Flourensia cernua is a bush that grows in the semi-desert regions of Mexico. It has been used in traditional medicine due to its healing properties and currently represents an alternative source of bioactive molecules for different areas of the agri-food and health industries. The objective of this study was to extract and characterize the purified polyphenolic compounds (PPCs) and essential oils (EOs) of F. cernua leaves, determine the total flavonoid content, evaluate the antioxidant activity by three different assays, and determine, for the first time, its inhibitory effect against enzymes involved in the degradation of carbohydrates (α-amylase and α-glucosidase). In addition, the analysis of functional groups (by FTIR-ATR assay) and the identification of the chemical constituents present in both essential oils and phenolic compounds were carried out by GC/MS and UPLC-QToF/MS², respectively. The results indicate that PPCs and EOs are rich in flavonoid-type compounds. In addition, they showed potential for free-radical scavenging and the inhibition of the lipid oxidation process. The analyzed EOs and PPCs had potential against α-amylase and α-glucosidase enzymes, which are related to high blood sugar levels. FTIR-ATR analysis allowed for the identification of functional groups characteristic of polyphenolic compounds and the chemical constituents of EOs. Finally, compounds such as caryophyllene, caryophyllene oxide, and germacrene-D were identified by GC/MS assay and luteolin 7-O-rutinoside and apigenin-6-C-glucosyl-8-C-arabinoside by UPLC/QToF-MS². The results indicate that the PPCs and EOs of F. cernua have the potential to be used as antioxidant and enzyme inhibitor agents.

Flourensia cernua and Jatropha dioica are non-timber forest species that grow in the desert regions of northern Mexico. Currently, they are recognized as a viable alternative to traditional remedies used to address certain ailments. However, the low yields of bioactive compounds obtained propitiate the use of more effective extraction techniques such as ultrasound-assisted extraction (UAE) in combination with statistical models such as Taguchi to maximize the yields of bioactive compounds. The objective of the research was to determine the optimal conditions for maximizing the yields of purified polyphenolic compounds from F. cernua and J. dioica using the Taguchi statistical model. In addition, we evaluated the total flavonoid content, antioxidant activity (ABTS•+, FRAP, and OH^• hydroxyl radical inhibition), and the in vitro evaluation of α-amylase inhibition and ex vivo hemolysis inhibition. Furthermore, a functional group analysis was conducted using FTIR. The optimization process revealed that the maximum yields of bioactive compounds for F. cernua and J. dioica were achieved with a particle size of 0.25 mm and 0.30 mm, temperatures of 60 °C for both, a water–ethanol concentration of 50% for both, and extraction times of 30 min and 5 min, respectively. The matrix bioactive compounds obtained contain flavonoid-type phenolic compounds, which exhibit properties such as inhibiting free radicals and acting as reducing agents; inhibiting α-amylase activity, which is involved in carbohydrate metabolism; and protecting red blood cells from damage by reactive oxygen species. Finally, the FTIR analysis allowed for the identification of functional groups associated with the polyphenolic compounds (O–H, C–H, C–C, C=O). These results suggest that both non-timber forest species are a rich source of bioactive compounds with potential for application in the agri-food and pharmaceutical industries However, it is necessary to carry out compound identification as well as toxicity analyses to guarantee the safety of these bioactive compounds.

The objective of this work was to optimize the ultrasound-assisted extraction process of bioactive compounds from byproducts of V. vinifera and C. sinensis using the Taguchi methodology. Moreover, the flavonoid content and the subsequent evaluation of the antioxidant potential through three different assays (ABTS^∙+ radical inhibition, ferric-reducing power (FRAP), and OH^• radical inhibition) were determined. Furthermore, the potential of these metabolites to inhibit the α-amylase enzyme and their protective effect to inhibit the hemolysis due to oxidative processes was assessed. In addition, functional group analysis was performed using Fourier transform infrared spectroscopy. The Taguchi L9 statistical model enabled the increase in bioactive compound yields by evaluating factors such as particle size, temperature, time, and solvent concentration. The samples were found to contain flavonoid-type compounds, which translated into their ability to inhibit free radicals (ABTS^∙+ and OH^•) and act as reducing agents (FRAP). They exhibited inhibitory effects on the α-amylase enzyme involved in the assimilation of starch and its derivatives, along with providing over 50% protection to erythrocytes in the presence of free radicals generated by AAPH. Furthermore, FTIR analysis facilitated the identification of characteristic functional groups of phenolic compounds (O–H, C–H, C=C, C–C, C=O). These findings suggest that the analyzed byproducts can effectively serve as sources of bioactive compounds with potential applications in the formulation of functional foods and medicines. However, it is necessary to conduct compound identification and toxicity analysis to ensure the safety of these bioactive compounds.

Salinity stress remains a major bottleneck for agriculture in arid regions. While Opuntia ficus-indica is known for its resilience, its young cladodes maintain a misleadingly healthy visual appearance and stable biomass even under heavy saline pressure, making traditional vegetation indices and standard statistics unreliable for early diagnosis. The objective of this study was to develop a non-destructive phenotyping framework for the early detection of latent salinity stress in young Opuntia cladodes. Controlled experiments were conducted using hyperspectral data cubes (400–1000 nm) acquired from plants exposed to six distinct salinity levels ranging from 2 to 21 dS m⁻¹. Our methodology integrates these high-dimensional spatial–spectral data with a tailor-made 3D Convolutional Neural Network (3D-CNN). Seven physiological vegetation indices—NDVI, PRI, WI, PSRI, MCARI, SIPI, and NDRE were extracted to track sub-clinical shifts and processed as a volumetric depth dimension within the network to preserve spatial–spectral integrity. The optimized 3D-CNN framework achieved a validation accuracy of 99.7% and a weighted F1-score of 99.1%, delivering 100% precision at critical stress thresholds (13 and 21 dS m⁻¹). Spatial confidence maps (Softmax > 0.95) further confirmed the high reliability of the diagnostic output. Requiring a training duration of approximately 8 s, this framework provides a robust basis for precision early-warning irrigation systems to sustain Opuntia cultivation in challenging environments.