用红外光谱结合导数分析，提升血糖预测精度
Derivative-Based Mir Spectroscopy for Blood Glucose Estimation Using Pca-Driven Regression Models
- 提出两种导数检测方法，融合吸光度与关键点信息
- SVR模型下r2提升达27%，岭回归最高提升36%
- 适合糖尿病监测、光谱数据分析等场景
本研究提出两种创新方法——阈值导数法（TBD）和自适应导数峰检测法（ADPD），用于提升基于中红外（MIR）光谱的血糖估计学习模型精度。通过整合吸光度数据及其导数中的关键点信息，显著增强模型表现。血液样本采用傅里叶变换红外（FTIR）光谱技术采集，并经过先进预处理。采用岭回归与支持向量回归（SVR）模型，以留一法交叉验证。结果表明，TBD与ADPD显著优于传统方法：在SVR模型中，TBD使r2提升约27%，ADPD提升约10%；岭回归模型中提升幅度在24%至36%之间。此外，两种方法误差率更低，临床准确性经Clarke与Parkes误差网格分析验证。
原文摘要 · Abstract (English)
In this study, we presented two innovative methods, which are Threshold-Based Derivative (TBD) and Adaptive Derivative Peak Detection(ADPD), that enhance the accuracy of Learning models for blood glucose estimation using Mid-Infrared (MIR) spectroscopy. In these presented methods, we have enhanced the model's accuracy by integrating absorbance data and its differentiation with critical points. Blood samples were characterized with Fourier Transform Infrared (FTIR) spectroscopy and advanced preprocessing steps. The learning models were Ridge Regression and Support Vector Regression(SVR) using Leave-One-out Cross-Validation. Results exhibited that TBD and ADPD significantly outperform basic used methods. For SVR, the TBD increased the r2 score by around 27%, and ADPD increased it by around 10%. these Ridge Regression values were between 36% and 24%. In addition, Results demonstrate that TBD and ADPD significantly outperform conventional methods, achieving lower error rates and improved clinical accuracy, validated through Clarke and Parkes Error Grid Analysis.
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