Rights statement: This document is the Accepted Manuscript version of a Published Work that appeared in final form in Analytical Chemistry, copyright ©2017 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see http://pubs.acs.org/doi/abs/10.1021/acs.analchem.7b01765
Accepted author manuscript, 2.07 MB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
| <mark>Journal publication date</mark> | 19/09/2017 |
|---|---|
| <mark>Journal</mark> | Analytical Chemistry |
| Issue number | 18 |
| Volume | 89 |
| Number of pages | 8 |
| Pages (from-to) | 9814-9821 |
| Publication Status | Published |
| Early online date | 15/08/17 |
| <mark>Original language</mark> | English |
Over-usage of antibiotics leads to the widespread induction of antibiotic resistance genes (ARGs). Developing an approach to allow real-time monitoring and fast prediction of ARGs dynamics in clinical or environmental samples has become an urgent matter. Vibrational spectroscopy is potentially an ideal technique towards the characterization of the microbial composition of microbiota as it is non-destructive, high-throughput and label-free. Herein, we employed attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and developed a spectrochemical tool to quantify the static and dynamic composition of kanamycin resistance in artificial microbiota to evaluate microbial antibiotic resistance. Second order differentiation was introduced in identifying the spectral biomarkers, and principal component analysis followed by linear discriminant analysis (PCA-LDA) was used for the multivariate analysis of the entire spectral features employed. The calculated results of the mathematical dispersion model coupled with PCA-LDA showed high similarity to the designed microbiota structure, with no significant difference (P >0.05) in the static treatments. Moreover, our model successfully predicted the dynamics of kanamycin resistance within artificial microbiota under kanamycin pressures. This work lends new insights into the potential role of spectrochemical analyses in investigating the existence and trends of antibiotic resistance in microbiota.