近红外光谱法糖浓度预测中测量条件控制与建模方法的系统研究

刘文博; 孙迪; 陈嘉宇; 姚明飞; 刘雪颖; 刘瑾; 韩同帅; 徐可欣

doi:10.3788/gzxb20265507.0717001

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近红外光谱法糖浓度预测中测量条件控制与建模方法的系统研究

医用光学与生物技术 | 更新时间：2026-06-12

- 近红外光谱法糖浓度预测中测量条件控制与建模方法的系统研究
- A Systematic Study on Measurement Condition Control and Modeling Methods for Glucose Concentration Prediction Using Near-Infrared Spectroscopy
- 光子学报 2026年页码：1-16
- 作者机构：
  
  1.天津大学精密测试技术及仪器全国重点实验室，天津大学，天津，300072
  2.先阳科技（天津）有限公司，天津，300192
- 作者简介：
  
  刘文博（1999—），男，博士研究生，主要研究方向为无创血糖测量。Email: wenbo_liu@tju.edu.cn
  刘雪颖（2001—），女，硕士研究生，主要研究方向为近红外光谱检测。Email: liuxveying@tju.edu.cn
  刘瑾（1979—），女，副教授，博士，主要研究方向为光谱检测与光谱分析。Email: liu_jin@tju.edu.cn
  韩同帅（1991—），男，助理研究员，博士，主要研究方向为无创血糖测量。Email: hts2014@tju.edu.cn
- 基金信息：
  
  国家自然科学基金(81971657)
- DOI：10.3788/gzxb20265507.0717001
  中图分类号： O433.4
- CSTR：32255.14.gzxb20265507.0717001
- 收稿：2026-03-07，
  
  修回：2026-04-15，
  
  录用：2026-04-19，
- 稿件说明：
移动端阅览
刘文博，孙迪，陈嘉宇，等. 近红外光谱法糖浓度预测中测量条件控制与建模方法的系统研究［J］.光子学报，2026，55（7）：0717001

LIU Wenbo， SUN Di， CHEN Jiayu， et al. A Systematic Study on Measurement Condition Control and Modeling Methods for Glucose Concentration Prediction Using Near-Infrared Spectroscopy［J］. Acta Photonica Sinica，2026，55（7）：0717001
刘文博，孙迪，陈嘉宇，等. 近红外光谱法糖浓度预测中测量条件控制与建模方法的系统研究［J］.光子学报，2026，55（7）：0717001 DOI： 10.3788/gzxb20265507.0717001. CSTR： 32255.14.gzxb20265507.0717001.

LIU Wenbo， SUN Di， CHEN Jiayu， et al. A Systematic Study on Measurement Condition Control and Modeling Methods for Glucose Concentration Prediction Using Near-Infrared Spectroscopy［J］. Acta Photonica Sinica，2026，55（7）：0717001 DOI： 10.3788/gzxb20265507.0717001. CSTR： 32255.14.gzxb20265507.0717001.

摘要

在基于近红外光谱法的糖浓度预测中，由于糖分子吸收与散射信号微弱，预测精度易受测量条件波动影响。本文结合糖水溶液模拟光谱体系与人体口服葡萄糖耐量试验，系统分析线性与非线性测量条件对光谱特征及模型性能的影响，并比较PLSR与MLP两种建模方法的抗干扰能力，提出“允许变化”与“不允许变化”的测量条件分类策略。仿真结果表明，温度与白噪声是“不允许变化”的，它们呈显著非线性特征，是影响测量精度的关键因素，需将它们分别控制在≤1 ℃和≤0.002 a.u.范围内，预测误差方可稳定在约0.2 mmol/L；而入射角度、光源色温及光源漂移等因素影响近似线性，在较大变化范围内仍可保持较低误差，是“允许变化”的。MLP在处理单一非线性温度干扰时略优于PLSR，但在温度波动较大时，两者误差均明显增大。人体口服葡萄糖耐量试验进一步验证了温控条件下光谱与血糖相关性显著增强，留一受试者法交叉验证预测误差为1.33 mmol/L，优于未温控组（1.63 mmol/L）。可见，合理的温度控制可有效提升预测精度。本研究为近红外光谱法无创血糖测量中的测量条件控制提供了参考。

Abstract

This study aimed to systematically investigate how measurement conditions affect the accuracy and stability of glucose concentration prediction based on near-infrared （NIR） spectroscopy， and to clarify the roles of physical control and modeling methods in improving predictive performance. By combining a simulated glucose-solution spectral system with human oral glucose tolerance test （OGTT） experiments， we examined the effects of linear and nonlinear disturbances on spectral characteristics and model behavior， compared the robustness of partial least squares regression （PLSR） and multilayer perceptron （MLP）， and proposed a classification strategy distinguishing measurement conditions as either “allowable variations” or “non-allowable variations.”A combined simulation-and-human-experiment framework was established. In the simulation study， an aqueous glucose solution was used as a simplified optical system to isolate the effects of controlled disturbances on NIR spectral response and prediction accuracy. Representative factors included liquid temperature variation， white noise， incident angle variation， light-source color temperature， and light-source drift. Their influences were analyzed at both the spectral and modeling levels. In particular， the relationship between disturbance magnitude and unit differential absorbance at a single wavelength was examined to determine whether the induced spectral variation followed an approximately linear or nonlinear pattern. Two modeling approaches were compared： PLSR as a representative linear regression method and MLP as a representative nonlinear learning method. Models were trained to predict glucose concentration under different disturbance scenarios， and performance was evaluated mainly by the root mean square error of prediction （RMSEP）. Based on spectral response patterns and error trends， measurement conditions were classified into “allowable variations，” referring to factors that could vary over a broad range while maintaining low prediction error， and “non-allowable variations，” referring to factors requiring strict control. To verify whether the simulation-based conclusions applied to physiological measurements， human OGTT experiments were also performed. NIR spectra were collected under temperature-controlled and non-temperature-controlled conditions. In the controlled condition， the skin was heated to approximately 36 °C and maintained near this level during measurement. Fingertip blood glucose values were collected intermittently as references. The correlation between spectral signals and blood glucose changes was analyzed under both conditions， with particular attention to features near 1550 nm. Model development included within-group modeling， combined dataset modeling， and leave-one-subject-out cross-validation （LOSOCV）.The simulation results showed that different disturbances had markedly different effects on spectral prediction. Liquid temperature variation and white noise exhibited clear nonlinear characteristics at the single-wavelength level： the relationship between disturbance amplitude and unit differential absorbance was non-proportional， indicating that these factors changed spectral response in a way that could not be adequately represented by simple linear correction. Such nonlinear disturbances altered both the spectral baseline and absorption peak distribution， making it difficult for the models to separate glucose-concentration information from interference. As the amplitudes of temperature fluctuation and noise increased， prediction errors rose substantially. These two factors were therefore identified as “non-allowable variations.” Only when temperature fluctuation was limited to no more than 1 °C and white noise was restricted to no more than 0.002 a.u. could RMSEP remain stably around 0.2 mmol/L. In contrast， incident angle variation， light-source color temperature， and light-source drift showed approximately linear effects within the investigated ranges. The induced spectral changes followed stable patterns， allowing the models to recover glucose-related information with higher robustness. Even when these factors varied over a larger range， prediction errors could still be maintained below about 0.2 mmol/L. These factors were therefore classified as “allowable variations.” Comparison of the two modeling methods showed that MLP had a slight advantage over PLSR when only a limited nonlinear temperature disturbance was present. However， once temperature fluctuation became larger， the errors of both models increased significantly， indicating that nonlinear modeling cannot replace physical control of critical measurement conditions. The human OGTT experiments further supported the simulation findings. Compared with the non-controlled condition， the temperature-controlled condition produced a stronger correlation between NIR spectra and blood glucose changes， with more prominent sensitivity near 1550 nm. Model performance also improved under this condition. In LOSOCV， the MLP model achieved an RMSEP of 1.33 mmol/L in the temperature-controlled group， whereas the corresponding RMSEP in the non-controlled group was 1.63 mmol/L. Similar trends were observed in within-group and combined dataset modeling.This study demonstrates that measurement-condition stability is a key determinant of accurate glucose concentration prediction using NIR spectroscopy. The combined evidence from simulation and human OGTT experiments indicates that nonlinear disturbances are the main factors limiting predictive performance. Temperature variation and white noise were identified as critical “non-allowable variations” because their effects were strongly nonlinear and could not be sufficiently compensated for by either PLSR or MLP. By contrast， incident angle variation， light-source color temperature， and light-source drift behaved approximately linearly and could therefore be tolerated within a wider range as “allowable variations”. Although MLP showed a limited advantage over PLSR under nonlinear interference， model optimization alone was insufficient when disturbance amplitude became large. Human experiments further confirmed that proper temperature control strengthened the spectral correlation with blood glucose and improved prediction under cross-subject validation. Overall， effective physical control of key nonlinear factors is a prerequisite for high-precision prediction.

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