Aging-associated, non-transmissible chronic diseases (NTCD) such as cancer, dyslipidemia, and neurodegenerative disorders have been challenged through several strategies including the consumption of healthy foods and the development of new drugs for existing diseases. Consumer health consciousness is guiding market trends toward the development of additives and nutraceutical products of natural origin. Fungi produce several metabolites with bioactivity against NTCD as well as pigments, dyes, antioxidants, polysaccharides, and enzymes that can be explored as substitutes for synthetic food additives. Research in this area has increased the yields of metabolites for industrial applications through improving fermentation conditions, application of metabolic engineering techniques, and fungal genetic manipulation. Several modern hyphenated techniques have impressively increased the rate of research in this area, enabling the analysis of a large number of species and fermentative conditions. This review thus focuses on summarizing the nutritional, pharmacological, and economic importance of fungi and their metabolites resulting from applications in the aforementioned areas, examples of modern techniques for optimizing the production of fungi and their metabolites, and methodologies for the identification and analysis of these compounds.

Cocoa honey, a by-product obtained during the processing of cocoa, is a juice rich in pectin, organic acids, minerals and phenolic compounds with antioxidant properties. Fresh cocoa honey is quickly fermented due to its high content of reducing sugars, such as fructose and glucose, which limits its shelf life. Currently, cocoa honey is only commercialized in frozen form, as logistical challenges prevent the wide distribution or export of this by-product for applications in the market of sweets, jellies, beverages, confectionery, and nutraceutical foods among others. Spray-drying technology is a viable prospect for the large-scale stabilization of products such as cocoa honey, with less heat exposure compared to other conventional drying methods. This work aimed to evaluate the efficacy of drying adjuvants for a rapid removal of the water present in cocoa honey via atomization, since this process minimizes the effects of glass transition temperature (Tg) related to materials with high sugar contents. Physical parameters such as the moisture content, hygroscopicity, particle size, and yield of the products obtained were determined. Cocoa honey presented 85.3 ± 0.20 g/100 g of moisture. The formulations successfully decreased moisture content, which was lower than 11.72 ± 0.08 g/100 g in the formulations. Water activity ranged between 0.1464 ± 0.0043 and 0.1562 ± 0.029, with no significant difference between the formulations. The hygroscopicity of cocoa honey powders ranged from 29.29 to 29.87 g of water/100 g of cocoa honey. The combination of 20% maltodextrin and 1% whey protein isolate (WPI) led to the best yield, resulting in a free-flowing powder as the final product. On the other hand, the formulation composed of maltodextrin and whey protein isolate in the ratio of 29:1, respectively, led to the most stable product, with less loss of phenolic compounds during the drying process (6.04%). Regarding particle diameter, 90% of the accumulated distribution did not exceed 57 μm. The greatest dispersion of particles occurs in the Ma20W10 formulation with a span of 2.72, inferring greater variation in size between small (7.01 ± 0.06 μm), medium (18.25 ± 0.37 μm), and large (56.65 ± 1.17 μm) particles. The use of whey protein isolate as an adjuvant proved to be an efficient drying process in the production of cocoa honey powder, and was also advantageous for enriching the nutritional content of the final product due to its protein origin. Furthermore, the combination of spray-drying technology and the use of whey protein isolate as adjuvant led to a free-flowing cocoa honey powder with an adequate particle size and benefits in terms of shelf-life extension, providing new opportunities for the commercialization of cocoa honey as an ingredient for the food industry, with benefits for the circular economy.

O mel de cacau é um subproduto da produção de cacau, cujo volume vem aumentando com o crescimento da indústria de chocolates e derivados. Este líquido produzido naturalmente durante a pós-colheita do cacau tem sabor agridoce e cor clara, possui compostos bioativos, açúcares, vitamina C e fibras dietéticas de importância para a indústria de alimentos. Sua aplicação industrial ainda requer estudos aprofundados e desenvolvimento tecnológico, porém, a literatura já aponta pesquisas para desenvolvimento de novos produtos contendo mel de cacau como sorvetes, geleias mistas, vinagres e bebidas alcoólicas. Atualmente o aproveitamento do mel de cacau ocorre de forma tímida, muitas vezes artesanal, limitada às propriedades produtoras de cacau devido a restrições para seu uso industrial e questões como sua conservação, que hoje em dia ocorre basicamente pelo processo de congelamento. Com os atuais esforços para fortalecimento da bioeconomia e de exploração sustentável das cadeias produtivas, há uma grande necessidade de desenvolvimento de produtos a partir de mel de cacau, de forma a evitar seu descarte e agregar renda para os produtores. Esta revisão descreve a obtenção, a composição físico-química, o potencial bioativo e tecnológico do mel de cacau, dentro da cadeia de beneficiamento do cacau.

Objective: This study aimed to apply artificial neural networks (ANNs) to the flotation stage in the treatment of effluents from a beverage industry. Theoretical Framework: Dissolved air flotation (DAF) is widely used in the treatment of industrial effluents, particularly for the removal of contaminants such as phosphorus and organic matter. Due to the complexity and nonlinear behavior of the involved processes, artificial neural networks have emerged as effective tools for modeling and forecasting in multivariable and dynamic systems. Method: A feedforward ANN model was used, trained with the Levenberg-Marquardt algorithm and implemented in MATLAB. The network was configured with two hidden layers, 250 neurons, and a tansig activation function. Results and Discussion: The simulations showed that the predicted values of total phosphorus were consistent with those observed in industrial practice. The network training resulted in a mean squared error (MSE) of 1.87×10⁻² and a correlation coefficient (R) close to 1, indicating the model’s accuracy in relation to actual data. Research Implications: The application of ANNs proved effective for modeling and analyzing multivariable chemical processes under transient conditions, offering decision-making support for the control and optimization of the flotation process. Originality/Value: The main contribution of this study lies in demonstrating the ability of artificial neural networks (ANNs) to accurately model the behavior of a dissolved air flotation (DAF) system, even in the presence of significant dispersion in operational data. The robustness of the network in the face of variations in input variables confirms its potential to support process optimization, reduce contaminants in the final effluent, and mitigate eutrophication.

Objective: The objective of this study was to apply Artificial Neural Networks (ANNs) to predict the drying curves of Capsicum chinense at different temperatures, aiming to optimize the drying process and ensure the quality of the final product. Theoretical Framework: Artificial Neural Networks (ANNs) have been widely used in modeling and predicting industrial processes, including the drying of agricultural products. Method: An analogy was drawn between the chemical kinetics equations applied to the drying unit operation and the determination of the parameters: kinetic constant (k) and reaction order (n) for various temperatures. The parameters obtained in this analogy fed the neural network modeling. With the data extracted from the drying curves and the aid of GeoGebra software, the drying kinetics parameters, n (reaction order) and k (reaction rate constant), were determined. An RNA (Recurrent Neural Network) was developed in MATLAB to model the drying curves of the 'pimenta-de-cheiro' pepper (Capsicum chinense) at different drying temperatures (40°C to 70°C). Results and Discussion: ANN training, performed with 245 data points and employing the Levenberg-Marquardt algorithm, resulted in high correlation coefficients (R = 1.0000 for training, 0.9999 for validation and 0.99995 for the overall set) and low mean squared error values ​​(minimum MSE of 9.94 × 10⁻¹¹). Furthermore, the ANN demonstrated generalization capacity when predicting drying curves for temperatures between 30°C and 80°C, confirming its applicability in modeling and optimizing the drying process of 'pimenta-de-cheiro' pepper. The neural network obtained was used to predict the drying curves of 'pimenta-de-cheiro' pepper (Capsicum chinense) at different drying temperatures (40°C to 70°C). Three-dimensional figures were obtained correlating the parameters temperature, kinetic constant (k) and reaction order (n). Due to the analogy made between the drying operation and chemical kinetics, in the drying operation, parameter (k) could be called drying kinetic constant, and parameter (n) could be called drying order. Research Implications: ANN modeling is effective in predicting and optimizing industrial processes, especially in the analysis of large volumes of data and complex systems, contributing to effective quality control in food production and improvement of industrial processes. Originality/Value: Artificial Neural Networks (ANNs) stand out as innovative and effective tools in optimizing industrial processes, especially in the drying of agricultural products. Inspired by the functioning of human neurons, ANNs allow for precise and adaptive predictions to solve complex and dynamic problems.

Two new rapid descriptive sensory evaluation methods have been gaining ground in the field sensory evaluation. The Projective Mapping method uses similarities and dissimilarities as a criterion, while Pivot Profile, uses reference criteria. This research aimed to assess panels with 12 and 24 judges, comparing its reproducibility, and evaluate if a non trained panel with a smaller numbers of judges is sufficient for results reliability. Samples of Pilsen beers in different packages were distributed, as well as a reference sample, with different sensorial characteristics. It was possible to observe a slight discrepancy between the results obtained in each of the applied tests. We observed a need for short-term training before the application of the test, aiming for better use of the descriptive terms by the judges. Also, the number of judges influenced the obtained results, being the panels of 24, in both tests, the ones that best described the indicated characteristics.