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Machine learning-enabled flexible luminescent sensor for non-destructive mapping antibiotics distribution on seafood

文献类型： 外文期刊

作者： Wu, Lidong 1 ; Li, Yuanxin 1 ; Xiang, Xueping 5 ; Qin, Haiyang 1 ; Zhao, Jinxue 1 ; Zhai, Xuejing 1 ; Li, Peiyi 1 ; Li, Zhibo 3 ;

作者机构： 1.Fisheries Engn Inst, State Key Lab Mariculture Biobreeding & Sustainabl, Beijing 100141, Peoples R China

2.Chinese Acad Fishery Sci, Beijing 100141, Peoples R China

3.Dalian Ocean Univ, Coll Food Sci & Engn, Dalian 116023, Peoples R China

4.Shanghai Ocean Univ, Coll Food Sci & Technol, Shanghai 201306, Peoples R China

5.Zhejiang Univ, Affiliated Hosp 2, Sch Med, Dept Pathol, Hangzhou 310009, Peoples R China

关键词： Luminescent hydrogel sensors; Machine learning; Non-destructive identification; Concentration-distribution patterns; Tetracycline

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 510 卷

页码：

收录情况： SCI

摘要： Fast, simultaneous, and non-destructive identification of antibiotic distribution on food is critical to public health. Here We present a novel methodology for mapping antibiotics using luminescent hydrogel sensors (LHS) enhanced by machine learning. The LHS comprises a hydrogel substrate with luminescent europium ions (Eu3+), undergoing chromatic alterations upon exposure to tetracycline (TC). These luminescent spectral variations are captured using a smartphone and used to train convolutional neural networks (CNNs), resulting in high accuracy (exceeding 92 %) for identifying and quantifying concentration-distribution patterns. The LHS displays a linear detection range of TC (5 similar to 100 mu g L-1) with a detection limit of 0.58 mu g L-1. The trained LHS-CNN system can simultaneously delineate the distribution of TC on cross-sections of animal-derived foods or moistened animal skin, representing a realistic and complex environmental setting. This method has the potential to advance non-destructive detection and identification of molecules on food substrates, including those with curved surfaces, without the need for enrichment or additional sample preparation.

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