International Science Index

8
10001843
Performance Comparison of a Low Cost Air Quality Sensor with a Commercial Electronic Nose
Abstract:
The Figaro AM-1 sensor module which employs TGS 2600 model gas sensor in air quality assessment was used. The system was coupled with a microprocessor that enables sensor module to create warning message via telephone. This low cot sensor system’s performance was compared with a DiagNose II commercial electronic nose system. Both air quality sensor and electronic nose system employ metal oxide chemical gas sensors. In the study experimental setup, data acquisition methods for electronic nose system, and performance of the low cost air quality system were evaluated and explained.
Paper Detail
1308
downloads
7
9998876
Integrated Flavor Sensor Using Microbead Array
Abstract:

This research presents the design, fabrication and application of a flavor sensor for an integrated electronic tongue and electronic nose that can allow rapid characterization of multi-component mixtures in a solution. The odor gas and liquid are separated using hydrophobic porous membrane in micro fluidic channel. The sensor uses an array composed of microbeads in micromachined cavities localized on silicon wafer. Sensing occurs via colorimetric and fluorescence changes to receptors and indicator molecules that are attached to termination sites on the polymeric microbeads. As a result, the sensor array system enables simultaneous and near-real-time analyses using small samples and reagent volumes with the capacity to incorporate significant redundancies. One of the key parts of the system is a passive pump driven only by capillary force. The hydrophilic surface of the fluidic structure draws the sample into the sensor array without any moving mechanical parts. Since there is no moving mechanical component in the structure, the size of the fluidic structure can be compact and the fabrication becomes simple when compared to the device including active microfluidic components. These factors should make the proposed system inexpensive to mass-produce, portable and compatible with biomedical applications.

Paper Detail
1066
downloads
6
9997663
Electronic Nose Based On Metal Oxide Semiconductor Sensors as an Alternative Technique for the Spoilage Classification of Oat Milk
Abstract:

The aim of the present study was to develop a rapid method for electronic nose for online quality control of oat milk. Analysis by electronic nose and bacteriological measurements were performed to analyze spoilage kinetics of oat milk samples stored at room temperature and refrigerated conditions for up to 15 days. Principal component analysis (PCA), Discriminant Factorial Analysis (DFA) and Soft Independent Modelling by Class Analogy (SIMCA) classification techniques were used to differentiate the samples of oat milk at different days. The total plate count (bacteriological method) was selected as the reference method to consistently train the electronic nose system. The e-nose was able to differentiate between the oat milk samples of varying microbial load. The results obtained by the bacteria total viable countsshowed that the shelf-life of oat milk stored at room temperature and refrigerated conditions were 20hrs and 13 days, respectively. The models built classified oat milk samples based on the total microbial population into “unspoiled” and “spoiled”.

Paper Detail
2317
downloads
5
10221
A Novel Approach to Optimal Cutting Tool Replacement
Abstract:
In metal cutting industries, mathematical/statistical models are typically used to predict tool replacement time. These off-line methods usually result in less than optimum replacement time thereby either wasting resources or causing quality problems. The few online real-time methods proposed use indirect measurement techniques and are prone to similar errors. Our idea is based on identifying the optimal replacement time using an electronic nose to detect the airborne compounds released when the tool wear reaches to a chemical substrate doped into tool material during the fabrication. The study investigates the feasibility of the idea, possible doping materials and methods along with data stream mining techniques for detection and monitoring different phases of tool wear.
Paper Detail
1261
downloads
4
7587
Application of Quality Index Method, Texture Measurements and Electronic Nose to Assess the Freshness of Atlantic Herring (Clupea harengus) Stored in Ice
Abstract:
Atlantic herring (Clupea harengus) is an important commercial fish and shows to be more and more demanded for human consumption. Therefore, it is very important to find good methods for monitoring the freshness of the fish in order to keep it in the best quality for human consumption. In this study, the fish was stored in ice up to 2 weeks. Quality changes during storage were assessed by the Quality Index Method (QIM), quantitative descriptive analysis (QDA) and Torry scheme, by texture measurements: puncture tests and Texture Profile Analysis (TPA) tests on texture analyzer TA.XT2i, and by electronic nose (e-nose) measurements using FreshSense instrument. Storage time of herring in ice could be estimated by QIM with ± 2 days using 5 herring per lot. No correlation between instrumental texture parameters and storage time or between sensory and instrumental texture variables was found. E-nose measurements could be use to detect the onset of spoilage.
Paper Detail
1009
downloads
3
6211
Source Direction Detection based on Stationary Electronic Nose System
Abstract:
Electronic nose (array of chemical sensors) are widely used in food industry and pollution control. Also it could be used to locate or detect the direction of the source of emission odors. Usually this task is performed by electronic nose (ENose) cooperated with mobile vehicles, but when a source is instantaneous or surrounding is hard for vehicles to reach, problem occurs. Thus a method for stationary ENose to detect the direction of the source and locate the source will be required. A novel method which uses the ratio between the responses of different sensors as a discriminant to determine the direction of source in natural wind surroundings is presented in this paper. The result shows that the method is accurate and easily to be implemented. This method could be also used in movably, as an optimized algorithm for robot tracking source location.
Paper Detail
774
downloads
2
10785
Gas Detection via Machine Learning
Abstract:
We present an Electronic Nose (ENose), which is aimed at identifying the presence of one out of two gases, possibly detecting the presence of a mixture of the two. Estimation of the concentrations of the components is also performed for a volatile organic compound (VOC) constituted by methanol and acetone, for the ranges 40-400 and 22-220 ppm (parts-per-million), respectively. Our system contains 8 sensors, 5 of them being gas sensors (of the class TGS from FIGARO USA, INC., whose sensing element is a tin dioxide (SnO2) semiconductor), the remaining being a temperature sensor (LM35 from National Semiconductor Corporation), a humidity sensor (HIH–3610 from Honeywell), and a pressure sensor (XFAM from Fujikura Ltd.). Our integrated hardware–software system uses some machine learning principles and least square regression principle to identify at first a new gas sample, or a mixture, and then to estimate the concentrations. In particular we adopt a training model using the Support Vector Machine (SVM) approach with linear kernel to teach the system how discriminate among different gases. Then we apply another training model using the least square regression, to predict the concentrations. The experimental results demonstrate that the proposed multiclassification and regression scheme is effective in the identification of the tested VOCs of methanol and acetone with 96.61% correctness. The concentration prediction is obtained with 0.979 and 0.964 correlation coefficient for the predicted versus real concentrations of methanol and acetone, respectively.
Paper Detail
1205
downloads
1
7067
Chilean Wines Classification based only on Aroma Information
Abstract:
Results of Chilean wine classification based on the information provided by an electronic nose are reported in this paper. The classification scheme consists of two parts; in the first stage, Principal Component Analysis is used as feature extraction method to reduce the dimensionality of the original information. Then, Radial Basis Functions Neural Networks is used as pattern recognition technique to perform the classification. The objective of this study is to classify different Cabernet Sauvignon, Merlot and Carménère wine samples from different years, valleys and vineyards of Chile.
Paper Detail
888
downloads