Advances in band assignment in near-infrared spectroscopy: Principles, methods, and applications

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Computational Chemistry

Background:

• Near-infrared (NIR) spectroscopy offers efficient, non-destructive, real-time monitoring.
• Challenges include overlapping spectral bands and weak absorption signals, hindering molecular structure correlation.
• Precise spectral band assignment is crucial for accurate analysis.

Purpose of the Study:

• To systematically review research progress in near-infrared spectral band assignment.
• To categorize and discuss mainstream methodologies for spectral band assignment.
• To highlight applications and future directions in NIR spectral analysis.

Main Methods:

• Experimental perturbation analysis: Modulating external conditions (temperature, concentration) to separate overlapping bands.
• Data-driven methods: Integrating chemometrics and explainable AI for characteristic wavelength extraction.
• Theoretical computation: Employing quantum chemistry and anharmonic vibrational algorithms for direct spectral prediction from molecular structures.

Main Results:

• Categorization of three primary approaches for NIR spectral band assignment.
• Demonstration of band assignment applications in quantitative analysis, process monitoring, material identification, and biomedical diagnostics.
• Advancement of spectral analysis from 'black-box' prediction to mechanistic insight.

Conclusions:

• Future directions include deep fusion of multi-paradigm approaches and enhanced AI integration.
• Advancement of miniaturized and interdisciplinary applications is proposed.
• Synergistic progress in theory, computation, and data will drive breakthroughs in NIR spectral band assignment.