Near-infrared chemical imaging for quantitative analysis of chlorpheniramine maleate and distribution homogeneity assessment in pharmaceutical formulations

Near infrared chemical imaging(NIR-CI)combines conventional near infrared(NIR)spectros-copy with chemical imaging,thus provides spectral and spatial information simult aneously.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 R^(2)_(val)0.981,RMSEC 0.384%,RMSECV 0.483%,RMSEP 0.631%,indicating that this model was reliable.

Rapid analysis of dyed safflowers by color objectification and pattern recognition methods

Objective:Rapid discrimination of three classes of safflowers,dyed safflower,adulterated safflower,and pure safflower using computer vision and image processing algorithms.Methods:A low cost computer vision system(CVS)was designed to measure the color of safflowers in the RGB(red,green,blue),L^*a^*b^*,and HSV(hue,saturation,vale)color spaces.The color moments in these three color spaces were extracted from the acquired images as color features of safflower.In addition,five kinds of pigments that are commonly used to dye safflowers were identified by high-performance liquid chromatography as a reference.Pattern recognition methods were investigated for rapid discrimination,including an unsupervised principal component analysis(PCA)algorithm and a supervised partial least squares discriminant analysis(PLS-DA)algorithm.Results:The mean error(e)between color values measured with the colorimeter and calculated with the CVS was 2.4%,with a high correlation coefficient(r)of 0.9905.This result indicated that the established CVS was reliable for color estimation of safflowers.The PLS-DA model,which had a total accuracy of 91.89%,outperformed the PCA model in classifying pure,adulterated,and dyed safflowers.Conclusion:The color objectification is a promising tool for rapid identification of dyed and adulterated safflowers.

NIR assignment of isopsoralen by 2D-COS technology and model application in Yunkang Oral Liquid

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^(2)_(cal))were 0.9987,0.9970 and 0.9982.The coefficient of determination in cross validation(R^(2)_(val)) T were 0.9985,0.9921 and 0.9982.The coe±cient of determination in prediction(R^(2)_(pre)) T 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).

Color spaces of safflower (Carthamus tinctorius L.) for quality assessment

Objective:In this study,safflower(Carthamus tinctorius L.)was taken as a representative example to examine the application of color characteristics to evaluate quality.Methods:A computer vision system was established for the objective and nondestructive assessment of color using image processing algorithms.Color parameters were investigated based on the RGB,L*a*b and HSV color spaces.The content of hydroxysafflor yellow A(HSYA),a major bioactive constituent of safflower,was determined by high-performance liquid chromatography.The relationship between HSYA content and color values was investigated by Pearson correlation analysis.A multiple linear regression model was established to predict the HSYA content from color values.Results:The red color and lightness of safflower were found to be significantly related to HSYA content.The prediction equation obtained by multiple regression was reliable with an R2 value of 0.805(P<.01).Conclusion:The results suggest that the computer vision technique could be used as a promising and non-destructive technology for color measurement and quality evaluation of CHM.

Estimation of multivariate detection limits of four quality parameters in licorice using MEMS–NIR spectrometry coupled with two sampling accessories

In this work,multivariate detection limits(MDL)estimator was obtained based on the microelectro-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 di®erent 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¯ber 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.