积分光谱与近红外光谱分析精度关系的研究

Study on the Relation Between Integration Spectrum and Accuracy of Near Infrared Spectroscopic Analysis

以MPA型傅里叶近红外光谱仪和WQF-400N型傅里叶近红外光谱仪上检测的奶粉光谱为原始光谱,利用积分法选择不同的积分区间对原始光谱进行积分,模拟不同分辨率下的光谱。采用相同的预处理方法——Savitzky-Golay平滑和一阶微分,利用偏最小二乘(PLS)回归对奶粉中的脂肪含量建立模型。分别比较两台仪器所建模型的相关系数(R),预测均方差(fRMSEP),平均相对误差(E)三个预测参数,得到两台仪器分别在分辨率为16 cm-1和64 cm-1时建模效果最好。研究结果表明,利用积分光谱,能起到压缩数据,提高模型预测精度的作用;同时说明对于具体样品的特定指标要选择合适的分辨率,在减少工作量的同时获得最佳的定量分析结果,而单纯的追求高分辨率是没有意义的。

The raw spectra of milk powder were detected by means of two Fourier near-infrared spectrometers MPA type and WQF-400N type. Primitive spectra were integrated in different integral intervals to simulate spectra with different resolutions. After being pre-treated with the same methods （Savitzky-Golay smoothing and the first derivative）, models for fat content in milk powder were established through partial least square regression （PLS）. Comparisons on correlation coefficient R, root mean square error of prediction fRMSEP and mean bias E between the models reveal that MPA type and WQF-400N type spectrometers have the best models at the resolutions of 16 cm 1 and 64 cm t respectively. These results show that integral spectrum could compress data and enhance the model＇s prediction accuracy, and that an adequate resolution should be selected for special characters of different samples. Both of them ensure the best quantitative analytical result while reducing the work load. It is of no significance to simply pursue higher and higher resolution.