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Spectroscopic Sensors and Spectral Analysis
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Spectroscopic Sensors and Spectral Analysis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 550

Special Issue Editors

Prof. Dr. Qiang Zeng

E-Mail Website
Guest Editor
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: spectroscopy and spectral analysis; laser-induced breakdown spectroscopy (LIBS)
Dr. Dan Feng

E-Mail Website
Guest Editor
Analytical & Testing Center, Northwestern Polytechnical University, Xi’an 710072, China
Interests: raman spectroscopy; laser-induced breakdown spectroscopy (LIBS); energy dispersive spectroscopy
Prof. Dr. Xiaoli Chu

E-Mail Website
Guest Editor
Sinopec Research Institute of Petroleum Processing Co., LTD., Beijing 100083, China
Interests: spectroscopy and spectral analysis; infrared spectroscopy

Special Issue Information

Dear Colleagues,

We are delighted to organize a Special Issue of Sensors, with the title “Spectroscopic Sensors and Spectral Analysis”. The main purpose of this SI is to report the latest advancements made in the development of spectroscopic sensors and related spectral analysis methods. To make it more practical, the development of spectroscopic sensors and sensing systems (related to XRF, LIBS, ICP-OES/MS, LIF, IR, Raman … and hyphenated techniques) will be emphasized. Ideally, there will be a clear vision for the development of spectroscopic sensors and novel methods of spectral analysis in the future.

Topics of interest include, but are not limited to, the following:

  • Novel spectroscopic sensors and sensing systems;
  • Chemometrics, machine Learning, and AI for spectral analysis;
  • Applications of spectroscopic sensors (agriculture, industrial, biological, geological, energy, environmental…);
  • Spectroscopic sensors applied for extreme conditions;
  • Microsensor and portable sensing system development;
  • Other related areas.

Prof. Dr. Qiang Zeng
Dr. Dan Feng
Prof. Dr. Xiaoli Chu
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • spectroscopic sensors
  • spectral sensing systems
  • applications of spectroscopic sensors (agriculture, industrial, biological, geological, energy, environmental…)
  • microsensor and portable sensing system development
  • laser-induced breakdown spectroscopy (LIBS)
  • raman spectroscopy
  • infrared spectroscopy

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Published Papers (1 paper)

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Research

22 pages, 8547 KB  
Article
High-Accuracy and Efficient Classification of Uranium Slag by Origin and Category via LIBS Integrated with Hybrid Machine Learning
by Mengjia Zhang, Hao Li, Luan Deng, Rong Hua, Xinglei Zhang, Debo Wu, Xizhu Wang, Xiangfeng Liu, Zuoye Liu and Xiaoliang Liu
Sensors 2026, 26(8), 2522; https://doi.org/10.3390/s26082522 - 19 Apr 2026
Viewed by 302
Abstract
Accurate classification of uranium slag origin and category is essential for nuclear environmental monitoring and safety. This study presents a hybrid framework combining laser-induced breakdown spectroscopy (LIBS), four preprocessing methods, and five machine learning algorithms for rapid uranium slag classification. A total of [...] Read more.
Accurate classification of uranium slag origin and category is essential for nuclear environmental monitoring and safety. This study presents a hybrid framework combining laser-induced breakdown spectroscopy (LIBS), four preprocessing methods, and five machine learning algorithms for rapid uranium slag classification. A total of nine sample categories were collected from three mining areas, with categories defined by their U concentration levels within each origin. Standard normal variate (SNV), Savitzky–Golay smoothing (SG), and their combinations (SNV-SG, SG-SNV) were applied to evaluate preprocessing effects. To address ultra-high-dimensional spectral data (49,242 points per spectrum), principal component analysis (PCA) and random forest (RF) were employed for feature engineering, integrated with support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbors (KNN) classifiers. Hyperparameter optimization via five-fold cross-validation and Bayesian optimization enhanced accuracy and efficiency. RF-based hybrid models consistently outperformed PCA-based counterparts. Remarkably, the RF-LDA model with SNV-SG preprocessing achieved 100% classification accuracy across all test sets with a processing time of only 10.46 s, demonstrating exceptional discriminative power and computational efficiency. These findings establish that combining RF feature selection with advanced machine learning offers a robust solution for LIBS-based nuclear material classification, with significant implications for both nuclear safety and resource management. Full article
(This article belongs to the Special Issue Spectroscopic Sensors and Spectral Analysis)
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