Next Article in Journal
Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks
Previous Article in Journal
Formation and Characterization of Self-Assembled Phenylboronic Acid Derivative Monolayers toward Developing Monosaccaride Sensing-Interface
Sensors 2007, 7(8), 1496-1508; doi:10.3390/s7081496

Differentiation of Toxic Molds via Headspace SPME-GC/MS and Canine Detection

1, 1, 1 and 1,*
1 Florida International University, International Forensic Research Institute, Miami, FL 33199, USA 2 Department of Chemistry and Biochemistry, University Park, Miami, FL 33199, USA 3 Department of Environmental Studies, University Park, Miami, FL 33199, USA 4 Florida Canine Academy, Safety Harbor, FL 34695, USA
* Author to whom correspondence should be addressed.
Received: 2 August 2007 / Accepted: 2 August 2007 / Published: 13 August 2007
View Full-Text   |   Download PDF [403 KB, uploaded 21 June 2014]   |   Browse Figures


Indoor mold growth has recently become a concern in the legal world in regards to insurance litigation. Hazardous mold exposure to humans has been linked to many acute and chronic adverse health effects including death. As it grows, mold produces several types of primary and secondary metabolites, including microbial volatile organic compounds (MVOCs). Microbial volatile organic compound emission may be used as a preliminary indication of a mold infestation that is invisible to the unaided eye. The objective of the study is to identify the unique odor signatures of three species of molds, Aspergillus versicolor, Penicillium chrysogenum, and Stachybotrys chartarum by SPME-GC/MS analysis. Determining the compounds that are emitted by the selected species has made it possible to conduct validation studies of canine detection of these mold species through a series of field tests.
Keywords: toxic molds; SPME-GC/MS; canine detection toxic molds; SPME-GC/MS; canine detection
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote |
MDPI and ACS Style

Griffith, R.T.; Jayachandran, K.; Whitstine, W.; Furton, K.G. Differentiation of Toxic Molds via Headspace SPME-GC/MS and Canine Detection. Sensors 2007, 7, 1496-1508.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here


[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert