Jia Yi-Fei, Zhang Ying-Ying, Xu Bing, Wang An-Dong, Zhan Xue-Yan
Beijing University of Chinese Medicine, Beijing 100102, China.
Zhongguo Zhong Yao Za Zhi. 2017 Jun;42(12):2298-2304. doi: 10.19540/j.cnki.cjcmm.20170710.001.
Near infrared model established under a certain condition can be applied to the new samples status, environmental conditions or instrument status through the model transfer. Spectral background correction and model update are two types of data process methods of NIR quantitative model transfer, and orthogonal signal regression (OSR) is a method based on spectra background correction, in which virtual standard spectra is used to fit a linear relation between master batches spectra and slave batches spectra, and map the slave batches spectra to the master batch spectra to realize the transfer of near infrared quantitative model. However, the above data processing method requires the represent activeness of the virtual standard spectra, otherwise the big error will occur in the process of regression. Therefore, direct orthogonal signal correction-slope and bias correction (DOSC-SBC) method was proposed in this paper to solve the problem of PLS model's failure to predict accurately the content of target components in the formula of different batches, analyze the difference between the spectra background of the samples from different sources and the prediction error of PLS models. DOSC method was used to eliminate the difference of spectral background unrelated to target value, and after being combined with SBC method, the system errors between the different batches of samples were corrected to make the NIR quantitative model transferred between different batches. After DOSC-SBC method was used in the preparation process of water extraction and ethanol precipitation of Lonicerae Japonicae Flos in this paper, the prediction error of new batches of samples was decreased to 7.30% from 32.3% and to 4.34% from 237%, with significantly improved prediction accuracy, so that the target component in the new batch samples can be quickly quantified. DOSC-SBC model transfer method has realized the transfer of NIR quantitative model between different batches, and this method does not need the standard samples. It is helpful to promote the application of NIR technology in the preparation process of Chinese medicines, and provides references for real-time monitoring of effective components in the preparation process of Chinese medicines.
在一定条件下建立的近红外模型可通过模型传递应用于新样品状态、环境条件或仪器状态。光谱背景校正和模型更新是近红外定量模型传递的两种数据处理方法，正交信号回归（OSR）是一种基于光谱背景校正的方法，其中使用虚拟标准光谱拟合主批次光谱与从批次光谱之间的线性关系，并将从批次光谱映射到主批次光谱以实现近红外定量模型的传递。然而，上述数据处理方法要求虚拟标准光谱具有代表性，否则在回归过程中会出现较大误差。因此，本文提出直接正交信号校正-斜率和偏差校正（DOSC-SBC）方法，以解决不同批次配方中PLS模型无法准确预测目标成分含量的问题，分析不同来源样品光谱背景差异及PLS模型预测误差。采用DOSC方法消除与目标值无关的光谱背景差异，与SBC方法结合后，校正不同批次样品间的系统误差，使近红外定量模型在不同批次间传递。本文将DOSC-SBC方法应用于金银花水提取醇沉制备过程中，新批次样品预测误差由32.3%降至7.30%，由237%降至4.34%，预测精度显著提高，可快速定量新批次样品中的目标成分。DOSC-SBC模型传递方法实现了近红外定量模型在不同批次间的传递，且该方法无需标准样品。有助于推动近红外技术在中药制备过程中的应用，为中药制备过程中有效成分的实时监测提供参考。
