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The main aim of this study was to optimize the conditions for bromelain extraction by reversed micelles from pineapple juice (Ananas comosus). The purification was carried out in batch extraction and a micro-column with pulsed caps for continuous extraction. The cationic micellar solution was made of BDBAC as a surfactant, isooctane as a solvent and hexanol as a co-solvent. For the batch process, a purification factor of 3 times at the best values of surfactant agent, co-solvent and salt concentrations, pH of the back and forward extractions were, 100 mM, 10% v/v, 1 M, 3.5 and 8, respectively. For the continuous operation, independent variables optimal point was determined: ratio between light phase flow rate and total flow rate equal to 0.67 and 1 second for the time interval between the pulses. This optimal point led to a productivity of 1.29 mL/min and a purification factor of 4.96.

Este trabalho propõe a realização de um estudo comparativo do desempenho de controladores Fuzzy e convencional PID aplicados ao controle de temperatura de um processo de precipitação de bromelina do extrato aquoso de resíduos de abacaxi. Uma análise quantitativa da não-linearidade do processo foi realizada baseada na metodologia de curva de reação, aplicada em diferentes momentos da batelada, caracterizando o sistema por possuir diferente sensibilidade às ações de controle ao longo do tempo. O controlador convencional foi sintonizado a partir da aplicação das equações de Ziegler-Nichols aos parâmetros do processo obtidos nos instantes iniciais do experimento, seguido de sintonia fina por tentativa-e-erro. A sintonia do controlador Fuzzy consistiu na alteração do universo de discurso, na base de regras e na disposição das funções de pertinência, utilizando-se para isto o conhecimento obtido na análise das curvas de reação obtidas. Foi observado um melhor desempenho do controlador Fuzzy, apresentando menor valor da integral de erro absoluto multiplicado pelo tempo (ITAE), maior recuperação de atividade enzimática e menor consumo de energia elétrica para o resfriamento do sistema.

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Pink peppercorns are among the most sophisticated condiments in the international cuisine. This culinary spice is obtained from dried fruits of Schinus terebinthifolius Raddi, a species native to South America. In this work, a methodology for the assessment of pink peppercorn quality under various drying conditions was defined. Experiments were performed in a pilot tray dryer, which ensured integrity of the product. A central composite rotatable design with 11 experiments was devised to study the influence of drying air temperature (35-75 ∘ C) and air velocity (0.3-0.9 m/s) on product quality, assessed by moisture content, color (CIELAB system), and volatile compounds. The essential oils of fresh and dried fruits were extracted by hydrodistillation and analyzed by gas chromatography coupled to mass spectrometry. Air temperature had the greatest influence on the quality parameters under study, while air velocity had no statistically significant effect. Considering all quality criteria, temperatures between 40 and 55 ∘ C provided the best compromise, yielding an adequate moisture content in the dried product without dramatic degradation of color and essential oil.

The growing consumption of low- and reduced-fat dairy products demands routine control of their authenticity by health agencies. The usual analyses of fat in dairy products are very simple laboratory methods; however, they require manipulation and use of reagents of a corrosive nature, such as sulfuric acid, to break the chemical bounds between fat and proteins. Additionally, they generate chemical residues that require an appropriate destination. In this work, the use of an artificial neural network based on simple instrumental analyses, such as pH, color, and hardness (inputs) is proposed for the classification of commercial yogurts in the low- and reduced-fat categories (outputs). A total of 108 strawberry-flavored yogurts (48 probiotic low-fat, 36 low-fat, and 24 full-fat yogurts) belonging to several commercial brands and from different batches were used in this research. The statistical analysis showed different features for each yogurt category; thus, a database was built and a neural model was trained with the Levenberg-Marquardt algorithm by using the neural network toolbox of the software MATLAB 7.0.1. Validation with unseen data pairs showed that the proposed model was 100% efficient. Because the instrumental analyses do not require any sample preparation and do not produce any chemical residues, the proposed procedure is a fast and interesting approach to monitoring the authenticity of these products.