Desarrollo de un frecuencímetro de conteo recíproco con un FPGA para sensores basados en resonador de cuarzo

Los sensores basados en resonadores de cuarzo son ampliamente utilizados en dispositivos de detección de olores conocidos como narices electrónicas (NE). Para medir la respuesta de este tipo de sensores es esencial el uso de un frecuencímetro. Existen diferentes métodos para implementar un frecuencímetro, tal como el método de conteo directo de frecuencia, el cual está restringido en la generación de datos, ya quesi se desea un incremento en el número de muestras por segundo, implicaría una pérdida en la resolución del instrumento debido a su principio de operación. Por otro lado, el método de conteo recíproco de frecuencia es un método que ofrece una alternativa en la medición de respuesta de sensores de gas. Por lo tanto en este trabajo se presenta el desarrollo de un frecuencímetro aplicando el método de conteo recíproco de frecuencia para sensores de gas utilizando un FPGA. Se obtuvieron resultados utilizando como patrón de entrada un generador de funciones. Este frecuencímetro tiene la ventaja de tener una mayor taza de muestreo y resolución que un frecuencímetro comercial. Finalmente se realizaron experimentos utilizando sensores de gas QCM a temperatura ambiente aplicando muestras de etanol para obtener datos reales y comprobar la efectividad del sistema obteniendo resultados satisfactorios, dado que los datos arrojados coinciden con el comportamiento típico de este tipo de sensores.Palabra(s) Clave(s): frecuencímetro, FPGA, nariz electrónica, sensores de gas QCM

Predicting human odor perception represented by continuous values from mass spectra of essential oils resembling chemical mixtures.

There have been recent advances in predicting odor characteristics using molecular structure parameters of chemicals. Although the molecular structure parameters are available for each chemical, they cannot be used for chemical mixtures. This study will elucidate a computational method of predicting human odor perception from the mass spectra of chemical mixtures such as essential oils. Furthermore, a method for obtaining similarity among odor descriptors has been proposed although the dataset contains binary values only. When the database indicates a set of odor descriptors for one sample, only binary data are available and the correlation between the similar descriptors disappears. Thus, the prediction performance degrades for not considering the similarity among the odor descriptors. Since mass spectra dataset is highly dimensional, we use auto-encoder to learn the compressed representation from the mass spectra of essential oils in its bottleneck hidden layer and then accomplishes the hierarchical clustering to create odor descriptor groups with similar odor impressions using a matrix of continuous value-based correlation coefficient as well as natural language processing. This work will help to expatiate the process of overcoming binary value problem and find out the similarity among odor descriptors using machine learning with natural language semantic representation of words. To overcome the problem of disproportionate ratio of positive and negative class for both the continuous value-based correlation coefficient and word similarity based models, we use Synthetic Minority Oversampling Technique (SMOTE). This model allows us to predict human odor perception through computer simulations by forming odor descriptors group. Accordingly, this study demonstrates the feasibility of ensembling machine learning with natural language processing and SMOTE approach for predicting odor descriptor group from mass spectra of essential oils

Study of Room Temperature Ionic Liquids as Gas Sensing Materials in Quartz Crystal Microbalances

Twenty-eight quartz crystal microbalance (QCM) sensors coated with different sensing films were tested and analyzed in this work; twenty-three sensors were coated in different room temperature ionic liquids (RTILs) and five additional QCM sensors were coated with conventional films commonly used as stationary phases in gas chromatography. Four volatile organic compounds (VOCs), in gaseous phase—hexanol, butyl acetate, 2-hexanone, and hexanoic acid—were measured. Two transducer mechanisms were used; resonant frequency shift and resistance shift of a QCM Mason equivalent circuit. The sensors were characterized by their sensitivity to the VOCs and their discrimination power of the four VOCs. The highest separation among VOCs was obtained when frequency and resistance information of both RTIL and conventional films was used, a sensor array composed by two RTILs (1-butyl-1-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide and 1-hexyl-3-methylimidazolium hexafluorophosphate) and two conventional films (tricresyl phosphate and apiezon-L) was found to improve the Wilks lambda separation for the tested gases two orders of magnitude compared to the Wilks lambda using only a conventional films array

Odor Impression Prediction from Mass Spectra.

The sense of smell arises from the perception of odors from chemicals. However, the relationship between the impression of odor and the numerous physicochemical parameters has yet to be understood owing to its complexity. As such, there is no established general method for predicting the impression of odor of a chemical only from its physicochemical properties. In this study, we designed a novel predictive model based on an artificial neural network with a deep structure for predicting odor impression utilizing the mass spectra of chemicals, and we conducted a series of computational analyses to evaluate its performance. Feature vectors extracted from the original high-dimensional space using two autoencoders equipped with both input and output layers in the model are used to build a mapping function from the feature space of mass spectra to the feature space of sensory data. The results of predictions obtained by the proposed new method have notable accuracy (R≅0.76) in comparison with a conventional method (R≅0.61)