Document Type
Journal Article
Publication Title
Food Chemistry: X
Volume
22
Publisher
Elsevier
School
Centre for Precision Health / School of Medical and Health Sciences
RAS ID
71164
Funders
Basic and Applied Basic Research Foundation of Guangdong Province
Grant Number
2022A1515220023
Abstract
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.
DOI
10.1016/j.fochx.2024.101507
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Comments
Mou, J. Y., Usman, M., Tang, J. W., Yuan, Q., Ma, Z. W., Wen, X. R., ... & Wang, L. (2024). Pseudo-Siamese network combined with label-free Raman spectroscopy for the quantification of mixed trace amounts of antibiotics in human milk: A feasibility study. Food Chemistry: X, 101507. https://doi.org/10.1016/j.fochx.2024.101507