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Chemosensors
Special Issues
Technological and Analytical Advances in Hyperspectral Analysis
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Technological and Analytical Advances in Hyperspectral Analysis

A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Analytical Methods, Instrumentation and Miniaturization".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 811

Special Issue Editors

Prof. Dr. José M. Amigo

E-Mail Website
Guest Editor
1. Department of Analytical Chemistry, University of the Basque Country UPV/EHU, Basque Country, P.O. Box 644, 48080 Bilbao, Spain
2. IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
Interests: hyperspectral; imaging; spectroscopy; machine learning; matlab
Special Issues, Collections and Topics in MDPI journals
Dr. Giulia Gorla

E-Mail Website
Guest Editor
Faculty of Science and Technology, University of the Basque Country, 48940 Leioa, Spain
Interests: analytical chemistry; chemometrics; molecular spectroscopies; hyperspectral imaging; process analytical technology (PAT); process monitoring

Special Issue Information

Dear Colleagues,

The field of hyperspectral analysis has seen significant advancements, driven by technological innovations and refined analytical methodologies. Hyperspectral imaging, with its ability to capture and process information across a wide spectrum of wavelengths, has revolutionized various sectors, including chemical science, agrifood science, remote sensing, medical diagnostics, and material science. This Special Issue aims to bring together cutting-edge research that highlights these advancements, providing insights into the latest tools, techniques, and applications of hyperspectral analysis.

Contributions to this Special Issue will cover a broad range of topics, from the development of novel hyperspectral sensors and imaging systems to the implementation of sophisticated algorithms for data processing and interpretation. We also welcome studies showcasing real-world applications that demonstrate the transformative impact of hyperspectral technology in diverse fields such as agriculture, environmental monitoring, and biomedical imaging.

Researchers and practitioners are invited to submit their original work to further the understanding and application of hyperspectral analysis.

Dr. José M. Amigo
Dr. Giulia Gorla
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. Chemosensors is an international peer-reviewed open access monthly 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 2000 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

  • hyperspectral imaging
  • spectral analysis
  • analytical chemistry
  • sensor technology
  • environmental monitoring
  • agricultural applications
  • food applications

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

Published Papers (1 paper)

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Research

19 pages, 4869 KB  
Article
Geographical Origin Identification of Rhizoma Atractylodis macrocephalae Using Hyperspectral Imaging Combined with Broad Learning System and SHapley Additive exPlanations
by Peng Li, Huaming Liu, Defang Liu, Liguo Han and Chuanzong Li
Chemosensors 2025, 13(11), 400; https://doi.org/10.3390/chemosensors13110400 - 19 Nov 2025
Viewed by 481
Abstract
Rhizoma Atractylodis macrocephalae (RAM) is a renowned food–medicine homologous herb in China, the quality and efficacy of which are inherently linked to its geographical origin. However, traditional origin identification methods for RAM are time-consuming, laborious, and destructive. This study introduces an innovative framework [...] Read more.
Rhizoma Atractylodis macrocephalae (RAM) is a renowned food–medicine homologous herb in China, the quality and efficacy of which are inherently linked to its geographical origin. However, traditional origin identification methods for RAM are time-consuming, laborious, and destructive. This study introduces an innovative framework integrating hyperspectral imaging (HSI), broad learning system (BLS), and SHapley Additive exPlanations (SHAP) for RAM origin identification. RAM samples were collected from three origins, 100 samples from per origin, and imaged using a visible and short-wave near-infrared HSI system. BLS was used to build identification models with full and important wavelengths, and compared against seven traditional algorithms, including K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), gradient boosting decision tree, (GBDT), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). Additionally, SHAP was used to enhance interpretability and identify important wavelengths highly correlated with RAM origin. Results showed that the full-wavelength BLS model achieved a test accuracy of 95.56%, which outperformed other models including KNN (77.78%), RF (85.56%), GBDT (88.89%), AdaBoost (90.00%), BPNN (91.11%), XGBoost (92.22%), and SVM (94.44%). SHAP identified important wavelengths similar to traditional methods (competitive adaptive reweighted sampling and successive projections algorithm), and the BLS model using SHAP-selected top 25 wavelengths achieved 94.44% accuracy with minimal performance loss. This study not only provides a rapid and accurate approach for RAM origin identification but also establishes a promising data-driven paradigm for non-destructive geographical origin traceability of other traditional Chinese medicines. Full article
(This article belongs to the Special Issue Technological and Analytical Advances in Hyperspectral Analysis)
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