The phytochemical profiles, antioxidant capacities, mineral composition, and antibacterial activities of Zingiber officinale (Z. officinal) and Piper nigrum (P. nigrum) were explored through aqueous, ethanolic, and methanolic extractions. The extracts were analyzed for polyphenols, flavonoids, and tannins, and their antioxidant potential was assessed using the DPPH assay. UPLC-HRMS identified major bioactive compounds, including 6-gingerol and shogaol in Z. officinale, and piperine and piperlonguminine in P. nigrum. Mineral analysis showed that P. nigrum was particularly rich in essential elements, including calcium (Ca), magnesium (Mg), and iron (Fe). In antibacterial testing, P. nigrum demonstrated wider zones of inhibition against E. coli, whereas Z. officinale was more active at lower concentrations, showing MICs as low as 3.91 µg/mL against Salmonella and S. aureus. PCA analysis revealed strong correlations between phenolic content and biological effects. These results underscore the potential of both spices as effective natural agents for use in food preservation and health-promoting applications.

Four aromatic and therapeutic plants, Thymus vulgaris, Rosmarinus officinalis, Pimpinella anisum, and Foeniculum vulgare, were examined in this comparative study. The objectives were to assess its phytochemical composition; polyphenol, flavonoid, and tannin content; antioxidant and antibacterial activity; bioactive molecule identification; and critical trace element quantification. Its aqueous and organic extracts were examined, focusing on ethanolic extracts. The ethanolic extract’s ability to neutralize free radicals was validated by phytochemical studies and antioxidant tests, underscoring their role in preventing oxidative stress. An Ultra-Performance Liquid Chromatography—High-Resolution Mass Spectrometry Orbitrap Exploris 120 (UPLC–HRMS Orbitrap) was used to identify the bioactive chemicals, and the results showed a variety of compounds having antibacterial and antioxidant properties. The important trace elements found in these plants were also measured using a Graphite Furnace-Atomic Absorption Spectrometer (GF-AAS). These components are essential to the biological characteristics of the plants, especially their antioxidant and antibacterial capacities. Among the aqueous extracts, it was observed that Rosmarinus officinalis and Foeniculum vulgare exhibited a MIC of 3.91 µg/mL against Staphylococcus. Additionally, R. officinalis also demonstrated a MIC of 3.91 µg/mL against Escherichia coli. All of the data were interpreted and shown using principal component analysis. The results were grouped and explained using this statistical method, which revealed a strong association between the abundance of antibacterial and antioxidant chemicals in the four plants under investigation.

Process modelling is an effective tool for describing and predicting the performance of an aerobic membrane bioreactor (AeMBR) for fish canning wastewater (FCWW) treatment under different operating conditions. Three machine learning (ML) algorithms were developed, random forest (RF), decision tree regressor (DTR) and adaptive boosting regression (AdaBoost-R), based on various physico-chemical characteristics of the influent and operating conditions, including hydraulic retention times (HRT), organic loading rates (OLR), total dissolved solids (TDS), aeration rate and permeate volumetric rates. Predicted values for chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total Kjeldahl nitrogen (TKN), and nitrate (NO3−) are compared with those reported from the experiment. As regards the quantitative assessment of the three predictive models, the DTR model demonstrated a modest determination coefficient (R2) value of 0.654, the AdaBoost-R model achieved 0.739, whereas the RF model showed the highest performance at 0.98. Due to its robustness and accuracy, the RF model was chosen for its superior ability to predict the performance of the AeMBR. Based on OLR of 4.27 (kg COD)∙(m3∙d)−1, a HRT of 24 h, a TDS of 3 g∙dm−3, an aeration rate of 1,300 Ndm3∙h−1 and a permeate volumetric rate of 15 dm3∙h−1, the average effluent characteristics comply with discharge and reuse limits.

This study provides a comprehensive evaluation of the phytochemical composition, antioxidant capacity, antibacterial activity, and mineral content of Trigonella foenum-graecum, Linum usitatissimum, and Panicum miliaceum extracts obtained using aqueous, ethanolic, and methanolic solvents. An integrated analytical strategy combining LC–MS/MS-based metabolite profiling, mineral analysis, and multivariate statistical tools (PCA) was applied to investigate the relationships between chemical composition and biological activities. The ethanolic extract of P. miliaceum showed the highest total phenolic content (TPC: 157.438 ± 0.521 µg GAE/mg extract), whereas L. usitatissimum exhibited the strongest antioxidant activity (IC₅₀ ≈ 65 µg/mL). Trigonella foenum-graecum displayed the most significant antibacterial activity, with a minimum inhibitory concentration (MIC) of 62.5 mg/mL against Staphylococcus aureus. LC–MS/MS analysis allowed the identification and structural characterization of more than twenty bioactive compounds through multiple reaction monitoring (MRM), including flavonoids, phenolic acids, and anthocyanins. Principal Component Analysis indicated that sample discrimination was mainly driven by solvent polarity rather than plant species, underlining the critical influence of extraction conditions on phytochemical profiles and associated bioactivities. These findings highlight the relevance of combining analytical and statistical approaches to better understand the interplay between plant origin, extraction conditions, and biological properties, and support the potential of these species as promising sources of nutraceutical and pharmaceutical compounds.