Conference Agenda

Chicken meat freshness is a critical quality and safety indicator that requires rapid and non-destructive detection methods. This study aimed to develop a spectral classification model to identify chicken freshness using NIR spectroscopy and machine learning algorithms. A total of 180 spectra (1100–2495 nm) were collected and labeled as “fresh” (days 1–3) or “spoiled” (days 4–5). After Savitzky–Golay smoothing and SNV correction, five models (SVM, LASSO, Ridge, Elastic Net, Random Forest) were trained using stratified cross-validation. The LASSO model achieved the best performance with 97.2% accuracy and AUC = 0.977, with no false negatives in the spoiled class, followed by SVM (94.4%). Results demonstrate the effectiveness of regularized linear architectures for detecting chemical changes associated with spoilage. This approach provides a rapid, non-invasive, and accurate tool for freshness monitoring in poultry production chains.

Precise temperature control is crucial for potato (Solanum tuberosum) cultivation in greenhouses, but conventional PID controllers are ineffective in the face of nonlinear dynamics and system disturbances. This work developed a fuzzy PID control system assisted by neural networks (ANFIS) to regulate the temperature in a simulated greenhouse, considering the conditions for potato cultivation. Following the VDI 2206 methodology and using MATLAB/Simulink, a first-order model of the greenhouse, was derived, and three control strategies were designed: classical PID, fuzzy PID, and ANFIS, the latter trained with data from the fuzzy PID model. System validation compared performance using standard metrics in step response and disturbance response. The results demonstrated that the ANFIS controller was superior, achieving a settling time of 126.34 s and maintaining the maximum deviation from external disturbances below 0.2 °C (specifically 0.15 °C), thus validating the main hypothesis. This performance significantly outperformed the classical PID controller (ts = 1053.3 s, Desv = 0.71 °C) and the fuzzy PID controller (ts = 224.94 s, Desv = 0.71 °C), showing a faster, more accurate, and more stable response. Although the neuro-fuzzy controller required greater computational effort, its performance demonstrated a considerable improvement in the speed and robustness of the system, establishing it as an effective solution for optimizing thermal stability in greenhouses used for potato cultivation.

This study develops and applies an economic–financial model to evaluate the feasibility and profitability of Teak (Tectona grandis) and Melina (Gmelina arborea) plantations established at the Panamerican Agricultural School, Zamorano, Honduras. The analysis integrates technical, economic, and financial components, including establishment and maintenance costs per hectare, wood volume estimates derived from growth records and allometric equations, and historical and projected timber market prices. Independent cash flow projections were developed for each species, considering their biological growth cycles and optimal harvest ages, with a 25-year evaluation horizon for Teak and a 19-year horizon for Melina.

Financial feasibility was assessed through the calculation of Net Present Value (NPV) and Internal Rate of Return (IRR) under two discount rate scenarios (8% and 15.41%), representing institutional and private opportunity costs of capital. In addition to deterministic evaluation, uncertainty and risk were incorporated through Monte Carlo simulations using the @RISK software, allowing the probabilistic assessment of key financial indicators and the sensitivity of project outcomes to variations in critical variables such as timber prices, production volumes, and investment costs.

The results show that although Teak exhibits a higher unit market value, its longer rotation period increases capital immobilization and financial exposure. In contrast, Melina achieves higher harvested volumes per hectare and faster investment recovery, which leads to superior financial performance under the evaluated conditions. The stochastic analysis indicates a higher probability of favorable economic outcomes for Melina, reflecting lower overall project risk. The findings confirm that both species are financially viable forestry alternatives in Zamorano.

Within the framework of foreign trade, participating organizations seek to become more competitive and develop capabilities that enable them to adapt to changing environments. In this sense, digital transformation is a fundamental element in remaining relevant in the market. This research analyzed the relationship between digital transformation and the perception of competitiveness of an organic banana exporting company in Peru, an emerging country that is heavily dependent on foreign trade, with agriculture being one of the sectors that contributes most to this trade. The main finding was to determine the positive and significant relationship between digital transformation and the perception of competitiveness in the company, with values obtained of 0.710 for the correlation coefficient and significance below 0.05. The method used was quantitative correlational cross-sectional, and questionnaires were used as instruments to measure each variable (validated and reliable according to Cronbach's Alpha).

The objective of this research was to evaluate the ecotoxicological effect of dissolved air flotation (DAF) sludge from a poultry processing plant on earthworms (Eisenia fetida) and corn seeds (Zea mays) using bioassays. The physicochemical characterization of the DAF sludge and the soil used in the bioassays was performed. In the earthworm bioassay, seven doses of DAF sludge (1.56% to 6.25%) plus a control were used, and in the corn seed phytotoxicity bioassay, six doses of DAF sludge (6.25% to 21.88%) plus a control were used. The LD₅₀ (median lethal dose) for DAF sludge on earthworms was 5.21%, and earthworm weight increased after 7 days of exposure, then decreased after 14 days of exposure. It was observed that cocoons formed at low doses of DAF sludge, and that there was a correlation between earthworm mortality (%) and pH and electrical conductivity. The ID50 (median inhibition dose) for corn seed germination was 11.1% DAF sludge. At 21 days, significant differences in the percentage of DAF sludge necrosis occurred, starting at 15.63%. A decrease in height, wet weight, dry weight, and number of leaves of Z. mays was observed as the DAF sludge dose increased, and finally, there was an association between corn seed germination and pH, and no association with electrical conductivity.

The study investigates the effect of acid (H₂SO₄) and base (KOH) catalyzed transesterification on the fatty acid profile of biodiesel derived from palm and sunflower oils. A factorial 2x2 experimental design was employed, comparing the two catalysts and oil types, with three repetitions per treatment. Results revealed that potassium hydroxide (KOH) promoted higher yields of monounsaturated fatty acids (e.g., oleic acid) in both oils, while sulfuric acid (H₂SO₄) favored polyunsaturated fatty acids (e.g., linoleic acid). Sunflower biodiesel showed a significant increase in linoleic acid (59%) with H₂SO₄, whereas palm biodiesel retained higher saturated fatty acids (e.g., palmitic acid) with KOH. The findings highlight the catalytic influence on biodiesel composition, impacting properties like oxidative stability and cold flow. Base catalysis proved more efficient for triglyceride conversion, while acid catalysis excelled in preserving unsaturated fatty acids.