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Sensors 2010, 10(8), 7122-7133; doi:10.3390/s100807122

Automated Signal Processing Applied to Volatile-Based Inspection of Greenhouse Crops

1
Wageningen University, Farm Technology Group/P.O. Box 17, 6700 AA Wageningen, The Netherlands
2
Wageningen University, Laboratory of Plant Physiology/P.O. Box 658, 6700 AR Wageningen, The Netherlands
3
Wageningen UR Greenhouse Horticulture / P.O. Box 644, 6700 AP Wageningen, The Netherlands
*
Author to whom correspondence should be addressed.
Received: 21 June 2010 / Revised: 15 July 2010 / Accepted: 20 July 2010 / Published: 28 July 2010
(This article belongs to the Special Issue Direct and Indirect Sensing of Odor and VOCs and Their Control)
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Abstract

Gas chromatograph–mass spectrometers (GC-MS) have been used and shown utility for volatile-based inspection of greenhouse crops. However, a widely recognized difficulty associated with GC-MS application is the large and complex data generated by this instrument. As a consequence, experienced analysts are often required to process this data in order to determine the concentrations of the volatile organic compounds (VOCs) of interest. Manual processing is time-consuming, labour intensive and may be subject to errors due to fatigue. The objective of this study was to assess whether or not GC-MS data can also be automatically processed in order to determine the concentrations of crop health associated VOCs in a greenhouse. An experimental dataset that consisted of twelve data files was processed both manually and automatically to address this question. Manual processing was based on simple peak integration while the automatic processing relied on the algorithms implemented in the MetAlignTM software package. The results of automatic processing of the experimental dataset resulted in concentrations similar to that after manual processing. These results demonstrate that GC-MS data can be automatically processed in order to accurately determine the concentrations of crop health associated VOCs in a greenhouse. When processing GC-MS data automatically, noise reduction, alignment, baseline correction and normalisation are required.
Keywords: automated; signal processing; plant volatiles; greenhouse automated; signal processing; plant volatiles; greenhouse
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Jansen, R.; Hofstee, J.W.; Bouwmeester, H.; Henten, E. Automated Signal Processing Applied to Volatile-Based Inspection of Greenhouse Crops. Sensors 2010, 10, 7122-7133.

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