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Keshari AK, Prabhakar Rao J, Sree Rama Murthy A, Jayaraman V. Design and development of instrumentation for the measurement of sensor array responses. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2020; 91:024101. [PMID: 32113421 DOI: 10.1063/1.5128967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 01/30/2020] [Indexed: 06/10/2023]
Abstract
Indigenous instrumentation has been designed and developed for the measurement of the concentration of analytes from eight conductometric metal oxide sensors. The hardware scheme of instrumentation is based on the astable multivibrator configuration. The hardware measures the resistance output from the sensors, conditions, processes, and displays the data on the liquid crystal display. An 8051 based processor averages the data, converts them into engineering units, and sends them to remote PC through ethernet communication for post-data analysis. A graphical user interface (GUI) is developed to acquire, monitor, and display the eight channels' sensor output. GUI plots the online data and offline data as a popup window. The hardware and software of the instrument were tested with standard resistors for calibration and found that in-house developed instrumentation is able to measure with an accuracy of ±0.5% with a resolution of 500 Ω. The instrument has been tested with a semiconductor metal oxide sensor, viz., chromium niobate (CrNbO4).
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Affiliation(s)
- Ajay Kumar Keshari
- Homi Bhabha National Institute, Mumbai, India and IGCAR Campus, Kalpakkam, Tamil Nadu 603102, India
| | - J Prabhakar Rao
- Materials & Fuel Chemistry Group, Materials Chemistry & Metal Fuel Cycle Group, Indira Gandhi Centre for Atomic Research, Kalpakkam, Tamil Nadu 603102, India
| | - A Sree Rama Murthy
- Materials & Fuel Chemistry Group, Materials Chemistry & Metal Fuel Cycle Group, Indira Gandhi Centre for Atomic Research, Kalpakkam, Tamil Nadu 603102, India
| | - V Jayaraman
- Materials & Fuel Chemistry Group, Materials Chemistry & Metal Fuel Cycle Group, Indira Gandhi Centre for Atomic Research, Kalpakkam, Tamil Nadu 603102, India
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Li H, Luo D, Sun Y, GholamHosseini H. Classification and Identification of Industrial Gases Based on Electronic Nose Technology. SENSORS 2019; 19:s19225033. [PMID: 31752238 PMCID: PMC6891334 DOI: 10.3390/s19225033] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 11/12/2019] [Accepted: 11/15/2019] [Indexed: 12/14/2022]
Abstract
Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function c = 10 and the degree of freedom d = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption.
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Affiliation(s)
- Hui Li
- School of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, China; (H.L.); (D.L.)
| | - Dehan Luo
- School of Information and Engineering, Guangdong University of Technology, Guangzhou 510006, China; (H.L.); (D.L.)
| | - Yunlong Sun
- School of Electric and Automatic Engineering, Changshu Institute of Technology, Changshu 215500, China
- Correspondence:
| | - Hamid GholamHosseini
- School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand;
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Schwanz TG, Bokowski LV, Marcelo MC, Jandrey AC, Dias JC, Maximiano DH, Canova LS, Pontes OF, Sabin GP, Kaiser S. Analysis of chemosensory markers in cigarette smoke from different tobacco varieties by GC×GC-TOFMS and chemometrics. Talanta 2019; 202:74-89. [DOI: 10.1016/j.talanta.2019.04.060] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/22/2019] [Accepted: 04/23/2019] [Indexed: 11/25/2022]
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Wu J, Yang SX. Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches. SENSORS 2019; 19:s19081778. [PMID: 31013918 PMCID: PMC6514745 DOI: 10.3390/s19081778] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 04/05/2019] [Accepted: 04/08/2019] [Indexed: 11/16/2022]
Abstract
The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of a bulk tobacco curing schedule were presented, which is significant for the systematic modelling of an intelligent tobacco flue-curing process. To fully imitate the curer’s control of the bulk tobacco curing schedule, three types of sensors were applied, namely, a gas sensor, image sensor, and moisture sensor. Feature extraction methods were given forward to extract the odor, image, and moisture features of the tobacco leaves individually. Three multi-sensor data fusion schemes were applied, where a least squares support vector machines (LS-SVM) regression model and adaptive neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments were conducted from July to September 2014, with a total of 603 measurement points, ensuring the results’ robustness and validness. The results demonstrate that a hybrid fusion scheme achieves a superior prediction performance with the coefficients of determination of the controlled parameters, reaching 0.9991, 0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed hybrid fusion scheme a feasible, reliable, and effective method to intelligently control over the tobacco curing schedule.
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Affiliation(s)
- Juan Wu
- School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
- Chongqing College of Electronic Engineering, Chongqing 401331, China.
| | - Simon X Yang
- School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada.
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Liu T, Zhang Q, Chang D, Niu Y, Lu W, Xiao Z. Characterization of Tobacco Leaves by Near-Infrared Reflectance Spectroscopy and Electronic Nose with Support Vector Machine. ANAL LETT 2018. [DOI: 10.1080/00032719.2017.1395034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Taiang Liu
- School of Materials Science and Engineering, Shanghai University, Shanghai, China
| | - Qing Zhang
- School of Materials Science and Engineering, Shanghai University, Shanghai, China
| | - Dongping Chang
- School of Materials Science and Engineering, Shanghai University, Shanghai, China
| | - Yunwei Niu
- School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai, China
| | - Wencong Lu
- Department of Chemistry, College of Sciences, Shanghai University, Shanghai, China
| | - Zuobing Xiao
- School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai, China
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Villière A, Le Roy S, Fillonneau C, Prost C. InnOscent system: Advancing flavor analysis using an original gas chromatographic analytical device. J Chromatogr A 2017; 1535:129-140. [PMID: 29329885 DOI: 10.1016/j.chroma.2017.12.053] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 12/18/2017] [Accepted: 12/19/2017] [Indexed: 10/18/2022]
Abstract
Despite continuous advances in analytical and physiological knowledge, the comprehension of an aroma is still a challenge. Gas chromatography coupled to olfactometry (GC-O) is an efficient method to identify and estimate individual potential of odorants, but there is a gap between this individual characterization and the effective contribution of compounds in the mixture, which is due to complex chemical and perceptual interactions. Therefore, recombination and omission experiments are often performed to achieve an understanding of food aromas. In this study, a chromatographic device, developed to facilitate aroma analysis, is presented. It was configured to perform both (1) conventional analyses by GC coupled with a mass spectrometer, olfactometric port(s), and a flame ionization detector (FID), and (2) omission or recombination experiments. This dual capability is due to the singular configuration of the system using an ingenious combination of splitter and Deans switch microfluidics transfer modules, and the existence of multiple outlets. The operational status of the system was tested using a purposely simple mixture of compounds. The similarity of retention times (RT) and FID peak areas obtained for each outlet demonstrates that the multiple outlets of the system are equivalent. The reproducibility of retention times (RT) and FID peak areas obtained in switching and non-switching conditions, also demonstrates the efficiency of switching operations. The validation of the system enables multiple detectors to be connected to the outlets and complementary information can be obtained from the eluate. The connection of recovery disposals to the outlets provides fraction collection and recombination possibilities, which contribute much to the understanding of aroma-aroma interactions. As an illustration of the InnOscent system relevance for the comprehension of more complex aromas, the device was used to study the aroma of a wine made from Cabernet Franc grape variety. An olfactometric profile was efficiently produced with the device configured as a GC-MS coupled to a dual olfactometric port. The main odorant active compounds were identified. The omission approach, carried out with the system on isopropyl- and isobutyl-methoxypyrazines, demonstrates the significant contribution of these compounds to the aroma of the wine studied, despite an individual perception among the weakest of the aromagram. A similar approach can be used to evaluate the contribution of any compound to any aroma. This approach overcomes constraints of current methodologies associated to reconstituted model solutions and paves the way for a better understanding of aroma construction.
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Affiliation(s)
- A Villière
- ONIRIS, Nantes-Atlantic College of Veterinary Medicine and Food Science, UMR GEPEA CNRS 6144, BP 82225, F-44307, Nantes, France; Université Nantes Angers Le Mans, France.
| | - S Le Roy
- ONIRIS, Nantes-Atlantic College of Veterinary Medicine and Food Science, UMR GEPEA CNRS 6144, BP 82225, F-44307, Nantes, France; Université Nantes Angers Le Mans, France
| | - C Fillonneau
- ONIRIS, Nantes-Atlantic College of Veterinary Medicine and Food Science, UMR GEPEA CNRS 6144, BP 82225, F-44307, Nantes, France; Université Nantes Angers Le Mans, France
| | - C Prost
- ONIRIS, Nantes-Atlantic College of Veterinary Medicine and Food Science, UMR GEPEA CNRS 6144, BP 82225, F-44307, Nantes, France; Université Nantes Angers Le Mans, France
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Fitz BD, Mannion BC, To K, Hoac T, Synovec RE. Evaluation of injection methods for fast, high peak capacity separations with low thermal mass gas chromatography. J Chromatogr A 2015; 1392:82-90. [DOI: 10.1016/j.chroma.2015.03.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Revised: 03/04/2015] [Accepted: 03/05/2015] [Indexed: 10/23/2022]
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