DOSC—SBC在近红外定量模型批次间传递中的应用

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[摘要] 模型傳递可使特定条件下建立的近红外模型能够应用于新的样品状态、环境条件或仪器状态。正交信号回归是一类基于“光谱背景校正”的模型传递方法，利用虚拟标准光谱拟合主从批次光谱间的线性关系，将从批次光谱向主批次光谱映射，以实现近红外定量模型的传递，但该方法对虚拟光谱的代表性要求较高，回归过程中易出现较大偏差。因此，该文提出一种直接正交信号校正法（direct orthogonal signal correction，DOSC）联合斜率截距校正算法（slope and bias correction，SBC）（DOSC-SBC）的数据处理方法，针对近红外定量模型对不同批次样本制剂过程中目标成分含量预测准确度较差的问题，分析不同批次样本间因组分差异带来的光谱背景差异和模型预测误差的性质，通过DOSC消除与目标值无关的光谱背景差异，联合SBC算法对不同批次间样本批次间系统误差进行校正，实现近红外定量模型在不同批次间传递。该研究将DOSC-SBC应用于金银花水提和醇沉制剂过程中，模型对新批次样本的预测误差由32.3%，237%降低到7.30%，4.34%，预测准确度显著提高，实现了制剂过程中新批次样本目标成分的快速定量。DOSC-SBC模型传递方法实现了近红外定量模型在不同批次间传递，且该方法不需要标准样品，有利于促进近红外技术在中药制剂过程的应用，为中药生产过程中有效成分的实时监测提供参考。

[关键词] DOSC-SBC； 近红外定量分析； 模型传递； 中药质量控制

[Abstract] 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 uelated 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.