研究目的
To review the achievements and applications of quantum cascade laser-based mid-infrared spectroscopy in biology and medicine, highlighting benefits such as high spectral power density and tuning capabilities.
研究成果
Quantum cascade laser-based mid-infrared spectroscopy offers significant advantages over traditional methods like FT-IR, including higher power density, faster measurements, and better beam properties, enabling applications in breath analysis, liquid sample quantification, tissue analysis, and rapid histopathology. However, challenges such as water interference, coherence effects, and clinical adoption barriers remain, but ongoing advancements are paving the way for broader biomedical use.
研究不足
Limitations include the high absorption of water in mid-infrared region affecting measurements, coherence issues in imaging leading to artifacts, need for specialized spectroscopic knowledge, cost of equipment, sample preparation requirements, potential long measurement times, and challenges in achieving clinical reproducibility and translation.
1：Experimental Design and Method Selection:
The review discusses various experimental setups using quantum cascade lasers (QCLs) for mid-infrared spectroscopy, including transmission spectroscopy, evanescent field absorption measurements, vibrational circular dichroism, and microspectroscopy. Methods involve using QCLs with different tuning capabilities (e.g., Fabry-Pérot, Distributed Feedback, External Cavity) and detection schemes (e.g., single element detectors, focal plane arrays).
2：Sample Selection and Data Sources:
Samples include gaseous samples (e.g., breath for analysis of CO2, CO, NO, acetone, ammonia), liquid samples (e.g., serum, biofluids for glucose and protein quantification), bulk tissue samples (e.g., porcine dermis, human skin for glucose monitoring), and tissue thin sections (e.g., cardiac, colon, breast tissues for histopathology). Data sources are from various cited studies.
3：List of Experimental Equipment and Materials:
Equipment includes QCLs (types: FP, DFB, EC), transmission cells, ATR crystals, microfluidic devices, detectors (e.g., MCT detectors, microbolometer arrays), microscopes, and computational tools for data analysis (e.g., PLS, random forest classifiers). Materials involve biological samples, chemicals like glucose, proteins, and standard reference materials.
4：Experimental Procedures and Operational Workflow:
Procedures involve illuminating samples with QCL radiation, measuring transmission or reflection, tuning wavelengths for hyperspectral data, scanning samples for mapping or imaging, and analyzing data with multivariate algorithms. Workflows include sample preparation (e.g., drying biofluids, sectioning tissues), calibration with reference methods, and validation against gold standards.
5：Data Analysis Methods:
Data analysis employs techniques such as partial least squares (PLS) regression, root mean squared error of prediction (RMSEP), limit of detection (LOD) calculations, machine learning classifiers (e.g., random forest, support vector machines), and statistical measures for accuracy and sensitivity.
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