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Advanced Vibrational Spectroscopy
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Advanced Vibrational Spectroscopy

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Analytical Chemistry".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1309

Special Issue Editor

Dr. Ke Lin

E-Mail Website
Guest Editor
School of Physics, Xidian University, Xi’an 710071, China
Interests: biological spectroscopy; biophysical chemistry; chemical dynamics

Special Issue Information

Dear Colleagues,

The molecular structure and its interactions in complex solutions are of significance for the comprehension of the properties of such solutions and their applications. Vibrational spectroscopy is frequently employed to study the molecular structure of solutions; however, the spectra of solvents interacting with solutes in complex solutions often overlap significantly with those of pure solvents. To address this challenge, two strategies have been devised to facilitate the study of related problems. The first of these is to adopt novel spectral data processing and analysis techniques, including excess spectroscopy, difference spectroscopy, ratio spectroscopy, multivariate curve resolution and two-dimensional correlation spectroscopy. A second strategy involves the use of special probes whose vibrational spectra are located within the spectral window. Examples of such probes include C-D bonds, azide bonds, SCN bonds and carbon-nitrogen bonds. The present Special Issue will focus on these novel spectroscopic analysis methods and the application of novel probes in complex solutions.

Dr. Ke Lin
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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

  • vibrational spectroscopy
  • molecular structure
  • molecular interaction
  • probe
  • excess spectroscopy
  • difference spectroscopy
  • ratio spectroscopy
  • multivariate curve resolution
  • two-dimensional correlation spectroscopy
  • Raman spectroscopy
  • infrared spectroscopy

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Published Papers (3 papers)

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Research

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18 pages, 2827 KB  
Article
SERS Mixture Recognition from Pure-Substance Spectra via Component Evidence Learning and Two-Stage Inference
by Li Fan, Daoyu Lin, Liang Shen, Junjun Guo, Ting Lian and Yazhou Qin
Molecules 2026, 31(9), 1412; https://doi.org/10.3390/molecules31091412 - 24 Apr 2026
Viewed by 184
Abstract
Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for molecular analysis, yet the recognition of mixed spectra remains challenging because severe peak overlap makes mixture-specific data expensive to acquire and difficult to cover exhaustively. Current machine-learning approaches often rely on labeled mixture datasets, [...] Read more.
Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for molecular analysis, yet the recognition of mixed spectra remains challenging because severe peak overlap makes mixture-specific data expensive to acquire and difficult to cover exhaustively. Current machine-learning approaches often rely on labeled mixture datasets, synthetic mixed spectra, or prior component-matching schemes, making their performance strongly dependent on task-specific mixture data. A pure-spectrum-trained framework for SERS mixture recognition is presented here based on component evidence learning and two-stage inference. Using paraquat, thiram, and tricyclazole as representative target compounds, the framework learns reusable constituent-level evidence directly from pure-substance spectra and converts it into mixture-category predictions within a unified recognition model. This design avoids mixture-specific parameter training while enabling direct recognition of binary and ternary mixtures. Experiments on SERS spectral datasets yielded a mixture recognition accuracy of 98.58%. The results show that pure-substance spectral learning can support accurate recognition of complex SERS mixtures and provide a scalable strategy for mixture analysis when labeled mixture data are limited. Full article
(This article belongs to the Special Issue Advanced Vibrational Spectroscopy)
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18 pages, 6056 KB  
Article
Developing an Integrated Toolbox for Raman Spectral Analysis with Both Artificial Neural Networks and Machine Learning Algorithms
by Xiangtao Kong, Jie Xu, Guodi Fan, Zixuan Zhang, Qidong Liu, Haorui An and Shuang Wang
Molecules 2026, 31(4), 666; https://doi.org/10.3390/molecules31040666 - 14 Feb 2026
Viewed by 601
Abstract
Based on its rich information of chemical specificity, Raman spectroscopy has been widely applied for in vivo biomedical investigations. For extracting quantitative information of target constitution, it is imperative to establish a robust model for unveiling the relationship between spectral features with/without priori [...] Read more.
Based on its rich information of chemical specificity, Raman spectroscopy has been widely applied for in vivo biomedical investigations. For extracting quantitative information of target constitution, it is imperative to establish a robust model for unveiling the relationship between spectral features with/without priori references. By integrating a variety of traditional machine learning and artificial neural network algorithms, an integrated Raman spectra analysis toolbox (AI-Assisted Raman Spectra Analysis Toolbox [AI-Raman] V 1.0) was developed for spectral processing, model training, and regression analysis by using MATLAB R2024a. Besides the utilization of back propagation artificial neural network and convolutional neural network algorithms, classical machine learning algorithms, such as partial least squares regression and support vector regression, were also compacted as the supporting functions of presented toolbox. A spectral dataset obtained from nailfold from different subjects was utilized to evaluated the feasibility and performance of the developed software, which demonstrated that the analysis software can predict glucose concentrations by in vivo Raman spectral measurement. With a friendly graphics interface, the analytical model can be customized and optimized for accomplishing the desired objectives, which will benefit many Raman-based inventions, especially for biomedical transformations. Full article
(This article belongs to the Special Issue Advanced Vibrational Spectroscopy)
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Review

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25 pages, 4623 KB  
Review
Machine Learning-Enabled Intelligent Analysis of Surface-Enhanced Raman Scattering: Methods, Applications, and Perspectives
by Zixing Li, Yu Wang, Zi Deng and Jingjing Zhao
Molecules 2026, 31(10), 1599; https://doi.org/10.3390/molecules31101599 - 10 May 2026
Abstract
Surface-enhanced Raman spectroscopy (SERS) enables ultrasensitive molecular detection but produces high-dimensional and substrate-dependent spectral data that are difficult to analyze using conventional methods. The integration of machine learning (ML) provides new opportunities for extracting chemical information from complex SERS datasets and for optimizing [...] Read more.
Surface-enhanced Raman spectroscopy (SERS) enables ultrasensitive molecular detection but produces high-dimensional and substrate-dependent spectral data that are difficult to analyze using conventional methods. The integration of machine learning (ML) provides new opportunities for extracting chemical information from complex SERS datasets and for optimizing nanostructured substrates that determine signal enhancement. This review summarizes recent advances in ML-assisted SERS across the analytical workflow. Data characteristics and preprocessing strategies are first outlined, followed by an overview of supervised, unsupervised, and deep learning approaches for spectral classification and quantitative analysis. Applications in biomarker discovery and spectral fingerprint recognition are discussed, with emphasis on model interpretability. In addition, ML-driven strategies for substrate optimization, including surrogate modeling and inverse design, are highlighted as emerging directions for improving enhancement efficiency. Current challenges, such as data scarcity, limited generalization, and real-time deployment constraints, are also examined. The convergence of ML and SERS is gradually shifting Raman-based analysis toward more predictive and integrated sensing frameworks. Full article
(This article belongs to the Special Issue Advanced Vibrational Spectroscopy)
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