Traditional methods for detecting foodstuff hazards are time-consuming, inefficient, and destructive. Spectral imaging techniques have been proven to overcome these disadvantages in detecting foodstuff hazards. Compared with traditional methods, spectral imaging could also increase the throughput and frequency of detection. This study reviewed the techniques used to detect biological, chemical, and physical hazards in foodstuffs including ultraviolet, visible and near-infrared (UV-Vis-NIR) spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging, and Raman spectroscopy. The advantages and disadvantages of these techniques were discussed and compared. The latest studies regarding machine learning algorithms for detecting foodstuff hazards were also summarized. It can be found that spectral imaging techniques are useful in the detection of foodstuff hazards. Thus, this review provides updated information regarding the spectral imaging techniques that can be used by food industries and as a foundation for further studies.

The rapid and precise identification of multiple pathogens is critical for ensuring food safety, controlling epidemics, diagnosing diseases, and monitoring the environment. However, traditional detection methods are hindered by complex workflows, the need for skilled operators, and reliance on sophisticated equipment, making them unsuitable for rapid, on-site testing. Optical biosensors, known for their rapid analysis, portability, high sensitivity, and multiplexing capabilities, offer a promising solution for simultaneous multi-pathogenic identification. This paper explores recent advancements in the utilization of optical biosensors for multiple pathogenic detection. First, it provides an overview of key sensing principles, focusing on colorimetric, fluorescence-based, surface-enhanced Raman scattering (SERS), and surface plasmon resonance (SPR) techniques, as well as their applications in pathogenic detection. Then, the research progress and practical applications of optical biosensors for multiplex pathogenic detection are discussed in detail from three perspectives: microfluidic devices, nucleic acid amplification technology (NAAT), and nanomaterials. Finally, the challenges presented by optical biosensing technologies in multi-pathogen detection are discussed, along with future prospects and potential innovations in the field.

Duck plague virus (DPV), a highly contagious α-herpesvirus in the livestock and poultry environment, poses a significant threat to the healthy growth of ducks, potentially causing substantial economic losses. Effective control of DPV requires the development of specific diagnostic tools. A new fluorescent biosensor (R-C-CHA) was developed to detect virulent strains of DPV. It combined recombinase polymerase amplification (RPA), a CRISPR/Cas12a system, and catalytic hairpin assembly (CHA) for signal enhancement. The RPA primers were specifically designed to target the conserved DPV-CHv UL2 gene region, allowing for the rapid, efficient amplification of the target nucleic acids in isothermal conditions. The CRISPR/Cas12a system was used for sequence-specific recognition, activating its lateral cleavage activity. Furthermore, the CHA cascade reaction was utilized for enzyme-free fluorescent signal amplification. The results showed that the R-C-CHA biosensor completed the detection process in 40 min with a detection limit of 0.02 fg/μL, which was an approximate five-fold improvement compared to traditional RPA-CRISPR/Cas12a biosensors. The R-C-CHA biosensor also demonstrated perfect consistency with clinical detection and polymerase chain reaction (PCR) diagnosis, highlighting its strong potential for rapid detection in livestock and poultry farming settings.