This article studies the development and implementation of different electronic devices for measuring signals during stress situations, specifically in academic contexts in a student group of the Engineering Department at the University of Pamplona (Colombia). For the research’s development, devices for measuring physiological signals were used through a Galvanic Skin Response (GSR), the electrical response of the heart by using an electrocardiogram (ECG), the electrical activity produced by the upper trapezius muscle (EMG), and the development of an electronic nose system (E-nose) as a pilot study for the detection and identification of the Volatile Organic Compounds profiles emitted by the skin. The data gathering was taken during an online test (during the COVID-19 Pandemic), in which the aim was to measure the student’s stress state and then during the relaxation state after the exam period. Two algorithms were used for the data process, such as Linear Discriminant Analysis and Support Vector Machine through the Python software for the classification and differentiation of the assessment, achieving 100% of classification through GSR, 90% with the E-nose system proposed, 90% with the EMG system, and 88% success by using ECG, respectively.

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Water quality control remains an important topic of public health since some diseases, such as diarrhea, hepatitis, and cholera, are caused by its consumption. The microbiological quality of drinking water relies mainly on monitoring of Escherichia coli, a bacteria indicator which serves as an early sentinel of potential health hazards for the population. In this study, an electronic nose coupled to a volatile extraction system (was evaluated for the detection of the emitted compounds by E. coli in water samples where its capacity for the quantification of the bacteria was demonstrated). To achieve this purpose, the multisensory system was subjected to control samples for training. Later, it was tested with samples from drinking water treatment plants in two locations of Colombia. For the discrimination and classification of the water samples, the principal component analysis method was implemented obtaining a discrimination variance of 98.03% of the measurements to different concentrations. For the validation of the methodology, the membrane filtration technique was used. In addition, two classification methods were applied to the dataset where a success rate of 90% of classification was obtained using the discriminant function analysis and having a probabilistic neural network coupled to the cross-validation technique (leave-one-out) where a classification rate of 80% was obtained. The application of this methodology achieved an excellent classification of the samples, discriminating the free samples of E. coli from those that contained the bacteria. In the same way, it was observed that the system could correctly estimate the concentration of this bacteria in the samples. The proposed method in this study has a high potential to be applied in the determination of E. coli in drinking water since, in addition for estimating concentration ranges and having the necessary sensitivity, it significantly reduces the time of analysis compared to traditional methods.

El presente estudio consiste de una nariz electrónica compuesta de 10 sensores de gases de tipo MQ para la clasificación de muestras de CLON ICS-95 de cacao. El desarrollo de las pruebas fue de tipo cualitativo, obteniendo una huella digital que caracterizó cada clase, las cuales fueron: Fermentado deseado: 144 horas, sobre-fermentado y mala fermentación cacao infectado con monilia. Todos los sensores usados en las diferentes pruebas fueron de material de óxidos metálicos con capacidad de medir diversos tipos de gases, butanos, propanos, alcoholes, monóxido de carbono en diferentes concentraciones, donde al hacer contacto con los volátiles asociados producen una alteración en el voltaje de salida. Las señales se adquirieron mediante un sistema de adquisición de datos basado en tarjeta Arduino y uso del software Labview, permitiendo el almacenamiento de los datos. El algoritmo para la extracción de parámetros, pre-procesamiento y procesamiento de datos se realizó mediante el uso de software Python. Los resultados se analizaron implementando análisis de componente principales PCA y ejecución de dos métodos de pre-procesamiento de datos, como el centrado y escalado de datos, logrando un porcentaje de varianza en los componentes principales de 97.8% y con el método Manhattan se obtuvo un 93.8% del porcentaje de varianza en la componente principal PC1. Con estos resultados se logró observar que el sistema de olfato electrónico fue capaz de clasificar los datos de acuerdo a las clases definidas, fermentado deseado: 144 horas, sobre-fermentado y mala fermentación cacao infectado con monilia