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This paper describes a methodology to establish an optimal process design for prickly pear wine production that preserves the peculiar and unique traits of traditional products, generating at the same time, technical information for appropriate design of both bioreactor and overall process. The strategy includes alcoholic fermentation optimization by the mixed native culture composed by Pichia fermentans and Saccharomyces cerevisiae, followed by malolactic fermentation optimization by Oenococcus oeni. The optimization criteria were based on multiple output functions: alcohol content, volatile compounds profile, organic acids profile, and compound contents related to color, which were analyzed by spectroscopy-chromatography methods and sensory analysis. The results showed that the mixed culture inoculated into a bioreactor containing prickly pear juice with 20 °Bx of fermentable sugars concentration, processed at a constant temperature of 20 °C for 240 h, leads to a fermented product with 9.93% (v/v) total alcohol content, and significant abundance of volatile compounds, which provide fruity and ethereal aromatic notes, complemented by a lively but not unpleasant acidity. This young wine was further subjected to malolactic fermentation at constant temperature (16 °C) for 192 h, decreasing malic acid, and balancing volatile compounds contents, thus resulting in a product with better aroma and flavor perception, and a velvety feeling of long aftertaste. Repeated assays showed that the process is stable, predictable, controllable, and reproducible. These results were used for process design and spreadsheet construction in order to simulate the process, and properly select and size the equipment required for such process.

It is difficult to imagine mathematics or engineering to date without nonlinear algebraic systems; these topics have deserved more diverse and vigorous interest over the years. In fact, one of the most important modern branches of mathematics originated in this context, topology, which has been inseparable from homotopy theory, describes a powerful intuitive tool to solve nonlinear algebraic systems. In the present work, we attempt to briefly summarize the theoretical beginnings of homotopy, its eventual transformation into a numerical method, its most reiterative applications in chemical engineering, and some current challenges in this field from an engineering-friendly perspective, especially approachable for chemical engineers.

The essential oil of citrus fruit contains components pleasant sensory characteristics that are appreciated in food, pharmaceutical, and cosmetics industries. In the case of sweet lime (Citrus limetta Risso), is necessary to characterize the essential oil components, to identify potential uses of this fruit. The essential oil of sweet lime was obtained from lime flavedo in four different maturation stages. Steam distillation was employed and then compared with hexane extraction. The identification of the components in the essential oil was carried out by gas chromatography and mass spectrometry. A total of 46 components were found in the essence of lime, among which the highest concentration of compounds present were aldehydes such as limonene. Linalool, sabinene, and bergamol were more abundant than in other varieties. The best extraction method was steam distillation, and the concentrations in stage III from the main terpenic portion were d-limonene with 74.4%, bergamol with 8.23%, and β-pinene with 7.62%.

Tecnolo ´gico y Garcı ´a Cubas s/n, Celaya, Gto 38010, Me ´xicoThe prediction of the oxygen transfer coefficients (k L ) in bubbling columns is studied. Bubbling columns have important applications as bioreactors. It is found that the coefficient is strongly influenced by the motion and geometric characteristics of the bubbles. In the experiments, it is observed that the maximum transfer coefficient is achieved when a measure of the bubble sphericity reaches a minimum and the slip velocity a maximum. The introduction of characteristic parameters to account for the bubble deformation is proposed. Their use greatly improves the prediction of the oxygen transfer coefficient using standard correlations.

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Artificial vision systems (AVS) have become very important in precision agriculture applied to produce high-quality and low-cost foods with high functional characteristics generated through environmental care practices. This article reported the design and implementation of a new fuzzy classification architecture based on the RGB color model with descriptors. Three inputs were used that are associated with the average value of the color components of four views of the tomato; the number of triangular membership functions associated with the components R and B were three and four for the case of component G. The amount of tomato samples used in training were forty and twenty for testing; the training was done using the Matlab© ANFISEDIT. The tomato samples were divided into six categories according to the US Department of Agriculture (USDA). This study focused on optimizing the descriptors of the color space to achieve high precision in the prediction results of the final classification task with an error of 536,995×10-6. The Computer Vision System (CVS) is integrated by an image isolation system with lighting; the image capture system uses a Raspberry Pi 3 and Camera Module Raspberry Pi 2 at a fixed distance and a black background. In the implementation of the CVS, three different color description methods for tomato classification were analyzed and their respective diffuse systems were also designed, two of them using the descriptors described in the literature.