Multi-channel piezoelectric quartz crystal sensor for organic vapours

Chang, Ping; Shih, Jeng‐Shong

doi:10.1016/s0003-2670(99)00664-9

Cited by 28 publications

(13 citation statements)

References 21 publications

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“…The eigenvalues and eigenvectors of the correlation matrix were calculated with the PCA-SAS program as described in our previous report. 19 The eigenvalues of the correlation matrix and the correlation coefficients of principal component (PC) factors with respect to 29 channels are summarized in Table 2. The first three factors in Table 2 are responsible for the major part of the total variance (98.66%).…”

Section: Resultsmentioning

confidence: 99%

“…[13][14] Principal component analysis (PCA), a well-known technique in statistics. [15][16][17][18][19] is useful in selecting appropriate coating materials for quartz crystals in a multi-channel piezoelectric crystal sensor. The central idea of PCA is to reduce the dimensionality of a dataset in which there are a large number of interrelated variables, while retaining variation present in the dataset as much as possible.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐Channel Piezoelectric Crystal Gas Sensor with Principal Component Analysis for Organic Solvent Pollutants from Polymer Plants

Wang

Shih

2006

J Chinese Chemical Soc

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A multi-channel piezoelectric (PZ) quartz crystal gas sensor coated with various organic adsorbents and a home-made microcomputer interface for data processing have been fabricated for the detection of various organic vapors such as methanol, formic acid, propionaldehyde, carbon disulfide and N,N-dimethyl formamide which are generally used as solvents in the polymer industry. The home-made computer interface includes a digital Altera and an Intel-8255 data processing system. The Principal Component Analysis (PCA) method was employed to analyze the frequency response signals from each channel with different coating materials and was applied to select the appropriate coating materials for these organic vapors. A dataset for the multi-channel sensor with 29 PZ quartz crystals for these organic vapors was collected. Six representative coating materials such as C60-polyphenyl acetylene (C60-PPA), polyethylene glycol, Nafion, triphenyl phosphine, cryptand-22 and polyvinyl pyrrolidone were selected after comparing the correlation between the 29 coating materials and the first four principal component (PC) factors of the PCA analysis. The 5 organic vapors can be effectively distinguished clearly by the six-channel piezoelectric crystal sensor and which can be obviously found from the PCA Scores Map. Furthermore, different organic vapors tend to have distinguishing profile discrimination maps with the six channel PZ crystal sensor. The profile discrimination maps can be applied as fingerprints for these organic molecules. The six-channel PZ crystal sensor has quite good detection limits for these organic vapors. Furthermore, the multivariate linear regression (MLR) analysis was also employed to compute the concentrations of methanol and carbon disulfide in the mixture of organic vapors.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi‐Channel Piezoelectric Crystal Gas Sensor with Principal Component Analysis for Organic Solvent Pollutants from Polymer Plants

Wang

Shih

2006

J Chinese Chemical Soc

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“…Chang and Shih [5] detected organic vapours by using multi-channel piezoelectric quartz crystal. By using an artificial neural network, they recognised organic analytes and demonstrated the distinction between these organic vapours.…”

Section: Theorymentioning

confidence: 99%

“…Many studies have shown the application of the QCM sensor for odour detection. Among those are gas mixture analysis [2], solvent vapour detection [3], detection of organic vapours in the environment [4,5], determination of single-odour substances in various types of botanical species such as fruit s and flowers [6], etc. In short, the advantages of the QCM sensor has been used for detection of various odours.…”

Section: Introductionmentioning

confidence: 99%

Detection of odour emissions from a composting facility using a QCM sensor array

2003

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Odour emissions from a composting facility were detected by using a quartz crystal microbalance (QCM) sensor array. The array consists of six sensor elements, which were coated with different materials. A series of tests under field conditions showed that considerable sensor frequency changes were detected during the compost-turning period. Using principal component analysis, it was found that more than 90% of the data variance could still be explained by use of two principal components.

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“…The signal was processed using a computer. Flow through systems for direct controlling of valves and monitoring of the data through a PC have been reported [4], [5], [6], [7] and [8].…”

Section: Introductionmentioning

confidence: 99%

Design and Development of Microcontroller Based Auto-Flow Assembly for Biosensor Application

Saini¹,

Suri²

2010

IJCA

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Multi-channel piezoelectric quartz crystal sensor for organic vapours

Cited by 28 publications

References 21 publications

Multi‐Channel Piezoelectric Crystal Gas Sensor with Principal Component Analysis for Organic Solvent Pollutants from Polymer Plants

Multi‐Channel Piezoelectric Crystal Gas Sensor with Principal Component Analysis for Organic Solvent Pollutants from Polymer Plants

Detection of odour emissions from a composting facility using a QCM sensor array

Design and Development of Microcontroller Based Auto-Flow Assembly for Biosensor Application

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