N. Laguarda-Miró

Low-Cost Capacitive Humidity Sensor for Application Within Flexible RFID Labels Based on Microcontroller Systems

This paper reports on the fabrication of a capacitive-type relative humidity (RH) sensor using screen printing processes for electrode film deposition. The applied measurement method based on microcontrollers is also reported. In this specific case, the microcontroller is used to measure RH by means of a capacitive sensor with a simple low-cost electronic system. In addition, a comparison between two different types of polyester substrates [Melinex (DuPont) and CG3460 (3M)] is shown. Both polyester substrates have similar properties, and only the thickness is different (175 μm for Melinex and 100 μm for CG3460). A nonlineal response has been obtained in this type of sensors. In order to linearize the response and reduce the external hardware, an artificial neural network embedded into the microcontroller has been used.

An Embedded Simplified Fuzzy ARTMAP Implemented on a Microcontroller for Food Classification

In the present study, a portable system based on a microcontroller has been developed to classify different kinds of honeys. In order to do this classification, a Simplified Fuzzy ARTMAP network (SFA) implemented in a microcontroller has been used. Due to memory limits when working with microcontrollers, it is necessary to optimize the use of both program and data memory. Thus, a Graphical User Interface (GUI) for MATLAB® has been developed in order to optimize the necessary parameters to programme the SFA in a microcontroller. The measures have been carried out by potentiometric techniques using a multielectrode made of seven different metals. Next, the neural network has been trained on a PC by means of the GUI in Matlab using the data obtained in the experimental phase. The microcontroller has been programmed with the obtained parameters and then, new samples have been analysed using the portable system in order to test the model. Results are very promising, as an 87.5% recognition rate has been achieved in the training phase, which suggests that this kind of procedures can be successfully used not only for honey classification, but also for many other kinds of food.

Glyphosate Detection by Means of a Voltammetric Electronic Tongue and Discrimination of Potential Interferents

A new electronic tongue to monitor the presence of glyphosate (a non-selective systemic herbicide) has been developed. It is based on pulse voltammetry and consists in an array of three working electrodes (Pt, Co and Cu) encapsulated on a methacrylate cylinder. The electrochemical response of the sensing array was characteristic of the presence of glyphosate in buffered water (phosphate buffer 0.1 mol·dm−3, pH 6.7). Rotating disc electrode (RDE) studies were carried out with Pt, Co and Cu electrodes in water at room temperature and at pH 6.7 using 0.1 mol·dm−3 of phosphate as a buffer. In the presence of glyphosate, the corrosion current of the Cu and Co electrodes increased significantly, probably due to the formation of Cu2+ or Co2+ complexes. The pulse array waveform for the voltammetric tongue was designed by taking into account some of the redox processes observed in the electrochemical studies. The PCA statistical analysis required four dimensions to explain 95% of variance. Moreover, a two-dimensional representation of the two principal components differentiated the water mixtures containing glyphosate. Furthermore, the PLS statistical analyses allowed the creation of a model to correlate the electrochemical response of the electrodes with glyphosate concentrations, even in the presence of potential interferents such as humic acids and Ca2+. The system offers a PLS prediction model for glyphosate detection with values of 098, −2.3 × 10−5 and 0.94 for the slope, the intercept and the regression coefficient, respectively, which is in agreement with the good fit between the predicted and measured concentrations. The results suggest the feasibility of this system to help develop electronic tongues for glyphosate detection.

Glyphosate detection with ammonium nitrate and humic acids as potential interfering substances by pulsed voltammetry technique

Pulsed voltammetry has been used to detect and quantify glyphosate on buffered water in presence of ammonium nitrate and humic substances. Glyphosate is the most widely used herbicide active ingredient in the world. It is a non-selective broad spectrum herbicide but some of its health and environmental effects are still being discussed. Nowadays, glyphosate pollution in water is being monitored but quantification techniques are slow and expensive. Glyphosate wastes are often detected in countryside water bodies where organic substances and fertilizers (commonly based on ammonium nitrate) may also be present. Glyphosate also forms complexes with humic acids so these compounds have also been taken into consideration. The objective of this research is to study the interference of these common pollutants in glyphosate measurements by pulsed voltammetry. The statistical treatment of the voltammetric data obtained lets us discriminate glyphosate from the other studied compounds and a mathematical model has been built to quantify glyphosate concentrations in a buffer despite the presence of humic substances and ammonium nitrate. In this model, the coefficient of determination (R(2)) is 0.977 and the RMSEP value is 2.96 × 10(-5) so the model is considered statistically valid.

An Electrochemical Impedance Spectroscopy-Based Technique to Identify and Quantify Fermentable Sugars in Pineapple Waste Valorization for Bioethanol Production

Electrochemical Impedance Spectroscopy (EIS) has been used to develop a methodology able to identify and quantify fermentable sugars present in the enzymatic hydrolysis phase of second-generation bioethanol production from pineapple waste. Thus, a low-cost non-destructive system consisting of a stainless double needle electrode associated to an electronic equipment that allows the implementation of EIS was developed. In order to validate the system, different concentrations of glucose, fructose and sucrose were added to the pineapple waste and analyzed both individually and in combination. Next, statistical data treatment enabled the design of specific Artificial Neural Networks-based mathematical models for each one of the studied sugars and their respective combinations. The obtained prediction models are robust and reliable and they are considered statistically valid (CCR% > 93.443%). These results allow us to introduce this EIS-based technique as an easy, fast, non-destructive, and in-situ alternative to the traditional laboratory methods for enzymatic hydrolysis monitoring.

An Electrochemical Impedance Spectroscopy System for Monitoring Pineapple Waste Saccharification

Electrochemical impedance spectroscopy (EIS) has been used for monitoring the enzymatic pineapple waste hydrolysis process. The system employed consists of a device called Advanced Voltammetry, Impedance Spectroscopy & Potentiometry Analyzer (AVISPA) equipped with a specific software application and a stainless steel double needle electrode. EIS measurements were conducted at different saccharification time intervals: 0, 0.75, 1.5, 6, 12 and 24 h. Partial least squares (PLS) were used to model the relationship between the EIS measurements and the sugar determination by HPAEC-PAD. On the other hand, artificial neural networks: (multilayer feed forward architecture with quick propagation training algorithm and logistic-type transfer functions) gave the best results as predictive models for glucose, fructose, sucrose and total sugars. Coefficients of determination (R2) and root mean square errors of prediction (RMSEP) were determined as R2 > 0.944 and RMSEP < 1.782 for PLS and R2 > 0.973 and RMSEP < 0.486 for artificial neural networks (ANNs), respectively. Therefore, a combination of both an EIS-based technique and ANN models is suggested as a promising alternative to the traditional laboratory techniques for monitoring the pineapple waste saccharification step.