MALDI-TOF MS for Next-Generation Food Adulteration Screening: Combined Novel Matrices, Microfluidics, and Artificial Intelligence for High-Throughput and Precise Detection

Abstract Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has emerged as a detection tool to ensure food safety and quality, especially for the rapid and accurate identification of adulterants. However, its application is challenged by limitations of commercial matrices, such as high background interference and low sensitivity. Complex sample preparation, labor-intensive data processing, and human errors further hinder its adoption in high-throughput workflows. Unlike previous reviews that primarily draw horizontal comparisons between MALDI-TOF MS and other conventional food detection methods, this review centers on complementary technical strategies to overcome the intrinsic analytical limitations of MALDI-TOF MS. It assesses innovative matrix materials for improved small-molecule detection performance. This work also provides a critical discussion of microfluidic-enabled automated sample preparation and machine learning-assisted spectral interpretation, which target current bottlenecks and expand practical applicability. The combination of MALDI-TOF MS with cutting-edge technologies provides promising new opportunities toward precise, automated, and high-throughput capabilities in food analysis.