Daniel Calvo

Beer classification by means of a potentiometric electronic tongue

In this work, an electronic tongue (ET) system based on an array of potentiometric ion-selective electrodes (ISEs) for the discrimination of different commercial beer types is presented. The array was formed by 21 ISEs combining both cationic and anionic sensors with others with generic response. For this purpose beer samples were analyzed with the ET without any pretreatment rather than the smooth agitation of the samples with a magnetic stirrer in order to reduce the foaming of samples, which could interfere into the measurements. Then, the obtained responses were evaluated using two different pattern recognition methods, principal component analysis (PCA), which allowed identifying some initial patterns, and linear discriminant analysis (LDA) in order to achieve the correct recognition of sample varieties (81.9% accuracy). In the case of LDA, a stepwise inclusion method for variable selection based on Mahalanobis distance criteria was used to select the most discriminating variables. In this respect, the results showed that the use of supervised pattern recognition methods such as LDA is a good alternative for the resolution of complex identification situations. In addition, in order to show an ET quantitative application, beer alcohol content was predicted from the array data employing an artificial neural network model (root mean square error for testing subset was 0.131 abv).

Multicomponent Titration of Calcium + Magnesium Mixtures Employing a Potentiometric Electronic‐Tongue

Abstract A fast novel potentiometric titration procedure is proposed, in which the detection system is formed by a potentiometric electronic tongue. The titration consists in a reduced number of fixed titrant additions to the sample and the recording of the potentials of an array of Ion Selective Electrodes. The obtained data matrix is entered to an Artificial Neural Network response model, previously trained to furnish concentrations of a multicomponent mixture. The principle is demonstrated with automated EDTA titration of mixtures of Ca2+ and Mg2+ at fixed pH 8.5. In this case, five 2 ml fixed volume additions plus the readings of four sensors were adequate for the resolution of the mixture. In the conditions used, titration was feasible up to 3.3 mM total ion concentration, the precision was estimated as 3.20% RSD for Ca2+ and 2.76% RSD for Mg2+ (n=5), and the detection limits 0.16 mM Ca2+ and 0.26 mM Mg2+. The procedure was applied to mineral waters and compared with reference methods (correlation=0.92 for Ca2+ and correlation=0.89 for Mg2+, n=14).

EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game

Video games are promising tools in educational environments since they have features that can promote learning in a playful environment. Formerly, we identified mathematics learning opportunities in a real time strategy video game. Going further, in order to precisely understand which information the students use to solve the challenges provided by the video game, this paper presents an eye tracker based tool to identify processes of mathematics problem solving while playing the game. The first preliminary results show the potential of the tool to further identify metacognitive and mathematics problem solving processes.

Procedure 45 An electronic tongue made of coated wire potentiometric sensors for the determination of alkaline ions: Use of artificial neural networks for its response model

Publisher Summary This chapter presents a procedure to prepare an array of potentiometric sensors (i.e., coated-wire type) for potassium and ammonium ions based on valinomycin and nonactin, respectively; and a third sensor with generic response to both ions based on dibenzo. The three electrodes form the sensor array for the electronic tongue. It also discusses the steps for preparation and measurement of a number of calibration standards that contain the ammonium ion, the potassium ion or both ions simultaneously. These two-dimensional combined standards are generated systematically, trying to provide the maximum variability to the system. The analytical information is obtained using the array of three potentiometric poly vinyl chloride membrane sensors, one for the ammonium ion, one for the potassium ion and one for the generic response potassium and ammonium, gathering at the same time crossed information because of interfering effects. The response surface to both ions may be fitted according to Nicolsky–Eisenman for each constructed electrode. Additionally, the construction of the response model is presented based on artificial neural networks, using the shareware program EasyNN-plus.