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A hydrogen bonding based SERS method for direct label-free L-hydroxyproline detection.

Zichen Yang¹, Guoqing Chen², Jialu Shen²

¹School of Science, Jiangnan University, Wuxi, China; School of Internet of Things Engineering, Jiangnan University, Wuxi, China; Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Wuxi, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy

|May 13, 2023

Summary

This summary is machine-generated.

A new Surface-enhanced Raman spectroscopy (SERS) method enables sensitive, label-free detection of L-hydroxyproline (L-Hyp) in milk. This advancement aids in identifying low-quality dairy products by detecting non-edible protein adulterants.

Keywords:

Amino acid detection L-hydroxyproline Milk adulteration Surface-enhanced Raman scattering (SERS)

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Area of Science:

Analytical Chemistry
Materials Science
Food Science

Background:

Detecting non-protein nitrogen adulterants in dairy products is challenging.
L-hydroxyproline (L-Hyp) is a marker for animal hydrolyzed protein, indicating low-quality milk.
Direct detection of L-Hyp in milk remains difficult.

Purpose of the Study:

To develop a sensitive, label-free method for L-Hyp detection in milk.
To investigate the hydrogen bond transition mechanism for L-Hyp detection.
To establish quantitative models for L-Hyp in aqueous and milk environments.

Main Methods:

Utilized an Ag@COF-COOH substrate for Surface-enhanced Raman Spectroscopy (SERS).
Explored hydrogen bond interactions and charge transfer (HOMO/LUMO) experimentally and computationally.
Developed quantitative models for L-Hyp detection.

Main Results:

Achieved a limit of detection (LOD) of 8.18 ng/mL for L-Hyp in an aqueous environment (R²=0.982).
Established a linear detection range of 0.5-1000 μg/mL for L-Hyp in milk.
Demonstrated a low LOD of 0.13 μg/mL for L-Hyp in milk.

Conclusions:

A novel SERS method based on hydrogen bond interactions enables label-free L-Hyp detection.
This technique offers high sensitivity and accuracy for identifying milk adulteration.
The study expands the application of SERS in dairy product analysis.