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<p>This paper explores the possibility of applying hyperspectral imaging and machine learning in detecting <i>infectious</i> coronavirus in the indoor environment. Currently, the polymerase chain reaction (PCR) test is the leading technique to detect the existence of SARS-CoV-2 virus in humans (serum or nasalpharyngelal specimens). While the test is a gold standard in detection of virus in humans, positive PCR results do not necessarily mean infectious virus is detected, as dead virus can trigger positive results in the nucleic acid amplification test. This paper proposes a novel method which is based on measuring <i>live cells being infected by infectious virus</i>, and has the potential to directly predict the infectiousness of the targeted virus within 48&#xa0;hours. The infection status was measured by a hyperspectral camera. A Partial Least Square - Discriminant Analysis (PLS-DA) model was trained on the hyperspectral images to do prediction. We believe this method opens a new avenue to detecting infectious virus in an indoor environment and can be potentially applied in SARS-CoV-2 detections on surfaces, hence used to aid in protection control measures and detecting virus contamination.</p>

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A Clinical Hygiene Ruler on the Basis of Hyperspectral Imaging and Machine Learning: A Proof-of-Concept Model for SARS-CoV-2 Virus Detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月15日

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  • Nabil Belbachir,
  • Rebecca Jane Cox,
  • Julio Hernández,
  • Arnoud Jochemsen,
  • Na Liu,
  • Kristin Greve-Isdahl Mohn,
  • Juha Ilmari Vahokoski,
  • Lars Kristian Vognild,
  • Fan Zhou,
  • Xuan Zhang

摘要

This paper explores the possibility of applying hyperspectral imaging and machine learning in detecting infectious coronavirus in the indoor environment. Currently, the polymerase chain reaction (PCR) test is the leading technique to detect the existence of SARS-CoV-2 virus in humans (serum or nasalpharyngelal specimens). While the test is a gold standard in detection of virus in humans, positive PCR results do not necessarily mean infectious virus is detected, as dead virus can trigger positive results in the nucleic acid amplification test. This paper proposes a novel method which is based on measuring live cells being infected by infectious virus, and has the potential to directly predict the infectiousness of the targeted virus within 48 hours. The infection status was measured by a hyperspectral camera. A Partial Least Square - Discriminant Analysis (PLS-DA) model was trained on the hyperspectral images to do prediction. We believe this method opens a new avenue to detecting infectious virus in an indoor environment and can be potentially applied in SARS-CoV-2 detections on surfaces, hence used to aid in protection control measures and detecting virus contamination.