4 Conclusions
Based on the study, it can be
concluded that the results obtained demonstrate that the QCL-GAP
operating in the 788 – 1884 cm-1 range produced
high-quality spectral information from the bacterial species studied:Sa , Se , and Ml . Although the bacteria under
investigation belong to the same family, the information provided by the
QCL-GAP had sufficient data to discriminate the bacteria mixtures from
the neat bacteria. This was demonstrated by the fact that in all PCA
analyses with various combinations, the tendency was to observe the
ellipse corresponding to the mixture positioned between the neat
bacteria combination ellipses. For example, the ellipse for the mixture
containing Se and Sa was positioned between the neatSe and Sa ellipses. The same effect was observed for the
other combinations, such as Se /Ml and Sa /Ml ,
where the ellipses were positioned between Se and Ml andSa and Ml neat bacteria, respectively. Developing PLS-DA
models to discriminate one microorganism at a time, results show that an
average of 99.2% of microorganisms were classified correctly,
Therefore, this study demonstrated the capability of QCL-GAP in
combination with PCA to discriminate between bacteria from the same
family. The development of this new methodology for analyzing bacteria
using QCL-GAP provides fast and accurate analysis for detecting
microorganisms. It is accompanied by a great potential to discriminate
between similar types of microorganisms, as was demonstrated in this
study.
Acknowledgments
I want to acknowledge and warmly thank my advisor, Professor Samuel P.
Hernández-Rivera, Ph.D., for his trust in my capabilities, potential,
and day-to-day support during my weakest moments during this journey.
You made this possible. His guidance and advice carried me through all
the stages of writing my project. I would also like to thank my
committee members, Prof. Carmen A. Vega-Olivencia, Dr. Nairmen
Mina-Camilde, Dr. Carlos Ríos-Velázquez, and Dr. Enrique
Meléndez-Martínez for their support in the revision of my publications
and dissertation, for making this challenging process an enjoyable
moment and for your brilliant comments and suggestions, thanks to you.
To my study partners and AbbVie, especially James Erker, Cormac Dalton,
and Dave Thompson, for allowing me to continue through these challenges
and support my goals.
I would also like to give special thanks to my family for their
continuous support and understanding when undertaking my research and
writing my project. Your prayer for me was what sustained me this far.
Finally, I would like to thank God for letting me through all the
difficulties, giving me strength, and blessing all my steps during this
process. I have experienced your guidance day by day. You are the one
who let me finish my degree. You will continue building my future,
career, and life goals and guide my family.
This material is based upon work
supported by the US Department of Homeland Security under Grant Awards
22STESE00001-02-00 and 21-CWDARI 00042-01-00 Science and Technology
Directorate, Office of University Programs. The views and conclusions
contained in this document are those of the authors. They should not be
interpreted as necessarily representing the official policies, either
expressed or implied, of the US Department of Homeland Security.
Additional support from the US Department of Agriculture, National
Institute of Food and Agriculture award # 2022-77040-37623 is also
acknowledged.