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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #305914

Title: An average enumeration method of hyperspectral imaging data for quantitative evaluation of medical device surface contamination

item LE, HANH - Food And Drug Administration(FDA)
item Kim, Moon
item HWANG, JEESEONG - National Institute Of Standards & Technology (NIST)
item YANG, YI - Food And Drug Administration(FDA)
item U-THAINUAL, PAWEENA - Food And Drug Administration(FDA)
item KANG, JIN - Johns Hopkins University
item KIM, DO-HYUN - Food And Drug Administration(FDA)

Submitted to: Journal of Biomedical Optics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/1/2014
Publication Date: 10/1/2014
Citation: Le, H.D., Kim, M.S., Hwang, J., Yang, Y., U-Thainual, P., Kang, J.U., Kim, D. 2014. An average enumeration method of hyperspectral imaging data for quantitative evaluation of medical device surface contamination. Journal of Biomedical Optics. 5:3613-3627.

Interpretive Summary: Recently, ARS scientists demonstrated the feasibility of utilizing hyperspectral imaging to analyze bacterial biofilm formation on food contact surfaces such as stainless steel. In collaboration with ARS, FDA and NIST researchers explored the potential use of the spectral imaging for detection of biofilm on medical devices. In this investigation, an image-based quantification method was developed and evaluated successfully as a means to measure contamination coverage on the surfaces of common medical devices. This research is beneficial to biomedical scientists, regulatory agencies, and medical communities responsible for ensuring that medical device surfaces are free of biofilm.

Technical Abstract: We propose a quantification method called Mapped Average Principal Component Analysis Score (MAPS) to enumerate the contamination coverage on common medical device surfaces. The method was adapted from conventional Principal Component Analysis (PCA) on non-overlapped regions on a full frame hyperspectral image to resolve the percentage of contamination from the substrate. The concept was proven by using controlled contamination sample using artificial test soil and color simulating organic mixture, and was further validated using bacterial system such as biofilm on stainless steel surface. We also compare the results of MAPS with other statistical spectral analysis such as Spectral Analysis Method (SAM) to validate our method. The proposed method provides alternative quantification method of hyperspectral imaging data which can be easily implemented by basic PCA analysis.