Abstract
A new approach of Fiber Enhanced Raman Spectroscopy (FERS) is described within this article based on the use of Hydrogel-Core microstructured Polymer Optical Fibers (HyC-mPOF). The incorporation of the hydrogel only on the core of the Hollow-Core microstructured Polymer Optical Fiber (HC-mPOF) enables to perform FERS measurements in a functionalized matrix, enabling high selectivity Raman measurements. The hydrogel formation was continuously monitored and quantified using a Principal Component Analysis verifying the coherence between the components and the Raman spectrum of the hydrogel. The performed measurements with high and low affinity target molecules prove the feasibility of the presented HyC-mPOF platform.
Highlights
New approaches of biosensors that join low-cost, high sensitivity and high specificity characteristics are widely pursued by the scientific community [1]
HyC-microstructured Polymer Optical Fibers (mPOF) were tested for measuring whether high affinity or low or low affinity target molecules in order to verify their feasibility as sensing platform
HyC-mPOF probe, several measurements were performed following the steps: HyC-mPOF probe, several measurements were performed following the steps: Firstly, Firstly, a Raman measurement focusing the hollow-core of the Hollow-Core microstructured Polymer Optical Fiber (HC-mPOF) probes was reca Raman measurement focusing the hollow-core of the HC-mPOF probes was recorded orded to ensure the correct state of the fiber
Summary
New approaches of biosensors that join low-cost, high sensitivity and high specificity characteristics are widely pursued by the scientific community [1]. The inelastic dispersion suffered by the incident beam when it strikes the sample, describes the intramolecular information as well as the intermolecular interactions of the target [4,5]. This technique has been widely employed in different fields [6,7,8], such as the pharmaceutical drug monitoring [9] or food quality studies [10]. The feasibility of this technique to combine with multivariate analysis based on Principal
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