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AI Chemistry
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Artificial Intelligence in Spectroscopic Techniques: From Data Processing to Discovery
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Artificial Intelligence in Spectroscopic Techniques: From Data Processing to Discovery

A special issue of AI Chemistry (ISSN 3042-6723).

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2165

Special Issue Editors

Prof. Dr. Yiping Zhao

E-Mail Website
Guest Editor
Department of Physics and Astronomy, The University of Georgia, Athens, GA 30602, USA
Interests: nanostructure/thin film fabrication and characterization; metamaterials and plasmonic nanostructures; chemical and biological sensors; nano-photocatalysts; antimicrobial materials; nanomotors and their applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bo Hu

E-Mail Website
Guest Editor
School of Life Science and Technology, Xidian University, Xi’an 710126, China
Interests: raman spectroscopy; surface-enhanced raman scattering; microfluidics; machine learning; computer vision; in vitro diagnosis

Special Issue Information

Dear Colleagues,

Spectroscopic techniques such as Raman, IR, UV-Vis, NMR, XPS, and mass spectrometry generate high-dimensional, information-rich data that are essential for chemical, biological, environmental, and material sciences. However, extracting meaningful patterns, reducing noise, interpreting complex spectra, and correlating spectral signatures with structural or functional properties remain challenging tasks. In recent years, artificial intelligence (AI) has emerged as a transformative tool for spectroscopic analysis. Techniques such as machine learning, deep learning, and generative modeling are reshaping how spectra are processed, interpreted, and applied—from enhanced preprocessing and feature extraction to classification, quantification, inverse design, and autonomous experimentation.

This Special Issue aims to showcase cutting-edge research at the intersection of AI and spectroscopy, highlighting both foundational methods and real-world applications. We welcome contributions that focus on AI-enhanced spectral data processing, spectral interpretation, compound identification, predictive modeling, the simulation of spectral data, and autonomous sensing systems across chemical, biomedical, environmental, and industrial domains.

This Special Issue aligns well the scope of AI Chemistry by emphasizing the application of data-centric, AI-enabled strategies to accelerate spectroscopic discovery, interpretation, and decision-making in chemical research.

Prof. Dr. Yiping Zhao
Prof. Dr. Bo Hu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI Chemistry is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • spectroscopy
  • data analysis
  • signal processing
  • SERS/Raman spectroscopy
  • spectral simulation
  • deep learning
  • spectral classification and quantification
  • spectral dimixing
  • inverse spectral design

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

18 pages, 4367 KB  
Article
Leveraging Bag Dissimilarity Regularized Multi-Instance Learning for Analyzing Infrared Spectra of Heterogeneous Objects
by Shiluo Huang and Zheyu Zou
AI Chem. 2026, 1(2), 6; https://doi.org/10.3390/aichem1020006 - 27 Mar 2026
Viewed by 282
Abstract
Infrared (IR) spectroscopy is a powerful tool for characterizing molecular structures and chemical groups, offering advantages such as low cost, rapid analysis, and non-destructive testing. When analyzing heterogeneous objects, spectra are typically measured from different regions to capture the local variations, presenting a [...] Read more.
Infrared (IR) spectroscopy is a powerful tool for characterizing molecular structures and chemical groups, offering advantages such as low cost, rapid analysis, and non-destructive testing. When analyzing heterogeneous objects, spectra are typically measured from different regions to capture the local variations, presenting a multi-instance learning (MIL) problem. However, existing methods primarily rely on multi-instance assumptions or explicit bag representations, often failing to fully capture the intrinsic information from implicit representations. We introduce a bag dissimilarity regularized MIL framework for analyzing IR spectra of heterogeneous objects, which integrates both explicit and implicit representations to effectively learn the MIL bags. Specifically, a bag dissimilarity regularization term is utilized to extract implicit representations, which subsequently guide the classifier based on explicit representations to enhance generalization performance. The proposed method was validated on two heterogeneous detection tasks: polydimethylsiloxane (PDMS) block assessment and polyethylene terephthalate (PET) fiber inspection. Experimental results demonstrate that our approach significantly outperforms existing methods on both datasets with a considerable margin. Full article
(This article belongs to the Special Issue Artificial Intelligence in Spectroscopic Techniques: From Data Processing to Discovery)
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17 pages, 3701 KB  
Article
BioRamanNet: A Neural Network Framework for Biological Raman Spectroscopy Classification
by Pengju Yin, Xin Li, Yuxuan Lv, Yan Li, Yiping Zhao and Bo Hu
AI Chem. 2026, 1(1), 3; https://doi.org/10.3390/aichem1010003 - 18 Nov 2025
Viewed by 1285
Abstract
Raman spectroscopy has become an important tool for biomedical analysis due to its ability to provide label-free, non-destructive molecular fingerprints of biological samples. However, existing deep learning approaches for classifying biological Raman spectra often focus on specific datasets and lack generalizability and interpretability. [...] Read more.
Raman spectroscopy has become an important tool for biomedical analysis due to its ability to provide label-free, non-destructive molecular fingerprints of biological samples. However, existing deep learning approaches for classifying biological Raman spectra often focus on specific datasets and lack generalizability and interpretability. In this study, BioRamanNet is presented, an interpretable and generalizable deep learning framework designed for classifying a wide range of biological Raman spectra. The model integrates adaptive one-dimensional convolutional layers and squeeze-and-excitation (SE) blocks within a residual network architecture to enhance feature extraction. BioRamanNet was evaluated using four representative Raman spectral datasets—breast cells, extracellular vesicles and particles (EVPs), viruses, and bacteria—achieving classification accuracies of 99.5%, 100%, 99.8%, and 85.3%, respectively. To improve model interpretability, a perturbation-based analysis using Voigt noise was introduced to identify key wavenumber regions influencing classification. These regions were found to correspond closely with known Raman biomarkers, validating their biological significance. The results of this work demonstrate that BioRamanNet is a powerful and interpretable tool for analyzing diverse biological Raman spectra and holds promise for advancing machine learning-assisted biomedical diagnostics. Full article
(This article belongs to the Special Issue Artificial Intelligence in Spectroscopic Techniques: From Data Processing to Discovery)
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