What does this research mean for the field?

While surface-enhanced Raman spectroscopy (SERS) offers outstanding sensitivity for trace-level food contaminant detection, its practical implementation and regulatory adoption are currently hindered by spectral irreproducibility, severe matrix interference, and a lack of standardized protocols. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To review surface-enhanced Raman spectroscopy (SERS) sensing strategies for major food contaminants and identify core technological bottlenecks preventing regulatory adoption and real-world deployment.

September 8, 2026FoodsOpen Access

SERS-Based Detection of Food Contaminants: From Laboratory Sensitivity to Practical Implementation—Bottlenecks and Pathways to Standardization

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Authors

DCDonglin CuiJiangsu University XZXin ZhouJiangsu University ZZZuqi ZhouJiangsu University

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Implication

Critical review uncovers implementation bottlenecks for SERS detection across food contaminant classes, highlighting pathways toward standardized, field-deployable regulatory screening.

Key Points

To review surface-enhanced Raman spectroscopy (SERS) sensing strategies for major food contaminants and identify core technological bottlenecks preventing regulatory adoption and real-world deployment.
Surveyed and categorized SERS-enabled analytical strategies across three major contaminant groups: pesticides, mycotoxins, and heavy metals.
Evaluated performance trade-offs, matrix interference mechanisms, and comparative utility alongside near-infrared spectroscopy (NIR) and hyperspectral imaging (HSI).
Detection pathways diverge by analyte: pesticides achieve parts-per-billion detection via substrate engineering and deep learning, mycotoxins reach picogram-per-milliliter levels through affinity recognition, and heavy metals require indirect functional probes.
Spectral irreproducibility, severe food matrix interference, and the absence of standardized testing protocols represent universal barriers impeding regulatory implementation.
Multispectral data fusion can mitigate throughput limitations, while portable hardware and explainable artificial intelligence present viable routes toward on-site testing.

Cite This Study

Cui et al. (2026) studied this question.

synapsesocial.com/papers/6a9fd79b58e84d0ff5b46730 https://doi.org/10.3390/foods15173152

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September 8, 2026FoodsOpen Access

SERS-Based Detection of Food Contaminants: From Laboratory Sensitivity to Practical Implementation—Bottlenecks and Pathways to Standardization

View Full Paper

Ask AI

Bookmark

Authors

DCDonglin CuiJiangsu University XZXin ZhouJiangsu University ZZZuqi ZhouJiangsu University

Discussion

Member takes

Implication

Critical review uncovers implementation bottlenecks for SERS detection across food contaminant classes, highlighting pathways toward standardized, field-deployable regulatory screening.

Key Points

To review surface-enhanced Raman spectroscopy (SERS) sensing strategies for major food contaminants and identify core technological bottlenecks preventing regulatory adoption and real-world deployment.
Surveyed and categorized SERS-enabled analytical strategies across three major contaminant groups: pesticides, mycotoxins, and heavy metals.
Evaluated performance trade-offs, matrix interference mechanisms, and comparative utility alongside near-infrared spectroscopy (NIR) and hyperspectral imaging (HSI).
Detection pathways diverge by analyte: pesticides achieve parts-per-billion detection via substrate engineering and deep learning, mycotoxins reach picogram-per-milliliter levels through affinity recognition, and heavy metals require indirect functional probes.
Spectral irreproducibility, severe food matrix interference, and the absence of standardized testing protocols represent universal barriers impeding regulatory implementation.
Multispectral data fusion can mitigate throughput limitations, while portable hardware and explainable artificial intelligence present viable routes toward on-site testing.

Cite This Study

Cui et al. (2026) studied this question.

synapsesocial.com/papers/6a9fd79b58e84d0ff5b46730 https://doi.org/10.3390/foods15173152

Also Consider

Synapse has enriched one closely related paper. Consider it for comparative context:

1Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples2026 · 8 citations

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