Clinical Pharmacist and Master’s student in Clinical Pharmacy with research interests in pharmacovigilance, behavioral interventions in mental health, and AI applications in clinical decision support. Experience includes digital health research with Bloomsbury Health (London) and pharmacovigilance practice in patient support programs. Published work covers drug awareness among healthcare providers, postpartum depression management, and patient safety reporting. 
Clinical Pharmacist and Master’s student in Clinical Pharmacy with research interests in pharmacovigilance, behavioral interventions in mental health, and AI applications in clinical decision support. Experience includes digital health research with Bloomsbury Health (London) and pharmacovigilance practice in patient support programs. Published work covers drug awareness among healthcare providers, postpartum depression management, and patient safety reporting. What was reviewed?
This review article surveys intelligent sensing for heavy metal detection alongside broader hazard monitoring in the food chain, emphasizing how optical, electrochemical, machine olfaction/gustation, acoustic, magnetic, and data-centric methods integrate with AI to deliver rapid, on-site analytics. By mapping sensing principles, signal processing, modeling, and deployment trends, the paper positions intelligent sensing for heavy metal detection as a linchpin for real-time verification, traceability, and certification workflows across processing, storage, and transport. The review also contextualizes technology limitations matrix effects, water absorption, sensitivity, and reproducibility, and outlines how miniaturization, multimodal data fusion, and blockchain/IoT can close the gap between laboratory performance and plant-floor reliability.
Who was reviewed?
The corpus spans methods and applications reported across food products, matrices, and supply-chain stages, rather than human participants. It synthesizes empirical results from seafood, meats, beverages, grains, teas, and water, with particular relevance to heavy metal contamination scenarios. Evidence includes LIBS workflows for trace Pb, Cr, and Cu, NIR/HSI models for elemental classification in mussels and water, electrochemical sensors for Pb, Cd, Cu, and Hg in milk, and E-tongue architectures for aqueous Pb²⁺ at nanomolar levels, as summarized in Table 2 and related case figures. This breadth demonstrates how intelligent sensing for heavy metal detection generalizes across diverse commodity types and processing environments and can be embedded into plant-level QA and field inspections.
Most important findings
| Critical point | Details |
|---|---|
| Optical LIBS targets metals directly | LIBS provides rapid, multielement, minimal-prep analysis; enhancements such as dual-pulse and LIBS-LIF push sensitivity. Lead in water achieved an LOD of 88 ng/L using resin enrichment plus LIBS-LIF, highlighting feasibility for trace-level certification screening; Fig. 1F illustrates LIBS architecture |
| NIR/HSI quantify metals indirectly | Chemometric models with NIRS/HSI classified Zn, Pb, Cd, and Cu in mussels with ≥95% accuracy and estimated Cu/Fe in water with GA-PLSR (R²≈0.73–0.75), enabling fast triage and hotspot mapping prior to confirmatory testing |
| Electrochemical sensors deliver ultralow LODs | Portable electrodes detected Cd, Pb, Cu, and Hg in milk with nanomolar LODs and 96–104% recoveries; DNAzyme- or aptamer-gated graphene transistors achieved sub-μg/L Pb²⁺ in dairy matrices, supporting in-line surveillance and batch release |
| E-tongue detects Pb in water at very low concentrations | Electronic-tongue platforms using nanofibers/cellulose/Ag composites discriminated Pb²⁺ in aqueous solutions down to 10 nmol L⁻¹, offering low-cost, rapid prescreening at receiving docks or field sites |
| THz methods add complementary signatures | Terahertz sensing, though challenged by water absorption, measured heavy metal ions using microalgae as a medium with up to 100% accuracy for Pb²⁺ class decisions; useful for lab-to-pilot workflows and moisture-controlled dry products |
| Preconcentration and interfaces matter | Electrospun nanofibrous membranes functionalized with Au/Ag nanoparticles boosted LIBS signals, yielding tea-infusion LODs of 5 μg/L (Cr) and 10 μg/L (Cu) with ~99–108% recoveries, underscoring the value of sample-interface engineering |
| Multimodal fusion improves robustness | Feature-level fusion of Vis-NIR and HSI outperformed single-modality models when predicting residues in meats; analogous fusion for metals promises better resilience to matrix effects and environmental drift |
| Edge, IoT, and blockchain strengthen compliance | Battery-free NFC freshness sensors, AI inference at the edge, and blockchain traceability form a pipeline from sensor to immutable record, reducing tampering risk and enabling auditable HTMC evidence |
| Known constraints and mitigations | NIR bands overlap and require chemometrics; THz is water-sensitive; LIBS has lower intrinsic sensitivity without augmentation; SERS/optics face matrix fluorescence. Preprocessing, enrichment, adaptive algorithms, and hardware co-design are recommended |
Key implications
For regulators, intelligent sensing for heavy metal detection enables auditable, near-real-time decisions and traceable data trails, tightening surveillance and deterrence. Certification programs should specify validated LODs, matrix-matched recoveries, and model performance metrics with drift control. Industry can deploy LIBS, electrochemical probes, and HSI for intake screening, line checks, and release testing. Research gaps include harmonized protocols, reference materials, and field-rugged calibration transfer. Practically, pair preconcentration with portable sensors, fuse modalities, log results to blockchain, and schedule periodic lab confirmation to maintain HTMC confidence.
Citation
Jiang W, Liu C, Liu W, Zheng L. Advancements in Intelligent Sensing Technologies for Food Safety Detection. Research. 2025;8:Article 0713. doi:10.34133/research.0713
Heavy metals are high-density elements that accumulate in the body and environment, disrupting biological processes. Lead, cadmium, arsenic, mercury, nickel, tin, aluminum, and chromium are of greatest concern due to persistence, bioaccumulation, and health risks, making them central to the HMTC program’s safety standards.
Lead is a neurotoxic heavy metal with no safe exposure level. It contaminates food, consumer goods and drinking water, causing cognitive deficits, birth defects and cardiovascular disease. HMTC’s rigorous lead testing applies ALARA principles to protect infants and consumers and to prepare brands for tightening regulations.
Cadmium is a persistent heavy metal that accumulates in kidneys and bones. Dietary sources include cereals, cocoa, shellfish and vegetables, while smokers and industrial workers receive higher exposures. Studies link cadmium to kidney dysfunction, bone fractures and cancer.
Mercury (Hg) is a neurotoxic heavy metal found in various consumer products and environmental sources, making it a major public health concern. Its regulation is critical to protect vulnerable populations from long-term health effects, such as neurological impairment and cardiovascular disease. The HMTC program ensures that products meet the highest standards for mercury safety.
