Hyung Gi Byun

Sensor array techniques for mimicking the mammalian olfactory system

Scales of human odour perception are subjective and there is much need for automated methods of odour measurement in a variety of industries. Organic conducting polymers have been developed as sensing devices, and many materials have been synthesised and characterised. The sensors show rapid adsorption and desorption characteristics and allow rapid measurements to be made. The responses are proportional to the concentration of the volatile chemical being sensed, and with calibration can be used to quantify single chemical species. Arrays of sensors produce patterns of responses that can be used as descriptors for discriminating complex odours. Examples of applications in food quality monitoring and agriculture malodours are given. The sensor array response may be correlated with olfactometric measurements in the case of pig slurry malodour.

Application of unsupervised clustering methods to the assessment of malodour in agriculture using an array of conducting polymer odour sensors

Abstract Odour sensing instrumentation based on arrays of sensors that display broad specificity and high sensitivity to odorous chemicals have been developed. Methods of unsupervised reduction of complex multidimensional data were examined, and useful algorithms were adapted for the use with broad specificity sensor arrays. As a result, a simple interface that would allow the human observer to decide easily whether a particular odour pattern could be distinguished from another on the basis of Euclidean distances between patterns, and calculation of 95% confidence limits around individual clusters of data was developed. The best performance was obtained from a combination of principal components analysis used as a starting point for Sammon mapping. This combined the invariance of the eigenvector calculation with the Euclidean distance mapping of the Sammon procedure, without the disadvantages of rotation of clusters when the order of patterns in a database was changed. The methods were applied to the assessment of odour differences of fresh pig slurry from pigs fed with different diets. The results show that the transformation of multidimensional data into two-dimensional clusters allowed easy visualisation of the difference in odour between slurries from the pigs fed with different diets.

Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches

Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials.

Volatile Organic Gas Recognition Using Conducting Polymer Sensor Array

A gas recognition system was fabricated using a conducting polymer (polypyrrole and polyaniline) sensor array that can recognize and analyze various kinds and quantities of volatile organic compounds (VOCs), such as ethanol, toluene, benzene, and chloroform. The sensors also exhibited different sensitivity curves to VOCs according to the different additive amounts and kinds of conducting polymer (polypyrrole or polyaniline) and electrode. Polypyrrole and polyaniline film sensors made by chemical polymerization were employed to detect the VOCs. The multi-dimensional sensor signals obtained from the sensor array were then analyzed using the principal component analysis (PCA) technique and a radial basis function network (RBFN). By implementing the sensing signals from the sensor array along with a multi-layer neural network using an Radial-Basis Function learning algorithm, the sensor array was successful in accurately classifying the gas species and also identifying the concentration of each VOC in a real-time process.

Sensing Mechanism of Conducting Polymer Sensor for Volatile Organic Compounds

In this study, the chemically polymerized PPy and PANi films with different selectivity by controlling dedoping time were fabricated. Additionally, the sensing properties and mechanism of VOCs adsorption to conducting polymers were investigated with contact angles measurement, a scanning probe microscope (SPM) and a UV-Vis-NIR spectrophotometer. The thin sensor had higher sensitivity compared to the thick one, which the dedoped sensor at 1-minute had the highest ; sensitivity. Upon gas absorption, polypyrrole exhibited positive sensitivity while polyaniline had negative sensitivity. PPy film showed hydrophilic property and PANi film showed hydrophobic property. After gas absorption, the sensitivity increased as a function of the polarity of the absorbed molecules. These behaviors are due to the polar molecules absorbed with the movable polaron or free carrier, which then interrupts or generates the movement of polaron and carrier, and then changes the conductivity of the polymer. We found that conducting polymer sensors are very sensitive to the difference in polarity of gas molecules.

Chemosensors and chemoreception

The objective of this topical collection is to report on the most recent research into and applications of chemical sensing and olfaction. It focuses in particular on chemosensors (gas sensors) and chemoreception (artificial olfaction), and the interactions between them. With the widespread use of chemicalsensor devices, there has been much investigation of highly sensitive, selective, and stable chemoresistors using oxide semiconductors, conducting polymers, graphenes, and carbon nanotubes. The diversity of sensing materials provides a variety of benefits, including flexible design, low temperature (power) operation, and integration into portable devices, in addition to crucial “3S” requirements (sensitivity, selectivity, and stability), which will enable innumerable new applications using chemosensors. To realize these benefits, understanding and investigation of the mechanisms and materials of chemosensors are essential. The information gathered from chemoresistors is extremely useful for enabling chemoreception in artificial olfaction devices. These devices, designed to mimic the mammalian sensory system, are gaining favor for odor and/or gas assessment applications in a variety of fields: environmental quality monitoring, food and beverage quality control, medical diagnosis, and others. In this paper collection, we invited front-line researchers to submit original research articles and critical reviews on chemosensors and chemoreception. Altogether, this collection of papers should be a valuable source of information for all readers with an interest in chemosensors and chemoreception or related fields. We thank all authors for submitting their interesting contributions for this issue, the referees for their critical but constructive comments, and the Editorial Office and the Editors for their friendly cooperation.

On Training Neural Network Algorithms for Odor Identification for Future Multimedia Communication Systems

Future multimedia communication system can be developed to identify, transmit and provide odors besides voice and image. In this paper, an improved odor identification method is introduced. We present an analysis of center-gradient and a new method of using convergence parameters in training RBFN-SVD-SG (radial basis function network using Singular Value Decomposition combined with stochastic gradient) algorithm for odor identification. Through mathematical analysis, it was found that the steady-state weight fluctuation and large values of convergence parameter can lead to an increase of variance of center-gradient, which induces ill-behaving convergence. The proposed method of using raised-cosine functions for time-decaying convergence parameter shows faster convergence and better recognition performance

Curation of datasets, assessment of their quality and completeness, and nanoSAR classification model development for metallic nanoparticles

Applications of machine learning techniques for the prediction of nanotoxicity are expected to reduce time and cost of nanosafety assessments. However, due to the rapid increases in literature data quantity and heterogeneity on nanomaterials, efficient screening of data based on their quality and completeness are becoming more important for the development of reliable nanostructure–activity relationship (nanoSAR) models. Herein, we have curated a nanosafety dataset of metallic NPs, with 2005 rows and 31 columns extracted from literature data mining of 63 published articles and gap filling by adapting data from manufacturer specification or references on the same nanomaterials. By using PChem scores based on physicochemical data quality and completeness, five datasets with different qualities and degrees of completeness were generated and used for the development of toxicity classification models of metallic NPs. Comparisons of these models, built with support vector machine and random forest algorithms, confirmed us that the datasets with higher quality and completeness (i.e., higher PChem score) produced better performing nanoSAR models than those with lower PChem scores. Further analysis of relative attribute importance showed that the physicochemical properties, core size and surface charge, and the experimental conditions of toxicity assays, dose and cell lines, are the four most important attributes to the toxicity of metallic NPs.

Sensor drift compensation algorithm based on PDF distance minimization

In this paper, a new unsupervised classification algorithm is introduced for the compensation of sensor drift effects of the odor sensing system using a conducting polymer sensor array. The proposed method continues updating adaptive Radial Basis Function Network (RBFN) weights in the testing phase based on minimizing Euclidian Distance between two Probability Density Functions (PDFs) of a set of training phase output data and another set of testing phase output data. The output in the testing phase using the fixed weights of the RBFN are significantly dispersed and shifted from each target value due mostly to sensor drift effect. In the experimental results, the output data by the proposed methods are observed to be concentrated closer again to their own target values significantly. This indicates that the proposed method can be effectively applied to improved odor sensing system equipped with the capability of sensor drift effect compensation