Powdered foods are matrices transformed into fine, loose solid particles through dehydration and/or milling, which enhances stability, storage, and transport. Due to their high commercial value and susceptibility to fraudulent practices, detecting adulterants in powdered foods is essential for ensuring food safety and protecting consumer health and the economy. Food fraud in powdered products, such as spices, cereals, dairy-based powders, and dietary supplements, poses an increasing risk to public health and consumer trust. These products were selected as representative matrices due to their high nutritional and economic relevance, which also makes them more susceptible to adulteration and hidden potential health risks from hidden contaminants. Recent studies highlight the potential of spectroscopic techniques combined with chemometrics as rapid, non-destructive, and cost-effective tools for authentication. This narrative review synthesizes recent literature (2020–2025) on the application of near-infrared (NIR) spectroscopy combined with chemometric techniques for adulterant detection in powdered foods. Advances in spectral preprocessing, variable selection, classification, and regression models are discussed alongside the most common adulterants and their nutritional and toxicological implications. Furthermore, the applicability of portable versus benchtop NIR devices is compared. The main contribution of this review lies in critically analyzing methodological frameworks, mapping current gaps, and identifying emerging trends, such as digital integration, self-adaptive chemometric models, and real-time on-site authentication, positioning NIR spectroscopy as a promising tool for food authentication and quality control.

The proposed Sustainable Energy Model is based on rice husk and the development of briquettes made from agricultural waste, which will increase efficiency in the domestic sector, and potentially replace conventional polluting fuels such as firewood. Large volumes of rice husks from millers are found scattered in rural agricultural areas of the San Martin region of Peru, where people are exposed daily to the emissions of polluting gases produced by burning these wastes, causing respiratory and lung diseases. Despite present circumstances, this waste has a great energetic potential that is not yet used by society, representing an opportunity to encourage socioenvironmental development and generate added value to the husk. Based on a compaction and drying process, briquettes were obtained with 4,040 kcal / kg of heat power and 80.39% combustion efficiency, allowing the little use of biofuel compared to firewood, and consequently, the utilization of this biofuel would reduce levels of deforestation. In contrast to similar projects, the sustainability of an energetic model of briquette production will be achieved when economic, environmental and social aspects are met, developing clean technologies and an efficient supply chain, from the supply of the husk to the commercialization of briquette

Servant leadership positively influences the performance of organizations since it focuses on developing the most critical capital of organizations – human beings. The study aims to analyze the influence of servant leadership on organizational performance mediated by organizational culture focusing on administrative staff in Peru. It uses a quantitative approach and non-experimental design. The data were obtained transversally from 465 union members-administrative staff through a virtual survey with a 20-item Likert-type questionnaire, verifying the data for compliance with the underlying assumptions. For data processing, the SPSS AMOS program was chosen, subjecting the data to confirmatory factor analysis and using the structural equation model to test the proposed hypotheses. The results show that, for administrative staff, organizational culture partially mediates the influence of servant leadership on organizational performance at 0.127. While servant leadership positively influences organizational performance (β = 0.341, p < 0.01), it also positively influences organizational culture (β = 0.492, p < 0.01). Furthermore, organizational culture positively influences organizational performance (β= 0.262, p < 0.01). The study confirms the importance of servant leadership in the performance of organizations. Also, it provides evidence to affirm that organizational culture is fundamental in explaining this influence, thus filling the research gap.

This study optimizes the 3D extrusion printing parameters—water-to-flour ratio (X₁), temperature (X₂), and printing speed (X₃)—for raw (RFB) and extruded (EFB) dehulled Andean fava bean flours to maximize print quality and minimize structural defects. A 2³ central composite design combined with response surface methodology (RSM) was used to identify the optimal conditions for achieving geometric precision, surface homogeneity, and textural stability. Physicochemical analyses showed that extrusion cooking substantially modified the composition and rheology of the flour. Compared with RFB, EFB exhibited lower protein and fiber contents, a higher proportion of digestible carbohydrates, and reduced rheological parameters (τ₀, K, G′, G″), which facilitated printing. The evaluation of different parameter combinations revealed notable differences between the two flours, with X₁ and X₂ exerting the greatest influence on print quality. For RFB, the highest desirability (0.853) was achieved at X₁ = 0.806, X₂ = 23.18 °C, and X₃ = 2470.5 mm/min, yielding more uniform and firmer printed structures. In contrast, EFB reached a desirability of 0.844 at X₁ = 1.66 °C, X₂ = 56.82 °C, and X₃ = 1505.43 mm/min, indicating its outstanding geometric accuracy and robustness. In conclusion, raw flour requires higher hydration and lower temperatures to prevent excessive viscosity. In contrast, extruded flour benefits from low water and high temperatures to achieve stable structures and firm textures. These findings demonstrate the feasibility of using Andean fava bean flour in 3D food printing to create nutrient-dense, functional foods with improved printability. This work offers practical applications for developing personalized foods—such as customized meals for individuals with specific dietary requirements—while contributing to sustainable and secure food production. Future research should address long-term storage, post-printing drying methods, and scaling production.

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.