What does this research mean for the field?

The integration of artificial intelligence, machine learning, and the Internet of Things with biosensor technologies enables rapid, scalable, and real-time multi-contaminant monitoring in food systems, facilitating the transition from prototype to practical deployment. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To review advancements in artificial intelligence-integrated biosensors for multi-contaminant food safety surveillance and evaluate pathways for their transition into real-time, field-deployable monitoring systems.

August 30, 2026Frontiers in Sustainable Food SystemsOpen Access

AI-integrated smart biosensors for real-time multi-contaminant detection in food systems

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MPMehak Puri

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Overview

Literature review demonstrates AI-integrated biosensing advancements for multi-contaminant detection in food matrices, highlighting pathways toward scalable, real-time food safety monitoring.

Key Points

To review advancements in artificial intelligence-integrated biosensors for multi-contaminant food safety surveillance and evaluate pathways for their transition into real-time, field-deployable monitoring systems.
Mini-review evaluating recent literature on the integration of artificial intelligence, machine learning, and Internet of Things architectures with smart biosensors.
Assessed sensing platforms including multi-analyte detection arrays, paper-based substrates, smartphone-coupled interfaces, and nanozyme-based systems across complex food matrices.
Artificial intelligence and machine learning algorithms substantially improved signal resolution and simultaneous multi-contaminant discrimination compared to conventional single-target laboratory assays.
Identified persistent translational bottlenecks including complex food matrix interference, sensor-data integration, algorithmic reliability, and deployment constraints in resource-limited settings.
Proposed a conceptual systems framework harmonizing physical biosensing, automated data analytics, and decision-support infrastructure to enable scalable food safety compliance.

Cite This Study

Mehak Puri (2026) studied this question.

synapsesocial.com/papers/6a93f00a6c1a8fb52e79c096 https://doi.org/10.3389/fsufs.2026.1905105

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August 30, 2026Frontiers in Sustainable Food SystemsOpen Access

AI-integrated smart biosensors for real-time multi-contaminant detection in food systems

View Full Paper

Ask AI

Bookmark

Authors

MPMehak Puri

Discussion

Member takes

Overview

Literature review demonstrates AI-integrated biosensing advancements for multi-contaminant detection in food matrices, highlighting pathways toward scalable, real-time food safety monitoring.

Key Points

To review advancements in artificial intelligence-integrated biosensors for multi-contaminant food safety surveillance and evaluate pathways for their transition into real-time, field-deployable monitoring systems.
Mini-review evaluating recent literature on the integration of artificial intelligence, machine learning, and Internet of Things architectures with smart biosensors.
Assessed sensing platforms including multi-analyte detection arrays, paper-based substrates, smartphone-coupled interfaces, and nanozyme-based systems across complex food matrices.
Artificial intelligence and machine learning algorithms substantially improved signal resolution and simultaneous multi-contaminant discrimination compared to conventional single-target laboratory assays.
Identified persistent translational bottlenecks including complex food matrix interference, sensor-data integration, algorithmic reliability, and deployment constraints in resource-limited settings.
Proposed a conceptual systems framework harmonizing physical biosensing, automated data analytics, and decision-support infrastructure to enable scalable food safety compliance.

Cite This Study

Mehak Puri (2026) studied this question.

synapsesocial.com/papers/6a93f00a6c1a8fb52e79c096 https://doi.org/10.3389/fsufs.2026.1905105

View Full Paper

Ask AI

Bookmark