RESUMO
OBJECTIVE： To establish the method for the rapidly non-destructive quality control of Liuwei dihuang capsule. METHODS： AOTF-NIR spectrometry was adopted. Taking 80 batches of Liuwei dihuang capsule produced by a manufacturer in recent three years as samples， HPLC chromatogram was adopted to determine the contents of loganin， morroniside， paeonol， paeoniflorin and ursolic acid； the content of water was determined according to general principles stated in 2015 edition of Chinese Pharmacopeia （part Ⅰ）. Taking 70 batches of samples as correction set， the partial least square method and the cross-validation algorithm were used to establish the NIR quantitative model of 6 indexes in Liuwei dihuang capsules with the Unscrambler quantitative analysis software. Taking residual 10 batches of samples as validation set， external validation was conducted for the model. RESULTS： The correlation coefficients （R2） of internal and external validation of loganin， morroniside， paeonol， paeoniflorin， the content of water quantitative model were all greater than 0.9； the correction of standand deviation （RMSEC） were 0.372 8， 0.025 4， 0.263 3， 0.288 5， 0.186 7 and 0.037 7； the prediction of standard deviation （RMSEP） were 0.462 2， 0.077 5， 0.472 1， 0.634 9， 0.293 4 and 0.206 9； the external verification showed that mean deviations of preclicted value to actual value were 6.04％， 6.05％， 5.87％， 6.97％， 5.62％ and 4.83％， with the mean deviation less than 10％.CONCLUSIONS：The established method can achieve rapidly non-destructive analysis Liuwei dihuang capsule.
RESUMO
OBJECTIVE:To establish the method for rapid judgement of blending endpoint of Jingqi shuangshen capsules and content determination of astragaloside Ⅳ. METHODS:AOTF-NIR combined with principal component analysis and Moving Block Standard Deviation method was used to identify the blending endpoint. First derivative combined with savitzky-golay filter method were used to spectrum pretreatment. The partial least square method was used to establish quantitative analysis model of the content of astragaloside Ⅳin mixed endpoint sample. The content of astragaloside Ⅳ in mixed endpoint sample was determined by HPLC-ELSD to validate the model. RESULTS:Methodology validation of content determination of astragaloside Ⅳ in mixed material sample and mixed endpoint sample was in line with the requirements. NIR monitoring results showed that the product reached the blending endpoint after 30 min. The results of NIR monitoring were generally consistent with the results of HPLC-ELSD. The principal component dimension of the quantitative model was 9;determination coefficients was 0.954 9;Root Mean Square of Calibration of the model was 0.039 2;Root Mean Square Error of Prediction of the model was 0.042 6. Predicted average value of astragaloside Ⅳ by NIR was 11.74 mg/g,and measured average value of astragaloside Ⅳ by HPLC-ELSD was 11.38 mg/g;average deviation was 3.16%. CONCLUSIONS:AOTF-NIR can rapidly judge the blending endpoint sample of Jingqi shuangshen capsules,rapidly determine the content of astragalosideⅣin mixed endpoint material,improve the quality control level of blending process and shorten blending cycle.
RESUMO
This study presents a systematized method for predicting water content, fat content and free acidity in olive fruits by on-line NIR Spectroscopy combined with chemometric techniques (PCA, LDA and PLSR). Three cultivar varieties of Olea europaea - Hojiblanca cv., Picual cv. and Arbequina cv. - were monitored. Five olive cultivation areas of Southern Spain (Andalucia) and Southern Portugal (Alentejo) were studied in 2011 and 2012. 465 olive samples were collected during the ripening process (non-mature olives) and compared with other 203 samples of mature olives collected at the final ripening stage. NIR spectra were measured directly in the olive fruits in the wavelength region from 1000 to 2300 nm in reflectance mode. The reference analyses were performed on the olive paste by oven drying for the moisture, by mini-Soxhlet extraction for the fat content and by acid titration of the oil extracted from the olive paste. Calibrations and predictive models were developed by Partial Least Square Regression (PLSR) previous Principal Component and Linear Discriminant analyses (PCA and LDA) were employed as exploratory and clean-up tools of data sets. The final models obtained for the total samples showed acceptable statistics of prediction with R(2)=0.88, RMSEV%=4.88 and RMSEP%=4.98 for water content, R(2)=0.76, RMSECV%=19.5 and RMSEP%=20.0 for fat content and R(2)=0.83, RMSECV%=36.8 and RMSEP%=38.8 for free acidity. Regression coefficients were better for only one maturity state (ripe period) than for olive fruit with different composition (ripening period). All models obtained were applied to predict LQPs on a new set of samples with satisfactory results, a good prediction potential of the models.
