Microfluidic platforms for food analysis: Materials, fabrication, analytical strategies, matrix handling, and translation.

BACKGROUND

Food analysis requires assessment of quality, authenticity, and safety in heterogeneous matrices. Microfluidic platforms can reduce sample and reagent consumption and integrate analytical operations, but chip-level performance does not necessarily translate into representative, reproducible, or field-compatible testing.

SCOPE AND APPROACH: This review critically assesses whether reported analytical gains remain meaningful across food-sample-to-result workflows. It connects materials and fabrication with recognition, readout, matrix handling, and testing decisions and compares reported fabrication and analytical metrics. Evidence from naturally contaminated, artificially inoculated or spiked, and pre-purified samples is distinguished. Advances in clustered regularly interspaced short palindromic repeats (CRISPR), magnetofluidics, and artificial intelligence (AI) are assessed by their gains and remaining limitations.

KEY FINDINGS AND

CONCLUSIONS

Reported detection limits and assay times often cover only on-chip stages, while sampling, matrix preparation, and reader requirements remain external or excluded. Spiked or purified inputs provide less direct evidence than naturally contaminated foods. Sensitivity, multiplexing, and automation gains may increase calibration, instrumentation, and manufacturing burdens. Poly(dimethylsiloxane) (PDMS) with soft lithography favors prototyping, printed paper supports low-cost screening, and injection-molded thermoplastics support high-volume production of reproducible closed cartridges. CRISPR, magnetofluidics, and AI have expanded sequence specificity, workflow automation, and data interpretation, respectively, but do not remove preparation and validation constraints. The transition from heterogeneous food lots to microliter volumes is a central bottleneck linked to pretreatment, manufacture, and validation. A food-specific strengths, weaknesses, opportunities, and threats (SWOT) analysis supports staged priorities in representative sampling and matrix-compatible pretreatment, enclosed multiparameter automation and cost-controlled manufacture, and reference-method comparison and interlaboratory validation.