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Applicability of fluorescent sensor arrays using kaleidoscopic indolizine for various analytes based on machine learning
- Author(s)
- 김현기
- Advisor
- 김은하
- Department
- 일반대학원 분자과학기술학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2022-02
- Language
- eng
- Keyword
- Indolizine; fluorescent sensor array; glucose; machine learning algorithm; pH; volatile organic compound
- Alternative Abstract
- As time has passed, humanity's interest in health has increased. Prior to treating a disease, it is often the aim of medics to identify the disease, as is currently the case with COVID-19. We suggest that the single most important factor is the cause of the disease to be treated. Consequently, we aimed to develop a simple and noninvasive sensor that could monitor health. We selected a fluorescent material that mimicked the function of mammalian olfactory receptors and simultaneously acted as a signal element. Fluorescent compounds based on indolizine structures are known to exhibit an existing intramolecular charge transfer (ICT) phenomenon, and it was expected that their photophysical properties would change due to electron density by interaction with the target analytes [1]. Our team have been able to develop numerous fluorescence frameworks through combinatorial chemical synthesis and have confirmed the emission of various fluorescent materials in the liquid and solid states at a single excitation wavelength (ex 365 nm) [2]. We term this the Kaleidoscopic Indolizine (KIz) system. One KIz fluorescent compound reacts with the analyte to produce a color difference value in response to exposure (before and after). Multiple designs of these fluorescent compounds form a specific pattern for the analyte, while forming an array of fluorescent sensors. Conventional array-type sensing systems require specialized operators with technical knowledge to measure the accuracy of electrochemical systems. To overcome this, we introduced an image analysis system that can detect various analytes using globally available mobile devices. A sensor array composed of multiple fluorescent compounds reacts with the analyte to produce a unique pattern. A highly accurate pattern-recognition machine learning algorithm was introduced into our sensing system. Pattern recognition and machine learning are commonly integrated fields of artificial intelligence (A.I). As our novel fluorescent sensor array is supported by machine learning, inexperienced users with no expertise can use it as a sensor with high accuracy. As a result, we developed a fluorescent sensor array that uses non-invasive methods to monitor health, and produces specific, identifiable patterns for volatile organic compounds, pH, and glucose. Based on the results of developing a sensor platform that can detect a variety of analytes using a chemical approach, if a bioengineering approach is adopted, it is expected that a diagnostic platform with higher performance will be built.
- Fulltext
- Appears in Collections:
- Graduate School of Ajou University > Department of Molecular Science and Technology > 4. Theses(Ph.D)
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