Pathogen detection using Raman spectroscopy is achieved through the use of a sandwich
immunoassay. Antibody-modified magnetic beads are used to capture and concentrate target
analytes in solution and surface-enhanced Raman spectroscopy (SERS) tags are conjugated with
antibodies and act as labels to enable specific detection of biological pathogens. The rapid detection
of biological pathogens is critical to first responders, thus assays to detect E.Coli and Anthrax have
been developed and will be reported. The problems associated with pathogen detection resulting
from the spectral complexity and variability of microorganisms are overcome through the use of
SERS tags, which provide an intense, easily recognizable, and spectrally consistent Raman signal.
The developed E. coli assay has been tested with 5 strains of E. coli and shows a low limit of
detection, on the order of 10 and 100 c.f.u. per assay. Additionally, the SERS assay utilizes
magnetic beads to collect the labeled pathogens into the focal point of the detection laser beam,
making the assay robust to commonly encountered white powder interferants such as flour, baking
powder, and corn starch. The reagents were also found to be stable at room temperature over
extended periods of time with testing conducted over a one year period. Finally, through a
specialized software algorithm, the assays are interfaced to the Raman instrument, StreetLab
Mobile, for rapid-field-deployable biological identification.
In recent years, several sensing devices capable of identifying unknown chemical and biological substances have been
commercialized. The success of these devices in analyzing real world samples is dependent on the ability of the on-board
identification algorithm to de-convolve spectra of substances that are mixtures. To develop effective de-convolution
algorithms, it is critical to characterize the relationship between the spectral features of a substance and its probability of
detection within a mixture, as these features may be similar to or overlap with other substances in the mixture and in the
library. While it has been recognized that these aspects pose challenges to mixture analysis, a systematic effort to
quantify spectral characteristics and their impact, is generally lacking. In this paper, we propose metrics that can be used
to quantify these spectral features. Some of these metrics, such as a modification of variance inflation factor, are derived
from classical statistical measures used in regression diagnostics. We demonstrate that these metrics can be correlated to
the accuracy of the substance's identification in a mixture. We also develop a framework for characterizing mixture
analysis algorithms, using these metrics. Experimental results are then provided to show the application of this
framework to the evaluation of various algorithms, including one that has been developed for a commercial device. The
illustration is based on synthetic mixtures that are created from pure component Raman spectra measured on a portable
device.
Identifying injuries, deformities, and diseases by non-invasive instrumental means has been a major innovation in medicine. Diagnostic and imaging medical devices have revolutionized diagnosis and surgery, providing more efficient way to identify injuries and diseased or damaged tissues. In this paper, identification of different animal tissues using a miniature near-infrared (NIR) spectrometer will be demonstrated. Each tissue type contains different amounts of moisture and proteins, and by using this miniature spectrometer, a miniature fiber-optic probe and chemometrics; the ability to recognize tissues spectral differences is established.
Identifying injuries, deformities, and diseases by non-invasive instrumental means has been a major innovation in medicine. Diagnostic and imaging medical devices have revolutionized diagnosis and surgery, providing more efficient way to identify injuries and diseased or damaged tissues. In this paper, identification of different animal tissues using a miniature near-infrared (NIR) spectrometer will be demonstrated. Each tissue type contains different amounts of moisture and proteins, and by using this miniature spectrometer, a miniature fiber-optic probe and chemometrics; the ability to recognize tissues spectral differences is established.
Course Instructor
SC802: Practical Near Infrared and Raman Spectroscopy Applications
This course provides attendees with a basic working knowledge of NIR and Raman spectroscopy, and the applications of these technologies. The course concentrates on current system designs and technologies and applications of existing systems in end-use applications. Many practical and useful examples are included throughout.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.