National University of Cajamarca

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.

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.

This study examined how social media content influences value co-creation among organic product consumers through the mediating roles of environmental awareness, green truth, and environmental attitude. Grounded in the Stimulus-Organism-Response (SOR) framework, social media content is conceptualized as a stimulus, environmental awareness, green trust, and environmental attitude as internal organism states, and value co-creation as the behavioral response. A cross-sectional quantitative design was applied using a 20-item questionnaire administered to 739 organic-product consumers. Data were analyzed using partial least-squares structural equation modeling (PLS-SEM). The results indicate that social media content does not directly affect value co-creation but significantly influences environmental awareness, green trust, and environmental attitude. Environmental awareness and green trust positively affect both environmental attitude and value co-creation, and environmental attitude emerges as the strongest direct predictor of value co-creation. These findings confirm the mediating role of cognitive and attitudinal mechanisms in transforming digital sustainability content into collaborative consumer behavior. This study contributes to the literature on sustainable consumption by integrating communication, cognitive, and attitudinal variables in a single explanatory model. Practically, the findings suggest that sustainability communication strategies in digital environments should prioritize credibility and environmental knowledge to foster consumer participation in value co-creation.

Andean agriculture faces several challenges, such as land use changes, land degradation, poverty, extreme events, and climate change. Such conditions compromise food production and security, highlighting the need to explore sustainable alternatives. This two-trial study evaluated the short-term performance of regenerative agricultural practices for potato production in the Peruvian Andes over two seasons (2022-2024), in terms of productivity (FTY: Fresh tuber yield), profitability (BCR: Benefit-cost ratio), C footprint (CF), and soil properties. One trial (Trial 1) tested tillage practices-minimum (MT) vs. zero (ZT); plastic barriers-with (wPB) vs. without (nPB); and mulch thicknesses-0.1 (M10) vs. 0.2 (M20) vs. 0.3 (M30) m. The other trial (Trial 2) tested cropping systems-monoculture (MO) vs. intercropping with faba bean (IN); a fungicide optimization tool-with (wDK) vs. without (nDK); and chicken manure doses-1 (CM1) vs. 2 (CM2) vs. 4 (CM4) t ha(-)(1) . Compared to conventional practices, numerically MT+nPB+M30 increased FTY by similar to 3.5 % and reduced CF by 19.9 % in Trial 1, while MO+wDK+CM4 increased FTY and BCR by 28 % and 12.4 %, respectively, in Trial 2. ZT and IN performed poorly in the short-term under Andean conditions, highlighting the need for long-term studies. In both trials, the short-term effect of regenerative practices improved soil organic matter with a mixed impact on pH. Regenerative practices in the Andes offer synergies and trade-offs, but integrating reduced tillage, mulching, and organic fertilization can enhance sustainability without lowering productivity. Long-term adoption is essential to restore soil carbon stocks, improve sustainability, and increase the resilience of Andean agriculture.