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Twenty Years of Dispersive Liquid-Liquid Microextraction: An Umbrella Review of Methodological Quality, Thematic
Hakim Faraji1, Adrián Conde Díaz1, Álvaro Santana Mayor1
1Departamento de Química, Área de Química Analítica, Facultad de Ciencias, Universidad de La Laguna (ULL), Avenida Astrofísico Francisco Sánchez s/n, 38206 San Cristóbal de La Laguna, Tenerife, Spain.
Abstract:
Over the past two decades, dispersive liquid-liquid microextraction (DLLME) has evolved from an emerging concept into a widely adopted approach within sustainable sample preparation. In parallel, a substantial body of review literature has accumulated, highlighting diverse applications and methodological developments. This umbrella review provides a structured synthesis of 59 review and systematic review articles published between 2006 and 2025, with the aim of examining how the review literature itself has shaped current understanding of DLLME. Methodological quality was appraised using the AMSTAR 2 framework, revealing considerable variability in review design and reporting practices. Key elements such as the transparent reporting of pre-defined review methods, fully reproducible search strategies, and structured assessments of bias were not routinely reported, and the majority of reviews were classified as critically low according to AMSTAR 2 criteria. To contextualize these findings, evidence redundancy was examined through structured overlap analysis, yielding a very low Corrected Covered Area (CCA = 0.0188), which indicates that existing reviews largely address complementary rather than repetitive aspects of DLLME. Thematic synthesis identified three dominant domains: methodological and mechanistic developments, green and sustainable extraction strategies, and application-driven advances in environmental and pharmaceutical analysis. Together, these findings provide a structured basis for improving future review design, evaluation, and editorial assessment in analytical chemistry, supporting more transparent, reproducible, and methodologically aligned evidence synthesis.
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