|CHUANG, YUNG-KUN - National Taiwan University|
|Yang, Chun Chieh|
|Delwiche, Stephen - Steve|
|LO, Y. MARTIN - University Of Maryland|
|CHEN, SUMING - National Taiwan University|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 10/18/2012
Publication Date: 10/18/2012
Citation: Chuang, Y., Yang, C., Kim, M.S., Delwiche, S.R., Lo, Y., Chen, S., Chan, D.E. 2012. Inspection of fecal contamination on strawberries using line-scan led-induced fluorescence imaging techniques. [abstract].
Technical Abstract: In the United States, fecal contamination of produce is a food safety issue because illnesses caused by associated pathogens such as Escherichia coli and Salmonella. Outbreaks of foodborne illnesses associated with consuming raw fruits and vegetables in the United States continue to be a public health concern. This problem not only threatens public health, but also results in economic losses from lost work hours, disruptions to the market chain, and the condemning of large lots. Therefore, the development of rapid and accurate detection methods such as optical sensing technologies for contaminant detection is urgent and essential. Among fruits, strawberry is a high-potential vector of fecal contamination and foodborne illnesses because it is often consumed raw or with minimal processing. In the present study, line-scan LED-induced fluorescence imaging techniques were applied to the inspection of fecal material on strawberries. The spectral characteristics at specific wavebands of strawberries spiked with dilute dried dairy manure were determined by simple mathematical detection algorithms. The results indicated that the combination of two-waveband intensity ratios, such as 680 nm / 688 nm and 680 nm / 704 nm, can successfully distinguish fecal contamination from the uncontaminated strawberry surface and the calyx. The resulting binary images showed that the algorithm could successfully detect all of the fecal contamination spots on the strawberry surfaces. The results of this study have the potential of providing essential information for the design of a nonchemical, automated, and rapid produce inspection system.