Mou Jing-Yi, Usman Muhammad, Tang Jia-Wei, Yuan Quan, Ma Zhang-Wen, Wen Xin-Ru, Liu Zhao, Wang Liang
Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China.
Department of Clinical Medicine, School of the 1 Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu Province, China.
Food Chem X. 2024 May 24;22:101507. doi: 10.1016/j.fochx.2024.101507. eCollection 2024 Jun 30.
The utilization of antibiotics is prevalent among lactating mothers. Hence, the rapid determination of trace amounts of antibiotics in human milk is crucial for ensuring the healthy development of infants. In this study, we constructed a human milk system containing residual doxycycline (DXC) and/or tetracycline (TC). Machine learning models and clustering algorithms were applied to classify and predict deficient concentrations of single and mixed antibiotics via label-free SERS spectra. The experimental results demonstrate that the CNN model has a recognition accuracy of 98.85% across optimal hyperparameter combinations. Furthermore, we employed Independent Component Analysis (ICA) and the pseudo-Siamese Convolutional Neural Network (pSCNN) to quantify the ratios of individual antibiotics in mixed human milk samples. Integrating the SERS technique with machine learning algorithms shows significant potential for rapid discrimination and precise quantification of single and mixed antibiotics at deficient concentrations in human milk.
抗生素在哺乳期母亲中使用普遍。因此，快速测定母乳中痕量抗生素对于确保婴儿的健康发育至关重要。在本研究中，我们构建了一个含有残留强力霉素（DXC）和/或四环素（TC）的母乳体系。通过无标记表面增强拉曼光谱（SERS），应用机器学习模型和聚类算法对单一和混合抗生素的不足浓度进行分类和预测。实验结果表明，在最优超参数组合下，卷积神经网络（CNN）模型的识别准确率为98.85%。此外，我们采用独立成分分析（ICA）和伪孪生卷积神经网络（pSCNN）对混合母乳样品中各抗生素的比例进行定量。将SERS技术与机器学习算法相结合，在快速鉴别和精确定量母乳中低浓度单一和混合抗生素方面显示出巨大潜力。
