New Publication | Decoding Complex Spectra with Machine Learning

We are excited to share our new Analytical Chemistry review article, “Decoding Complex Spectra with Machine Learning: Advances in Spectral Unmixing for Chemical Analysis.” 

Modern spectroscopy is indispensable in biomedical diagnostics, environmental monitoring, food safety, and materials characterization—but real-world spectra are often mixtures: multiple chemical components, overlapping peaks, strong backgrounds (e.g., fluorescence), and spatial mixing (e.g., imaging, remote sensing). These effects can make direct interpretation difficult and motivate the need for spectral unmixing methods that separate hidden components and quantify their contributions.

What This Review Covers

In this review, we map the evolution of spectral unmixing from classical mathematical decomposition to machine learning (ML) and deep learning paradigms, with a unified perspective across application domains.

Core concepts and modeling

• Clear framing of spectral unmixing and why it matters across hyperspectral imaging and vibrational spectroscopy

• Linear vs nonlinear mixing assumptions and what they imply for interpretability and accuracy

• How physical constraints (e.g., nonnegativity, sum-to-one abundance constraints) shape practical unmixing workflows

From traditional methods to ML

• Traditional foundations (geometry-based, statistical, sparse regression, Bayesian, etc.)—their strengths and when they break down

• ML-driven strategies categorized by learning paradigm:

  • Supervised unmixing (endmembers known)

  • Semi-supervised unmixing (partial priors available)

  • Unsupervised/blind unmixing (no labels; representation learning and generative approaches)

Challenges and future directions

• Robustness to noise, background interference, peak overlap, endmember variability, and domain shift (instrument/sample/environment differences)

• Opportunities in physics-informed ML, multimodal fusion, interpretability tools, and—critically—open, standardized datasets to enable benchmarking and reproducibility

Why This Matters

Spectral unmixing is rapidly becoming a “connective tissue” between spectroscopy, imaging, and AI. By organizing the field into a coherent technical landscape, this review aims to help researchers:

• Choose appropriate models (linear/nonlinear; classical/ML) based on the mixing physics and data conditions

• Understand tradeoffs between accuracy, interpretability, data requirements, and computational cost

• Identify high-impact research gaps—especially generalizable ML, reproducible evaluation, and community datasets

Publication Information

Pengju Yin, Yumeng Xiao, Chenyao Feng, Xiaoyao Wu, Xinyuan Wang, Dmitriy Klyuyev, Zhuo Wu, Yiping Zhao, Bo Hu, "Analytical Chemistry (Review)“Decoding Complex Spectra with Machine Learning: Advances in Spectral Unmixing for Chemical Analysis” Analytical Chemistry 98(6), 4432–4457. DOI: