Spectral Detection: Advancing Sensing Tools for Global Challenges

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

Deadline for manuscript submissions: closed (15 April 2026) | Viewed by 292

Editor

Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China
Interests: spectral detection; measurement techniques for atmospheric radicals; atmospheric chemistry

Special Issue Information

Dear Colleagues,

Spectral detection technologies are revolutionizing our ability to monitor and analyze complex chemical and biological systems with unparalleled precision. As global challenges such as environmental pollution, disease outbreaks, industrial safety, and food quality control escalate, there is an urgent need to advance sensing tools that offer real-time, non-invasive, and high-sensitivity detection capabilities. Innovations in spectral sensors—spanning ultraviolet–visible, infrared, Raman, fluorescence, and hyperspectral imaging—are paving the way for breakthroughs in early disease diagnosis, air/water contaminant tracking, hazardous material identification, and industrial process optimization.

This Special Issue focuses on cutting-edge advancements in spectral detection technologies, including novel sensor designs, miniaturized instrumentation, AI-driven spectral data processing, and multi-spectral fusion techniques. We welcome contributions addressing fundamental research, device engineering, and real-world applications, such as portable spectrometers for field analysis, wearable sensors for health monitoring, and satellite-based spectral systems for environmental surveillance. Submissions on interdisciplinary approaches integrating nanotechnology, machine learning, or IoT platforms to enhance spectral sensitivity, selectivity, and cost-efficiency are particularly encouraged. By showcasing the latest innovations in this field, we aim to foster collaboration between researchers and industries to accelerate the transition of spectral technologies from laboratories to global markets.

Dr. Renzhi Hu
Guest Editor

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Keywords

  • spectral detection technology
  • sensors
  • instruments
  • real-time monitoring
  • non-invasive detection
  • portable spectrometers
  • wearable sensors
  • environmental surveillance
  • early disease diagnosis
  • machine learning

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Published Papers (1 paper)

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Review

28 pages, 2891 KB  
Review
Orthogonal Multimodal Sensing and AI Fusion for the Recognition of Unknown Chemical Threats: A Critical Review
by Min-Kun Kim, Ku Kang, Shin Hum Cho, Yoon Jeong Jang, Soohwan Kim, Jin Yoo, Myeongsik Shin, Sungbong Kim and Doo-Hee Lee
Chemosensors 2026, 14(9), 189; https://doi.org/10.3390/chemosensors14090189 (registering DOI) - 22 Aug 2026
Abstract
Real-time detection of chemical warfare agents (CWAs) and toxic industrial chemicals underpins military protection, counter-terrorism, and emergency response. Yet field instruments usually fail for a reason unrelated to sensitivity: they cannot identify agents that are not already in their reference libraries, such as [...] Read more.
Real-time detection of chemical warfare agents (CWAs) and toxic industrial chemicals underpins military protection, counter-terrorism, and emergency response. Yet field instruments usually fail for a reason unrelated to sensitivity: they cannot identify agents that are not already in their reference libraries, such as novel analogs, mixtures, and degradation products. We argue that this unknown-agent problem is a structural limitation of single-modality sensing, because any one class of information (molecular bonds, ion mobility, elemental composition, or chemical reactivity) is rarely sufficient to resolve an unfamiliar threat. We review the dominant field modalities, including FTIR, Raman/SERS, ion mobility and field-asymmetric ion mobility spectrometry, laser- and spark-induced plasma spectroscopy, metal-oxide sensor arrays, and portable mass spectrometry, and show that their weaknesses are largely complementary. We then set out the principle of orthogonal multimodal sensing, in which complementary information axes are combined by machine learning with anomaly and open-set detection so that unfamiliar agents are recognized as such rather than misidentified. Four hybrid architectures are critically compared, and we examine spark-induced decomposition diagnostics, consumable-free self-decontaminating field systems with edge AI, and the open challenges of standardized datasets, calibration transfer, and validation, before outlining a roadmap toward field-relevant recognition of unidentified chemical threats. Full article
(This article belongs to the Special Issue Spectral Detection: Advancing Sensing Tools for Global Challenges)
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