Yang Ruizhao, Li Yun, Zheng Jincun, Qiu Jie, Song Jinwen, Xu Fengxia, Qin Binyi
Key Laboratory of Complex System Optimization and Big Data Processing, Guangxi Colleges and Universities, Yulin Normal University, Yulin 537000, China.
Optoelectronic Information Research Center, School of Physics and Telecommunication Engineering, Yulin Normal University, Yulin 537000, China.
Materials (Basel). 2022 Sep 2;15(17):6093. doi: 10.3390/ma15176093.
Benzimidazole fungicide residue in food products poses a risk to consumer health. Due to its localized electric-field enhancement and high-quality factor value, the metamaterial sensor is appropriate for applications regarding food safety detection. However, the previous detection method based on the metamaterial sensor only considered the resonance dip shift. It neglected other information contained in the spectrum. In this study, we proposed a method for highly sensitive detection of benzimidazole fungicide using a combination of a metamaterial sensor and mean shift machine learning method. The unit cell of the metamaterial sensor contained a cut wire and two split-ring resonances. Mean shift, an unsupervised machine learning method, was employed to analyze the THz spectrum. The experiment results show that our proposed method could detect carbendazim concentrations as low as 0.5 mg/L. The detection sensitivity was enhanced 200 times compared to that achieved using the metamaterial sensor only. Our present work demonstrates a potential application of combining a metamaterial sensor and mean shift in benzimidazole fungicide residue detection.
食品中的苯并咪唑类杀菌剂残留会对消费者健康构成风险。由于其具有局部电场增强和高品质因数的特性，超材料传感器适用于食品安全检测相关应用。然而，先前基于超材料传感器的检测方法仅考虑了共振峰的位移，忽略了光谱中包含的其他信息。在本研究中，我们提出了一种结合超材料传感器和均值漂移机器学习方法用于高灵敏度检测苯并咪唑类杀菌剂的方法。超材料传感器的单元结构包含一根切割线和两个裂环谐振器。均值漂移作为一种无监督机器学习方法，被用于分析太赫兹光谱。实验结果表明，我们提出的方法能够检测低至0.5mg/L的多菌灵浓度。与仅使用超材料传感器相比，检测灵敏度提高了200倍。我们目前的工作展示了超材料传感器和均值漂移相结合在苯并咪唑类杀菌剂残留检测中的潜在应用。
