The purpose of this narrative review is to provide a critical comparative assessment of the advanced analytical technologies for the detection of economically motivated adulteration (EMA), highlighting their applications, strengths, limitations and regulatory relevance across different food matrices.
The methodology employed involved a structured narrative review focused on analyzing scientific research articles, academic papers, regulatory reports and government databases related to food adulteration and its implications for public health. The literature search was mainly based on publications between 2010 and 2026 retrieved from sources such as Scopus, Web of Science, PubMed and Google Scholar to reflect recent advances in analytical methods and regulatory issues. Research unrelated to the identification of food adulteration or authenticity was excluded.
During the past decade, substantial progress has been made in the development of analytical tools for the detection of different types of food adulterants. Modern approaches include chromatographic methods, spectroscopic techniques, DNA-based authentication, biosensor-based detection, artificial intelligence (AI)-assisted chemometric analysis, portable analytical devices, blockchain-enabled digital traceability and multi-omics approaches for comprehensive food authentication. These technologies have enhanced the sensitivity, accuracy and speed of detection of adulteration. All of the analytical techniques, however, have limitations in cost, complexity of operation and applicability.
This narrative review comparatively evaluates the advanced detection technologies based on their analytical principles, applications, analytical performance, strengths, limitations and regulatory relevance, providing a practical framework for selecting appropriate authentication strategies for different food matrices and EMA scenarios.
