Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics

In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Motivated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectrograms from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple classifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier

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

Drift Correction Methods for gas Chemical Sensors in Artificial Olfaction Systems: Techniques and Challenges

In this chapter the authors introduce the main challenges faced when developing drift correction techniques and will propose a deep overview of state-of-the-art methodologies that have been proposed in the scientific literature trying to underlying pros and cons of these techniques and focusing on challenges still open and waiting for solution

Design Issues and Challenges of File Systems for Flash Memories

This chapter discusses how to properly address the issues of using NAND flash memories as mass-memory devices from the native file system standpoint. We hope that the ideas and the solutions proposed in this chapter will be a valuable starting point for designers of NAND flash-based mass-memory devices

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

Microorganisms’ discrimination using an electronic nose-chemometric approach

Mestrado de dupla diplomação com a Université Libre de TunisThe detection/identification of microorganisms is of major relevance for food quality and safety. Traditional analytical procedures (e.g., culture methods, immunological techniques, and polymerase chain reaction), while accurate and widely used, are time-consuming, costly, and generate a large amount of waste. Sensor-based instruments have evolved as quicker and sensitive complementary identification tools for yeasts, bacteria and fungi. Electronic noses (E-noses), in combination with chemometrics, have been effectively employed for the detection/discrimination of different microorganisms, providing a green, quick, cost-effective, and non-destructive/non-invasive assessment. The successful use of the E-noses may be related to the generation of distinctive olfactory fingerprints of certain volatile organic compounds (VOCs) during the microorganism's growth. These devices have already been used to detect/discriminate fungi and bacteria (e.g.,Enterococcus faecalis, Escherichia coli, Klebsiella pneumonia, Listeria monocytogenes, Pseudomonas aeruginosa), namely in milk, juice, soups,goat and pork meat,fruits and vegetables. Thus, a lab-made E-nose, with nine metal oxide semiconductor sensors, was applied to detect, differentiate, andquantify four common food contamination/quality indicator bacteria, including two Gram positive (E. faecalisand S. aureus) and two Gram negative (E. coli and P. aeruginosa). Besides, to support the E-nose performance the volatile profiles generated by these bacteria were also assessed by headspace solid-phase micro extraction gas-chromatography-mass spectrometry. The volatile profiles comprised 15 identified VOCs, being 10 of them emitted by at least one of the four bacteria evaluated, namely two alcohols (1-butanol, and 1-nonanol), three pyrazines (2-ethyl-6-methyl-pyrazine, 3-ethyl-2,5-dimethylpyrazine,and trimethylpyrazine), three terpenes (camphene, D-limonene, and ƒÒ-pinene), and two other compounds (2,4-thujadiene and indole). The four bacteria could be distinguished using the electrical resistance signals produced by the E-nose in combination with linear discriminate analysis (90% of correct classifications for leave-one-out cross-validation). Additionally, multiple linear regression models, with root mean square errors lower than 4 colony forming units, were successfully established (0.9428 . R2.0.9946). Overall, the E-nose proved to be an effective qualitative-quantitative tool for analyzing bacteria in solid matrices, being foreseen it possible application to solid food matrices.A deteccao/identificacao de microorganismos e de grande relevancia para a qualidade e seguranca dos alimentos. Os procedimentos analiticos tradicionais (por exemplo, metodos de cultura, tecnicas imunologicas e reacao em cadeia de polimerase), embora precisos e amplamente utilizados, sao demorados, dispendiosos e geram uma grande quantidade de residuos. Os instrumentos baseados em sensores evoluiram como ferramentas de identificacao complementares mais rapidas e sensiveis para leveduras, bacterias e fungos. Os narizes eletronicos, em combinacao com a quimetria, foram efetivamente empregados para a deteccao/discriminacao de diferentes microorganismos, proporcionando uma avaliacao verde, rapida, economica e nao destrutiva/nao invasiva. O uso bem-sucedido dos E-noses pode estar relacionado a geracao de impressoes olfativas distintivas de certos compostos organicos volateis (VOCs) durante o crescimento do microorganismo. Estes dispositivos ja foram usados para detectar/discriminar fungos e bacterias (por exemplo, Enterococcus faecalis, Escherichia coli, Klebsiella pneumonia, Listeria monocytogenes, Pseudomonas aeruginosa), nomeadamente no leite, suco, sopas, carne de cabra e de porco, frutas e legumes. Assim, um E-nose feito em laboratorio, com nove sensores semicondutores de oxido de metal, foi aplicado para detectar, diferenciar e quantificar quatro bacterias comuns de contaminacao alimentar / indicador de qualidade, incluindo duas Gram positivas (E. faecalis e S. aureus) e duas Gram negativas. (E. coli and P. aeruginosa). Alem disso, para apoiar o desempenho do nariz E, os perfis volateis gerados por essas bacterias tambem foram avaliados por micro-espectrometria de extraccao de gas-cromatografia-massa de fase solida.Os perfis Volateis consistiam em 15 VOCs identificados, sendo 10 deles emitidos por pelo menos uma das quatro bacterias avaliadas, ou seja, dois alcoois (1-butanol e 1-nonanol), tres pirazinas (2-etil-6-metil-pirazina, 3-etil-2,5-dimetilpyrazina, e trimethylpyrazine), tres terpenos (hcampene, D-limonene e-pinene), e outros dois compostos. (2,4-thujadiene and indole). As quatro bacterias poderiam ser distinguidas usando os sinais de resistencia eletrica produzidos pelo E-nose em combinacao com a analise discriminante linear (90% das classificacoes corretas para a validacao cruzada de abandono-um-out). Alem disso, foram estabelecidos com sucesso multiplos modelos de regressao linear, com erros medios do quadrado raiz inferiores a 4 unidades de formacao de colonia (0,9428 . R2.0,9946). No geral, o E-nose provou ser uma ferramenta qualitativa-quantitativa eficaz para analisar bacterias em matrizes solidas, prevendo-se a possivel aplicacao a matrizs de alimentos solidos

Active Wavelength Selection for Chemical Identification Using Tunable Spectroscopy

Spectrometers are the cornerstone of analytical chemistry. Recent advances in microoptics manufacturing provide lightweight and portable alternatives to traditional spectrometers. In this dissertation, we developed a spectrometer based on Fabry-Perot interferometers (FPIs). A FPI is a tunable (it can only scan one wavelength at a time) optical filter. However, compared to its traditional counterparts such as FTIR (Fourier transform infrared spectroscopy), FPIs provide lower resolution and lower signal-noiseratio (SNR). Wavelength selection can help alleviate these drawbacks. Eliminating uninformative wavelengths not only speeds up the sensing process but also helps improve accuracy by avoiding nonlinearity and noise. Traditional wavelength selection algorithms follow a training-validation process, and thus they are only optimal for the target analyte. However, for chemical identification, the identities are unknown. To address the above issue, this dissertation proposes active sensing algorithms that select wavelengths online while sensing. These algorithms are able to generate analytedependent wavelengths. We envision this algorithm deployed on a portable chemical gas platform that has low-cost sensors and limited computation resources. We develop three algorithms focusing on three different aspects of the chemical identification problems. First, we consider the problem of single chemical identification. We formulate the problem as a typical classification problem where each chemical is considered as a distinct class. We use Bayesian risk as the utility function for wavelength selection, which calculates the misclassification cost between classes (chemicals), and we select the wavelength with the maximum reduction in the risk. We evaluate this approach on both synthesized and experimental data. The results suggest that active sensing outperforms the passive method, especially in a noisy environment. Second, we consider the problem of chemical mixture identification. Since the number of potential chemical mixtures grows exponentially as the number of components increases, it is intractable to formulate all potential mixtures as classes. To circumvent combinatorial explosion, we developed a multi-modal non-negative least squares (MMNNLS) method that searches multiple near-optimal solutions as an approximation of all the solutions. We project the solutions onto spectral space, calculate the variance of the projected spectra at each wavelength, and select the next wavelength using the variance as the guidance. We validate this approach on synthesized and experimental data. The results suggest that active approaches are superior to their passive counterparts especially when the condition number of the mixture grows larger (the analytes consist of more components, or the constituent spectra are very similar to each other). Third, we consider improving the computational speed for chemical mixture identification. MM-NNLS scales poorly as the chemical mixture becomes more complex. Therefore, we develop a wavelength selection method based on Gaussian process regression (GPR). GPR aims to reconstruct the spectrum rather than solving the mixture problem, thus, its computational cost is a function of the number of wavelengths. We evaluate the approach on both synthesized and experimental data. The results again demonstrate more accurate and robust performance in contrast to passive algorithms

Environmental engineering applications of electronic nose systems based on MOX gas sensors

Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors’ applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications

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