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Automated ELISA and Machine Learning-Enhanced Accuracy in Biosensing and Colorimetric Measurements
Automated ELISA and Machine Learning-Enhanced Accuracy in Biosensing and Colorimetric Measurements
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105230
- ISBN
- 9798291567180
- DDC
- 610
- 서명/저자
- Automated ELISA and Machine Learning-Enhanced Accuracy in Biosensing and Colorimetric Measurements
- 발행사항
- [Sl] : University of Michigan, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 186 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-02, Section: B.
- 주기사항
- Advisor: Fan, Xudong;Guo, L. Jay.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2025.
- 초록/해제
- 요약Modern biomedical diagnostics demand platforms that are accurate, compact, automated, and cost-effective. Conventional ELISA (enzyme-linked immunosorbent assay) systems, while reliable, are often bulky, manual, and expensive, limiting accessibility for decentralized testing. This dissertation addresses this gap by developing a miniaturized ELISA platform with a production cost of about $1200, integrating simple rotary and linear movements as the only required mechanical motions with custom three-dimensional printed components to automate liquid handling. Using interleukin-6 (IL-6) as a model system, the platform achieved an R² of 0.9937 with duplicate measurements and a limit of detection (LOD) of 7.13 pg/mL, matching or exceeding the performance of high-end commercial systems while offering significant reductions in size, complexity, and cost. Beyond automation, this dissertation focuses on leveraging machine learning to enhance optical biosensing accuracy without modifying the hardware. In both primary sensing methods, which are peak shift tracking and fixed-wavelength intensity modulation, traditional approaches rely on one-dimensional fitting and overlook the rich spectral information available. First, a Ridge Regression framework is applied to combine multiple resonant peak shifts in silicon nanorod arrays, achieving approximately three orders of magnitude improvement in mean squared error (MSE) of refractive index prediction compared to traditional single-peak tracking. Full-spectrum modeling is then explored across two optical structures, one based on silicon supporting sharp Mie resonances for peak shift-based sensing and another based on titanium where intensity modulation is the dominant response to the refractive index change. It is found that linear regression based full-spectrum analysis significantly enhances accuracy for intensity modulation-based index sensing, while for peak shift-based sensing, combining multiple peaks provides superior performance. Finally, experimental validation using colorimetric measurements shows that applying machine learning with twelve selected wavelengths results in more than a 5700-fold MSE reduction relative to the best single-wavelength result.
- 일반주제명
- Biomedical engineering
- 일반주제명
- Engineering
- 일반주제명
- Nanotechnology
- 키워드
- Machine learning
- 키워드
- Biosensing
- 기타저자
- University of Michigan Electrical and Computer Engineering
- 기본자료저록
- Dissertations Abstracts International. 87-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798291567180
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a610
■1001 ▼aAalizadeh, Majid.
■24510▼aAutomated ELISA and Machine Learning-Enhanced Accuracy in Biosensing and Colorimetric Measurements
■260 ▼a[Sl]▼bUniversity of Michigan▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a186 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-02, Section: B.
■500 ▼aAdvisor: Fan, Xudong;Guo, L. Jay.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2025.
■520 ▼aModern biomedical diagnostics demand platforms that are accurate, compact, automated, and cost-effective. Conventional ELISA (enzyme-linked immunosorbent assay) systems, while reliable, are often bulky, manual, and expensive, limiting accessibility for decentralized testing. This dissertation addresses this gap by developing a miniaturized ELISA platform with a production cost of about $1200, integrating simple rotary and linear movements as the only required mechanical motions with custom three-dimensional printed components to automate liquid handling. Using interleukin-6 (IL-6) as a model system, the platform achieved an R² of 0.9937 with duplicate measurements and a limit of detection (LOD) of 7.13 pg/mL, matching or exceeding the performance of high-end commercial systems while offering significant reductions in size, complexity, and cost. Beyond automation, this dissertation focuses on leveraging machine learning to enhance optical biosensing accuracy without modifying the hardware. In both primary sensing methods, which are peak shift tracking and fixed-wavelength intensity modulation, traditional approaches rely on one-dimensional fitting and overlook the rich spectral information available. First, a Ridge Regression framework is applied to combine multiple resonant peak shifts in silicon nanorod arrays, achieving approximately three orders of magnitude improvement in mean squared error (MSE) of refractive index prediction compared to traditional single-peak tracking. Full-spectrum modeling is then explored across two optical structures, one based on silicon supporting sharp Mie resonances for peak shift-based sensing and another based on titanium where intensity modulation is the dominant response to the refractive index change. It is found that linear regression based full-spectrum analysis significantly enhances accuracy for intensity modulation-based index sensing, while for peak shift-based sensing, combining multiple peaks provides superior performance. Finally, experimental validation using colorimetric measurements shows that applying machine learning with twelve selected wavelengths results in more than a 5700-fold MSE reduction relative to the best single-wavelength result.
■590 ▼aSchool code: 0127.
■650 4▼aBiomedical engineering
■650 4▼aEngineering
■650 4▼aNanotechnology
■653 ▼aMachine learning
■653 ▼aBiomedical diagnostics
■653 ▼aBiosensing
■653 ▼aLimit of detection
■653 ▼aColorimetric measurements
■690 ▼a0541
■690 ▼a0537
■690 ▼a0652
■690 ▼a0800
■71020▼aUniversity of Michigan▼bElectrical and Computer Engineering.
■7730 ▼tDissertations Abstracts International▼g87-02B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359879▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
