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In order to realize the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, this paper first prepared the sulphur-fumigated Achyranthis Bidentatae Radix samples with the usage amount of sulphur being 0, 2.5%, and 5% of the mass of Achyranthis Bidentatae Radix pieces. The SO_2 content in different batches of sulphur-fumigated Achyranthis Bidentatae Radix was determined using the method in Chinese Pharmacopoeia, followed by the acquisition of their hyperspectral data within both visible-near infrared(435-1 042 nm) and short-wave infrared(898-1 751 nm) regions by hyperspectral imaging. Meanwhile, the first derivative, AUTO, multiplicative scatter correction, Savitzky-Golay(SG) smoothing, and standard normal variable transformation algorithms were used to pre-process the original hyperspectral data, which were then subjected to characteristic band extraction based on competitive adaptive reweighted sampling(CARS) and the partial least square regression analysis for building a quantitative model of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix. It was found that the accuracy of the quantitative model built depending on the visible-near infrared spectra was high, with the determination coefficient of prediction set(R■) reaching 0.900 1. The established quantitative model has enabled the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, which can serve as an effective supplement to the method described in Chinese Pharmacopeia.
Assuntos
Imageamento Hiperespectral , Análise dos Mínimos Quadrados , Raízes de Plantas , EnxofreRESUMO
Objective To establish an online quantitative analysis model for moisture content assay of hydroxychloroquine sulfate particles by near infrared (NIR) spectroscopy. Methods The NIR spectra were collected in real time when the material particles were dried in the fluidized bed. Meanwhile the water content of the particles was measured with the standard moisture tester. The multiplicative signal correction (MSC) and first derivative followed by Karl Norris smoothing were used for spectra pretreatment. Two spectral range (4 935−5 336 cm−1 and 6 911−7 297 cm−1) were selected for the quantitative model with the partial least squares (PLS) regression. Results The quantitative calibration model had good correlation coefficients with Rc value=0.952 9 and Rp value=0.936 6. The root mean square error of calibration (RMSEC) was 0.408 and the root mean square error of prediction error (RMSEP) was 0.435. The ratio of standard deviation of validation set to prediction standard deviation (RPD) was 5.18. There was no significant difference between the predicted value and the reference value by t test when the established model was applied in large-scale production. Conclusion The online model established for monitoring water content has high accuracy and stability, which can be applied in industrial scale process to monitor the particle moisture in real time.
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Objective To establish a fast detection method of sodium aescinate by using near infrared (NIR) spectroscopy analysis method for the determination of the content of sodium aescinate for injection. Methods OPUS software was used to optimize the collected spectrum. PLS algorithm and factorization algorithm were used to establish quantitative model and qualitative model. Results The correlation coefficient of the quantitative model reached 0.9926, the RMSECV deviation was 0.253. The deviation between the predicted value of the sample and the true measured value was less than 5%, which could accurately predict the content of sodium aescinate. Conclution The qualitative model can effectively distinguish the samples of other varieties that have not participated in the modeling, and provide a reference for the rapid screening of the drug.
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Objective: To establish a better near infrared quantitative model for quality control of Glycyrrhizae Radix et Rhizoma of components (moisture,total ash,liquiritin and glycyrrhizic acid) in liquorice,in order to realize rapid detection.Method: The contents of moisture,total ash,liquiritin and glycyrrhizic acid were determined in 97 samples based on the methods set forth in Chinese Pharmacopoeia.Meanwhile,the near infrared spectrum was scanned using near infrared spectroscope.R software was used to screen out the spectral pretreatment and build the quantitative models.Result: The optimum spectral pretreatment method for establishing the near infrared quantitative model of moisture and liquiritin was the first order derivative.For moisture,the correlation coefficients of test and validation were 0.930 0 and 0.929 9,and the root mean square errors were 0.243 2 and 0.203 8,respectively.For liquiritin,the correlation coefficients of test and validation were 0.930 3 and 0.907 6,and the root mean square errors were 0.093 9 and 0.128 9,respectively.The optimum spectral pretreatment method for establishing the near infrared quantitative model of total ash was MSC.The correlation coefficients of test and validation were 0.926 5 and 0.917 7,and the root mean square errors were 0.109 6 and 0.103 7,respectively.The optimum spectral pretreatment method for establishing the near infrared quantitative model of glycyrrhizic acid was SNV.The correlation coefficients of test and validation were 0.918 1 and 0.915 7,and the root mean square errors were 0.274 8 and 0.236 0,respectively.Conclusion: In this study,a better near infrared quantitative models for quality control of components of Glycyrrhizae Radix et Rhizoma were established,with a high accuracy,which laid a foundation for rapid detection of the components in Glycyrrhizae Radix et Rhizoma.
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Objective:To quantitatively analyze the water content in Pinellia ternata by near-infrared spectroscopy to plant seed-ling cultivation and determine the quality of Pinellia ternata.Methods: Near-infrared spectroscopy was used to establish the model , and the moisture was measured by the drying method .The quantitative model was established by partial least squares method .Results:The quantitative model was established with the correlation coefficient of 0.9903 and the RMSECV of 0.205.The internal cross valida-tion and the external verification were both less than 1.0％.Conclusion:The established model can accurately predict the content of water in Pinellia ternata, which can be used for planting seedling cultivation , medicine storage management and quality control .
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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.
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Objective: To established a near-infrared spectroscopy quantitative model for the rapid determination of volatile oils from Rhizoma wenyujin concisum. Methods:Firstly, the volatile oils from Rhizoma wenyujin was determined by the distillation method described in Chinese Pharmacopoeia. The quantitative calibration model was established and optimized by fourier transformation near-infrared spectroscopy ( FT-NIR) combined with partial least square ( PLS) regression. The calibration model was evaluated by the coef-ficient (r), root-mean-square error of calibration (RMSEC) and root mean square of cross-validation (RMSECV) of the calibration model as well as the root mean square of prediction ( RMSEP) of prediction model. Results: In the combination of FT-NIR and PLS regression, the spectrum of 7189-4227 cm-1 , 8813-7478 cm-1 and"second spectrum+MSC" were chosen to establishe and optimize the quantitative calibration model. For the quantitative calibration model, the r, RMSEC and RMSECV of volatile oils was 0. 9769, 0. 0907 and 0. 3773, respectively. For the prediction model, the r and RMSEP of volatile oils was 0. 9053 and 0. 1960, respective-ly. Conclusion:The established near-infrared spectroscopy quantitative model is relatively stable, accurate and reliable in the simulta-neous quantitative analysis of volatile oils, and is expected to be used for the rapid determination of volatile oils from Rhizoma wenyujin concisum.
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Objective: To established a near-infrared spectroscopy quantitative model for the rapid determination of volatile oils from Rhizoma wenyujin concisum. Methods:Firstly, the volatile oils from Rhizoma wenyujin was determined by the distillation method described in Chinese Pharmacopoeia. The quantitative calibration model was established and optimized by fourier transformation near-infrared spectroscopy ( FT-NIR) combined with partial least square ( PLS) regression. The calibration model was evaluated by the coef-ficient (r), root-mean-square error of calibration (RMSEC) and root mean square of cross-validation (RMSECV) of the calibration model as well as the root mean square of prediction ( RMSEP) of prediction model. Results: In the combination of FT-NIR and PLS regression, the spectrum of 7189-4227 cm-1 , 8813-7478 cm-1 and"second spectrum+MSC" were chosen to establishe and optimize the quantitative calibration model. For the quantitative calibration model, the r, RMSEC and RMSECV of volatile oils was 0. 9769, 0. 0907 and 0. 3773, respectively. For the prediction model, the r and RMSEP of volatile oils was 0. 9053 and 0. 1960, respective-ly. Conclusion:The established near-infrared spectroscopy quantitative model is relatively stable, accurate and reliable in the simulta-neous quantitative analysis of volatile oils, and is expected to be used for the rapid determination of volatile oils from Rhizoma wenyujin concisum.
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Objective: To develop a model for rapid and non-destructive determination of amoxilcillin sodium and sulbactam sodium for injection using the analysis of near infrared diffuse reflectance spectroscopy (NIR) and chemometrics.Methods: Totally 41 batches of commercial samples and 20 batches of laboratory samples were analyzed by NIR and the legal methods.The first derivative and vector normalization were selected as the preprocessing methods and 8 720-5 446 cm-1 was selected as the frequency range.Results: The quantitative model was constructed based on 16 batches of commercial samples and 15 batches of laboratory samples (0.75 g) and the content ranged from 4.45% to 61.82% for amoxilcillin and 15.75% to 30.25% for sulbactam.The root mean square errors of cross validation (RMSECV), determination coefficients (R 2) and root mean square errors of prediction (RMSEP) respectively was 0.858 , 0.998 1 and 0.936 for amoxilcillin, and respectively was 0.541 , 0.988 1 and 0.423 for sulbactam.The model was tested based on 5 batches of commercial samples and 5 batches of laboratory samples (0.75 g) and the results well met with those of the legal methods with difference ≤ 1.5 %.The model also applied in 10 batches of commercial samples (1.5 g) and 2 batches of samples from the other manufacturers.Conclusion: The non-destructive quantitative NIR methods are accurate with good reproducibility, and applicable for the rapid analysis of amoxilcillin sodium and sulbactam sodium for injection.
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Objective: To quantitatively analyze the content of glucose in glucose injection by near infrared spectroscopy to control the quality of the product.Methods: A quantitative model was established by near infrared reflectance spectroscopy and the injection came from pharmaceutical enterprises with different concentrations of glucose and the solution samples came from laboratories with different concentrations of glucose.The liquid sample accessories were selected, a quantitative model was established by a partial least squares method, and the effect of environmental temperature on the model was studied as well.Results: According to the quantitative model, the correlation coefficient reached up to 0.999 3, and RMS deviation (RMSECV) was 0.077 4.In the verification test, the results of various liquid formulas containing glucose were similar to those of the laboratory results.The prediction error was less than 5% within the temperature range of 20-35℃.Conclusion: The model of near-infrared partial least squares (PLS) can accurately predict the content of glucose in glucose products without sodium chloride, and can be used for the quality control of glucose intermediates and the finished products in the large infusion manufacturers
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Objective:To develop a method of near infrared reflectance spectroscopy ( NIRS) for the rapid determination of peflox-acin mesylate for injection .Methods:The quantitative models were established by the collection of NIR spectra of pefloxacin mesylate for injection.The spectra were pretreated with the methods of vector normalization , and the spectral ranges of 9 176.2-8 169.5 cm-1 , 6 051.9-5 716.3 cm-1 and 4 509-3 999.9 cm-1 were chosen.The partial least square (PLS) was used as the regression method .Re-sults:The prediction model was established by the internal cross validation ,and the concentration range was 7.55%-77.69%.The root mean square error of cross validation (RMSECV) was 1.61%, and the correlation coefficient was 0.992 4.Conclusion: The method of near infrared reflectance spectroscopy can be used for the rapid quantitative analysis of pefloxacin mesylate for injection .
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OBJECTIVE:To establish a method for the rapid identification of Sanjiu weitai granule and quantitative analysis of moisture. METHODS:Near-infrared (NIR) spectroscopy was adopted. 38 batches of Sanjiu weitai granule were collected,NIR spectrum was determined by integrating sphere diffuse-reflectance. Conformity test model was performed by comparing consistency index(CI)value and CI limit,and quantitative model of moisture was established using partial least squares(PLS)algorithm. RE-SULTS:When CI value was less than 6,the established model can accurately distinguish Sanjiu weitai granule,the model was proven feasible;the correlation coefficient (r2) of quantitative model was 97.44,the root mean square error of cross validation (RMSECV) was 0.453,the root mean square error of prediction (RMSEP) was 0.748. CONCLUSIONS:The method is simple and rapid without destroying,which can be applied to fast screening of Sanjiu weitai granule in site and fast determination of mois-ture content.
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To rapidly identify the compound Yiganling tablets from different manufacturers by near-infrared fibre-opti-cal spectroscopy and detect the content of silymarin. Methods:The ninety-three batches of compound Yiganling tablets from six manu-facturers were used as the analysis objects. Clustering analysis model of compound Yiganling tablets was established. The spectra were pretreated with the second derivative and vector normalization;the number of smoothing point was thirteen; the wavelength range was 9 000-4 100 cm-1;the distance between the spectra was Euclidean distance, the distance between the spectra and the class was the sum of square of the variance(Ward’s method). The quantitative model was established using the same samples. The spectra were pretreated with the first Derivative and vector normalization;the number of smoothing point was seventeen;the wavelength ranges were 9 100-7 300 cm-1 and 7 100-4 100 cm-1;the regression method was partial least squares ( PLS) algorithm. Results:The clustering model could identify the samples of training set, and validate the thirty spectra of test set accurately. For the quantitative model of the content of silymarin, the root mean square error of cross validation (RMSECV) was 0. 082 8 and the determination coefficient R2 was 97. 98%. The root mean square error of prediction (RMSEP) was 0. 044 1 and the correlation coefficient R2 was 98. 95%. Conclu-sion:The clustering analysis and the quantitative model are rapid and simple, accurate and reliable, and can be applied in fast drug a-nalysis.
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The near infrared ( NIR ) spectra of 76 batches of the amoxicillin capsules from different manufacturers and their corresponding content powder without capsules cell were used to find some quantitative indicators to evaluate whether the piecewise direct standardization ( PDS ) algorithm succeeded in NIR quantitative model updating. 54 batches were used to construct the NIR quantitative model for capsule preparation. All the NIR spectra of amoxicillin capsules were divided into five classes by cluster analysis, and each class can be regarded as a homology sample set. The average spectrum for each homology sample set was calculated. Ten to Fifteen spectra were selected from each homology sample set as the corresponding master spectra of the PDS algorithm to correct all the NIR spectra of the amoxicillin content powder respectively. Then the corrected spectra were predicted by the constructed NIR quantitative model for amoxicillin capsules. The prediction error for each corrected powder spectrum, and the correlation coefficient between each corrected powder spectrum and the average spectrum of the corresponding homolog sample set which the PDS master spectra came from, were calculated. Finally, the relationship between the prediction error and its corresponding correlation coefficient were studied. It was found that the correction results correlated closely with the selected master spectra set in PDS algorithm. The bigger the correlation coefficient ( r) , the better the correction results. In general, when r is less than 99%, it can be judged that the PDS correction is failed. At this condition, the prediction error is often more than 5%. Therefore, the correlation coefficient between the corrected spectrum and its corresponding average spectrum of the homology sample set can be used as an indicator to evaluate the efficiency of the PDS correction.
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A multiple regression analysis using ordinary least square (OLS) is frequently used for the projection of healt expenditure as well as for the identification of factors affecting health care costs. Data for the analysis often have mixed characteristics of time series and cross section. Parameters as a result of OLS estimation, in this case, are no longer the best linear unbiased estimators (BLUE) because the data do not satisfy basic assumptions of regression analysis. The study theoretically examined statistical problems induced when OLS estimation was applied with the time series cross section data. Then both the OLS regression and time series cross section regression (TSCS regression) were applied to the same empirical data. Finally, the difference in parameters between the two estimations were explained through residual analysis.