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A voting model weighting algorithm-driven multi-algorithm framework enhances intelligent point-of-care testing for edible oil safety

文献类型： 外文期刊

作者： Li, Zhiqiang 1 ; Hu, Yating 1 ; Zhang, Wen 1 ; Zhang, Qi 1 ; Li, Peiwu 1 ; Tang, Xiaoqian 1 ;

作者机构： 1.Chinese Acad Agr Sci, Qual Inspection & Test Ctr Oilseed Prod, Minist Agr & Rural Affairs,Key Lab Biol & Genet Im, Oil Crops Res Inst,Key Lab Detect Mycotoxin, Wuhan 430062, Peoples R China

2.China Agr Univ, Coll Engn, Beijing 100091, Peoples R China

3.HuBei Univ, Food Safety Res Inst, Wuhan 430062, Peoples R China

4.Hubei Hongshan Lab, Wuhan 430070, Peoples R China

5.Xianghu Lab, Hangzhou 311231, Peoples R China

关键词： Edible oil; Benzo[ a ]pyrene; Dibutyl phthalate; Metal-organic frameworks; Point-of-care testing; Intelligent analysis

期刊名称：CHEMICAL ENGINEERING JOURNAL （ 影响因子：13.2； 五年影响因子：13.5 ）

ISSN： 1385-8947

年卷期： 2025 年 520 卷

页码：

收录情况： SCI

摘要： Edible oils, a major source of dietary fat, are frequently contaminated with dibutyl phthalate (DBP) and benzo(a) pyrene (BaP), posing serious food safety risks. To enhance the accuracy of immunoassays in complex oil matrices, we developed a point-of-care testing platform that integrates bimetallic porous carbon nanomaterials as multifunctional signaling probes with a multi-algorithm framework driven by a voting model weighting algorithm. The porous carbon material Zn-CN was synthesized via organic solution coordination followed by hightemperature pyrolytic derivatization. Pt-Zn-CN was subsequently prepared by incorporating platinum nanoparticles into Zn-CN through reductive stirring at room temperature. This nanomaterial exhibits excellent photothermal properties and dispersibility, and its large surface area and porous architecture facilitate efficient antibody coupling and target enrichment. The resulting nanoprobes act as multifunctional signal transducers to enhance the recognition and amplification of antigen-antibody interactions. The multi-algorithm framework leverages bionic swarm intelligence and autonomous decision-making to perform high-precision image segmentation, attaining 97.6 % contour recognition and 95.7 % detection accuracy within 0.03 s. This platform enables ultra-fast early warning and achieves ultra-low detection limits of 0.184 ng/mL for DBP and 0.096 ng/ mL for BaP. A user-friendly graphical user interface supports real-time quantification of BaP and DBP, providing intuitive safety warnings for edible oils.

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