Marilu Mestanza

Cryopreservation of ovine semen is associated with oxidative stress and structural sperm damage, which may compromise post-thaw sperm quality. Natural antioxidants derived from agro-industrial by-products have emerged as potential alternatives to reduce cryo-induced damage. The present study evaluated the effect of lyophilized coffee grounds extract (LCGE) supplementation on sperm quality during refrigeration and cryopreservation of Dorper and Creole ram semen. Semen samples from ten rams (five per breed) were allocated to three treatments based on LCGE concentration: 0 mg/mL (control), 1.0 mg/mL, and 1.5 mg/mL. Cryopreservation significantly affected all CASA-derived sperm parameters, with breed-dependent responses detected for several kinetic traits. LCGE supplementation had limited effects on CASA-derived sperm motility and kinetic parameters, with a significant dose effect detected only for total motility. In contrast, higher plasma membrane functionality, acrosomal integrity, DNA integrity, and chromatin condensation were observed in cryopreserved semen supplemented with LCGE, particularly at 1.5 mg/mL. LCGE-treated samples also exhibited a lower occurrence of moderate and high oxidative stress categories than cryopreserved control samples. Overall, the findings indicate that LCGE supplementation was associated with higher plasma membrane functionality, acrosomal integrity, DNA integrity, and chromatin condensation in cryopreserved semen. Further studies are required to determine the biological mechanisms involved and to evaluate the effects of LCGE supplementation on fertility under in vivo conditions.

Colored potato cultivars are rich in phenolic compounds that confer high antioxidant capacity; however, these beneficial metabolites could be susceptible to oxidation by polyphenol oxidases (PPOs), leading to enzymatic browning and the loss of antioxidant potential. Despite the agronomic relevance of this trade-off, the dynamics of the PPO gene family (StPPOs) gene expression in pigmented potatoes remains poorly characterized. Here, we present an integrated biochemical and molecular analysis of two purple-fleshed Peruvian landraces (Siriñacha and Angashungo), a partially pigmented landrace (Sapa), and non-pigmented cultivars, including the commercial cultivar Desirée. We quantified the total phenolic content, antioxidant capacity, and enzymatic browning index (EBI) using colorimetric and spectrophotometric methods. We also generated gene expression profiles of ten StPPO genes using semi-quantitative and digital PCR. Purple-fleshed cultivars exhibited significantly higher phenolic content and antioxidant capacity but also displayed accelerated browning kinetics compared to non- or partially pigmented genotypes. Expression analysis revealed cultivar-specific StPPO patterns, with StPPO2 and StPPO8 being markedly upregulated in pigmented materials, particularly StPPO8. These findings provide the first integrated biochemical and transcriptional evidence linking specific StPPO isoforms to enzymatic browning in colored potatoes, and highlight their potential for biotechnological applications.

This study aims to provide an empirical, decision-oriented comparison of four widely used sustainability assessment frameworks applied to agri-food systems (LCA, MESMIS, SAFA, and RISE). Using a Scopus-based search (2000–2025), we compiled a purposively balanced corpus of 89 empirical applications and coded each study with a standard rubric spanning normative, systemic, and procedural dimensions. Dimension indices were constructed using polychoric PCA (first component) and rescaled to 0–1, and a global index was computed as the mean of the three dimensions. Within the constructed corpus, MESMIS shows the highest mean global index (0.54), followed by LCA (0.46), SAFA (0.45), and RISE (0.32). LCA leads the normative dimension (0.56), while MESMIS leads the systemic (0.64) and procedural (0.53) dimensions; SAFA presents a balanced profile and explicitly incorporates governance considerations. Findings are interpreted as descriptive patterns within the constructed corpus rather than population-level estimates of inherent method superiority. We conclude that framework choice should be driven by evaluation purpose and information conditions and that hybrid approaches can combine complementary strengths across methods.

This study evaluated the integration of non-invasive remote sensing and colorimetry to classify the maturity stages of Coffea arabica fruits across four varieties: Caturra Amarillo, Excelencia, Milenio, and Típica. Multispectral signatures were captured using a Parrot Sequoia camera at wavelengths of 550 nm, 660 nm, 735 nm, and 790 nm, while colorimetric parameters L*, a*, and b* were measured with a high-precision colorimeter. We conducted multivariate analyses, including Principal Component Analysis (PCA) and multiple linear regression (MLR), to identify color patterns and develop predictors for fruit maturity. Spectral curve analysis revealed consistent changes related to ripening: a decrease in reflectance in the green band (550 nm), a progressive increase in the red band (660 nm), and relative stability in the RedEdge and near-infrared regions (735–790 nm). Colorimetric analysis confirmed systematic trends, indicating that the a* component (green to red) was the most reliable indicator of ripeness. Additionally, L* (lightness) decreased with maturity, and the b* component (yellowness to blue) showed varying importance depending on the variety. PCA accounted for over 98% of the variability across all varieties, demonstrating that these three parameters effectively characterize maturity. MLR models exhibited strong predictive performance, with adjusted R² values ranging between 0.789 and 0.877. Excelencia achieved the highest predictive accuracy, while Milenio demonstrated the lowest, highlighting varietal differences in pigmentation dynamics. These findings show that combining multispectral imaging, colorimetry, and statistical modeling offers a non-destructive, accessible, and cost-effective method for objectively classifying coffee maturity. Integrating this approach into computer vision or remote sensing systems could enhance harvest planning, reduce variability in specialty coffee lots, and improve competitiveness by ensuring greater consistency in cup quality.

Este estudio analiza el potencial de la inteligencia artificial para promover la sostenibilidad de los agronegocios peruanos, un sector clave que representa el 6,5 % del producto interno bruto y emplea al 25 % de la población económicamente activa. A través de una metodología cualitativa que incluye revisión bibliográfica, análisis de contenido y triangulación de resultados, se evalúan tecnologías de inteligencia artificial como el aprendizaje automático, la visión por computadora y la robótica, aplicadas a cultivos estratégicos como el café, la quinua y el maíz. Los resultados destacan que la inteligencia artificial optimiza el uso de recursos, reduce el impacto ambiental y mejora la productividad, contribuyendo a los Objetivos de Desarrollo Sostenible (ODS 2, 8, 12 y 15). Sin embargo, barreras como el costo y la falta de infraestructura limitan su adopción. Se concluye que la colaboración entre el Gobierno, la academia y el sector privado es vital para garantizar la implementación de estas soluciones, promoviendo agronegocios sostenibles y competitivos en el Perú.

Postharvest handling and storage of yellow pitahaya ( Hylocereus megalanthus ) posed challenges due to the variability in physicochemical and bioactive properties at different maturity ( M ) stages and over time. This study is aimed at assessing the influence of the M and storage time ( T ) on the physicochemical and bioactive characteristics of yellow pitahaya. Fruits at three M stages (green, semiripe, and ripe) were collected, stored at 16°C with 85% relative humidity, and evaluated over 20 days (Days 1, 5, 10, 15, and 20). The research analyzed how M and T affected key quality attributes and bioactive components, identifying the M stage that optimized these properties for consumption. Physicochemical changes, as well as chemical composition and antioxidant capacity of 1‐day fruit powders, were assessed. Fruit dimensions, moisture, and firmness were mainly influenced by M , while weight loss, pH, soluble solids, and acidity were influenced by T . Color showed significant sensitivity to both factors. Green fruits were the smallest, with the highest firmness and lowest volume, while ripe fruits had the lowest epicarp percentage and thickness. During storage, significant reductions in dimension, volume, epicarp thickness, soluble solids, and acidity were observed, while weight loss, mesocarp + endocarp, color, pH, and M index increased. Chemical composition and bioactive properties varied notably with M stage, with semiripe and ripe fruits exhibiting the most favorable bioactive characteristics. In conclusion, both M and T significantly influenced most physicochemical and bioactive properties of yellow pitahaya, highlighting the semiripe and ripe stages as optimal for harvest, offering practical insights for improving postharvest handling and storage practices.

Livestock farming is vital to a country's economy, and technological innovations in animal genetics and reproductive biotechnologies are key for environmental and socioeconomic development. This study aimed to?evaluate the antioxidant capacity...