Food fraud and the lack of reliable dietary traceability systems in meat products represent a growing challenge for food safety, consumer trust, and the competitiveness of the poultry sector, particularly in contexts where verification of animal feeding relies on documentary records that may be prone to error or manipulation. In this framework, this study aimed to evaluate the capability of VIS–NIR hyperspectral imaging combined with machine learning to discriminate feeding regimes in broiler chickens (Gallus gallus domesticus), specifically assessing the influence of anatomical region on classification performance. A controlled experiment was designed using 60 broilers distributed into three contrasting feeding groups, and hyperspectral images were acquired from four anatomical regions of the carcass (breast, tail/uropygial region, thigh, and drumstick/leg). Spectra were preprocessed using Savitzky–Golay filtering and SNV normalization, and several supervised classification models, including linear and non-linear algorithms, were trained under strict individual-wise validation schemes. The results showed that the breast provided the highest discriminative capability, reaching an external accuracy close to 0.98 with an optimized Ridge model, whereas the tail/uropygial region, thigh, and leg exhibited considerably lower performance, reflecting greater structural variability and weaker diet-related spectral signatures. Furthermore, band reduction techniques successfully compressed the input space from 300 to 120 spectral bands without compromising predictive capability. Overall, these findings highlight the strong potential of VIS–NIR hyperspectral imaging as a non-destructive tool to support dietary traceability in chicken meat.
The early detection of internal damage caused by Elasmopalpus lignosellus in fresh asparagus constitutes a challenge for the agro-export industry due to the limited sensitivity of traditional visual inspection. This study evaluated the potential of VIS-NIR hyperspectral imaging (390-1036 nm) combined with machine-learning models to discriminate between infested (PB) and sound (SB) asparagus spears. A balanced dataset of 900 samples was acquired, and preprocessing was performed using Savitzky-Golay and SNV. Four classifiers (SVM, MLP, Elastic Net, and XGBoost) were compared. The optimized SVM model achieved the best results (CV Accuracy = 0.9889; AUC = 0.9997). The spectrum was reduced to 60 bands while LOBO and RFE were used to maintain high performance. In external validation (n = 3000), the model achieved an accuracy of 97.9% and an AUC of 0.9976. The results demonstrate the viability of implementing non-destructive systems based on VIS-NIR to improve the quality control of asparagus destined for export.
Sensory Architecture has been recognized as a relevant factor in the emotional experience of children and adolescents with autism spectrum disorder (ASD); however, a persistent gap remains in the systematic incorporation of empirical evidence into the architectural design process, particularly in Latin American urban contexts. Within this framework, the present study analyzed the relationship between Sensory Architecture and Emotional Well-Being in children and adolescents with ASD attending therapeutic centers in the district of San Juan de Lurigancho, Lima, with the aim of translating empirical findings into evidence-based architectural design criteria. A quantitative, non-experimental, cross-sectional, and correlational approach was adopted. The unit of analysis consisted of children and adolescents with ASD, whose emotional experience was assessed through proxy informants, specifically family members. The sample comprised 100 family informants selected using non-probabilistic convenience sampling. Data were collected through a structured questionnaire consisting of 25 items measured on a five-point Likert scale, which demonstrated high internal consistency (Cronbach’s alpha = 0.93). As the data did not follow a normal distribution (Kolmogorov–Smirnov, p < 0.05), Spearman’s Rho coefficient was applied. The results revealed positive and statistically significant associations between the dimensions of Sensory Architecture and Emotional Well-Being, with Spatial Configuration emerging as the dimension with the strongest associative weight (ρ = 0.652; p < 0.001). Based on this empirical hierarchy, an evidence-based architectural design proposal for a therapeutic center was developed. Study limitations include the cross-sectional design and the absence of post-occupancy evaluation, which point to future research directions focused on longitudinal studies and empirical validation of architectural performance.
The high dependence on fossil fuels for energy supply in hospitals compromises their operational sustainability, increases costs, and contributes significantly to polluting emissions. This study evaluates the technical, economic, and environmental feasibility of integrating photovoltaic and solar thermal systems in a hospital located in a tropical Caribbean environment, characterized by continuous operation and high energy demand. The methodology combines advanced simulation using PVsyst for the photovoltaic subsystem and the f-chart method for the solar thermal system, using real data on electricity and domestic hot water demand. The proposed system achieves an installed photovoltaic power of close to 390 kWp, with an annual production of around 0.7 GWh and an average performance ratio of 0.80, demonstrating high technical performance. The solar thermal subsystem covers approximately two-thirds of the annual domestic hot water demand, supported by thermal storage suitable for hospital operation. From an economic standpoint, the total estimated investment is recovered in less than 10 years, with a positive net present value, confirming the system’s profitability over its useful life. In environmental terms, hybrid integration avoids more than 400 t of CO2 per year, contributing significantly to the decarbonization of the health sector and the strengthening of energy security. The results obtained demonstrate that photovoltaic–thermal integration in tropical hospitals is technically and economically viable and constitutes a replicable solution for regions with high solar radiation and energy vulnerability. This research provides a comprehensive and reproducible methodological framework that can support sustainable energy planning and the design of public policies aimed at low-emission healthcare infrastructure.
The sustainable processing of coffee requires not only improving the efficiency of conventional operations but also advancing the recovery and valorization of bioactive compounds across the coffee value chain. In this context, emerging technologies offer eco-efficient alternatives to conventional extraction methods. This review summarizes recent advances in ultrasound-assisted extraction (UAE), high-pressure extraction (HPE), cold atmospheric plasma (CAP), and microwave-assisted extraction (MAE) applied to coffee beans and major coffee side streams, including pulp, husk, parchment, silverskin, and spent coffee grounds. The physicochemical principles of each technology, the main operating parameters, and their influence on extraction yield, phenolic composition, antioxidant capacity, and heat-sensitive compound preservation are discussed. Furthermore, potential synergies between combined techniques (UAE-MAE or HPE-UAE) and trends toward industrial scaling and integral valorization within a circular economy framework are highlighted. Overall, the evidence indicates that emerging technologies can intensify coffee extraction processes, increase phenolic recovery (often achieving up to two-fold improvements in total phenolic content compared to conventional techniques), and significantly reduce processing times (commonly reaching 2.5–15 min), supporting more sustainable and industrially relevant value chains.
