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Updated: May 18, 2026
08:06
The Use of a β-lactamase-based Conductimetric Biosensor Assay to Detect Biomolecular Interactions
Published on: February 1, 2018
Elimination of influence by interference substance on amperometric enzyme biosensor response using wavelet
Isao Shitanda1, Saori Tanaka, Masayuki Itagaki
1Department of Pure and Applied Chemistry, Faculty of Science and Technology, Tokyo University of Science, Chiba, Japan. shitanda@rs.noda.tus.ac.jp
Summary
Wavelet transformation effectively removes interfering ascorbic acid signals from lactose biosensor measurements. This signal processing technique achieved a 98% elimination of interference, improving biosensor accuracy.
Area of Science:
- Electrochemistry
- Biosensor Technology
- Signal Processing
Background:
- Amperometric biosensors are susceptible to interference from coexisting substances.
- Ascorbic acid is a common interfering agent in biosensor applications.
- Accurate analyte detection requires effective methods to mitigate interference.
Purpose of the Study:
- To investigate the application of wavelet transformation for eliminating interfering signals in biosensing.
- To assess the effectiveness of wavelet transformation in removing ascorbic acid interference from a lactose biosensor.
Main Methods:
- Amperometric current responses of a bi-enzyme lactose biosensor were analyzed.
- The biosensor was exposed to solutions containing both lactose and ascorbic acid.
- Wavelet transformation was applied to the recorded current signals to analyze frequency components.
Main Results:
- The power spectrum density of ascorbic acid interference was identified at approximately 0.125 Hz.
- Wavelet transformation successfully isolated and removed the ascorbic acid signal component.
- A 98% reduction in the current response attributed to ascorbic acid was achieved.
Conclusions:
- Wavelet transformation is a viable and highly effective method for eliminating interfering substances in amperometric biosensors.
- This signal processing approach significantly enhances the selectivity and reliability of biosensors.
- The findings support the use of wavelet transformation for improving biosensor performance in complex matrices.
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