Unscrambling complex sample composition, variability and multi-scale interference in optical spectroscopy

Rui C. Martins

doi:10.1117/12.2527586

3 October 2019 Unscrambling complex sample composition, variability and multi-scale interference in optical spectroscopy

Rui C. Martins

Author Affiliations +

Proceedings Volume 11207, Fourth International Conference on Applications of Optics and Photonics; 1120722 (2019) https://doi.org/10.1117/12.2527586
Event: IV International Conference on Applications of Optics and Photonics (AOP 2019), 2019, Lisbon, Portugal

Abstract

Spectral information is characterized by multi-scaled interference, convolution and variability. Spectral lines are fragmented and diffused along the spectra. In many cases, matrix and physical effects do not allow to determine specific bands. Despite this limitation, the observed spectra contains significant amounts of information about the sample composition and characteristics, which once understood, can make spectroscopy an ideal technology for analyzing complex samples, such as bodyfluids and tissues. Breaking down and deciphering the structure of spectral information is paramount for the development of reagent-free point-of-care devices. A self-learning artificial intelligence was developed to take advantage of spectral complex information structure. It determines the relationships between composition and/or spectral features in high-dimensional space, where different sub-spaces correlate to specific constituents or characteristics. It also establishes a knowledgebase, by feature space transformations and optimizing co-variance search direction under the correct ’matrix effect’ context. An example of hemogram analysis with erythrocyte and leucocyte counts is presented to demonstrate the advantages of the developed methodology.

Citation Download Citation

Rui C. Martins "Unscrambling complex sample composition, variability and multi-scale interference in optical spectroscopy", Proc. SPIE 11207, Fourth International Conference on Applications of Optics and Photonics, 1120722 (3 October 2019); https://doi.org/10.1117/12.2527586

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

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Blood

Artificial intelligence

Spectroscopy

3D modeling

Convolution

Optical spectroscopy

Chemometrics

Show All Keywords

Keywords/Phrases

Search In:

Publication Years