Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

In recent years, cacao and its derivatives have gained significant attention due to their potential health benefits. The primary bioactive compounds in cacao are polyphenols and methylxanthines, predominantly represented by theobromine. Their concentrations vary widely, influenced by cacao variety, growth region, and postharvest processing. Fermentation typically leads to a marked decrease in polyphenols and theobromine, with further reductions during drying and roasting. This review aims to consolidate current knowledge on how these factors affect compound levels, providing insights crucial for optimizing practices to enhance the health benefits and quality of cacao products. Literature consistently shows that cacao properties are shaped by genetics, environmental conditions, and processing stages. Moreover, the unique polyphenol and theobromine profiles can serve as distinctive fingerprints to differentiate cacao origins. Understanding these dynamics is essential for improving both nutritional value and industrial applications.

Cacao cultivation is one of the main agricultural products of Ecuador, known internationally for its quality and aroma. However, it is affected by fungal diseases including Moniliophthora roreri, Moniliophthora perniciosa, Phytophthora spp., and Colletotrichum spp. The genus Colletotrichum spp. is known for its characteristics that complicate traditional taxonomic identification. In cacao cultivation, it is one of the most frequently found species as an endophyte of leaves and fruits, yet it is also reported to cause the disease known as anthracnose on leaves and fruits. The objective of this work was to identify at the species level 16 Colletotrichum isolates, 13 from healthy leaf endophytes and 3 from pods with symptoms, through multilocus analysis of the ITS1, 5.8S, and ITS2 region, and partial sequences of the TUB2 and GAPDH genes. Subsequently, their pathogenicity was evaluated by inoculating healthy cacao pods and measuring the damage caused. The 16 isolates were identified as follows: from the gloeosporioides complex, C. siamense 6, C. chrysophilum 6, C. theobromicola 2 and from the boninense complex, C. karstii 2. The most frequently found species were those that caused symptoms, especially C. siamense, to which the strains were isolated from symptomatic pods belonged. This work provides relevant and accurate information about the diversity of Colletotrichum species that colonize cocoa plantations and identifies which species might cause the disease known as anthracnose. Additionally, it allows for a more precise diagnosis and consequently better treatment.

Floriculture is a sector of growing global importance, contributing to employment generation, income creation, and the promotion of biodiversity and sustainability. This study aimed to identify the factors influencing the adoption of floriculture as an alternative crop in the province of Leoncio Prado, Peru, and to assess its economic viability. A total of 269 farmers were surveyed, analyzing attitudes, land suitability, and socioeconomic and environmental factors. Influential factors were identified using descriptive analysis, chi-square tests, and logistic regression (p < 0.1). Additionally, multiple machine learning algorithms (Decision Trees, Logistic Regression, KNN, SVM, Ensemble, Neural Networks, Naive Bayes) with cross-validation (k = 5) and AUC metrics were employed to model adoption intentions. Scenarios were developed to increase the willingness to adopt floriculture, and an economic analysis of eight tropical species (Red Ginger, Anthurium, Emperor's Staff, Heliconia, Gardenia, Parrot's Beak, Golden Heliconias, Maracas) was conducted. The results reveal that willingness to change crops, participation in awareness campaigns, allocation of areas for conservation, and cost control are key factors. The neural network model achieved an AUC of 83.3%, and improved scenarios indicate that adoption could increase by up to 11.32%. Red Ginger demonstrated high profitability (NPV S/10428; IRR 51%; PBP 0.7 years). In conclusion, floriculture represents an economically and environmentally viable alternative that contributes to agricultural diversification and sustainability.

Water stress has caused major losses in the agricultural productivity of crops, inducing the search for alternatives for sustainable cultivation. In this context, the objective of this study was to evaluate the tolerance of watermelon under water stress, inoculated with bacterial strains of the genus Bacillus spp., regarding the biochemical and enzymatic variables in the flowering stage. A randomized block design was adopted in a split-plot 4x4 factorial scheme, with plots consisting of four levels of soil water availability (40%, 60%, 80% and 100% of field capacity - FC) and subplots consisting of four inoculations (Negative Control (NC); XX6.9 bacteria; P6.2 bacteria; MIX – co-inoculation of XX6.9 and P6.2 bacteria), with five replicates. XX6.9 bacteria and NC were the treatments most affected by severe water stress, since at the soil water availability (SWA) level of 40% FC they showed high contents of the oxidative marker (MDA) and proline. Although the inoculation with XX6.9 bacteria promoted a higher content of osmoregulators such as proteins, total soluble sugars and reducing sugars, it was not enough to attenuate the effects of water deficit. On the other hand, treatments with P6.2 bacteria and MIX of bacteria showed reduced levels of MDA at the SWA level of 40% FC, accompanied by high enzymatic activity of POD and CAT, which may contribute to the tolerance of the watermelon crop to water stress.

Himatanthus sucuuba is important in folk medicine and is widely used as an antitumor, antifungal, vermifuge and anti-anemic agent1. In this context, the objective of this study was to develop a protocol for in vitro germination and micropropagation of H. sucuuba. The seeds were immersed in a 1.0% (v/v) Cabrio Top solution for one hour on a magnetic stirrer and then in a 0.1% (v/v) diluted NaOCl solution for 30 minutes under agitation, followed by immersion in 70% alcohol for 1 minute. Subsequently, the seeds were rinsed four times with sterile distilled water and then inoculated in MS medium supplemented with the auxins AIA, ANA and AIB at concentrations of 0.0; 1.0; 3.0; 5.0 mg L-1. The experimental design was completely randomized, using 10 treatments with 3 replicates of 10 seeds (n = 30). It was observed that the MS medium supplemented with IAA (5.0 mg L-1) resulted in 80% germination and seedlings with 5.97 cm in height and 4.2 nodal segments. To stimulate rooting, the nodal segments were cut and inoculated in MS medium supplemented with BAP (0.1 mg L-1) and in interaction with the auxins IAA, 2,4-D and ANA, at concentrations of 0.0; 3.0; 5.0 and 8.0 mg L-1 and kept in a growth room at 25 ± 2 °C, with a photoperiod of 16 h. The combination BAP+IAA (0.1 + 8.0 mg L-1) showed the best results with 100% sprouting, 40% callus formation and 30% rooting. In conclusion, in vitro propagation is a promising technique to produce H. sucuuba seedlings, however, hormonal adjustments are necessary.

Sapote gum (SG) is a new biopolymer with promissory functional properties. This study aimed to determine if SG is a suitable emulsifier for obtaining stable grape seed oil (GSO) emulsions. In the first stage, coarse emulsion concentrations of SG and grapeseed oil - GSO were evaluated, applying the Central Composite Rotational Design (0.59% to 3.41% of SG and 12.93% to 27.07% GSO). For the second stage, using a Centered Face Design – CFD, the resulting emulsion was sonicated at 90, 270, and 450 Watts at 5, 10, and 15 min. Finally, a validation was made. Emulsions were evaluated through microstructure, droplet size, kinetic stability, heat stress, and rheology. Micrographs of the first-stage emulsions showed droplets up to 3.8 μm diameter and a creaming index between 0.00% and 28.39% after 24 h. Optimization indicates that the higher the concentration of gum (3.5%) and GSO (25%), the more kinetically stable emulsions are produced. Ultrasonic emulsions showed no significant difference in droplet size and kinetic stability before 14 days of rest. Ultrasonic validation was made at 450 W for 6 min, resulting in emulsions stable for 20 days and with rheological characteristics interesting for food or cosmetic industries.

The objective of this research was to predict the live weight of Corriedale lambs using morphological measurements and machine learning algorithms. A total of 291 five-month-old lambs from the Corpacancha Production Unit of SAIS PACHACÚTEC SAC were used. These animals represented a homogeneous group in terms of age, sex, and genetics, as they belonged to the Corriedale breed and were offspring of "Category A" ewes. Morphological measurements recorded included Body Length (BL), Withers Height (WH), Thoracic Girth (TG), Rump Width (RW), Abdominal Girth (AG), Cannon Bone Length (CBL), Chest Depth (CD), and Live Weight (LW). The models evaluated were Multiple Linear Regression, Ridge Regression, Decision Trees, Random Forest, and XGBoost. The comparative analysis of the machine learning models identified ModG and Ridge as the most accurate and stable options, standing out for their low Mean Squared Error (MSE = 0.083) and Root Mean Squared Error (RMSE ≈ 0.287 – 0.288). Additionally, they exhibited the highest coefficients of determination (R2 = 0.89, RAdj2 = 0.88), indicating excellent predictive capability and data fit. Their low coefficient of variation (CV%) confirms their stability, establishing them as the best choices for applications where precision is paramount, such as predicting critical values in production processes and high-demand scientific studies. While XGBoost proved to be a robust alternative with an MSE of 0.119, an RMSE of 0.345, and a relative error of 2.22%. These findings confirm that prioritizing models that balance accuracy, interpretability, and stability enable faster, data-driven decision-making in Corriedale sheep production. Such an approach optimizes feed allocation, classifies lambs by market weight, and promptly detects growth deviations, thereby improving overall flock profitability.

Synthesis, characterization and biological activity of the Cu(II)-1,3-PDTA complex was performed by two synthetic routes: sodium and carbonate pathways. The Cu (II)-1,3-PDTA complex can be easily produced by the 1,3-PDTA binder, commercially available as Trilon F (BASF) which is available as sodium or acid salt for the coordination of Cu (II). The biological activity of the Cu (II)-1,3-PDTA [Cu (II)-PDTA] complex was performed to analyze the activity against the bacterium Erwinia chrysanthemi Bancroft. The bacterium is the causative agent of soft rot in the parsley maniac (Arracacia xanthorrhiza Bancroft). The bacterium was isolated from parsley manioc infected with soft rot. The metal complex sensitivity tests were performed by diffusion antibiogram. The mean values of the halos and standard deviations of the zones of inhibition were obtained for the [Cu (II) (PDTA)] and Streptomycin complexes. The concentrations of the complexes evaluated were 10-3, 10-2 and 10-1 M (equivalent to 36.3 g L-1 (3.63%), 3.63 g L-1 (0.363%) and 0.363 g L-1 (0.0363%), respectively). The halos were 25.0 ± 1.2 mm for control. For [Cu (II) (PDTA)] complexes, the obtained values were 9.5 ± 0.6, 15.0 ± 0.8 and 24.0 ± 0.8 via sodium and 12.0 ± 1.6, 25.0 ± 0.8 and 31.0 ± 1.9 via carbonate, for 10-3, 10-2 and 10-1 M, respectively. The results showed that the bactericidal activity of the Cu (II)-1,3-PDTA complex obtained by the two synthetic routes are adequate for the control of Erwinia chrysanthemi Bancroft.

The presence of foliar phytopathogenic fungi causes severe damage to leave and fruits of peach (Prunus persica L.), in producing areas of southern Chihuahua, Mexico, which has caused a decrease in production by 30%. The objective of this work was to identify morphologically and molecularly the foliar phytopathogenic fungi associated with the peach tree crop, evaluating the pathogenicity in one-year-old plants against Trichoderma asperelloides. Leaves with brown and brown lesions were collected from mummified fruits on the plant from 19 commercial peach orchards distributed in three municipalities of regional and national production in the State of Chihuahua, Mexico. Fungal identification of four representative isolates was performed using morphological methods. characterization and phylogenetic analysis based on the internal transcribed spacer region (ITS1 and ITS4) of ribosomal DNA, part of the translation elongation factor 1-alpha (TEF) a second secondary primer for each of the genera for, Collectotrichum ACT-512F and ACT-583R, Fusarium with EF1. For plant confrontations, a concentration of 1x106 conidia was inoculated. mL-1 of pathogens such as T. asperelloides, evaluating leaf diameter and length, height, severity and incidence. It was possible to identify the presence of Fusarium sambucinum, Collectotrichum gleosporoides and Monilinia frutícola, in addition the B-F-M1-A2-ACCH-3 strain of F. Sambucinum obtained the highest values ​​in the inhibition of the response variables and in severity Monilinia frutícola presented 61.23% as the highest value. It is recommended to use strain 3 as a biological control of foliar phytopathogens.

The emergence of Machine Learning (ML) technologies and their integration into agriculture has demonstrated a significant impact on disease detection in crops, enabling continuous monitoring and enhancing risk planning and management. This study applied image processing techniques such as thresholding, gamma correction, and the Stretched Neighborhood Effect Color to Grayscale (SNECG) method, alongside ML, to develop a predictive model for identifying five types of rice diseases. The ML techniques used included Logistic Regression, Multilayer Perceptron, Support Vector Machines, Decision Trees, and Random Forests (RF). Hyperparameters were optimized and evaluated through 5-fold cross-validation. In the results, the SNECG method successfully converted images to grayscale, capturing essential features of lesions on rice leaves. The ML models developed with these techniques showed evaluation metrics exceeding 80%, with the RF model (precision = 88.31%) demonstrating superior performance. Additionally, the RF model was integrated into an interface designed for agricultural decision-making. The practical application of the developed model could significantly improve the ability to detect and manage diseases in rice crops.