为实现人体血液甘油三酯(TG)含量无创检测, 应用近红外光谱技术(NIRS), 对单体TG进行定量分析。 通过离体实验优选检测波段(5 700～5 600和4 600～4 400 cm-1), 优化设计检测探头, 综合预处理方法等手段, 对其中TG含量进行定标和预测。 以期提升单体TG无创检测精度及稳定性。 无创采集54组单体光谱数据, 并对其中TG含量进行定标预测, 经对比分析确定平滑滤波(SG)结合偏最小二乘方法(PLS)的定标模型稳健性最优, 对预测集1, 2样品最佳分析结果: 预测标准偏差RMSEP分别为12和12.8 mg·dL-1, 相对预测标准偏差RSD为16.25%和17.33%, 预测精度理想, 基本可用于单人TG的日常监测。 鉴于SG-PLS模型在单体TG无创测量及日变化趋势预测方面的良好表现, NIRS分析技术将在TG无创检测与日常管理领域有更为广泛的应用前景。

To facilitate noninvasive detection of human triglyceride (TG) in blood, near infrared spectroscopy (NIRS) was applied to quantitatively analyze the TG level of single person. By optimizing the proper NIRS detection band (5 700～5 600 and 4 600～4 400 cm-1) in in-vitro experiment, preferably designing the fiber probe, and comparatively evaluating several preprocessing algorithms, we intend to further promote the non-invasive detection accuracy and stability of single-person TG level. Then we noninvasively collected 54 samples of spectral data from the same volunteer and made quantitative analysis for TG level. Savitzky-Golay (SG) combined with partial least squares (PLS) was confirmed to be the most robustness calibration model. The optimal analysis of the predictive 1 and 2 sets is as follows. The standard deviation (RMSEP) was 12 and 12.8 mg·dL-1, respectively; the relative standard deviation (RSD) was 16.25% and 17.33%, respectively. The prediction accuracy was ideal and able to be used for routine monitoring of single TG. In view of the well performance of SG-PLS model in non-invasive detection of single-person TG level and the trend of daily variation, NIRS analysis technology has potential for human TG non-invasive detection and daily management.