Hazardous Odor Recognition by CMAC Based Neural Networks

Electronic noses are being developed as systems for the automated detection and classification of odors, vapors, and gases. Artificial neural networks (ANNs) have been used to analyze complex data and to recognize patterns, and have shown promising results in recognition of volatile compounds and odors in electronic nose applications. When an ANN is combined with a sensor array, the number of detectable chemicals is generally greater than the number of unique sensor types. The odor sensing system should be extended to new areas since its standard style where the output pattern from multiple sensors with partially overlapped specificity is recognized by a neural network or multivariate analysis. This paper describes the design, implementation and performance evaluations of the application developed for hazardous odor recognition using Cerebellar Model Articulation Controller (CMAC) based neural networks

Recent development in electronic nose data processing for beef quality assessment

Beef is kind of perishable food that easily to decay. Hence, a rapid system for beef quality assessment is needed to guarantee the quality of beef. In the last few years, electronic nose (e-nose) is developed for beef spoilage detection. In this paper, we discuss the challenges of e-nose application to beef quality assessment, especially in e-nose data processing. We also provide a summary of our previous studies that explains several methods to deal with gas sensor noise, sensor array optimization problem, beef quality classification, and prediction of the microbial population in beef sample. This paper might be useful for researchers and practitioners to understand the challenges and methods of e-nose data processing for beef quality assessment

Gas sensing technologies -- status, trends, perspectives and novel applications

The strong, continuous progresses in gas sensors and electronic noses resulted in improved performance and enabled an increasing range of applications with large impact on modern societies, such as environmental monitoring, food quality control and diagnostics by breath analysis. Here we review this field with special attention to established and emerging approaches as well as the most recent breakthroughs, challenges and perspectives. In particular, we focus on (1) the transduction principles employed in different architectures of gas sensors, analysing their advantages and limitations; (2) the sensing layers including recent trends toward nanostructured, low-dimensional and composite materials; (3) advances in signal processing methodologies, including the recent advent of artificial neural networks. Finally, we conclude with a summary on the latest achievements and trends in terms of applications.Comment: arXiv admin comment: This version has been removed by arXiv administrators as the submitter did not have the rights to agree to the license at the time of submissio

Meat Quality Assessment by Electronic Nose (Machine Olfaction Technology)

Over the last twenty years, newly developed chemical sensor systems (so called “electronic noses”) have made odor analyses possible. These systems involve various types of electronic chemical gas sensors with partial specificity, as well as suitable statistical methods enabling the recognition of complex odors. As commercial instruments have become available, a substantial increase in research into the application of electronic noses in the evaluation of volatile compounds in food, cosmetic and other items of everyday life is observed. At present, the commercial gas sensor technologies comprise metal oxide semiconductors, metal oxide semiconductor field effect transistors, organic conducting polymers, and piezoelectric crystal sensors. Further sensors based on fibreoptic, electrochemical and bi-metal principles are still in the developmental stage. Statistical analysis techniques range from simple graphical evaluation to multivariate analysis such as artificial neural network and radial basis function. The introduction of electronic noses into the area of food is envisaged for quality control, process monitoring, freshness evaluation, shelf-life investigation and authenticity assessment. Considerable work has already been carried out on meat, grains, coffee, mushrooms, cheese, sugar, fish, beer and other beverages, as well as on the odor quality evaluation of food packaging material. This paper describes the applications of these systems for meat quality assessment, where fast detection methods are essential for appropriate product management. The results suggest the possibility of using this new technology in meat handling

Classification of Agarwood Oil Using an Electronic Nose

Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples

Using Generative Adversarial Networks to Classify Structural Damage Caused by Earthquakes

The amount of structural damage image data produced in the aftermath of an earthquake can be staggering. It is challenging for a few human volunteers to efficiently filter and tag these images with meaningful damage information. There are several solution to automate post-earthquake reconnaissance image tagging using Machine Learning (ML) solutions to classify each occurrence of damage per building material and structural member type. ML algorithms are data driven; improving with increased training data. Thanks to the vast amount of data available and advances in computer architectures, ML and in particular Deep Learning (DL) has become one of the most popular image classification algorithms producing results comparable to and in some cases superior to human experts. These kind of algorithms need the input images used for the training to be labeled, and even if there is a large amount of images most of them are not labeled and it takes structural engineers a large amount of time to do it. The current data earthquakes image data bases do not contain the label information or is incomplete slowing significantly the advance of a solution and are incredible difficult to search. To be able to train a ML algorithm to classify one of the structural damages it took the architecture school an entire year to gather 200 images of the specific damage. That number is clearly not enough to avoid overfitting so for this thesis we decided to generate synthetic images for the specific structural damage. In particular we attempt to use Generative Adversarial Neural Networks (GANs) to generate the synthetic images and enable the fast classification of rail and road damage caused by earthquakes. Fast classification of rail and road damage can allow for the safety of people and to better prepare the reconnaissance teams that manage recovery tasks. GANs combine classification neural networks with generative neural networks. For this thesis we will be combining a convolutional neural network (CNN) with a generative neural network. By taking a classifier trained in a GAN and modifying it to classify other images the classifier can take advantage of the GAN training without having to find more training data. The classifier trained in this way was able to achieve an 88\% accuracy score when classifying images of structural damage caused by earthquakes

Signal and data processing for machine olfaction and chemical sensing: A review

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing

Electronic noses based on metal oxide nanowires: A review

Metal oxides are ideal for the fabrication of gas sensors: they are sensitive to many gases while allowing the device to be simple, tiny, and inexpensive. Nonetheless, their lack of selectivity remains a limitation. In order to achieve good selectivity in applications with many possible interfering gases, the sensors are inserted into an electronic nose that combines the signals from nonselective sensors and analyzes them with multivariate statistical algorithms in order to obtain selectivity. This review analyzes the scientific articles published in the last decade regarding electronic noses based on metal oxide nanowires. After a general introduction, Section 2 discusses the issues related to poor intrinsic selectivity. Section 3 briefly reviews the main algorithms that have hitherto been used and the results they can provide. Section 4 classifies the recent literature into fundamental research, agrifood, health, security. In Section 5, the literature is analyzed regarding the metal oxides, the surface decoration nanoparticles, the features that differentiate the sensors in a given array, the application for which the device was developed, the algorithm used, and the type of information obtained. Section 6 concludes by discussing the present state and points out the requirements for their use in real-world applications