Information Content Analysis for Spectral Data ID: 2016-034

A novel data analysis method leveraging information content (IC) to interpret complex spectral data more intuitively.

Technology Overview

This technology introduces a method for analyzing depth profiles in XPS, ToF-SIMS, and LC-MS data using Shannon's entropy to calculate information content. It represents a significant advancement in data analysis, offering a more intuitive approach to understanding complex spectra by associating larger entropy values with increased complexity. Developed at Brigham Young University, this method provides a fresh perspective over traditional analysis techniques like PCA and cluster analysis.

Key Advantages

• Offers a more intuitive understanding of complex spectral data
• Improves effectiveness in revealing transitions or interfaces in materials
• Provides a novel alternative to traditional analysis methods such as PCA and cluster analysis
• Utilizes Shannon's entropy, enhancing the interpretative quality of spectral analysis

Problems Addressed

• Complexity in interpreting spectral data from various mass spectrometry techniques
• Difficulty in revealing material transitions or interfaces using traditional data analysis methods
• Challenges in analyzing depth profiles in material sciences

Market Applications

• Material sciences, particularly in analyzing depth profiles of various materials
• Academic research, offering a new tool for complex data analysis
• Industrial sectors such as essential oil and petroleum, for quality control and characterization
• Potential commercialization in software and analytical tools for XPS, To

Additional Information

Technology ID: 2022-002
Sell Sheet: Download the Sell Sheet here
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Date Published: 28 March, 2025