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<p>This study presents a portable and accurate nitrite detection system that integrates a differential gradient analysis with smartphone-assisted microfluidic colorimetry to improve detection efficiency and precision. A bidirectional concentration gradient was established via diffusion within a PDMS-based microfluidic chip, significantly reducing the dependency on standard calibration and shortening the total detection time to within 15 minutes. Sample images were captured using a smartphone camera, and nitrite concentrations were quantified through RGB color analysis using a custom-developed mobile application. The implementation of the differential colorimetric method significantly improved detection accuracy, particularly at low concentrations, reducing the measurement error from 9.12% (conventional methods) to 1.93%. The platform demonstrates compatibility with various smartphone models, thereby eliminating the need for bulky instrumentation or specialized training. These features collectively enhance its potential for rapid point-of-care testing. Overall, the proposed method offers a cost-effective and efficient solution for quantifying colorimetric-responsive analytes in resource-limited settings. The successful integration of microfluidic technology with smartphone-based analysis not only advances nitrite detection performance but also opens new avenues for detecting other analytes, underscoring its broad applicability in field applications.</p>

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Portable gradient colorimetry platform for rapid and accurate nitrite detection

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • Longqian Zhang,
  • Fang Wang,
  • Yuan Gao,
  • Zhenrong Xu,
  • Jie Shu,
  • Junke Wang,
  • Li Zhang

摘要

This study presents a portable and accurate nitrite detection system that integrates a differential gradient analysis with smartphone-assisted microfluidic colorimetry to improve detection efficiency and precision. A bidirectional concentration gradient was established via diffusion within a PDMS-based microfluidic chip, significantly reducing the dependency on standard calibration and shortening the total detection time to within 15 minutes. Sample images were captured using a smartphone camera, and nitrite concentrations were quantified through RGB color analysis using a custom-developed mobile application. The implementation of the differential colorimetric method significantly improved detection accuracy, particularly at low concentrations, reducing the measurement error from 9.12% (conventional methods) to 1.93%. The platform demonstrates compatibility with various smartphone models, thereby eliminating the need for bulky instrumentation or specialized training. These features collectively enhance its potential for rapid point-of-care testing. Overall, the proposed method offers a cost-effective and efficient solution for quantifying colorimetric-responsive analytes in resource-limited settings. The successful integration of microfluidic technology with smartphone-based analysis not only advances nitrite detection performance but also opens new avenues for detecting other analytes, underscoring its broad applicability in field applications.