Noninvasive near infrared spectroscopy on living tissue with multivariate calibration approaches

208 Pages·2017·6.12 MB·English

University of Iowa
Iowa Research Online
Teses and Dissertations
Fall 2010
Noninvasive near infrared spectroscopy on living
tissue with multivariate calibration approaches
Chuannan Bai
University of Iowa
Copyright 2010 Chuannan Bai
Tis dissertation is available at Iowa Research Online: htp://ir.uiowa.edu/etd/776
Recommended Citation
Bai, Chuannan. "Noninvasive near infrared spectroscopy on living tissue with multivariate calibration approaches." PhD (Doctor of
Philosophy) thesis, University of Iowa, 2010.
htp://ir.uiowa.edu/etd/776.
Follow this and additional works at: htp://ir.uiowa.edu/etd
Part of the Chemistry Commons
NONINVASIVE NEAR INFRARED SPECTROSCOPY ON LIVING TISSUE WITH
MULTIVARIATE CALIBRATION APPROACHES
by
Chuannan Bai
An Abstract
Of a thesis submitted in partial fulfillment
of the requirements for the Doctor of
Philosophy degree in Chemistry
in the Graduate College of
The University of Iowa
December 2010
Thesis Supervisor: Professor Mark A. Arnold
1
ABSTRACT
Near infrared (NIR) spectroscopy is being developed on living tissue models for
noninvasively measuring in vivo glucose concentrations in individuals with diabetes.
Multivariate calibration models have been built and the selectivity of each multivariate
signature has been evaluated by several means. The primary objective of the research
detailed in this dissertation is to practically apply noninvasive NIR glucose measurements
on animal models for both short-term and long-term studies and preview future human
subject evaluations.
In the animal study, living tissue spectra were collected through a modified
optical interface with hyper- and hypo-glycemia control. Selective measurements of
glucose molecules are illustrated by the partial lease squares (PLS) algorithm, net analyte
signal (NAS) vector, and hybrid linear analysis (HLA). Each model demonstrates the
ability to predict prospective glucose concentrations in the short term.
A restraint platform was developed for the long-term study on conscious animals.
Conscious animal spectra were collected on multiple days. The anesthetized animal
experiment follows on the final day. Principal component analysis (PCA) of spectra
collected on different days demonstrates no significant difference between conscious
animal spectra and anesthetized animal spectra. Moreover, an NAS vector analysis from
conscious animal spectra has the ability predict glucose concentrations which follow the
blood glucose transient during the anesthetized animal experiment. This procedure has
great potential to be applied in future NIR glucose monitoring device.
Before the application of this noninvasive NIR technology on people with
diabetes, the impact of skin difference must be determined. In this human subject study,
human skin color and baseline spectra were collected and analyzed to determine
differences among individuals and within groups of people. To compare in vivo NIR
spectra with different skin characteristics, PCA was performed to obtain principal
2
component (PC) scores. Poor correlation between PC scores and skin characteristics
concludes that noninvasive near-infrared technology is insensitive to different types of
skin. In addition, glucose prediction was performed by a NAS analysis. The prediction
results demonstrate that it is feasible to build a NAS glucose model for noninvasive NIR
glucose predictions in human subjects.

Abstract Approved: ___________________________________
Thesis Supervisor
_
Title and Department
_
Date
NONINVASIVE NEAR INFRARED SPECTROSCOPY ON LIVING TISSUE WITH
MULTIVARIATE CALIBRATION APPROACHES
by
Chuannan Bai
A thesis submitted in partial fulfillment
of the requirements for the Doctor of
Philosophy degree in Chemistry
in the Graduate College of
The University of Iowa
December 2010
Thesis Supervisor: Professor Mark A. Arnold
Graduate College
The University of Iowa
Iowa City, Iowa
CERTIFICATE OF APPROVAL
_______________________
PH.D. THESIS
_______________
This is to certify that the Ph.D. thesis of
Chuannan Bai
has been approved by the Examining Committee
for the thesis requirement for the Doctor of Philosophy
degree in Chemistry at the December 2010 graduation.
Thesis Committee: __________________________________
Mark A. Arnold, Thesis Supervisor
__________________________________
Gary W. Small
__________________________________
Lei Geng
__________________________________
Claudio J. Margulis
__________________________________
Julie L. P. Jessop
To my wife Wei Wang and my loving parents
ii
ACKNOWLEDGMENTS
I would like to thank everyone who has helped and supported me during my
graduate study. Their direct and indirect contributions to this dissertation are highly
appreciated.
At the very first, I am honored to express my deepest and sincerest gratitude to
my research advisor, Dr. Mark A. Arnold, for his continuous support during my Ph.D.
study. His patience, motivation, enthusiasm, and immense knowledge have made my
research life smooth, productive, and full of good memories. I am also thankful for the
excellent example that he provided as successful scientist and chemist.
I am also extremely grateful to Dr Gary W. Small for the invaluable discussions
and instructions. Dr. Small is more like my second research advisor and who led me into
this chemometrics world. Anytime when I had difficulties in my research, he was always
there to help with his intelligence, diligence, and patience.
It is my pleasure to thank my other committee members: Dr. Lei Geng, Dr.
Claudio J. Margulis, Dr. Julie L. P. Jessop, and Dr. Daniel M. Quinn for the valuable
discussion and suggestions about my research.
Special thanks also go to Dr. Jonathon T. Olesberg and Dr. Lingzhi Liu for the
animal study training and generous sharing of many computer codes. Without their initial
setup and studies, this work would not been half as complete. Their success in this
research has set an excellent example for me.
I would also like to thank Terry L. Graham for her help in the animal study and
data collection. It was she who taught me the right way to handle animals. She was more
like a translator between the animals and I.
I am indebted to every member in Arnold Research Group to support me. I would
like to thank Dong, Lingzhi, Min, David, Sherif, Wenjiao, Valerie, Joo Young, Natasha,
iii
Jue, Ryan, and Bo for a variety of help in my experiments, support during my
presentation practice, and discussions in the group meeting.
I wish to thank my parents for providing me a loving environment and supporting
me for whatever decision I made.
Last but not least, I would like to thank my wife, Wei Wang, for the support
throughout my graduate study and encouragement during my thesis writing.
iv
ABSTRACT
Near infrared (NIR) spectroscopy is being developed on living tissue models for
noninvasively measuring in vivo glucose concentrations in individuals with diabetes.
Multivariate calibration models have been built and the selectivity of each multivariate
signature has been evaluated by several means. The primary objective of the research
detailed in this dissertation is to practically apply noninvasive NIR glucose measurements
on animal models for both short-term and long-term studies and preview future human
subject evaluations.
In the animal study, living tissue spectra were collected through a modified
optical interface with hyper- and hypo-glycemia control. Selective measurements of
glucose molecules are illustrated by the partial lease squares (PLS) algorithm, net analyte
signal (NAS) vector, and hybrid linear analysis (HLA). Each model demonstrates the
ability to predict prospective glucose concentrations in the short term.
A restraint platform was developed for the long-term study on conscious animals.
Conscious animal spectra were collected on multiple days. The anesthetized animal
experiment follows on the final day. Principal component analysis (PCA) of spectra
collected on different days demonstrates no significant difference between conscious
animal spectra and anesthetized animal spectra. Moreover, an NAS vector analysis from
conscious animal spectra has the ability predict glucose concentrations which follow the
blood glucose transient during the anesthetized animal experiment. This procedure has
great potential to be applied in future NIR glucose monitoring device.
Before the application of this noninvasive NIR technology on people with
diabetes, the impact of skin difference must be determined. In this human subject study,
human skin color and baseline spectra were collected and analyzed to determine
differences among individuals and within groups of people. To compare in vivo NIR
spectra with different skin characteristics, PCA was performed to obtain principal
v