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Digital immunoassay system integrating automated imaging equipment with deep learning algorithms for detecting enrofloxacin residues
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Zeng-Lin WANG1, Ying LI2, Han ZHANG3, Hui-Hui WANG1, Nan XIA3, Du-Bang MAO1, *, Yi-Ping CHEN2, *
Journal of Food Safety & Quality | 2026, 17(8) : 85 - 92
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Journal of Food Safety & Quality | 2026, 17(8): 85-92
• Special Topic: Application of artificial intelligence in food safety detection •
Digital immunoassay system integrating automated imaging equipment with deep learning algorithms for detecting enrofloxacin residues
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Zeng-Lin WANG1, Ying LI2, Han ZHANG3, Hui-Hui WANG1, Nan XIA3, Du-Bang MAO1, *, Yi-Ping CHEN2, *
Affiliations
  • 1.School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116034, China
  • 2.School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China
  • 3.School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China
doi: 10.19812/j.cnki.jfsq11-5956/ts.20250821001
Outline
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Objective

To develop an intelligent, highly sensitive rapid detection platform for enrofloxacin (ENR) to address the issues of high cost and lengthy processing times associated with traditional detection methods, as well as the reliance on manual operation and insufficient identification accuracy of existing digital immunoassay technologies.

Methods

Based on automated imaging equipment and deep learning algorithms, a digital immunoassay system using polystyrene microspheres as signal carriers was constructed, combining immunoreaction and magnetic separation technologies to achieve quantitative detection. The system integrated a high-precision automated microdisplacement platform, adopted the Faster-RCNN algorithm to achieve rapid recognition of microspheres, and introduced the NAFNet image enhancement algorithm to improve the imaging quality.

Results

The platform was capable of achieving high sensitivity detection of ENR (limit of detection 19.35 pg/ml, linear range 0.5–100.0 ng/mL), with test recoveries ranging from 104%–111%, and had made significant breakthroughs in detection efficiency, automation and anti-interference capability.

Conclusion

The study develops a deep learning-based automated immunoassay platform capable of stable image acquisition and accurate recognition of microspheres. The deep learning algorithm provides accurate and reliable counting, and the immunoassay system establishes features a wide linear range, low detection limit and high sensitivity. It performs well in the detection of milk samples and is suitable for the rapid automated detection of enrofloxacin in food.

enrofloxacin  /  digital immunoassay system  /  polystyrene microspheres  /  deep learning
王增林, 李莹, 张涵, 王慧慧, 夏楠, 毛杜邦, 陈翊平. 结合自动化成像设备与深度学习算法的数字化免疫分析系统检测牛奶中的恩诺沙星残留. 食品安全质量检测学报, 2026 , 17 (8) : 85 -92 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250821001
Zeng-Lin WANG, Ying LI, Han ZHANG, Hui-Hui WANG, Nan XIA, Du-Bang MAO, Yi-Ping CHEN. Digital immunoassay system integrating automated imaging equipment with deep learning algorithms for detecting enrofloxacin residues[J]. Journal of Food Safety & Quality, 2026 , 17 (8) : 85 -92 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250821001
Year 2026 volume 17 Issue 8
13
4
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Article Info
doi: 10.19812/j.cnki.jfsq11-5956/ts.20250821001
  • Receive Date:2025-08-21
  • Online Date:2026-11-05
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History
  • Received:2025-08-21
Affiliations
    1.School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116034, China
    2.School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China
    3.School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
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Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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