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Bacillus cereus is a foodborne opportunistic pathogen commonly found in humans and animals. It produces various toxins, causing frequent food safety incidents. Therefore, establishing a fast and accurate method for detecting B. cereus enterotoxin is crucial for disease diagnosis and food safety. In this study, Haemolysin BL comprising Hbl B and L2 was obtained from a prokaryotic expression system and then used to immunize mice for antibody preparation. Paired antibodies 2A10–5C7 against Hbl B and 1E2–10A4 against Hbl L2 were screened using the chessboard method and then used to construct a double-antibody sandwich detection method and a lateral flow immunochromatographic assay (LFIA) to quantify the concentrations of Hbl B and L2 in PBS and milk. The limits of detection for Hbl B and L2 in milk were 0.74 ng mL−1 and 1 ng mL−1 with detection ranges of 1.48–645.5 ng mL−1 and 2.33–391.5 ng mL−1. The spiked recoveries ranged from 82.2% to 105.67% and there was no cross-reactivity with common microbial toxins. The established LFIA was low in cost and rapid and was comparable with commercially available detection kits for food samples. [ABSTRACT FROM AUTHOR]

Foodborne pathogens cause diseases in humans. The traditional methods of detecting foodborne pathogens are time-consuming. The lateral flow immunoassay (LFIA) has become a widely used detection platform for onsite testing of various foodborne pathogens due to its time-efficiency, cost-effectiveness, portability, and ease of use. With the development of novel nanomaterials, the sensitivity of the LFIA has improved tremendously compared with traditional colorimetric LFIA sensors. This review first summarizes the principles and corresponding formats of the LFIA. Then, a detailed classification of nanomaterial label (e.g., metallic, carbon and selenium, fluorescent, and magnetic nanoparticles) synthesis, signal amplification strategy, and detection principles are discussed as related to food safety. Subsequently, the LFIA used in the detection of pathogenic bacteria, including Escherichia coli, Vibrio parahaemolyticus, Staphylococcus aureus, Listeria monocytogenes, and Salmonella, are classified and summarized. Multiple signal modes have been explored that improve the sensitivity of foodborne pathogen detection. Further improvement should focus on the design and preparation of high signal-to-noise ratio nanomaterials to achieve highly sensitive detection, and multitarget and multimode sensing. [ABSTRACT FROM AUTHOR]