AI-Driven Methods for Chemometric Spectroscopy: Algorithms and Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Chemical Sensors".
Deadline for manuscript submissions: 27 July 2026 | Viewed by 5
Special Issue Editors
Interests: deep learning; chemometric modeling
Special Issues, Collections and Topics in MDPI journals
Interests: nondestructive testing; intelligent food inspection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of artificial intelligence (AI) and big data analytics with spectroscopic sensing has opened up new frontiers in chemical and material analysis. Traditional chemometric approaches often face limitations when dealing with high-dimensional, nonlinear, and noisy spectral data. Emerging AI-driven methods, including deep learning, transfer learning, and interpretable machine learning, offer powerful tools for extracting meaningful patterns, enhancing model robustness, and enabling real-time decision-making in complex sensing environments.
This Special Issue invites original research and review articles focused on the development and application of intelligent algorithms for chemometric spectroscopy. Topics of interest include AI-based spectral preprocessing, regression and classification models, feature extraction, anomaly detection, and data fusion from multi-modal or multi-sensor platforms. We particularly welcome contributions demonstrating real-world applications in fields such as environmental monitoring, food quality, healthcare, process control, and portable sensing systems.
By bridging AI and spectroscopic chemometrics, this Special Issue will provide a forum for advancing next-generation intelligent sensing technologies and data-driven analytical frameworks.
Dr. Liang Zou
Dr. Yue Huang
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence (AI)
- spectroscopic sensing
- chemical and material analysis
- chemometric spectroscopy
- AI-based spectral preprocessing
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