Guidelines for pattern recognition using differential receptors and indicator displacement assays.

Area of Science:

• Analytical Chemistry
• Chemical Sensing
• Spectroscopy

Background:

• Discriminating structurally similar analytes is challenging in chemical sensing.
• Traditional sensor arrays often rely on specific binding stoichiometries.
• Advanced data analysis is crucial for extracting meaningful information from sensor responses.

Purpose of the Study:

• To develop a colorimetric sensor array for discriminating structurally similar carboxylic acids.
• To investigate the role of non-1:1 guest-receptor binding stoichiometry in analyte discrimination.
• To evaluate the impact of data preselection and preprocessing on sensor array performance.

Main Methods:

• Design and synthesis of specific receptors for carboxylic acids.
• Fabrication of a colorimetric sensor array using designed receptors and metal salts.
• Application of principal component analysis (PCA) with data preselection and preprocessing.
• Analysis of sensor responses based on varying guest-receptor binding stoichiometries.

Main Results:

• The sensor array successfully discriminated between structurally similar carboxylic acid analytes.
• Non-1:1 binding stoichiometries (e.g., 1:2, 2:1) provided valuable information for discrimination.
• Data preselection and preprocessing significantly enhanced the discrimination in PCA score plots.
• A single designed receptor, combined with metal salts and optimized data analysis, proved sufficient for analyte differentiation.

Conclusions:

• Colorimetric sensor arrays can effectively discriminate complex chemical mixtures.
• Exploiting higher-order binding stoichiometries and advanced data processing improves sensor performance.
• Optimized data analysis is as critical as receptor design for achieving high discrimination in chemical sensing.