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Figures & Tables

Fig.1

Unprocessed NIR spectra for glucose levels below 5.0 mM (blue lines) or above 7.0 mM (red lines).
Unprocessed NIR spectra for glucose levels below 5.0 mM (blue lines) or above 7.0 mM (red lines).

Fig.2

Measured resistance as a function of frequency for glucose levels below 5.0 mM (blue lines) or above 7.0 mM (red lines).
Measured resistance as a function of frequency for glucose levels below 5.0 mM (blue lines) or above 7.0 mM (red lines).

Fig.3

Measured reactance as a function of frequency for glucose levels below 5.0 mM (blue lines) or above 7.0 mM (red lines).
Measured reactance as a function of frequency for glucose levels below 5.0 mM (blue lines) or above 7.0 mM (red lines).

Fig.4

Correlation between measured resistance at different frequencies, and blood glucose level.
Correlation between measured resistance at different frequencies, and blood glucose level.

Fig.5

Correlation between measured reactance at different frequencies, and blood glucose level.
Correlation between measured reactance at different frequencies, and blood glucose level.

Fig.6

Correlation between measured phase angle at different frequencies, and blood glucose level.
Correlation between measured phase angle at different frequencies, and blood glucose level.

Fig.7

Correlation between measured conductance at different frequencies, and blood glucose level.
Correlation between measured conductance at different frequencies, and blood glucose level.

Fig.8

Correlation between measured susceptance at different frequencies, and blood glucose level.
Correlation between measured susceptance at different frequencies, and blood glucose level.

Fig.9

PLS model with three components based on NIR, resistance and reactance. Individually trained until the start of intake of sugar-containing soft drink for the second time (blue dots) and thereafter used for prediction (orange dots).
PLS model with three components based on NIR, resistance and reactance. Individually trained until the start of intake of sugar-containing soft drink for the second time (blue dots) and thereafter used for prediction (orange dots).

Fig.10

PLS model with five components based on NIR, resistance and reactance. Globally trained until the start of intake of sugar-containing soft drink for the second time (blue dots) and thereafter used for prediction orange dots).
PLS model with five components based on NIR, resistance and reactance. Globally trained until the start of intake of sugar-containing soft drink for the second time (blue dots) and thereafter used for prediction orange dots).

Fig.11

ANN model with one hidden layer and five nodes based on NIR, resistance and reactance. Globally trained until the start of intake of sugar-containing soft drink for the second time (blue dots) and thereafter used for prediction orange dots).
ANN model with one hidden layer and five nodes based on NIR, resistance and reactance. Globally trained until the start of intake of sugar-containing soft drink for the second time (blue dots) and thereafter used for prediction orange dots).
Language: English
Page range: 133 - 138
Submitted on: Oct 12, 2019
Published on: Dec 31, 2019
Published by: University of Oslo
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2019 Jan-Hugo Andersen, Olav Bjerke, Fatos Blakaj, Vilde Moe Flugsrud, Fredrik Alstad Jacobsen, Marius Jonsson, Eirik Nobuki Kosaka, Petter André Langstrand, Øyvind Grannes Martinsen, Alexander Stene Moen, Emily Qing Zang Moen, Øyvind Knutsen Nystad, Eline Olesen, Mahum Qureshi, Victor Jose Østrem Risopatron, Simen Kristoffer Ruud, Nikolai Stensø, Fredrik Lindseth Winje, Eirik Vetle Winness, Sisay Abie, Vegard Munkeby Joten, Christian Tronstad, Ole Elvebakk, Ørjan Grøttem Martinsen, published by University of Oslo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.