Abstract

Fluorescence microscopy is an established technique for qualitative and quantitative surface imaging. Because many "real world" surfaces of Interest are optically rough (vertical variations on the order of 100 μm or more) it is of great interest to minimize the amount of spectral information lost because of depth-of-focus variations. Multivariate statistics on normalized spectra provide an ideal method for extracting information from such data. The following study examines nylon 66 gel on production pack screens via multivariate fluorescence imaging.

Fluorescence spectral imaging for characterization of tissue based on multivariate statistical analysis

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Decrease in fluorescence lifetime by glycation of collagen and its application in determining advanced glycation end-products in human dentin

Shuichiro Fukushima, Masato Shimizu, Jiro Miura, Yusuke Matsuda, Mizuho Kubo, Mamoru Hashimoto, Takuya Aoki, Fumio Takeshige, and Tsutomu Araki
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High quality computational ghost imaging using multi-fluorescent screen

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J. Opt. Soc. Am. A 32(2) 323-328 (2015)

Multivariate optical computing using a digital micromirror device for fluorescence and Raman spectroscopy

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Fluorescence-guided optical coherence tomography imaging for colon cancer screening: a preliminary mouse study

Nicusor Iftimia, Arun K. Iyer, Daniel X. Hammer, Niyom Lue, Mircea Mujat, Martha Pitman, R. Daniel Ferguson, and Mansoor Amiji
Biomed. Opt. Express 3(1) 178-191 (2012)