近红外光谱法无损快速测定全血中血糖的含量

doi:10.5246/jcps.2025.11.079

中国药学(英文版) ›› 2025, Vol. 34 ›› Issue (11): 1051-1057.DOI: 10.5246/jcps.2025.11.079

近红外光谱法无损快速测定全血中血糖的含量

李春燕¹^,², 薛金涛²^,³^,^*(), 叶利明³^,^*()

^1. 豫北医学院生物与基础医学实验教学中心, 河南新乡 453003
^2. 河南医药大学药学院, 河南新乡 453002
^3. 四川大学华西药学院, 四川成都 610041

收稿日期:2025-04-23 修回日期:2025-06-15 接受日期:2025-07-27 出版日期:2025-12-02 发布日期:2025-12-02
通讯作者: 薛金涛, 叶利明

Fast analysis of blood glucose in whole blood by near-infrared spectroscopy

Chunyan Li¹^,², Jintao Xue²^,³^,^*(), Liming Ye³^,^*()

¹ Biological and Basic Medical Experimental Teaching Center, North Henan Medical University, Xinxiang 453003, Henan, China
² School of Pharmacy, Henan Medical University, Xinxiang 453002, Henan, China
³ West China School of Pharmacy, Sichuan University, Chengdu 610041, Sichuan, China

Received:2025-04-23 Revised:2025-06-15 Accepted:2025-07-27 Online:2025-12-02 Published:2025-12-02
Contact: Jintao Xue, Liming Ye
Supported by:
The University Key Research Projects of Henan Province (Grant No. 25B360004), and the Backbone Teachers Program of North Henan Medical University (Sanquan College of Xinxiang Medical University) (Grant No. SQ2025GGJS08).

摘要/Abstract

摘要：

糖尿病是一种以高血糖为特征的终身性疾病, 频繁测定血糖是糖尿病管理的重要组成部分。常规血糖分析使用成本较高, 本研究旨在开发一种绿色环保、低成本的在全血中无损快速分析血糖含量的近红外光谱法。通过高糖高脂饮食4周和链脲佐菌素联合诱导糖尿病模型大鼠, 采用偏最小二乘法对近红外光谱通过筛选优化光谱预处理方法、建模光谱范围和建模因子数, 建立最佳血糖测定的近红外光谱模型。最优化近红外光谱模型为采用消除常数偏移量进行光谱预处理, 建模光谱范围为7502.0−5446.2 cm^–1, 建模因子数为10, 其建模集和验证集的相关系数分别为0.9621和0.9481, 内部验证均方差为0.612, 外部验证均方差为0.420, 残留预测偏差为3.48。此外还对各组大鼠的生化参数进行了分析。本研究建立的近红外光谱方法稳健准确, 可快速分析全血中的血糖含量, 具有重要的应用价值。

关键词: 近红外光谱, 血糖测定, 全血, 偏最小二乘法, 糖尿病

Abstract:

Diabetes remains one of the most pressing global metabolic disorders, necessitating regular and precise monitoring of blood glucose levels for effective disease management. In this study, we developed a rapid and reliable method for quantifying glucose in whole blood using near-infrared (NIR) spectroscopy. A diabetic rat model was established through a high-fat, high-sugar diet followed by administration of streptozotocin (STZ) over a period of 4 weeks. To construct the NIR calibration model, partial least-squares (PLS) regression was employed, with optimization tailored to spectral range, preprocessing techniques, and the number of latent variables. The optimal model was achieved within the spectral window of 7502.0–5446.2 cm^–1, using Constant Offset Elimination for spectral pretreatment and a factor number of 10. This optimized model yielded a strong correlation coefficient (R) of 0.9621, with a root mean square error of cross-validation (RMSECV) of 0.612, a residual predictive deviation (RPD) of 3.48, and a root mean square error of prediction (RMSEP) of 0.420. Additionally, biochemical indices were evaluated across all experimental groups to validate the model’s performance. Overall, the proposed NIR-based analytical strategy demonstrated high accuracy, robustness, and reproducibility, offering a promising tool for rapid glucose assessment in whole blood.

Key words: Near-infrared spectroscopy, Blood glucose assay, Whole blood, Partial least squares, Diabetes

Supporting:

李春燕, 薛金涛, 叶利明. 近红外光谱法无损快速测定全血中血糖的含量[J]. 中国药学(英文版), 2025, 34(11): 1051-1057.

Chunyan Li, Jintao Xue, Liming Ye. Fast analysis of blood glucose in whole blood by near-infrared spectroscopy[J]. Journal of Chinese Pharmaceutical Sciences, 2025, 34(11): 1051-1057.

图/表 5

Figure 1. The NIR spectrum of the rats’ whole blood samples.

Table 1. Calibration and validation results for the NIR model.

Figure 2. Relationship between the values of the correlation coefficients (R), RMSECV, and the factors (F).

Table 2. Water intake, urine volume, and urine glucose of rats.

Table 3. Biochemistry assay results for each group of rats.

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近红外光谱法无损快速测定全血中血糖的含量

Fast analysis of blood glucose in whole blood by near-infrared spectroscopy

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