Spotting Live Bugs Keeps a Cleanroom Clean
When it comes to keeping pharmaceutical cleanrooms clean and profitable, it is important to be a good judge of the quick and the dead. Of the two classes of particles that contaminate cleanrooms, the quick are the more troublesome because a single live microorganism can multiply into many.
If process engineers can identify the type of offending microbe, they can track down the contaminating source and eliminate it. However, conventional methods involve growing the captured microorganisms in various media, which can take a day or more.
To identify cleanroom-contaminating bioaerosols, the researchers first collect the airborne particles on a substrate (left). They use fluorescence imaging to weed out those that are dead (middle) and employ Raman spectroscopy to identify the strain and species of the remaining particles of interest (right). Images courtesy of Jürgen Popp. ©2006, American Chemical Society.
“These contaminations need to be identified on short timescales,” said Jürgen Popp, a professor of physical chemistry at Friedrich Schiller Universität in Jena, Germany.
Popp and his colleagues from the university and other German institutions have developed a rapid way to detect and identify biotic particles through a combination of fluorescence imaging and Raman spectroscopy. The approach cuts the identification time significantly and enables automation of the task, so a system could spot a single living bioparticle in minutes.
A A fully automated prototype of a bioaerosol identification system includes visible and fluorescence microscopes and a Raman setup with a fiber-coupled spectrometer.
In developing the technique, the investigators used fluorescence to categorize particles on a substrate as biotic or abiotic. In a typical cleanroom, the ratio of the two ranges from 1:100 to 1:10,000 or less. By determining which were biotic, they substantially decreased the number of particles that required further investigation. In the next step, they used Raman spectroscopy to identify the biotic particles with regard to type, employing pattern recognition to match the measured spectra against those of known microorganisms.
They did this with a support vector machine, an adjustable classification program supplied by Universität Freiburg. The combination of Raman spectra and computer algorithms led to good results, according to Popp.
In a demonstration of the technique reported in the online edition of Analytical Chemistry on March 4, the scientists collected fluorescence images using a cooled Zeiss CCD array spectrometer and a fluorescence microscope, with an excitation wavelength of 365 nm. To capture Raman spectra, they used a Horiba Jobin Yvon microspectrometer with a 532-nm line from a frequency-doubled Coherent Nd:YAG laser for a light source.
With these instruments, they classified various microbes mixed among similar-size particles of melamine resin, TiO2 and polymethyl methacrylate. Tests indicated that only 1.5 percent of the biotic particles were incorrectly identified as abiotic and that the strain and species of 125 of 130 bacterial particles were identified correctly. Popp said that better results might be obtained by changing the Raman excitation wavelength, which is under evaluation.
The researchers also built a prototype of an automated system that combined the separate instruments into one. In use, there might be a need to adjust the fluorescence feature threshold that distinguishes between biotic and abiotic particles. Raman spectra might also have to be captured for microbes specific to a particular cleanroom. Those enhancements may be part of further research and development.
Popp noted that the system has potential applications in other areas, such as in the rapid identification of infectious diseases.
Contact: Jürgen Popp, Friedrich Schiller Universität, Jena, Germany; e-mail: firstname.lastname@example.org.
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