Multivariate optical computing for fluorochrome discrimination

Ryan J. Priore; Joseph A. Swanstrom

doi:10.1117/12.2080996

9 March 2015 Multivariate optical computing for fluorochrome discrimination

Ryan J. Priore, Joseph A. Swanstrom

Author Affiliations +

Proceedings Volume 9332, Optical Diagnostics and Sensing XV: Toward Point-of-Care Diagnostics; 933212 (2015) https://doi.org/10.1117/12.2080996
Event: SPIE BiOS, 2015, San Francisco, California, United States

Abstract

Multivariate Optical Elements (MOEs) are thin-film devices that encode a broad band, spectroscopic pattern allowing a simple broadband detector to generate a highly sensitive and specific detection for a target analyte. MOE filter sets are capable of sensing projections of the original sparse spectroscopic space enabling a small set of MOEs to discriminate a multitude of target analytes. This presentation will summarize the design and fabrication of compressed detection MOE filter sets for detecting multiple fluorochromes simultaneously with strong spectroscopic interference as well as comparing the detection performance of the MOE sensor with traditional optical band pass filter methodologies.

Citation Download Citation

Ryan J. Priore and Joseph A. Swanstrom "Multivariate optical computing for fluorochrome discrimination", Proc. SPIE 9332, Optical Diagnostics and Sensing XV: Toward Point-of-Care Diagnostics, 933212 (9 March 2015); https://doi.org/10.1117/12.2080996

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE