Rational Selection of Minimal Sensor Arrays for Analyte Fingerprinting.

Area of Science:

• Analytical Chemistry
• Chemometrics
• Sensor Technology

Background:

• High-dimensional sensor arrays offer powerful chemical fingerprinting but face challenges in selecting reliable sensor subsets from limited data.
• Practical applications are hindered by variability and overlapping analyte responses in screening data with few replicates.

Purpose of the Study:

• To develop a transparent and data-efficient analysis framework for rational sensor array reduction using limited experimental data.
• To enable robust sensor selection that overcomes challenges of variability and partial overlap in analyte responses.

Main Methods:

• Constructing analyte-specific decision regions from measured responses to evaluate individual sensor contributions.
• Ranking sensors based on their contribution to resolving difficult-to-distinguish analytes.
• Utilizing an intuitive trade-off curve to identify the minimal sensor subset for desired classification performance.

Main Results:

• The framework produces stable and reproducible sensor selections even with small replicate numbers.
• Demonstrated robustness through controlled variability alteration and application to three independent fluorescence-based sensor libraries.
• Showcased that a few selected sensors achieve low classification error with interpretable decision maps.

Conclusions:

• The presented open-source, platform-agnostic strategy provides compact and interpretable solutions for screening data analysis and sensor array design.
• The approach is well-suited for data-limited conditions common in chemical and biological sensing.
• Enables rational reduction of sensor arrays for efficient and reliable chemical fingerprinting.