Evidence map›Paper›PMID 40672876›Full record

ReviewFood chemistry: X2025

Nanoparticle-based detection of foodborne pathogens: Addressing matrix challenges, advances, and future perspectives in food safety.

Himanshu Jangid, Mitali Panchpuri, Joydeep Dutta, Harish Chandra Joshi, Maman Paul, Arun Karnwal, Akil Ahmad, Mohammed B Alshammari, Kaizar Hossain, Gaurav Pant and 1 more

Abstract readReview
In one paragraph

Review in Food chemistry: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Article
  8. Article
  9. Review
  10. Review
  11. Review
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Himanshu JangidSchool of Bioengineering and Biosciences, Lovely Professional University, Jalandhar, Punjab, India.
Mitali PanchpuriSchool of Pharmaceutical and Population Health Informatics, DIT University, Dehradun 248009, Uttarakhand, India.
Joydeep DuttaSchool of Bioengineering and Biosciences, Lovely Professional University, Jalandhar, Punjab, India.
Harish Chandra JoshiDepartment of Chemistry, Graphic Era (Deemed to be University), Dehradun 248002, Uttarakhand, India.
Maman PaulDepartment of Physiotherapy, Guru Nanak Dev University, Amritsar, Punjab, India.
Arun KarnwalDepartment of Microbiology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India.
Akil AhmadDepartment of Chemistry, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
Mohammed B AlshammariDepartment of Chemistry, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
Kaizar HossainDepartment of Environmental Science, Asutosh College, University of Calcutta, 92, Shyama Prasad Mukherjee Rd, Bhowanipore, Kolkata 700026, West Bengal, India.
Gaurav PantDepartment of Microbiology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India.
Gaurav KumarSchool of Bioengineering and Biosciences, Lovely Professional University, Jalandhar, Punjab, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Foodborne diseases pose significant public health and economic challenges worldwide, with conventional pathogen detection methods, such as culture-based assays and PCR, often hindered by the complex food matrix in categories like dairy, seafood, fresh produce, and processed foods. These matrices, containing fats, proteins, biofilms, and salts, interfere with detection accuracy, reducing the sensitivity and robustness of traditional approaches. Nanoparticle-based detection systems have emerged as transformative tools to overcome these challenges, offering enhanced sensitivity, rapid detection, and adaptability to real-time monitoring. Gold, silver, magnetic, polymeric, and hybrid nanoparticles leverage their unique optical, magnetic, and functional properties to facilitate specific pathogen identification while mitigating food matrix interference. Recent advancements include nanoparticle-functionalized biosensors, magnetic separation platforms, and smart detection systems integrated with IoT and blockchain for traceability and real-time contamination alerts. However, challenges such as high production costs, regulatory gaps, and scalability hinder their full-scale adoption. This review critically examines matrix-specific adaptations of nanoparticle-based detection technologies, their comparative advantages over traditional methods, and their integration with smart technologies to ensure food safety. Future directions emphasize interdisciplinary collaboration, eco-friendly synthesis, and regulatory frameworks to address commercialization hurdles and revolutionize pathogen detection across the global food industry.

Indexed as

Detection SystemsFoodborne PathogensFood MatricesFood SafetyNanoparticlesSmart Technologies

Identifiers

PMID40672876
PMCPMC12266482

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.