Classification of Chinese Soybean Paste by Fourier Transform Near-Infrared (FT-NIR) Spectroscopy and Different Supervised Pattern Recognition

Discovery and verification of Q-markers for promoting blood circulation and removing stasis of raw and wine-steamed Vaccaria segetalis based on pharmacological evaluation combined with chemometrics

ETHNOPHARMACOLOGICAL RELEVANCE

Dried and mature seeds of Vaccaria segetalis (Neck.) Garcke ex Asch. (VS) are known for their therapeutic effects, as they stimulate blood circulation, promote menstruation and diuresis and eliminate gonorrhoea. However, due to its hard shell, the dissolution of its active ingredients is often improved by steaming and frying in clinical applications. Among the processed products, wine-steamed Vaccaria segetalis (WVS) is one of the commonly used ones. Numerous historical records have shown that wine steaming can enhance the efficacy of drugs to promote blood circulation and remove blood stasis. However, the differences in the efficacy of VS and WVS in promoting blood circulation and removing blood stasis have not been thoroughly studied, and the possible reasons for these differences have not been reported.

AIM OF THE STUDY

The objective of this study was to identify quality markers (Q-markers) that could differentiate the efficacy of promoting blood circulation and removing blood stasis of VS and WVS, which could serve as a basis for the rational application of VS and WVS in clinical settings.

MATERIALS AND METHODS

A pharmacodynamic comparison between the water extracts of VS and WVS was carried out based on a mouse acute blood stasis model (ABS) and thrombus zebrafish model. The potential bioactive substances of WVS were screened by investigating the correlation between common peaks identified for 10 batches of WVS by ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS/MS) and their rate of thrombosis inhibition in zebrafish. Furthermore, multivariate statistical analysis of chemical components between VS and WVS was conducted to speculate the Q-markers combined with the results of the bioactive components. Based on the efficacy verification of Q-markers, the content of Q-markers in 10 batches of WVS was evaluated.

RESULTS

The results of efficacy comparison assays demonstrated that the efficacy of WVS was more prominent than VS at the same dose. Five components were screened as effective components of WVS for promoting blood circulation and removing blood stasis by correlation analysis. Furthermore, a total of 24 common ingredients were identified in VS and WVS extracts, and 9 of them showed increased dissolution rate after wine steaming, including 4 active ingredients, Hypaphorine, Vaccarin, Saponarin, and Isovitexin-2″-O-arabinoside, which were screened out by correlation analysis. The monomer test suggested that these 4 components could activate blood circulation and remove blood stasis in a dose-dependent manner. Consequently, Hypaphorine, Vaccarin, Saponarin, and Isovitexin-2″-O-arabinoside were selected as Q-markers to distinguish between VS and WVS. The content determination showed that the total contents of 4 Q-markers of WVS from 10 batches with different origins ranged from 0.478% to 0.716%.

CONCLUSIONS

This study compared the efficacy of VS and WVS in promoting blood circulation and resolving stasis and revealed Q-markers that reflected the difference in efficacy between them for the first time, which laid the foundation for establishing quality standards for WVS.

Rapid discrimination and screening of volatile markers for varietal recognition of Curcumae Radix using ATR-FTIR and HS-GC-MS combined with chemometrics

ETHNOPHARMACOLOGICAL RELEVANCE

Curcumae Radix (Yujin) has a long medicinal use history in China, which is used to cure diseases like jaundice, cholelithiasis caused by dampness-heat of gallbladder and liver, and so on. It comes from the dried tuberous roots of C. kwangsiensis (Guiyujin), C. longa (Huangyujin), C. phaeocaulis (Lvyujin) and C. wenyujin (Wenyujin). Though there are differences in chemical compositions and pharmacological activities among the four species of Yujin, they have not been differentiated well in clinical application due to their similar morphological characterizations.

AIM OF THE STUDY

In this study, the four species of Yujin were rapidly and accurately discriminated. The potential volatile markers for varietal recognition were identified.

MATERIALS AND METHODS

Attenuated total reflection fourier transformed infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to rapidly discriminate the four species of Yujin. Headspace-gas chromatography-mass spectrometry (HS-GC-MS) technology coupled with chemometrics was employed to characterize volatile profiling, differentiate species and select potential markers for varietal recognition of Yujin.

RESULTS

By applying PCA (principal components analysis) and HCA (hierarchical cluster analysis), HS-GC-MS realized complete differentiation of the four species of Yujin, while ATR-FTIR only recognized Guiyuijin. Back propagation neural network (BP-NN), KNN (K-nearest neighbor) and LDA (linear discriminant analysis) models based on spectral data achieved 100% discriminant accuracies. Support vector machines (SVM), KNN and PLS-DA (partial least square discriminant analysis) models based on volatile compounds also realized 100% discriminant accuracies. Additionally, the potential volatile markers for varietal recognition of Yujin were screened using PLS-DA, including 2 for Guiyujin, 6 for Lvyujin, 9 for Wenyujin and 13 for Huangyujin.

CONCLUSIONS

The present study developed reliable methods for the varietal discrimination and volatile compounds characterization of Yujin, which will provide references for its quality control and clinical efficacy.

Discriminant analysis of Chinese patent medicines based on near-infrared spectroscopy and principal component discriminant transformation

Principal component discriminant transformation was applied for discrimination of different Chinese patent medicines based on near-infrared (NIR) spectroscopy. In the method, an optimal set of orthogonal discriminant vectors, which highlight the differences between the NIR spectra of different classes, is designed by maximizing Fisher's discriminant function. Therefore, a model for discriminating a class and the others can be obtained with the tiny differences between the NIR spectra of different classes. Furthermore, because NIR spectra contain a large amount of redundant information, principal component analysis (PCA) is employed to reduce the dimension. On the other hand, continuous wavelet transform (CWT) is taken as the pretreatment method to remove the variant background. For identifying the method, different medicines and the same medicine from different manufactures were studied. The results show that all the models can provide 100% discrimination.

Identification of solid state fermentation degree with FT-NIR spectroscopy: Comparison of wavelength variable selection methods of CARS and SCARS

The use of wavelength variable selection before partial least squares discriminant analysis (PLS-DA) for qualitative identification of solid state fermentation degree by FT-NIR spectroscopy technique was investigated in this study. Two wavelength variable selection methods including competitive adaptive reweighted sampling (CARS) and stability competitive adaptive reweighted sampling (SCARS) were employed to select the important wavelengths. PLS-DA was applied to calibrate identified model using selected wavelength variables by CARS and SCARS for identification of solid state fermentation degree. Experimental results showed that the number of selected wavelength variables by CARS and SCARS were 58 and 47, respectively, from the 1557 original wavelength variables. Compared with the results of full-spectrum PLS-DA, the two wavelength variable selection methods both could enhance the performance of identified models. Meanwhile, compared with CARS-PLS-DA model, the SCARS-PLS-DA model achieved better results with the identification rate of 91.43% in the validation process. The overall results sufficiently demonstrate the PLS-DA model constructed using selected wavelength variables by a proper wavelength variable method can be more accurate identification of solid state fermentation degree.

Classification of washing powder brands using near-infrared spectroscopy combined with chemometric calibrations

In this study, near-infrared (NIR) spectroscopy is applied for rapid and objective classification of 5 different brands of washing powder. Chemometric calibrations including partial least square discriminant analysis (PLS-DA), back propagation neural network (BP-NN) and least square support vector machine (LS-SVM) are investigated and compared to achieve an optimal result. Firstly, principal component analysis (PCA) is conducted to visualize the difference among washing powder samples of different brands and principal components (PCs) are extracted as inputs of BP-NN and LS-SVM models. The number of PCs and parameters of such models are optimized via cross validation. In experimental studies, a total of 225 spectra of washing powder samples (45 samples for each brand) were used to build models and 75 spectra of washing powder samples (15 samples for each brand) were used as the validation set to evaluate the performance of developed models. As for the comparison of the three investigated models, both BP-NN model and LS-SVM model successfully classified all samples in validation set according to their brands. However, the PLS-DA model failed to achieve 100% of classification accuracy. The results obtained in this investigation demonstrate that NIR spectroscopy combined with chemometric calibrations including BP-NN and LS-SVM can be successfully utilized to classify the brands of washing powder.