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Updated: Jun 28, 2026
08:03
Multi-analyte Biochip (MAB) Based on All-solid-state Ion-selective Electrodes (ASSISE) for Physiological Research
Published on: April 18, 2013
Multivariate data analysis of dynamic amperometric biosensor responses from binary analyte mixtures-application of
Eva Dock1, Jakob Christensen, Mattias Olsson
1Department of Analytical Chemistry, Lund University, P.O. Box 124, SE-22100 Lund, Sweden.
Talanta
|October 31, 2008
Summary
This study shows a single biosensor can quantify analytes in mixtures using multivariate analysis. Correction algorithms improve accuracy for phenolic compounds, enabling on-line measurements without extensive calibration.
Area of Science:
- Analytical Chemistry
- Biosensor Technology
- Electrochemistry
Background:
- Quantitative analysis of binary mixtures often requires selective sensors or complex separation techniques.
- Biosensors offer potential for rapid, on-site analysis but can suffer from instability and batch-to-batch variations.
- Phenolic compounds like catechol and 4-chlorophenol are common environmental pollutants requiring accurate detection methods.
Purpose of the Study:
- To demonstrate the quantitative determination of individual analytes within binary mixtures using a single tyrosinase-modified biosensor.
- To evaluate the effectiveness of multivariate data analysis, specifically partial least squares regression (PLS-R), for deconvolution of mixture responses.
- To assess the impact of a correction algorithm on biosensor performance, accounting for ageing and preparation variability.
Main Methods:
- Flow injection analysis of binary mixtures of catechol and 4-chlorophenol using a graphite electrode modified with tyrosinase enzyme.
- Application of a correction algorithm using in-between sample reference measurements to compensate for sensor drift and preparation differences.
- Quantitative analysis of analyte concentrations using partial least squares regression (PLS-R) with dynamic flow injection peak responses.
Main Results:
- After correction, relative prediction errors for catechol and 4-chlorophenol in binary mixtures were 7.4% and 5.5%, respectively, using a single biosensor.
- Validation with a new biosensor yielded relative prediction errors of 7.0% for catechol and 16.0% for 4-chlorophenol when using the first sensor's data for calibration.
- The correction algorithm significantly improved the accuracy of analyte quantification in mixtures, even with variations in biosensor batches.
Conclusions:
- A single-receptor biosensor coupled with multivariate data analysis can quantitatively determine individual analytes in binary mixtures.
- Correction algorithms are crucial for compensating biosensor instability and preparation variations, enabling reliable on-line measurements.
- This approach offers a promising alternative to traditional calibration methods, reducing the need for time-consuming recalibration procedures.
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