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学者姓名：孙启
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Abstract ：
Zearalenone (ZEN) is a common contaminant in crops with serious food safety implications. By leveraging the etching effect of H2O2 on gold nanorods (Au NRs) and the catalysis of Fenton reaction, we have successfully developed two enzyme-linked immunosorbent assay (ELISA) kits with distinct principles. ZEN can be accurately quantified by both methods by measuring changes in the longitudinal surface plasmon resonance (LSPR) absorption peaks of Au NRs. Direct detection kit is simpler and faster, with a linear range of 0.2-4000 ng/mL, while indirect competitive kit is more complex and has a narrower linear range of 0.02-10 ng/mL. This study is expected to provide an effective analytical strategy for rapid screening and accurate monitoring of mold contaminants in food and agricultural products.In this study, a novel dual-mode ELISA system was developed for the detection of zearalenone (ZEN) using Au NRs and their longitudinal surface plasmon resonance (LSPR) properties. By integrating H2O2-mediated nano-etching and Fenton reaction catalysis, the system enables both direct (0.2-4000 ng/mL) and indirect competitive (0.02-10 ng/mL) detection modes. The main work includes: (1) replacing the traditional enzyme colorimetric signal with an LSPR shift for accurate UV-visible quantification and visual multicolor readout; (2) optimizing the nanoenzyme catalytic system for improved reaction sensitivity; (3) eliminating the need for enzyme-labeled secondary antibodies in direct mode, thereby reducing cost and improving stability. The system was validated on grain samples with recoveries of 93.1-107.1% and detection limits of 0.2 ng/mL (direct) and 0.02 ng/mL (indirect). The modular design of the method allows it to be used for the detection of other mycotoxins (e.g., aflatoxin, ochratoxin) and offers great potential for food safety screening, agricultural monitoring, and environmental assessment. This work provides a cost-effective, sensitive and versatile platform for mycotoxin analysis.
Keyword ：
Food safety Food safety Gold nanorods Gold nanorods Visual detection Visual detection Zearalenone Zearalenone
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| GB/T 7714 | Hu, Yuanlong , Chen, Jiamin , Wu, Sifang et al. A colorimetric immunoassay for visual detection of zearalenone based on the Fenton reaction on gold nanorods [J]. | JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION , 2025 , 19 (9) : 6665-6676 . |
| MLA | Hu, Yuanlong et al. "A colorimetric immunoassay for visual detection of zearalenone based on the Fenton reaction on gold nanorods" . | JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 19 . 9 (2025) : 6665-6676 . |
| APA | Hu, Yuanlong , Chen, Jiamin , Wu, Sifang , Zhong, Yi , Ye, Jialin , Ye, Lingqing et al. A colorimetric immunoassay for visual detection of zearalenone based on the Fenton reaction on gold nanorods . | JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION , 2025 , 19 (9) , 6665-6676 . |
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Abstract ：
Formaldehyde (FA), a known carcinogen, is occasionally used illegally as a preservative in seafood, while traditional detection methods for FA residues often fail to meet the practical needs for nondestructive detection. In this study, a approach was developed by combining a portable Raman spectrometer with the InceptionTime deep learning model without sample pretreatment. Model were trained by FA-negative and FA-positive Raman spectral data from the shrimp surface and achieved accuracies of 84.40 % and 85.17 % at detection thresholds of 5 mg/kg (the primary safety detection threshold) and 100 mg/kg (the abuse-level contamination threshold), respectively. Metabolomic analysis and weight visualization indicated that the model particularly focused on Raman peaks associated with specific amino acids and astaxanthin-binding proteins. Two amino acid metabolites, timonacic and spinacine, were also identified as direct indicators of FA addition. Our model offers a fielddeployable and practical approach for real-time and on-site FA detection scenario.
Keyword ：
Deep learning Deep learning Formaldehyde detection Formaldehyde detection Nondestructive detection Nondestructive detection Raman spectroscopy Raman spectroscopy Shrimp Shrimp
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| GB/T 7714 | Wei, Chencheng , Zhang, Jiheng , Li, Gaozheng et al. Rapid and non-destructive detection of formaldehyde adulteration in shrimp based on deep learning-assisted portable Raman spectroscopy [J]. | FOOD CHEMISTRY , 2025 , 492 . |
| MLA | Wei, Chencheng et al. "Rapid and non-destructive detection of formaldehyde adulteration in shrimp based on deep learning-assisted portable Raman spectroscopy" . | FOOD CHEMISTRY 492 (2025) . |
| APA | Wei, Chencheng , Zhang, Jiheng , Li, Gaozheng , Zhong, Yi , Ye, Zhaoting , Wang, Handong et al. Rapid and non-destructive detection of formaldehyde adulteration in shrimp based on deep learning-assisted portable Raman spectroscopy . | FOOD CHEMISTRY , 2025 , 492 . |
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Fast and accurate detection of infectious bacteria in wounds is crucial for effective clinical treatment. However, traditional methods take over 24 h to yield results, which is inadequate for urgent clinical needs. Here, we introduce a deep learning-driven framework that detects and classifies four bacteria commonly found in wounds: Acinetobacter baumannii (AB), Escherichia coli (EC), Pseudomonas aeruginosa (PA), and Staphylococcus aureus (SA). This framework leverages the pretrained ResNet50 deep learning architecture, trained on manually collected periodic bacterial colony-growth images from high-content imaging. In in vitro samples, our method achieves a detection rate of over 95% for early colonies cultured for 8 h, reducing detection time by more than 12 h compared to traditional Environmental Protection Agency (EPA)-approved methods. For colony classification, it identifies AB, EC, PA, and SA colonies with accuracies of 96%, 97%, 96%, and 98%, respectively. For mixed bacterial samples, it identifies colonies with 95% accuracy and classifies them with 93% precision. In mouse wound samples, the method identifies over 90% of developing bacterial colonies and classifies colony types with an average accuracy of over 94%. These results highlight the framework's potential for improving the clinical treatment of wound infections. Besides, the framework provides the detection results with key feature visualization, which enhance the prediction credibility for users. To summarize, the proposed framework enables high-throughput identification, significantly reducing detection time and providing a cost-effective tool for early bacterial detection.
Keyword ：
Bacterial detection Bacterial detection Deep learning Deep learning High-content imaging High-content imaging Wound infection Wound infection
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| GB/T 7714 | Zhang, Ziyi , Gao, Lanmei , Zheng, Houbing et al. High-content imaging and deep learning-driven detection of infectious bacteria in wounds [J]. | BIOPROCESS AND BIOSYSTEMS ENGINEERING , 2024 , 48 (2) : 301-315 . |
| MLA | Zhang, Ziyi et al. "High-content imaging and deep learning-driven detection of infectious bacteria in wounds" . | BIOPROCESS AND BIOSYSTEMS ENGINEERING 48 . 2 (2024) : 301-315 . |
| APA | Zhang, Ziyi , Gao, Lanmei , Zheng, Houbing , Zhong, Yi , Li, Gaozheng , Ye, Zhaoting et al. High-content imaging and deep learning-driven detection of infectious bacteria in wounds . | BIOPROCESS AND BIOSYSTEMS ENGINEERING , 2024 , 48 (2) , 301-315 . |
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