Paper
30 August 2013 Possibilities of visible-near infrared spectroscopy for rapid diagnosis of diseases
Jing Zhao, Ming Liu, Xiaozuo Lu, Gang Li
Author Affiliations +
Proceedings Volume 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications; 891019 (2013) https://doi.org/10.1117/12.2034084
Event: ISPDI 2013 - Fifth International Symposium on Photoelectronic Detection and Imaging, 2013, Beijing, China
Abstract
A noninvasive and rapid method for the diagnosis of disease was developed on the basis of an analysis of visible and near-infrared (Vis-NIR) spectra from tongue tip. Reflectance spectrum in the 463.87-1737.26 nm region from the tongue tips of 149 volunteers were collected and the samples were separated into two parts: calibration sample and test sample. Spectra were then subjected to two different analysis methods: a partial least squares (PLS) regression analysis and an interval partial least squares (iPLS) regression analysis. PLS and iPLS model gave the best results for test samples with correlation equal to 0.902 and 0.932, and with classification accuracy equal to 75% and 85%, respectively. The results showed that the iPLS method seem more robust than PLS model in full-spectrum region. The result also showed that application of the spectra for disease diagnosis is promising, and may provided a fast and simple diagnostic tools for clinical.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jing Zhao, Ming Liu, Xiaozuo Lu, and Gang Li "Possibilities of visible-near infrared spectroscopy for rapid diagnosis of diseases", Proc. SPIE 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications, 891019 (30 August 2013); https://doi.org/10.1117/12.2034084
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Reflectivity

Data modeling

Liver

Tongue

Statistical modeling

Arteries

Heart

Back to Top