Líbia Diniz Santos

Packaging systems play an important role in maintaining the storage stability of fermented specialty green coffee beans, thereby contributing to the preservation of attributes associated with coffee quality. This study advances the understanding of how multilayer oxygen-barrier packaging influences the storage stability of fermented and non-fermented green Arabica coffee beans. Samples were analyzed after 3, 6, and 9 months for moisture sorption behavior and content, instrumental color, texture, and packaging mechanical resistance. Moisture content remained within the recommended range for green coffee (8.5–11%) in all systems, while rupture force values ranged from 480 to 570 N. Fermented samples showed limited moisture variation, whereas non-fermented coffees exhibited greater variability, particularly in thinner and more permeable packaging. The GAB model showed superior fitting performance for moisture sorption data, with R² values up to 0.99, indicating better predictive accuracy than the BET model. Color analysis showed progressive changes during storage, with non-fermented coffee exhibiting greater color variation than fermented coffee under similar conditions. Among the evaluated systems, the thickest multilayer packaging (Packaging 2) showed superior mechanical strength (56.63 MPa), and barrier performance, approximately three times higher than the other systems. Overall, high-barrier multilayer packaging combined with fermentation effectively preserved green coffee physicochemical quality during long-term storage.

Volcanic fermentation is an innovative technique in post-harvest coffee processing that involves forming conical mounds, called “volcanoes”, to create specific biotransformation conditions. This study investigates the impact of different volcano fermentation methods on the chemical composition and sensory attributes of coffee. Four methods were evaluated: asphalt patio (E1), on pallets (E2), in steel containers under the sun (E3), and in steel containers in the shade (E4). The chemical composition was analyzed in terms of sugars (sucrose, glucose, fructose), organic acids (citric, malic, succinic, lactic, acetic) and alcohols (glycerol, ethanol). In addition, color differences (ΔE) and sensory analysis of the fermented coffees were evaluated. The results of this study reveal that volcanic fermentation produces high-quality specialty coffees, but with divergent profiles of acids and alcohols, thus influencing the sensory characteristics of the resulting beverage. However, the different methods of volcanic fermentation did not significantly affect pH and soluble solids, indicating that the microbiota developed an efficient and consistent fermentation regardless of the solar exposure conditions. The most frequently mentioned sensory descriptors were chocolate, citrus fruits, honey/molasses, caramel, floral, and brown sugar. These findings highlight the significant influence of the volcanic fermentation method on the chemical and sensory quality of coffee fermented.

Supervised machine learning is widely researched nowadays. Several works have already been developed using genetic algorithms (GAs) for classification tasks evolving IF-THEN classification rules. Oftentimes, these methods are built using integers and real values from one’s chromosome structure. In this paper, new and important improvements are proposed to Non-linear Computation Evolutionary Environment (NLCEE), a GA-based rule-set generator proposed by Amaral and Hruschka. The proposed GA, called BIN-NLCEE, uses binary representation in its chromosome structure to simplify its mutation and also produce a higher search space. The main goal is to have a rule-set generator that produces simple and interpretable classification rules with good accuracy values and better converge rates. The BIN-NLCEE performance was compared with other GAs-based and four traditional classifiers in five medical domain datasets. The results showed a better convergence rate and higher fitness values for BIN-NLCEE when compared with the GA-based CEE and NLCEE. In 20 comparisons, BIN-NLCEE achieved better results in 9 (45%), and, according to the confidence interval, equivalent results were obtained in 11 (55%). In this way, BIN-NLCEE was better or equal to NLCEE and CEE in 100% of the comparisons. Also, BIN-NLCEE outperformed all traditional classifiers’ results, i.e., achieved better results in 100% of comparisons.

Studies have shown that a diverse and metabolically active microbiota exists throughout different stages of coffee processing, from pre- to post-harvest. This microbiota originates from both the cultivation and processing environments. Additionally, microorganisms from the soil can be found on the fruit due to the transfer between them. This study reviews the microbiota present in Arabica coffee fruits and the soils where the plants are grown. It examines how microbial profiles are related to coffee variety, altitude, cultivation region, and processing method, and establishes a connection between the microbiota in soil and fruit. A diverse microbiota was observed in both coffee fruits and soils, with similar microorganisms identified across different growing regions, processing methods, and coffee varieties. However, exclusive detections of some microorganisms were also observed. These differences highlight the influence of terroir on coffee’s microbial composition, confirming that environmental conditions, genetic factors, and processing methods shape coffee microbiota. Since microbial development during coffee fermentation can affect the beverage’s quality, the data presented in this review offer valuable insights for researchers and producers. Understanding the influence of processing methods, coffee varieties, and cultivation regions on coffee microbiota enables the selection of specific fermentation conditions or starter cultures to enhance terroir characteristics or adjust microbial populations to favor or introduce microorganisms beneficial for coffee quality. Graphical abstract