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Near infrared chemical imaging (NIR-CI) combines conventional near infrared (NIR) spectroscopy with chemical imaging, thus provides spectral and spatial information simultaneously. It could be utilized to visualize the spatial distribution of the ingredients in a sample. The data acquired using NIR-CI instrument are hyperspectral data cube (hypercube) containing thousands of spectra. Chemometric methodologies are necessary to transform spectral information into chemical information. Partial least squares (PLS) method was performed to extract chemical information of chlorpheniramine maleate in pharmaceutical formulations. A series of samples which consisted of different CPM concentrations (w/w) were compressed and hypercube data were measured. The spectra extracted from the hypercube were used to establish the PLS model of CPM. The results of the model were Rval2 0.981, RMSEC 0.384%, RMSECV 0.483%, RMSEP 0.631%, indicating that this model was reliable.

The present study aimed at investigating the relationship between tablet hardness and homogeneity of different Yinhuang dispersible tablets by near-infrared chemical imaging (NIR-CI) technology. The regularity of best hardness was founded between tablet hardness and the spatial distribution uniformity of Yinhuang dispersible tablets. The ingredients homogeneity of Yinhuang dispersible tablets could be spatially determined using basic analysis of correlation between analysis (BACRA) method and binary image. Then different hardnesses of Yinhuang dispersible tablets were measured. Finally, the regularity between tablet hardness and the spatial distribution uniformity of Yinhuang dispersible tablets was illuminated by quantifying the agglomerate of polyvinyl poly pyrrolidone (PVPP). The result demonstrated that the distribution of PVPP was unstable when the hardness was too large or too small, while the agglomerate of PVPP was smaller and more stable when the best tablet hardness was 75N. This paper provided a novel methodology for selecting the best hardness in the tabletting process of Chinese Medicine Tablet.

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 $(R_{\rm cal}^{2})$ were 0.9987, 0.9970 and 0.9982. The coefficient of determination in cross validation $(R_{\rm val}^{2})$ were 0.9985, 0.9921 and 0.9982. The coefficient of determination in prediction $(R_{\rm pre}^{2})$ 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).

In this work, multivariate detection limits (MDL) estimator was obtained based on the micro-electro-mechanical systems–near infrared (MEMS–NIR) technology coupled with two sampling accessories to assess the detection capability of four quality parameters (glycyrrhizic acid, liquiritin, liquiritigenin and isoliquiritin) in licorice from different geographical regions. 112 licorice samples were divided into two parts (calibration set and prediction set) using Kennard–Stone (KS) method. Four quality parameters were measured using high-performance liquid chromatography (HPLC) method according to Chinese pharmacopoeia and previous studies. The MEMS–NIR spectra were acquired from fiber optic probe (FOP) and integrating sphere, then the partial least squares (PLS) model was obtained using the optimum processing method. Chemometrics indicators have been utilized to assess the PLS model performance. Model assessment using chemometrics indicators is based on relative mean prediction error of all concentration levels, which indicated relatively low sensitivity for low-content analytes (below 1000 parts per million (ppm)). Therefore, MDL estimator was introduced with alpha error and beta error based on good prediction characteristic of low concentration levels. The result suggested that MEMS–NIR technology coupled with fiber optic probe (FOP) and integrating sphere was able to detect minor analytes. The result further demonstrated that integrating sphere mode (i.e., MDL0.05,0.05, 0.22%) was more robust than FOP mode (i.e., MDL0.05,0.05, 0.48%). In conclusion, this research proposed that MDL method was helpful to determine the detection capabilities of low-content analytes using MEMS–NIR technology and successful to compare two sampling accessories.