Abstract Carotenoids have a low solubility in water, and therefore their incorporation in foods is impaired. The nanoemulsions are able to protect these compounds and enhance bioavailability. The objective of this study was to develop nanoemulsions with added beta-carotene, evaluating the influence of process variables and the stability at a long storage period. To assess the effects of these variables a Box-Behnken design was performed. The best condition to form a stable emulsion was 7% (w.w-1) of surfactant and 40 °C. All formulations subjected to a thermal stress test remained stable after the test. The use of a higher concentration of soy lecithin (7%, w.w-1) in the development of emulsions conferred greater stability after a freeze-defrost cycle. Furthermore, with this concentration of surfactant, and using a high-speed homogenizer under conditions of 10,000 rpm, 30 °C, and 20 min of shaking time, it was possible to develop stable nanoemulsion with an average diameter of 429 nm. Moreover, the zeta potential indicated system stability. Thus, it was possible to obtain stable nanoemulsion without the use of subsequent equipment, which makes the process less expensive.

Neste trabalho, foi construído um estimador neuro-fuzzy da concentração de biomassa da microalga Synechococcus nidulans a partir de concentrações iniciais da batelada, visando possibilitar a predição da produtividade. Nove experimentos em réplica foram realizados. O crescimento foi acompanhado diariamente pela transmitância do meio e mantido até o final da fase exponencial de crescimento. O treinamento das redes ocorreu segundo delineamento experimental 3³, os fatores foram o número de dias no vetor de entrada (3, 5 e 7 dias), o número de clusters (10, 30 e 50 clusters) e o valor de abrandamento do filtro interno (Sigma) (0,30, 0,45 e 0,60). A variável resposta foi o somatório do erro quadrático das validações. Estas possuíam 24 (A) e 18 (B) dias de crescimento. As validações demonstraram que, em experimentos de longo período (Validação A), é necessário usar poucos clusters e Sigmas altos. Já, em curtos períodos (Validação B), o Sigma não gera alterações. O ponto ótimo ocorreu com 3 dias na entrada, com 10 clusters e Sigma de 0,60, cujo coeficiente de determinação médio foi 0,95. O estimador neuro-fuzzy mostrou-se uma alternativa robusta para predição do crescimento desta microalga.

In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days), number of clusters (10, 30 and 50 clusters) and internal weight softening parameter (Sigma) (0.30, 0.45 and 0.60). These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A) and 18 (B) days of culture growth. The validations demonstrated that in long-term experiments (Validation A) the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B), Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.