Data classification methodology for electronic noses using uniform manifold approximation and projection and extreme learning machine

The classification and use of robust methodologies in sensor array applications of electronic noses (ENs) remain an open problem. Among the several steps used in the developed methodologies, data preprocessing improves the classification accuracy of this type of sensor. Data preprocessing methods, such as data transformation and data reduction, enable the treatment of data with anomalies, such as outliers and features, that do not provide quality information; in addition, they reduce the dimensionality of the data, thereby facilitating the tasks of a machine learning classifier. To help solve this problem, in this study, a machine learning methodology is introduced to improve signal processing and develop methodologies for classification when an EN is used. The proposed methodology involves a normalization stage to scale the data from the sensors, using both the well-known min-max approach and the more recent mean-centered unitary group scaling (MCUGS). Next, a manifold learning algorithm for data reduction is applied using uniform manifold approximation and projection (UMAP). The dimensionality of the data at the input of the classification machine is reduced, and an extreme learning machine (ELM) is used as a machine learning classifier algorithm. To validate the EN classification methodology, three datasets of ENs were used. The first dataset was composed of 3600 measurements of 6 volatile organic compounds performed by employing 16 metal-oxide gas sensors. The second dataset was composed of 235 measurements of 3 different qualities of wine, namely, high, average, and low, as evaluated by using an EN sensor array composed of 6 different sensors. The third dataset was composed of 309 measurements of 3 different gases obtained by using an EN sensor array of 2 sensors. A 5-fold cross-validation approach was used to evaluate the proposed methodology. A test set consisting of 25% of the data was used to validate the methodology with unseen data. The results showed a fully correct average classification accuracy of 1 when the MCUGS, UMAP, and ELM methods were used. Finally, the effect of changing the number of target dimensions on the reduction of the number of data was determined based on the highest average classification accuracy.This work was funded in part with resources from the Fondo de Ciencia, Tecnología e Innovación (FCTeI) del Sistema General de Regalías (SGR) from Colombia. The authors express their gratitude to the Administrative Department of Science, Technology, and Innovation–Colciencias with the grant 779–“Convocatoria para la Formación de Capital Humano de Alto Nivel para el Departamento de Boyacá 2017” for sponsoring the research presented herein. This study has been partially funded by the Spanish Agencia Estatal de Investigación (AEI)-Ministerio de Economía, Industria y Competitividad (MINECO), and the Fondo Europeo de Desarrollo Regional (FEDER) through research projects DPI2017-82930-C2-1-R and PGC2018-097257-B-C33; and by the Generalitat de Catalunya through research projects 2017-SGR-388 and 2017-SGR-1278.Peer ReviewedPostprint (published version

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate

A novel humid electronic nose combined with an electronic tongue for assessing deterioration of wine

We report herein the use of a combined system for the analysis of the spoilage of wine when in contact with air. The system consists of a potentiometric electronic tongue and a humid electronic nose. The potentiometric electronic tongue was built with thick-film serigraphic techniques using commercially available resistances and conductors for hybrid electronic circuits; i.e. Ag, Au, Cu, Ru, AgCl, and C. The humid electronic nose was designed in order to detect vapours that emanate from the wine and are apprehended by a moist environment. The humid nose was constructed using a piece of thin cloth sewn, damped with distilled water, forming five hollows of the right size to introduce the electrodes. In this particular case four electrodes were used for the humid electronic nose: a glass electrode, aluminium (Al), graphite and platinum (Pt) wires and an Ag-AgCl reference electrode. The humid electronic nose together with the potentiometric electronic tongue were used for the evaluation of the evolution in the course of time of wine samples. Additionally to the analysis performed by the tongue and nose, the spoilage of the wines was followed via a simple determination of the titratable (total) acidity. © 2011 Elsevier B.V.We thank the Spanish Government (project MAT2009-14564-C04) and the Generalitat Valenciana (project PROME-TEO/2009/016) for support. Luis Gil-Sanchez also thanks the Universidad Politecnica de Valencia for support (Primeros Proyectos de Investigacion - PAID-06-09) and the Generalitat Valenciana (proyectos de I+D para grupos de investigacion emergentes - 2009/8650).Gil Sánchez, L.; Soto Camino, J.; Martínez Mañez, R.; García Breijo, E.; Ibáñez Civera, FJ.; Llobet Valero, E. (2011). A novel humid electronic nose combined with an electronic tongue for assessing deterioration of wine. Sensors and Actuators A: Physical. 171(2):152-158. https://doi.org/10.1016/j.sna.2011.08.006S152158171

Classification of smoke contaminated Cabernet Sauvignon berries and leaves based on chemical fingerprinting and machine learning algorithms

Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00%; Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a sample collection of grapes for chemical analysis of smoke taint markers

Metal Oxide Sensors for Electronic Noses and Their Application to Food Analysis

Electronic noses (E-noses) use various types of electronic gas sensors that have partial specificity. This review focuses on commercial and experimental E-noses that use metal oxide semi-conductors. The review covers quality control applications to food and beverages, including determination of freshness and identification of contaminants or adulteration. Applications of E-noses to a wide range of foods and beverages are considered, including: meat, fish, grains, alcoholic drinks, non-alcoholic drinks, fruits, milk and dairy products, olive oils, nuts, fresh vegetables and eggs

Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas

Sensores: De los biosensores a la nariz electrónica

The recent advances in sensor devices have allowed the developing of new applications in many technological fields. This review describes the current state-of-the-art of this sensor technology, placing special emphasis on the food applications. The design, technology and sensing mechanism of each type of sensor are analysed. A description of the main characteristics of the electronic nose and electronic tongue (taste sensors) is also given. Finally, the applications of some statistical procedures in sensor systems are described briefly.Los recientes avances en los sistemas de sensores han permitido el desarrollo de nuevas aplicaciones en muchos campos tecnológicos. Este artículo de revisión describe el estado actual de esta nueva tecnología, con especial énfasis en las aplicaciones alimentarias. El diseño, la tecnología y el mecanismo sensorial de cada tipo de sensor son analizados en el artículo. También se describen las principales características de la nariz y la lengua electrónica (sensores de sabor). Finalmente, se describe brevemente el uso de algunos procedimientos estadísticos en sistemas de sensores