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Quantitative analysis of blood glucose by FT‐Raman spectroscopy and multivariate statistical analysis

Wenyi Sun, Shuai Song, Bairen Qian, Da Wen, Daying Jiang, Yongqing (Richard) Fu, Qiaoyun Wang*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    5 Citations (Scopus)
    62 Downloads (Pure)

    Abstract

    Diabetes is one of the common chronic diseases in worldwide and increasing among young children. Testing glycated hemoglobin levels as well as blood glucose levels is a method for detecting diabetes. In this article, with multivariate statistical analysis was used to monitor the blood glucose levels by the Fourier Transform Raman spectroscopy. Principal component regression with direct orthogonal signal correction is developed and applied to analyze the glucose concentration with the blood Raman spectroscopy. The root means square error of calibration is 3.0824 mg/dL (0.1712 mmol/L), the root means square error of prediction is 3.2688 mg/dL (0.1816 mmol/L), the correlation coefficients (R 2) is.9946 and ratio of performance to deviation is 6.4789 and, respectively. The results show that this method can be used as a strong potential diagnostic tool for diabetes mellitus.

    Original languageEnglish
    Article numbere33860
    Pages (from-to)1-7
    Number of pages7
    JournalMicrowave and Optical Technology Letters
    Volume66
    Issue number1
    Early online date6 Aug 2023
    DOIs
    Publication statusPublished - 1 Jan 2024

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

    Keywords

    • blood glucose
    • direct orthogonal signal correction
    • principal component regression
    • Raman spectroscopy

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