Label-free optical detection of type II diabetes based on surface-enhanced Raman spectroscopy and multivariate analysis

Authors

  • Jinyong Lin,

    1. Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
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    • These authors contributed equally to the work.

  • Zufang Huang,

    1. Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
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    • These authors contributed equally to the work.

  • Shangyuan Feng,

    1. Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
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    • These authors contributed equally to the work.

  • Juqiang Lin,

    Corresponding author
    1. Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
    • Correspondence to: Juqiang Lin, Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China.

      E-mail: jqlin@fjnu.edu.cn

      Correspondence to: Rong Chen, Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China.

      E-mail: chenr@fjnu.edu.cn

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  • Nenrong Liu,

    1. Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
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  • Jing Wang,

    1. Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
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  • Ling Li,

    1. The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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  • Yongyi Zeng,

    1. The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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  • Buhong Li,

    1. Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
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  • Haishan Zeng,

    1. Imaging Unit – Integrative Oncology Department, British Columbia Cancer Research Centre, Vancouver, BC, Canada
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  • Rong Chen

    Corresponding author
    1. Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
    • Correspondence to: Juqiang Lin, Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China.

      E-mail: jqlin@fjnu.edu.cn

      Correspondence to: Rong Chen, Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education and Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China.

      E-mail: chenr@fjnu.edu.cn

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Abstract

Surface-enhanced Raman scattering (SERS) spectroscopy was first employed to detect oxyhemoglobin (OxyHb, the common type of hemoglobin) variation in type II diabetic development without using exogenous reagents. Using silver nanoparticles as SERS-active substrate, high-quality SERS spectra are obtained from blood OxyHb samples of 49 diabetic patients and 40 healthy volunteers. Tentative assignment of the observed SERS bands indicates specific structural changes of OxyHb molecule in diabetes, including heme transformation and globin variation. Furthermore, partial least squares and principal component analysis combined with linear discriminate analysis diagnostic algorithms are employed to analyze and classify the SERS spectra acquired from diabetic and healthy OxyHb, yielding the diagnostic accuracies of 90.0% and 95.5%, respectively. This exploratory work suggests that the silver nanoparticles-based OxyHb SERS method in combination with multivariate statistical analysis has great potential for the label-free detection of type II diabetes. Copyright © 2014 John Wiley & Sons, Ltd.

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