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Lactate and sequential lactate-glucose sensing using surface-enhanced Raman spectroscopy.

Nilam C Shah¹, Olga Lyandres, Joseph T Walsh

¹Chemistry Department, Northwestern University, Evanston, Illinois 60208, USA.

Analytical Chemistry

|August 11, 2007

Summary

This study demonstrates a novel surface-enhanced Raman spectroscopy (SERS) sensor for real-time lactate detection. The reversible sensor shows potential for monitoring physiological conditions and strenuous activity performance.

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Area of Science:

Analytical Chemistry
Biomedical Engineering
Spectroscopy

Background:

Lactate levels indicate human performance, fatigue, and hydration.
Elevated lactate is linked to medical conditions like heart failure, hypoxia, and diabetic ketoacidosis.
Real-time lactate monitoring is crucial for medical and performance evaluation.

Purpose of the Study:

To demonstrate a proof-of-concept for a lactate sensor using surface-enhanced Raman spectroscopy (SERS).
To explore the potential for a multianalyte sensing platform.
To characterize the reversibility and quantify lactate levels using the developed sensor.

Main Methods:

A mixed decanethiol/mercaptohexanol partition layer was employed for SERS lactate sensing.
Sensor surface reversibility was assessed by alternating exposure to lactate solutions and buffer.
Physiological lactate levels were quantified using multivariate analysis in a phosphate-buffered saline medium.

Main Results:

The sensor surface exhibited reversible partitioning and departitioning of lactate with time constants of approximately 30 seconds.
Physiological lactate concentrations (6-240 mg/dL) were quantified with a root-mean-square error of prediction of 39.6 mg/dL.
Sequential exposure to glucose and lactate demonstrated complete partitioning and departitioning for both analytes, indicating platform potential.

Conclusions:

The SERS-based sensor provides a viable method for real-time lactate detection.
The reversible nature of the sensor surface supports its use in dynamic monitoring applications.
This work paves the way for developing a multianalyte sensing platform for comprehensive physiological assessment.