Data analysis for electronic nose systems
By Simon Scott, D. James, Zulfiqur Ali. 2006
455 citations recorded by the source corpus.
Subfields: Bioengineering, Biomedical Engineering, Spectroscopy
View Paper: https://www.semanticscholar.org/paper/1d9e5364ec4cc7ab2cefcf6039384391b15f95d4
How researchers see this paper
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In Bioengineering
In Biomedical Engineering
- J. Belnap
- Agustin Gutierrez-Galvez
- Mahdi Ghasemi-Varnamkhasti
- Santiago Marco
- Di Wu
- Seyed Saeid Mohtasebi
- Duckshin Park
- V. Tran
In Spectroscopy
Citations in the corpus
- Indoor Air Pollution, Related Human Diseases, and Recent Trends in the Control and Improvement of Indoor Air Quality (2020, methodology): “By using the chemical sensor arrays in combination with the classification algorithms, E-noses can easily monitor target gases by detecting and discriminating types and concentrations [178].”
- Signal and Data Processing for Machine Olfaction and Chemical Sensing: A Review (2012, background): “There have been recent reviews on particular aspects of the data processing methodology [4], [5], [6].”
- An electronic tongue for honey classification (2008, unknown): “prove the obtained classification performance [20].”
- Detection of Adulteration in Saffron Samples Using Electronic Nose (2015, methodology): “Saffron color and aroma are determined by different equipment such as UV-Vis spectrometry, HPLC, and gas chromatography-mass spectrometry (GC-MS).[4,14] However, these methods are expensive and require trained experts.”
- Building capacity in biodiversity monitoring at the global scale (2017, background): “Due to the interest of industry in automated olfaction systems, systems of analysis and data curation have already been developed (Scott et al. 2006) and derived systems for biodiversity monitoring may therefore be relatively easy to develop.”
- Application of Electronic Nose with Multivariate Analysis and Sensor Selection for Botanical Origin Identification and Quality Determination of Honey (2015, background): “The signal produced by the e-nose results in a matrix of semi-independent variables (the sensor array output) and a set of dependent variables (classes or quality features) (Scott et al. 2006).”