This was combined with the integration of multivariate techniques for analysis to improve existing methods for nondestructive quality assessment of tomato fruits. Variations in tomato characteristics were revealed by selecting spectral indices, leading to changes in reflection spectra at different wavelengths. To achieve more accurate predictions, the use of vectors containing various spectral indices became necessary. This study found that predicting key tomato characteristics relies more on the informativeness and predictive ability of vectors derived from spectral indices than on the method used to reduce data volume. Regression models were developed to automate the prediction of six tomato characteristics based on the spectral data. The developed models for vitamin C content, titratable organic acids, and lycopene showed the highest predictive efficiency (over 90%) among the investigated features. These results highlight the potential of regression models to be integrated into automated systems for assessing the quality of tomato fruits, providing sufficiently accurate information to enhance quality control processes.

The degradation of pastures and meadows is a global problem with a wide range of impacts. It affects farmers in different ways, such as decreases in cattle production, milk yield, and forage quality. Still, it also has other side effects, such as loss of biodiversity, loss of resources, etc. In this study, the degradation of a semi-natural pasture near the village of Obichnik, Bulgaria, was evaluated using machine learning algorithms, and an unmanned aerial vehicle (UAV) obtained visual spectrum images. A high-quality (HQ) orthomosaic of the area was created and numerous regions of interest were manually marked for training and validation purposes. Three machine learning algorithms were used—Maximum likelihood, Random trees (RT), and Support Vector Machine (SVM). Furthermore, object-based and pixel-based approaches were utilized. The obtained results indicate that the object-based RT and SVM models provide significantly better accuracy, with their Cohen’s Kappa reaching 0.86 and 0.82, respectively. The performed classification showed that approximately 61% of the investigated pasture area is covered with grass, which indicates light-to-medium degradation.