Extended disjoint principal-components regression analysis of SAW vapor sensor-array responses
| Autor: | Stuart Batterman, Mingwei Han, Tinsu Pan, Edward T. Zellers, Samuel J. Patrash |
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| Rok vydání: | 1993 |
| Předmět: |
inorganic chemicals
Materials science technology industry and agriculture Metals and Alloys food and beverages Binary number Regression analysis Disjoint sets equipment and supplies Condensed Matter Physics complex mixtures Surfaces Coatings and Films Electronic Optical and Magnetic Materials Sensor array Principal component analysis Materials Chemistry Principal component regression Gas detector Physics::Atomic Physics Electrical and Electronic Engineering Ternary operation Biological system Instrumentation Physics::Atmospheric and Oceanic Physics |
| Zdroj: | Sensors and Actuators B: Chemical. 12:123-133 |
| ISSN: | 0925-4005 |
| DOI: | 10.1016/0925-4005(93)80008-y |
| Popis: | The application of a disjoint principal-components regression method to the analysis of sensor-array response patterns is demonstrated using published data from ten polymer-coated surface-acoustic-wave (SAW) sensors exposed to each of nine vapors. Use of the method for the identification and quantitation of the components of vapor mixtures is shown by simulating the 36 possible binary mixtures and 84 possible ternary mixtures under the assumption of additive responses. Retaining information on vapor concentrations in the classification models allows vapors to be accurately identified, while facilitating prediction of the concentrations of individual vapors and the vapors comprising the mixtures. The effects on the rates of correct classification of placing constraints on the maximum and minimum vapor concentrations and superimposing error on the sensor responses are investigated. |
| Databáze: | OpenAIRE |
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