Lab-on-a-Chip-Surface Enhanced Raman Scattering Combined with the Standard Addition Method : Toward the Quantification of Nitroxoline in Spiked Human Urine Samples

Titelangaben

Hidi, Izabella J. ; Jahn, Martin ; Weber, Karina ; Bocklitz, Thomas ; Pletz, Mathias W. ; Cialla-May, Dana ; Popp, Jürgen:
Lab-on-a-Chip-Surface Enhanced Raman Scattering Combined with the Standard Addition Method : Toward the Quantification of Nitroxoline in Spiked Human Urine Samples.
In: Analytical Chemistry. Bd. 88 (2016) Heft 18 . - S. 9173-9180.
ISSN 1520-6882
DOI: https://doi.org/10.1021/acs.analchem.6b02316

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Abstract

The emergence of antibacterial resistance and the development of new drugs lead to a continuous change of guidelines for medical treatments. Hence, new analytical tools are required for the detection of drugs in biological fluids. In this study, the first surface enhanced Raman scattering (SERS) detection of nitroxoline (NTX) in purified water and in spiked human urine samples is reported. Insights concerning the nature of the molecule–metal interaction and its influence on the overall SERS signal are provided. Furthermore, three randomly collected urine samples originating from a healthy volunteer were spiked to assess the limit of detection (LOD), the limit of quantification (LOQ), and the linear dynamic range of the lab-on-a-chip SERS (LoC-SERS) method for NTX detection in human urine. The LOD is ∼3 μM (0.57 mg/L), LOQ ∼ 6.5 μM (1.23 mg/L) while the linear range is between 4.28 and 42.8 μM (0.81–8.13 mg/L). This covers the minimum inhibitory concentration (MIC) values of the most commonly encountered uropathogens. Finally, seven clinical samples having an “unknown” NTX concentration were simulated. The LoC-SERS technique combined with the standard addition method and statistical data analysis provided a good prediction of the unknown concentrations. Additionally, it is also demonstrated that the predictions carried out by multicurve resolution alternating least-squares (MCR-ALS) algorithm provides reliable results, and it is preferred to a univariate statistical approach.