2015
DOI: 10.1016/j.chemolab.2015.02.021
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Signal model of electronic noses with metal oxide semiconductor

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Cited by 9 publications
(5 citation statements)
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References 24 publications
(22 reference statements)
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“…We assume that an electronic nose system is made up of sensors. When this system is used to measure samples and the measuring time is for each sample, a three-way data array is obtained According to our previous study [ 38 ], each slice of can be expressed as a two-way data , and be decomposed as follows: …”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…We assume that an electronic nose system is made up of sensors. When this system is used to measure samples and the measuring time is for each sample, a three-way data array is obtained According to our previous study [ 38 ], each slice of can be expressed as a two-way data , and be decomposed as follows: …”
Section: Methodsmentioning
confidence: 99%
“…In this paper, we establish a new data processing method for the discrimination of similar or complex odor samples. A novel signal model based on the true response mechanism of a metal oxide semiconductor (MOS) sensor array had been developed in our previous work and had shown success in the discrimination of perfume samples [ 38 ]. Here, we develop a new algorithm for the decomposition of this signal mode, and further propose a conception of abstract odor factor maps (AOFMs).…”
Section: Introductionmentioning
confidence: 99%
“…Thus, the development of fast, low-cost and green sensorbased techniques, which may be applied on-line, to monitor in-situ perfume aroma-fragrance profiles is highly envisaged by the industry. Electronic noses (E-noses) have been proposed for perfume analysis namely for discriminating original brand perfumes or recognizing fake counterparts [5][6][7]; for identifying simple aromas [8][9][10][11][12]; for recognizing unknown fragrance mixtures [13]; to classify different perfume classes [14]; as quality control method of musk samples [15]; for generating analyte-specific fingerprints of odorants [16]; to differentiate perfumes by brand [16,17]; or, for highlight the differences of perfumes according to the producers, using odorant maps [18]. An Enose was also applied to detect counterfeit perfumed cleaner products as well as to quantify the perfume added amount [19].…”
Section: Introductionmentioning
confidence: 99%
“…Gas identification technology has huge potential applications in medical diagnoses, food industries, early warning of poisonous gas leakage, fire prevention, antiterrorism, military, etc. At present, electronic noses are the main gas identification means, which include four main subunits: the sensor array, signal acquisition and processing unit, pattern recognition unit, and reference-library database. Several types of gas sensors may be used in electronic noses, and the most common is the conductive sensors (such as metal-oxide semiconductor (MOS) sensors and conducting polymer sensors) and piezoelectric sensors (such as surface acoustic sensors and quartz crystal microbalances). Compared with the traditional gas identification methods such as the sensory evaluation, gas chromatography, and gas chromatography–mass spectrometry, electronic noses have the merits such as a relative low cost, a fast response, and freedom from professional operation. However, as the sensing response of each sensor element in the sensor array is influenced by the environmental temperature and humidity, the performance of an electronic nose is also affected by environmental conditions and in a sophisticated way due to the large number of sensors employed. Moreover, as each sensor element in the sensor array has the time drifting problem, an electronic nose usually also has this problem and its identification performance drifts with time in a sophisticated way due to the multiple number of sensors employed. Thus, reducing the number of sensor elements in an electronic nose can simplify the structure, increase the long-term stability, and widen the application range of electronic noses.…”
Section: Introductionmentioning
confidence: 99%