Near infrared (NIR) assignment of Isopsoralen was performed using deuterated chloroform solvent and two-dimensional correlation spectroscopy (2D-COS) technology. Yunkang Oral Liquid was applied to study Isopsoralen, the characteristic bands by spectral assignment as well as the bands by interval partial least squares (iPLS) and synergy interval partial least squares (siPLS) were used to establish partial least squares (PLS) model. The coefficient of determination in calibration (R2cal) were 0.9987, 0.9970 and 0.9982. The coefficient of determination in cross validation (R2val) were 0.9985, 0.9921 and 0.9982. The coefficient of determination in prediction(R2pre) were 0.9987, 0.9955 and 0.9988. The root mean square error of calibration (RMSEC) were 0.27, 0.40 and 0.31 ppm. The root mean square error of cross validation (RMSECV) were 0.30, 0.67 and 0.32 ppm. The root mean square error of prediction (RMSEP) were 0.23, 0.43 and 0.22 ppm. The residual predictive deviation (RPD) were 31.00, 16.58 and 32.41. It turned out that the characteristic bands by spectral assignment had the same results with the chemometrics methods in PLS model. It provided guidance for NIR spectral assignment of chemical compositions in Chinese Materia Medica (CMM).

以氘代氯仿为溶剂,以中药化学标准品厚朴酚为载体,采用二维相关光谱(Tow-dimensional correlation spectroscopy,2D-COS)技术,采集近红外光谱(near infrared spectroscopy,NIR),由氘代氯仿纯溶剂与厚朴酚原始光谱的二维相关同步谱可知,厚朴酚在1 365~1　455,1　600~1　720,2　000~2　181和2　275~2　465 nm处有特征吸收,其中1　440 nm为酚基O—H伸缩振动基频的一级倍频谱带,1 679 nm为芳基C—H及与芳基相连的甲基C—H伸缩振动一级倍频谱带,2 117,2 304,2 339和2 370 nm为芳基C—H伸缩振动、弯曲振动和变形振动的组合频,2 445 nm为芳基相连的甲基C—H弯曲振动基频二级倍频谱带,这些波段为厚朴酚的特征归属.以藿香正气口服液复杂体系为载体,以厚朴酚光谱解析的特征波段与间隔偏最小二乘(interval partial least squares,iPLS)和组合间隔偏最小二乘(synergy interval partial least squares,SiPLS)筛选的特征波段分别建立偏最小二乘(partial least squares,PLS)定量模型,模型的决定系数R2cal 和R2pre均大于0.99,校正均方根误差(root mean of square error of calibration,RMSEC),交叉验证均方根误差(root mean of square error of cross validation,RMSECV)和预测均方根误差(root mean of square error of prediction,RMSEP)均较小.结果表明,2D-COS技术解析厚朴酚所得波段建立的定量模型与iPLS和SiPLS波段筛选的模型均相对稳定,这使定量模型的波段选择更具有解释性.该研究为中药化学成分NIR光谱解析特征波段的归属提供方法参考,同时为NIR建模波段筛选提供借鉴和指导.

近红外光谱(near infrared spectroscopy, NIR)作为目前中药关键质量属性快速评价热门技术, 广泛应用于提取、 浓缩、 醇沉、 纯化等中药生产过程质量控制领域。 由于谱带严重重叠, 谱峰较宽, 吸收信号弱等特征, NIR需采用化学计量学建立模型。当前, 光谱解析是NIR分析的研究热点, 结合作者的研究成果, 率先综述国内外NIR光谱解析的研究进展及主要方法。 其中包括: 物质吸收的光谱差异对特征信号提取的主成分分析法; 光谱波段筛选, 寻找物质特征吸收波段, 解析物质结构与特征波段关系的偏最小二乘法; 针对扰动(温度、 浓度、 压力等)引起的光谱变化, 通过同步、 异步光谱相关峰对光谱进行分析, 解析引起光谱变化的特征基团的二维相关光谱法; 用量子力学方法对物质结构的能量变化进行计算, 解析分子能量变化与光谱变化关系的密度泛函理论法。 最后, 结合实例, 考虑中药组分复杂性特点, 从单一药效指标成分出发, 采用氘代DMSO技术指认不同浓度的绿原酸NIR吸收特征, 验证金银花提取过程中在线NIR模型绿原酸成分变量筛选的可靠性, 阐明间隔偏最小二乘法筛选的特征波段与绿原酸光谱特征吸收的一致性, 建立了一种中药NIR关键质量属性的光谱解析策略, 为中药NIR关键质量属性快速准确评价提供支撑。