Stability assessment of extracts obtained from Arbutus unedo L. fruits in powder and solution systems using machine-learning methodologies.

Arbutus unedo L. (strawberry tree) has showed considerable content in phenolic compounds, especially flavan-3-ols (catechin, gallocatechin, among others). The interest of flavan-3-ols has increased due their bioactive actions, namely antioxidant and antimicrobial activities, and by association of their consumption to diverse health benefits including the prevention of obesity, cardiovascular diseases or cancer. These compounds, mainly catechin, have been showed potential for use as natural preservative in foodstuffs; however, their degradation is increased by pH and temperature of processing and storage, which can limit their use by food industry. To model the degradation kinetics of these compounds under different conditions of storage, three kinds of machine learning models were developed: i) random forest, ii) support vector machine and iii) artificial neural network. The selected models can be used to track the kinetics of the different compounds and properties under study without the prior knowledge requirement of the reaction system.

Assessment of neural networks and time series analysis to forecast airborne Parietaria pollen presence in the Atlantic coastal regions.

Pollen forecasting models are a useful tool with which to predict episodes of type I allergenic risk and other environmental or biological processes. Parietaria is a wind-pollinated perennial herb that is responsible for many cases of severe pollinosis due to its high pollen production, the long persistence of the pollen grains in the atmosphere and the abundant presence of allergens in their cytoplasm and walls. The aim of this paper is to develop artificial neural networks (ANNs) to predict airborne Parietaria pollen concentrations in the northwestern part of Spain using a 19-year data set (1999-2017). The results show a significant increase in the length of time Parietaria pollen is in the air, as well as significant increases in the annual Parietaria pollen integral and mean daily maximum pollen value in the year. The Neural models show the ability to forecast airborne Parietaria pollen concentrations 1, 2, and 3 days ahead. A developed model with five input variables used to predict concentrations of airborne Parietaria pollen 1 day ahead shows determination coefficients between 0.618 and 0.652.

Application of transit data analysis and artificial neural network in the prediction of discharge of Lor River, NW Spain.

Transit data analysis and artificial neural networks (ANNs) have proven to be a useful tool for characterizing and modelling non-linear hydrological processes. In this paper, these methods have been used to characterize and to predict the discharge of Lor River (North Western Spain), 1, 2 and 3 days ahead. Transit data analyses show a coefficient of correlation of 0.53 for a lag between precipitation and discharge of 1 day. On the other hand, temperature and discharge has a negative coefficient of correlation (-0.43) for a delay of 19 days. The ANNs developed provide a good result for the validation period, with R(2) between 0.92 and 0.80. Furthermore, these prediction models have been tested with discharge data from a period 16 years later. Results of this testing period also show a good correlation, with R(2) between 0.91 and 0.64. Overall, results indicate that ANNs are a good tool to predict river discharge with a small number of input variables.

Airborne castanea pollen forecasting model for ecological and allergological implementation.

Castanea sativa Miller belongs to the natural vegetation of many European deciduous forests prompting impacts in the forestry, ecology, allergological and chestnut food industry fields. The study of the Castanea flowering represents an important tool for evaluating the ecological conservation of North-Western Spain woodland and the possible changes in the chestnut distribution due to recent climatic change. The Castanea pollen production and dispersal capacity may cause hypersensitivity reactions in the sensitive human population due to the relationship between patients with chestnut pollen allergy and a potential cross reactivity risk with other pollens or plant foods. In addition to Castanea pollen's importance as a pollinosis agent, its study is also essential in North-Western Spain due to the economic impact of the industry around the chestnut tree cultivation and its beekeeping interest. The aim of this research is to develop an Artificial Neural Networks for predict the Castanea pollen concentration in the atmosphere of the North-West Spain area by means a 20years data set. It was detected an increasing trend of the total annual Castanea pollen concentrations in the atmosphere during the study period. The Artificial Neural Networks (ANNs) implemented in this study show a great ability to predict Castanea pollen concentration one, two and three days ahead. The model to predict the Castanea pollen concentration one day ahead shows a high linear correlation coefficient of 0.784 (individual ANN) and 0.738 (multiple ANN). The results obtained improved those obtained by the classical methodology used to predict the airborne pollen concentrations such as time series analysis or other models based on the correlation of pollen levels with meteorological variables.

Multilayer perceptron neural network for flow prediction.

Artificial neural networks (ANNs) have proven to be a tool for characterizing, modeling and predicting many of the non-linear hydrological processes such as rainfall-runoff, groundwater evaluation or simulation of water quality. After proper training they are able to generate satisfactory predictive results for many of these processes. In this paper they have been used to predict 1 or 2 days ahead the average and maximum daily flow of a river in a small forest headwaters in northwestern Spain. The inputs used were the flow and climate data (precipitation, temperature, relative humidity, solar radiation and wind speed) as recorded in the basin between 2003 and 2008. Climatic data have been utilized in a disaggregated form by considering each one as an input variable in ANN(1), or in an aggregated form by its use in the calculation of evapotranspiration and using this as input variable in ANN(2). Both ANN(1) and ANN(2), after being trained with the data for the period 2003-2007, have provided a good fit between estimated and observed data, with R(2) values exceeding 0.95. Subsequently, its operation has been verified making use of the data for the year 2008. The correlation coefficients obtained between the data estimated by ANNs and those observed were in all cases superior to 0.85, confirming the capacity of ANNs as a model for predicting average and maximum daily flow 1 or 2 days in advance.

Evaluation of atmospheric Poaceae pollen concentration using a neural network applied to a coastal Atlantic climate region.

In the South of Europe an important percentage of population suffers pollen allergies, being the Poaceae pollen the major source. One of aerobiology's objectives is to develop statistical models enabling the short- and long-term prediction of atmospheric pollen concentrations to take preventative measures to protect allergic patients from the severity of the atmospheric pollen season. The implementation of a computational model based on supervised MLP neural network was applied for the prediction of the atmospheric Poaceae pollen concentration. There is a good correlation between the values predicted by the ANN for the training cases in comparison with the real pollen concentrations. A high coefficient of linear regression (R(2)) of 0.9696 was obtained. The accuracy of the neural network developed was tested with data from 2006 and 2007, which was not taken into account to establish the aforementioned models. Neural networks provided us a good tool to forecasting allergenic airborne pollen concentration helping the automation of the prediction system in the aerobiological information diffusion to the population suffering from allergic problems.