Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs)

Hay un gran número de cultivos de tomate con una amplia gama de características morfológicas, químicas, nutricionales y sensoriales. Son muchos los factores conocidos que influyen en el contenido de nutrientes de cultivos de tomate. Un completo estudio de los efectos de estos factores requeriría un exhaustivo diseño experimental, un enfoque científico multidisciplinario y un método estadístico adecuado. Algunas técnicas de análisis multivariante como análisis de componentes principales (PCA) o el análisis factorial (FA) han sido ampliamente aplicados para buscar patrones en el comportamiento y reducir la dimensión de un conjunto de datos por un nuevo conjunto de variables latentes no correlacionados. Sin embargo, en algunos casos no es útil para sustituir las variables originales con estas variables latentes. En este estudio, la interacción automática (ayuda) del algoritmo de detección y los modelos de una red neuronal artificial (RNA) se aplican como alternativa a la PCA, AF y otras técnicas de análisis multivariante para identificar los componentes fitoquímicos relevantes para la caracterización y la autenticación de los tomates. Para demostrar la viabilidad de la ayuda del algoritmo y del modelo Ann para lograr el propósito de este estudio, ambos métodos se aplican sobre un conjunto de datos con veinticinco parámetros químicos analizados en 167 muestras de tomate de Tenerife (España). Cada muestra de tomate fue definida por tres factores: cultivo, prácticas agrícolas y fecha de cosecha. El modelo lineal general (GLM ligada a la ayuda-AID) de estructura de árbol se organiza en 3 niveles de acuerdo con el número de factores. El ácido p-Coumaric era el compuesto permitido para distinguir las muestras de tomate según el día de la cosecha. Era necesario más de un parámetro químico para distinguir entre las diferentes prácticas agrícolas y entre los cultivos de tomate. Fueron desarrollados varios modelos de ANN, con 25 y 10 variables de entrada, para la predicción de cultivo, prácticas agrícolas y fecha de cosecha. Por último, los modelos con 10 variables de entrada fueron elegidos por situarse entre el 44 y el 100%. El menor encaje recayó en los cultivos y la clasificación, de modo que debe emplearse otro tipo de parámetro químico para identificar los cultivos de tomate.There are a large number of tomato cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics. Many factors are known to affect the nutrient content of tomato cultivars. A complete understanding of the effect of these factors would require an exhaustive experimental design, multidisciplinary scientific approach and a suitable statistical method. Some multivariate analytical techniques such as Principal Component Analysis (PCA) or Factor Analysis (FA) have been widely applied in order to search for patterns in the behaviour and reduce the dimensionality of a data set by a new set of uncorrelated latent variables. However, in some cases it is not useful to replace the original variables with these latent variables. In this study, Automatic Interaction Detection (AID) algorithm and Artificial Neural Network (ANN) models were applied as alternative to the PCA, AF and other multivariate analytical techniques in order to identify the relevant phytochemical constituents for characterization and authentication of tomatoes. To prove the feasibility of AID algorithm and ANN models to achieve the purpose of this study, both methods were applied on a data set with twenty five chemical parameters analysed on 167 tomato samples from Tenerife (Spain). Each tomato sample was defined by three factors: cultivar, agricultural practice and harvest date. General Linear Model linked to AID (GLM-AID) tree structured was organized into 3 levels according to the number of factors. p-Coumaric acid was the compound the allowed to distinguish the tomato samples according to the day of harvest. More than one chemical parameter was necessary to distinguish among different agricultural practices and among the tomato cultivars. Several ANN models, with 25 and 10 input variables, for the prediction of cultivar, agricultural practice and harvest date, were developed. Finally, the models with 10 input variables were chosen with fit’s goodness between 44 and 100%. The lowest fits were for the cultivar classification, this low percentage suggests that other kind of chemical parameter should be used to identify tomato cultivars.• Junta de Galicia. Consellería de Cultura, Educación e Ordenación Universitaria: Beca postdoctoral (Plan 12C) P.P.0000 421S 140.08 • Junta de Extremadura: Ayuda GR10084peerReviewe

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

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 to predict the adsorption capacity of microplastics

Nowadays, there is an extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro- and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict different microplastic/water partition coefficients (log Kd) using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients above 0.92 in the query phase, which indicates that these types of models could be used for the rapid estimation of the absorption of organic contaminants on microplastics.Ministerio de Universidades | Ref. FPU2020/0614

Modeling the Behavior of Amphiphilic Aqueous Solutions

Two types of predictive models based on artificial neural networks (ANN) and quadratic regression model developed in our laboratory will be summarized in this book chapter. Both models were developed to predict the density, speed of sound, kinematic viscosity and surface tension of amphiphilic aqueous solutions. These models were developed taking into account the concentration, the number of carbons and the molecular weight values. The experimental data were compiled from literature and included different surfactants: i) hexyl, ii) octyl, iii) decyl, iv) tetradecyl and v) octadecyl trimethyl ammonium bromide. Neural models present better adjustment values, with R2 values above 0.902 and AAPD values under 2.93% (for all data), than the quadratic regression models. Finally, it is concluded that the quadratic regression and the neural models can be powerful prediction tools for the physical properties of surfactants aqueous solutions

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

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

Essential oils as antimicrobials in crop protection

At present, organic crops have reached an important boom in a society increasingly interested in the conservation of the environment and sustainability. It is evident that a part of the population in the Western world focuses their concern on how to obtain our food and on doing it in a way that is as respectful as possible with the environment. In this review, we present a compilation of the work carried out with the use of essential oils as an alternative in the fight against different bacteria and fungi that attack crops and related products. Given the collected works, the efficacy of essential oils for their use as pesticides for agricultural use is evident

Modelling and prediction of monthly global irradiation using different prediction models

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.Universidade de VigoXunta de Galicia | Ref. POS-B / 2016/001Xunta de Galicia | Ref. K645 P.P.0000 421S 140.0

Pomegranate peel as suitable source of high-added value bioactives: tailored functionalized meat products

In the last few years, the consumer’s concern with the relationship between health and diet has led to the search of foods with functional properties beyond the nutritional. In this framework, the consumption of pomegranate has increased due to their sensorial attributes and remarkable amounts of bioactive compounds, which generate, at the same time, huge amounts of by-products. A search in the Scopus database for the last 10 years has revealed the rising interest in pomegranate peel (PP), the main residue from this fruit. The meat industry is a food sector that has had to search for new alternatives to substitute the use of synthetic preservatives by new natural additives, to extend the self-life and keep the quality attributes of their processed products. This review sets out the main bioactivities of PP extracts, and their incorporation in meat products is elaborated. PP is a good source of bioactive compounds, including phenolic acids, flavonoids and hydrolyzable tannins, which have beneficial health effects. It can be concluded that the reformulation of meat products with PP extracts is a suitable strategy for enhancing their technological characteristics, in addition to conferring functional properties that make them healthier and potentially more acceptable for the consumer.GAIN (Axencia Galega de Innovación) | Ref. IN607A2019/0