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基于金纳米集群的机器学习辅助光发光传感器阵列,用于区分抗生素与测试纸的抗生素

Jinming Xu¹, Xihang Chen¹, Huangmei Zhou¹

¹State Key Laboratory of Precision Spectroscopy, East China Normal University, No.500, Dongchuan Rd., Shanghai, 200241, China.

|August 31, 2023

概括

使用蛋白质稳定黄金纳米集群 (AuNCs) 的新型光发光传感器阵列在环境样本中有效检测四环素和类抗生素. 这种方法为抗生素残留监测提供了一种简单而有效的方法.

科学领域:

环境科学环境科学
分析化学分析化学
纳米技术纳米技术

背景情况:

环境中的抗生素残留物对生态系统和人类健康构成重大风险.
迫切需要快速,灵敏和可靠的抗生素检测方法.
现有的检测方法可能是复杂和耗时的,限制了它们在现场监测中的应用.

研究的目的:

开发一种光发光传感器阵列,以便轻松高效地检测四环素和类抗生素.
调查使用蛋白质稳定黄金纳米集群 (AuNCs) 作为传感元件.
评估传感器阵列在各种矩阵中对抗生素的分类和定量方面的性能,包括环境水样.

主要方法:

通过牛血清白蛋白 (BSA) 和卵白蛋白 (OVA) 稳定的金纳米集群 (AuNC) 的合成.
使用AuNCs对不同抗生素的独特光发光 (PL) 反应制造传感器阵列.
机器学习算法的应用 (线性差异分析和ExtraTrees) 用于数据分析.
传感器阵列的验证使用尖的河水样本和现实世界的环境样本.

主要成果:

传感器阵列成功地区分和定量了七种类型的抗生素 (四环素和类) 的高精度.
通过定量分析,在0-100μM的度范围内,R2值至少达到0.99.
该阵列在区分抗生素混合物和干扰物质方面表现出强大,在河水样本中保持100%的分类准确性.
开发了一种智能手机辅助的测试纸格式,用于快速,即时的抗生素检测.

结论:

蛋白稳定AuNC为开发抗生素敏感和选择性光发光传感器提供了一个有前途的平台.
开发的传感器阵列为环境抗生素监测提供了一个简单,高效和潜在的低成本方法.
智能手机辅助检测系统有可能在现场实时分析抗生素污染.

关键词:

黄金纳米集群中的金色纳米集群.线性差异分析线性差异分析机器学习机器学习昆诺隆是一种诺.测试纸上的测试纸.环素的四环环素是一种.

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