We discussed the classification protocols created throughout the document that handle the forward scattered light signal described in the previous chapter. We also show some pre-liminary results that serve as proof of concept for the developed classifier using synthetic particles. Additionally, we have reported the obtained results of the classifier performance in the two case studies proposed in the outline, providing an analysis of the results and possible explanations for the successes or failures of the models, the results being visible
56 OPTICALTWEEZERS DEVELOPMENT AS A TOOL FOR BIOMEDICAL DIAGNOSIS
in Table5.1. The results point toward the possibility of the use of this methodology in all three of the tested environments, whether as a particle classification tool in the case of the molecular imprinted polymers, or a cataloging instrument in the case of the algae.
Classifier Type Mean Best Worst Synthetic 64.75% 95.38% 38.34%
MIPs 82.3% 99.4% 44.4%
Micro-algae 77.83% 100% 47.5%
TABLE 5.1: Results table for the case studies, complete with the mean success rate, the best and the worst classification results.
Chapter 6
Conclusion and Prospective Work
With this dissertation, we set out to investigate the potential applications of a conven-tional optical tweezers setup as a tool for biomedical diagnosis. To accomplish this, we deployed a live classification tool based on the assumption that distinct particles with dif-ferent constituents behave difdif-ferently while caught under the optical tweezers laser beam.
A quadrant photodetector is used to assess the forward scattered light from the confined particles, treating the output data through the appropriate analysis methods and con-structing the classifying predictive models with machine learning algorithms.
Beginning with a description of the physical principles of the trapping phenomenon in the two major optical limits, we proceed to provide a detailed explanation of the equipar-tition and power spectral density methods, which allow for force/stiffness computations, which are at the heart of the classification tool. In addition, an overview of the experimen-tal setup is supplied, along with a description of the main components and their purposes.
Furthermore, we describe the system calibrations and sample preparation techniques for organic and non-organic materials. The collected data is then utilized to train and evalu-ate the previously mentioned cevalu-ategorization method.
Following the setup’s characterization, we take a close look at the constructed graph-ical user interface for the optgraph-ical tweezers system. In this section, we examine the many layers of the program and their respective responsibilities, beginning with the thought process behind the construction of the component drivers and finishing with a tour of the interface’s functions. The purpose of this effort was to make the data collecting pro-cedure easier for novice users while also allowing advanced personnel to write specific subroutines for their needs.
57
58 OPTICALTWEEZERS DEVELOPMENT AS A TOOL FOR BIOMEDICAL DIAGNOSIS
With this knowledge, we progress to describe the classification process, beginning with the development of models that identify particles based on their optical proper-ties under the trapping mechanism. We detail the output data pre-processing technique before discussing the use of Principal Component Analysis in Fourier space, which al-lows the data to be represented in the directions that most magnify its variance, pulling data points closer together if they have comparable features and vice versa. We also pro-vide early findings for the identification of 3mum PMMA, 3mum PS, 4mum PS, 8mum PMMA, and 8mum PS, with a relative success rate of 64.75 %, with a best result of 95.38%
and a worst result of 38.34%, arguing for the potential of the model as a particle classifier.
Then, the classifier is tested in case study conditions, using Carboxy PMMA beads that could be bound or unbound to a streptavidin protein. This protein has a very high affinity to biotin, also known as vitamin B7, and their interaction mimics the bonding reactions that occur in molecular imprinted polymers (MIPs), which are our main object of study. For this undertaking, we obtained an overall success rate 82.3%, with a best classification score of 99.4% and a worst result of 44.4%. This establishes a proof of concept for the classifier’s use in the molecular imprinted polymers, furthering our objective of developing this technique for biomedical use. Moreover, we provide a new tool for the classification of micro algae, exploring the protocol as a cataloging methodology. We obtained an overall success rate of 83%, with a best result of 100% and a worst result of 47.5%.
In summary, the obtained results validate our initial hypothesis that the optical tweez-ers setup, accompanied by the developed classification procedure, are capable to discrim-inate several types of particles in a plethora of contexts, such as organic and non-organic particles, proving its versatility and utility in scientific areas, Furthermore, we can infer its use in the development of biomedical sensors and diagnostic tools, with the possibility of combination with other instruments, to create a sensor fusion approach to screening methods in medical technology, being of utmost importance in the field of biomedical science.
6.1 Prospective Work
As a result of the dissertation’s constrained timeline, we were unable to follow various areas of research that were interesting in their own right. Their implications for this work are important for the main objective, thus they are worthy of a brief discussion.
6. CONCLUSION ANDPROSPECTIVEWORK 59 One proposed modification to the setup is the employment of a spatial light modulator (SLM) to regulate the light intensity in the sample plane, hence boosting particle entrap-ment. The spatial light modulator’s capacity to project light in a precise pattern allows for experimentation with a holographic optical tweezers configuration, the benefits of which are succinctly outlined in Chapter1. One of the attractive features of this technique is the possible use of non-gaussian laser beam, such as Laguerre-Gaussian beams, that could improve the classification and trapping process. This technique is also capable of pro-ducing multiple traps, which could be finely tuned to suit our identification needs. Trap stiffness coefficients for non-spherical specimens with the trapping of anE. Colibacteria in a given orientation have been determined in some studies in this subject [66]. Direc-tion, and even trajectory dependent trapping could help solve the problem outlined in Section5.5. Furthermore, its nanometric spatial resolution is sufficient for the trapping of molecular imprinted polymers, indicating promise for a sensor based on this technology.
Finally, we plan to add a Raman spectroscopy component to the optical tweezers setup for a sensor fusion approach, obtaining the well-known Raman Tweezers configuration.
The setup, which is widely used in the biology/medicine sensor development fields, is capable of combining the optical properties determination of the optical trapping phe-nomenon with the biochemical information provided by the Raman spectroscopy sys-tem, undoubtedly assisting in particle classification and identification. Furthermore, both methods are non-invasive, which is critical for biological sensors. Results obtained with the Raman Tweezers apparatus of the classification of colorectal cancer cells using ma-chine learning algorithms yielded promising results [25], indicating a possible new gen-eration of optical sensor based cancer screening procedures, allowing in this fashion a much faster detection process in the oncology field.
Appendix A
Experimental Methods and results
A.1 Fast Fourier Transform (FFT) based results for preliminary testing
FIGUREA.1: FFT based results using the Random Forest algorithm.
61
62 OPTICALTWEEZERS DEVELOPMENT AS A TOOL FOR BIOMEDICAL DIAGNOSIS
FIGUREA.2: FFT based results using the K-Nearest Neighbor algorithm.