Chemical reactivity in microheterogeneous media

Since the second half of the last century, the science of colloids has undergone a true revolution, from being little more than a collection of qualitative observations of the macroscopic behavior of some complex systems to becoming a discipline with substantial theoretical foundations [...

The Potential of Seaweeds as a Source of Functional Ingredients of Prebiotic and Antioxidant Value

Two thirds of the world is covered by oceans, whose upper layer is inhabited by algae. This means that there is a large extension to obtain these photoautotrophic organisms. Algae have undergone a boom in recent years, with consequent discoveries and advances in this field. Algae are not only of high ecological value but also of great economic importance. Possible applications of algae are very diverse and include anti-biofilm activity, production of biofuels, bioremediation, as fertilizer, as fish feed, as food or food ingredients, in pharmacology (since they show antioxidant or contraceptive activities), in cosmeceutical formulation, and in such other applications as filters or for obtaining minerals. In this context, algae as food can be of help to maintain or even improve human health, and there is a growing interest in new products called functional foods, which can promote such a healthy state. Therefore, in this search, one of the main areas of research is the extraction and characterization of new natural ingredients with biological activity (e.g., prebiotic and antioxidant) that can contribute to consumers? well-being. The present review shows the results of a bibliographic survey on the chemical composition of macroalgae, together with a critical discussion about their potential as natural sources of new functional ingredients.Fil: Gomez Zavaglia, Andrea. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos; ArgentinaFil: Prieto Lage, Miguel Ángel. Universidad de Vigo; EspañaFil: Jiménez López, Cecilia. Universidad de Vigo.; EspañaFil: Mejuto, Juan Carlos. Universidad de Vigo. Facultad de Ciencias de Ourense; EspañaFil: Simal Gándara, Jesús. Universidad de Vigo; Españ

Influence of Amphiphiles on Percolation of AOT-Based Microemulsions Prediction Using Artificial Neural Networks

In this chapter, the ability of artificial neural networks was evaluated to predict the influence of amphiphiles as additive upon the electrical percolation of dioctyl sodium sulfosuccinate (AOT)/isooctane/water microemulsions. In particular, water/AOT/isooctane microemulsion behaviour has been modelled. These microemulsions have been developed in presence of 1-n-alcohols, 2-n-alcohols, n-alkylamines and n-alkyl acids. In all cases, a neural network has been obtained to predict with accuracy the experimental behaviour to identify the physico-chemical variables (such as additive concentration, molecular mass, log P, pKa or chain length) that exert a greater influence on the model. All models are valuable tools to evaluate the percolation temperature for AOT-based microemulsions

Assessment of different machine learning methods for reservoir outflow forecasting

Reservoirs play an important function in human society due to their ability to hold and regulate the flow. This will play a key role in the future decades due to climate change. Therefore, having reliable predictions of the outflow from a reservoir is necessary for early warning systems and adequate water management. In this sense, this study uses three approaches machine learning (ML)-based techniques—Random Forest (RF), Support Vector Machine (SVM) and artificial neural network (ANN)—to predict outflow one day ahead of eight different dams belonging to the Miño-Sil Hydrographic Confederation (Galicia, Spain), using three input variables of the current day. Mostly, the results obtained showed that the suggested models work correctly in predicting reservoir outflow in normal conditions. Among the different ML approaches analyzed, ANN was the most appropriate technique since it was the one that provided the best model in five reservoirs.Ministerio de Ciencia e Innovación | Ref. FPU2020/0614

Machine learning applied to the oxygen-18 isotopic composition, salinity and temperature/potential temperature in the Mediterranean sea

This study proposed different techniques to estimate the isotope composition (δ18O), salinity and temperature/potential temperature in the Mediterranean Sea using five different variables: (i–ii) geographic coordinates (Longitude, Latitude), (iii) year, (iv) month and (v) depth. Three kinds of models based on artificial neural network (ANN), random forest (RF) and support vector machine (SVM) were developed. According to the results, the random forest models presents the best prediction accuracy for the querying phase and can be used to predict the isotope composition (mean absolute percentage error (MAPE) around 4.98%), salinity (MAPE below 0.20%) and temperature (MAPE around 2.44%). These models could be useful for research works that require the use of past data for these variables.Universidade de Vigo | Ref. 0000 131H TAL 641Xunta de Galicia | Ref. ED431C 2018/42Xunta de Galicia | Ref. POS-B / 2016/00

Global solar irradiation modelling and prediction using machine learning models for their potential use in renewable energy applications

Global solar irradiation is an important variable that can be used to determine the suitability of an area to install solar systems; nevertheless, due to the limitations of requiring measurement stations around the entire world, it can be correlated with different meteorological parameters. To confront this issue, different locations in Rias Baixas (Autonomous Community of Galicia, Spain) and combinations of parameters (month and average temperature, among others) were used to develop various machine learning models (random forest -RF-, support vector machine -SVM- and artificial neural network -ANN-). These three approaches were used to model and predict (one month ahead) monthly global solar irradiation using the data from six measurement stations. Afterwards, these models were applied to seven different measurement stations to check if the knowledge acquired could be extrapolated to other locations. In general, the ANN models offered the best results for the development and testing phases of the model, as well as for the phase of knowledge extrapolation to other locations. In this sense, the selected ANNs obtained a mean absolute percentage error (MAPE) value between 3.9 and 13.8% for the model development and an overall MAPE between 4.1 and 12.5% for the other seven locations. ANNs can be a capable tool for modelling and predicting monthly global solar irradiation in areas where data are available and for extrapolating this knowledge to nearby areas

Oxidation of aldehydes used as food additives by peroxynitrite

Benzaldehyde and its derivatives are used as food supplements. These substances can be used mainly as flavorings or as antioxidants. Besides, peroxynitrite, an oxidizing agent, could be formed in canned food. Both species could react between them. The present article has focused on the kinetic study of the oxidation of aldehydes by peroxynitrite. A reaction mechanism that justifies all the experimental results is proposed. This mechanism, in acidic media, passes through three competitive pathways: (a) a radical attack that produces benzoic acid. (b) peracid oxidation, and (c) a nucleophilic attack of peroxynitrous acid over aldehyde to form an intermediate, X, that produces benzoic acid, or, through a Cannizzaro-type reaction, benzoic acid and benzyl alcohol. All rate constants involved in the third pathway (c) have been calculated. These results have never been described in the literature in acid media. A pH effect was analyzed

Artificial Intelligence Models to Predict the Influence of Linear and Cyclic Polyethers on the Electric Percolation of Microemulsions

This book chapter presents three predictive models, based on artificial neural networks, to determine the percolation temperature of different AOT microemulsions in the presence of different additives (crown ethers, glymes, and polyethylene glycols), which were developed in our laboratory by different authors. An artificial neural network model has been developed for each additive. The models developed, multilayer perceptron, were implemented with different input variables (chosen among the variables that define the packing or its chemical properties) and different intermediate layers. The best model for crown ethers has a topology of 10-8-1, for glymes the selected topology is 5-5-1, and for polyethylene glycol, the best topology was 5-8-8-5-1. The selected models are capable of predicting the electrical percolation temperature with good adjustments in terms of the root mean square error (RMSE), presenting values below 1°C for glymes and polyethylene glycols. According to these results, it can be concluded that the models presented good predictive capacity for percolation temperature. Nevertheless, the adjustments obtained for the crown ethers model indicate that it would be convenient to study new input variables, increase the number of cases, and even use other training algorithms and methods

Encapsulation of Essential Oils by Cyclodextrins: Characterization and Evaluation

The essential oils normally had low physicochemical stability and low solubility in water. These facts limit their industrial applications in general and in food formulations particularly. This chapter characterizes the physicochemical properties and the antioxidant and antimicrobial activities of three encapsulated essential oils – guava leaf, yarrow and black pepper essential oils – in hydroxypropyl-β-cyclodextrin (HPβCD)

Functional foods based on the recovery of bioactive ingredients from food and algae by-products by emerging extraction technologies and 3D printing

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUG3D food printing is an emerging technology developed to facilitate the life of consumers and food enterprises. This technology allows to obtain any type of new foods according to our wishes. It is possible to develop a food with the exact nutritive value necessary for our body, with the most benefiting nutrients we want, or without any ingredients that we have an allergy, and even predict or personalize the taste, the color, the shape, and the size of a food. Therefore, 3D food printing is considered a promising strategy for developing healthy foods. On the other hand, many foods enterprises release high amounts of waste from their processing activities. These wastes contain many bioactive ingredients such as polyphenols, carotenoids, vitamins, minerals, fibers, unsaturated fatty acids, among others, which have physiological and health benefits. Similarly, several bioactive compounds have been identified in algae. They can be extracted by conventional methods with solvents such as water, ethanol, methanol, chloroform, acetone, and many others, but with some limits like environmental contamination, human toxicity, and low extraction rate. For these reasons, it will be interesting to use emerging extraction technologies to recover bioactive compounds and use them in a 3D food printer to make functional foods that can bring a targeted health benefit to consumers