This study evaluated the impact of different drying methods on the physicochemical, microbiological, and sensory qualities of coffees produced in the Campos das Vertentes (CV) and Alta Mogiana (AM) regions of Brazil. The sun-drying (S), sun-drying combined with rotary mechanical dryer (SM), and CoffeeDryer^® mechanical dryer (C) methods were compared at different harvest times for the same crop (2024). The results indicated that CoffeeDryer^® preserved relatively high levels of phenolic compounds and antioxidant activity, reaching 3.24 g of gallic acid equivalents per 100 g (g EAG·100 g⁻¹) and 47.96% antioxidant protection in the coffees produced in Alta Mogiana, whereas the sun-dried coffees presented relatively low values (2.20 g EAG·100 g⁻¹ and 28.96% protection). In the Campos das Vertentes region, C maintained 2.78 g EAG·100 g⁻¹ phenolic compounds and 50.29% antioxidant protection, outperforming combined drying (2.48 g EAG·100 g⁻¹ and 41.17%). Regardless of the region and time of harvest, the coffees dried by C had a water activity of less than 0.6 and more stable moisture content (7.73–10.42%), reducing the possibility of proliferation of filamentous fungi and, consequently, mycotoxins. In the sensory evaluation, CoffeeDryer^® guaranteed higher scores for fragrance/aroma and flavor, allowing the coffees to reach 80 to 81 points on the SCA scale, which is classified as special. Thus, the use of CoffeeDryer^® proved to be an efficient alternative for optimizing coffee drying, preserving its chemical and microbiological qualities, and enhancing its commercial and sensory value.

Phenolic compounds are recognized for their significant bioactivity, playing a role in plant protection against abiotic stresses and providing therapeutic benefits to human health. Although interest in functional foods has driven the demand for quality analysis, conventional analytical methods for determining antioxidant capacity are costly and require high operational time. In this context, the application of Artificial Intelligence tools emerges as a strategic alternative for process optimization. The present study aimed to predict the antioxidant activity of different fruit matrices using Artificial Neural Networks (ANNs). The database was structured from ten scientific articles, from which phenolic profiles and antioxidant activity values (DPPH) were extracted. For model development, a Multilayer Perceptron (MLP) architecture was used with 13 input variables and three hidden layers. Data were normalized using the Min-Max method and divided into training (70%) and testing (30%) sets. Different activation functions and optimization algorithms were tested to evaluate predictive accuracy. The results demonstrated that the combination of the hyperbolic tangent activation function (tanh) with the Stochastic Gradient Descent (SGD) optimizer yielded the best performance, achieving a Mean Absolute Percentage Error (MAPE) of 3.93×10⁻⁶ and a Mean Squared Error (MSE) of 2.28×10⁻¹⁰. It is concluded that ANNs constitute a robust, fast, and low-cost methodology for predicting antioxidant capacity, with high potential for application in quality control within the food industry.

The development of sustainable adhesive systems based on renewable resources is essential for advancing environmentally friendly lignocellulosic materials. However, limited information is available on the direct modification of vinyl acetate-ethylene (VAE) adhesives with Kraft lignin for bamboo particleboards, particularly regarding the relationship between adhesive thermal behavior, interfacial morphology, and panel performance. This study evaluated the effect of incorporating Kraft lignin (0, 5, 10, 15, and 20%) into VAE adhesive on the physical, mechanical, morphological, and thermal properties of bamboo particleboards. TGA/DTG and scanning electron microscopy (SEM) were used to investigate adhesive thermal behavior and particle–adhesive interfacial morphology, respectively. Lignin incorporation was associated with improved dimensional stability and mechanical performance. Water absorption decreased from 17.04% to 6.62% after 2 h and from 80.31% to 36.29% after 24 h. At 15% lignin, the modulus of rupture, modulus of elasticity, and internal bond increased from 38.02 to 60.45 MPa, 2219 to 5449 MPa, and 0.31 to 0.68 MPa, respectively. TGA/DTG showed that lignin did not substantially alter the overall degradation profile of VAE but increased carbonaceous residue at high temperatures. SEM revealed improved interfacial cohesion up to 15% lignin, with reduced fiber pull-out, fewer voids, and a more continuous adhesive matrix, whereas 20% lignin promoted agglomeration and microvacuole formation. Thermal conductivity remained stable at 0.13–0.15 W m⁻¹ K⁻¹. Among the formulations evaluated, 10 and 15% Kraft lignin showed the most favorable combination of dimensional stability and mechanical performance, demonstrating the potential of lignin as a renewable modifier for VAE-based bamboo particleboards.