Physicochemical Responsive Integrated Similarity Measure (PRISM) for a Comprehensive Quantitative Perspective of...

Area of Science:

• Analytical Chemistry
• Chemometrics
• Spectroscopy

Background:

• Sample similarity is fundamental in analytical chemistry for calibration, classification, and outlier detection.
• Quantifying similarity is challenging, especially for unlabeled spectral data.
• Existing methods often lack a comprehensive approach to integrate spectral and contextual information.

Purpose of the Study:

• To develop a novel method for quantifying sample similarity using spectral data, particularly for unlabeled target samples.
• To introduce the physicochemical responsive integrated similarity measure (PRISM) for enhanced similarity assessment.
• To demonstrate the utility of PRISM across diverse analytical applications.

Main Methods:

• Developed PRISM, a hybrid approach integrating spectral similarity with contextual information (analyte content, model predictions).
• Applied PRISM to four near-infrared (NIR) datasets across different application areas.
• Evaluated PRISM for prediction reliability, model updating, outlier detection, and matrix matching.

Main Results:

• PRISM effectively quantifies similarity between target and source samples by amplifying physicochemical properties from spectra.
• Demonstrated PRISM's efficacy in prediction reliability, model generalizability, outlier detection, and matrix matching evaluations.
• PRISM shows promise for biological samples and other measurement systems affected by matrix effects.

Conclusions:

• PRISM provides a robust quantitative measure of sample similarity, integrating spectral and contextual data.
• The method enhances model performance and reliability in various analytical chemistry applications.
• PRISM's adaptability suggests broad applicability across different spectral data types and measurement systems.