Decision Tree and Linear Discriminant Analysis-Assisted Design of Polydopamine Nanoparticle-Based Colorimetric Array...

Area of Science:

• Biochemistry
• Analytical Chemistry
• Materials Science

Background:

• Physiological phosphates are vital in biological processes and disease mechanisms.
• Accurate quantification of diverse phosphate mixtures is essential for biological and medical research.
• Existing methods for phosphate analysis often lack the specificity and efficiency required for complex biological samples.

Purpose of the Study:

• To develop a novel, highly selective, and efficient colorimetric sensor array for the detection and quantification of physiological phosphates.
• To establish an intuitive evaluation model for serum phosphate levels for potential disease diagnosis.
• To demonstrate the sensor's capability in monitoring biologically relevant processes like kinase activity.

Main Methods:

• Preparation of three polydopamine nanoparticles (PDA NPs) with varied surface modifications using dopamine and biothiols.
• Design of a high-dimensional dual-signal array sensor integrating N-PDA NPs, specific metal ions (Fe3+, Ag+, Hg2+), and TMB.
• Application of decision tree and LDA algorithms for sensor optimization and data analysis.

Main Results:

• The N-PDA NPs-metal ion-TMB sensor successfully discriminated seven distinct phosphate species at equal concentrations and quantified varying concentrations and mixtures.
• Achieved 100% discrimination from interfering substances and accurate prediction of unknown phosphate species, demonstrating high selectivity and robustness.
• Demonstrated successful monitoring of kinase activity and achieved high recovery rates (96-114%) for phosphate in spiked serum samples.

Conclusions:

• The developed colorimetric array sensor offers a sensitive, selective, and robust platform for physiological phosphate analysis.
• This study presents the first intuitive evaluation model for serum phosphate levels, paving the way for diagnosing phosphate-related diseases.
• The sensor's ability to analyze complex mixtures and real biological samples highlights its significant potential in clinical diagnostics and biochemical research.