Universal strategy for rapid design and analysis of gas detection peptide chips with positional preference

Abstract

The design and analysis of gas detection chips directly affect their detection efficiency and applicability. Detection devices are currently restricted by detection principles, facing drawbacks like intricate structural design, limited applicability, and low detection efficiency. We have designed a complete set of design and analysis scheme for a peptide gas detection chip. First, we selected specific and high-affinity peptide combinations from existing peptide-gas affinity datasets. Then, the peptide chip's arrangement was grouped according to the variations in peptides' affinity towards different gases. Peptides were arranged based on their affinity levels within each group, striking a balance between discrimination and flexibility in the design of the chip. Finally, we evaluated the analysis methods by generating simulated data based on a reference affinity matrix constructed from actual data. Due to the preprocessing role of chip design on affinity data, all methods can effectively accomplish gas classification. In gas concentration prediction tasks, our method reduced mean square error to 0.41, significantly outperforming other methods. This gas detection scheme shortens the development cycle of chip design and analysis methods, fully utilizing the specificity of peptides, enhancing gas analysis effectiveness, and demonstrating the agile development of gas detection chips.

Keywords

• Multicomponent gas mixtures
• Gas recognition
• Peptide chip
• Affinity matrix
• Machine learning