Commercial Raman systems generally conduct imaging and spectroscopy measurements at subcentimeter scales. Such small spatial ranges cannot be used to inspect food samples with large surface areas (e.g., tomato fruit and beef steak), which is not convenient for food experiments. A line-scan macro-scale Raman system has been developed using a 785 nm line laser to implement high-throughput Raman chemical imaging (RCI) for food safety and quality research. A one-axis positioning table is used to move the samples to accumulate hyperspectral data using a pushbroom method. A dispersive Raman spectrograph is used in the system, which can be configured to backscattering RCI mode for surface inspection and spatially offset Raman spectroscopy (SORS) mode for subsurface inspection. In-house developed LabVIEW software is used to fulfill functions for system control, hardware parameterization, and data transfer. The systems is flexible and versatile for food test, and it has been used to evaluate safety and quality of various food and agricultural products, such as detecting chemical adulterants mixed in food powders, mapping carotenoid content on carrot cross section, imaging whole surface of pork shoulder, and authenticating foods and ingredients through packages.
Outbreaks of foodborne illness due to pathogenic bacteria have been identified worldwide and have been associated with the consumption of contaminated agricultural products. The main objective of this research is to develop a rapid method for pathogen detection using Raman spectroscopy (RS). Direct detection in culture media and surface-enhanced Raman scattering (SERS) were used to identify Escherichia coli, Escherichia coli O157:H7, Salmonella spp., Listeria monocytogenes, Staphylococcus aureus, Bacillus cereus, and Bacillus thuringiensis. Bacterial isolates were cultured on selective media for 24 h at 37°C or 30°C and then tested with RS. A portable 785 nm point-scan Raman system was developed at ARS USDA for this purpose and multiple laser current and exposure times were tested to establish optimal conditions. Seven nanoparticles and three substrates were evaluated for optimal bacterial detection using label-free SERS. Raman peaks were very weak in direct detection and the bacteria were not identified using direct or SERS approaches. However, two gold nanoparticles consistently showed SERS peaks at 878.9, 1086, and 1455 cm-1 and relative differences in Raman intensity were observed among each of the tested bacteria. This method can be used to lay a foundation for future research such as SERS combined with chemometric analysis and label-based SERS approaches.
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.