Special Issue on Intelligent Detection and Data-Driven Food Safety

Aims and Scope:

Recent advances in point-of-need testing (PONT), intelligent sensing systems, and artificial intelligence (AI) are reshaping food safety analysis, driving a transition from centralized laboratory testing toward rapid, on-site, and data-informed monitoring across the entire food supply chain, including farms, processing facilities, border checkpoints, distribution networks, and retail environments.

Emerging rapid detection platforms—such as lateral flow immunoassays, isothermal amplification methods including LAMP, RPA, and RCA, CRISPR-based diagnostics involving Cas12, Cas13, and Cas14, nucleic acid-guided programmable proteins such as Argonaute and TnpB/Tas systems, digital PCR, and optical or electrochemical biosensors—are increasingly capable of delivering laboratory-level sensitivity and specificity in portable and field-deployable formats. These technologies are further enhanced by functional nanomaterials, including plasmonic nanoparticles, perovskite nanocrystals, aggregation-induced emission luminogens, DNA-templated nanoclusters, metal-organic frameworks, magnetic nanoparticles, and nanozymes, which enable efficient target enrichment, robust signal amplification, and multimodal readout.

At the same time, the growing availability of high-throughput sequencing data, including whole-genome sequencing, metagenomics, and targeted amplicon sequencing, together with machine learning, chemometrics, and big-data analytics, is creating new opportunities for predictive risk assessment, antimicrobial resistance surveillance, outbreak source tracking, food authenticity evaluation, and intelligent decision-making in food safety management. When integrated with smart packaging, Internet of Things platforms, and end-to-end digital traceability systems, these approaches support a shift toward proactive, real-time, and intelligence-driven food safety control across the agri-food value chain.

This Special Issue welcomes high-quality original research articles, critical reviews, and perspective papers addressing the development, validation, and application of advanced technologies for rapid and intelligent food safety testing. We particularly encourage submissions that demonstrate performance in real-world matrices, including agricultural commodities, processed food products, animal-derived foods, and environmental samples, rather than studies conducted solely in buffer or simplified model systems.

Keywords: Point-of-Need Testing; Food Safety; Artificial Intelligence; Intelligent Sensing; Food Safety Big Data; Biosensors; Functional Nanomaterials; CRISPR Diagnostics; Optical Spectroscopy; Electrochemical Detection; High-Throughput Sequencing; Smart Packaging; Digital Traceability

Important Deadlines:

Submission Deadline: Jan 31, 2027

Publication Date: July 2027

Submission Instructions:

Please read the Guide for Authors before submitting. All articles should be submitted online; please select SI: Intelligent Detection and Data-Driven Food Safety

Special Issue Editor:

Fang Zhang, Associate Professor

College of Biological Science and Engineering, Fuzhou University, China

E-mail: zhangfang921@gmail.com

Jianlong Wang, Professor

College of Food Science and Engineering, Northwest Agriculture & Forestry University, China

E-mail: wanglong79@nwsuaf.edu.cn

Yunlei Xianyu, Professor

College of Biosystems Engineering and Food Science, Zhejiang University, China

Department of Clinical Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine

E-mail: xianyu19@zju.edu.cn

Xueming He, Professor

College of Food Science and Engineering, Nanjing University of Finance and Economics, China

E-mail: he_xueming@nufe.edu.cn