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Updated: Oct 1, 2026
Fluorescence detection methods for microfluidic droplet platforms
Published on: December 10, 2011
Raman/SERS-based identification, sorting and validation in droplet microfluidics: potential and challenges for
Haixia Zhao1, Shijie Zheng1, Yi Duan1
1College of Optoelectronic Engineering, Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, Key Disciplines Lab of Novel Micro-Nano Devices and System Technology, Chongqing University, Chongqing 400044, China. CL2009@cqu.edu.cn.
Abstract:
High-throughput microbial metabolite detection and screening support strain development and the design-build-test-learn (DBTL) cycle in biomanufacturing. Droplet microfluidics provides independent microreactors for detection, sorting and recovery, but reliable screening also requires accurate identification, controlled separation and independent validation. This review discusses cell-laden and non-cell-laden droplets, detection methods, integration of surface-enhanced Raman scattering (SERS) with droplet microfluidics, fermentation-matrix effects, Raman-activated cell and droplet sorting, and performance assessment. SERS provides signals through molecular fingerprints or specific recognition systems to inform sorting decisions. However, hot spots, probe state, matrix composition, droplet position and acquisition conditions affect absolute intensity, making single measurements unreliable for quantification without adequate calibration. Internal standards, matrix-matched calibration and averaging signals from droplets containing the same sample can improve quantitative reliability, while HPLC, LC-MS or functional assays can provide independent post-sorting validation. Many well-developed microdroplet SERS demonstrations use standards or biomedical models; complete detection, sorting and validation workflows in genuine fermentation samples and cell-laden droplets remain limited. Organised around target identification, physical sorting and post-sorting validation, this review evaluates the signal-throughput trade-off, matrix compatibility, detection-sorting timing and inconsistent evaluation criteria. A staged validation route from model droplets to fermentation samples and cell-laden droplets is discussed. By linking fermentation-matrix effects, sorting reliability and independent validation, the review helps identify sources of screening errors and assess the potential and limitations of these approaches in biomanufacturing.
