Rapid and simultaneous determination of moisture and berberine content in Coptidis Rhizoma and Phellodendri Chinensis Cortex by near-infrared spectroscopy and chemometrics

Coptidis Rhizoma(Chinese:Huanglian)and Phellodendri Chinensis Cortex(Chinese:Huangbo)are widely used Traditional Chinese Medicine,and often used in combination because of their similar pharmacological effects in clinical practice.However,the quality control methods of the two drugs are different and complicated,which is time consuming and laborious in practical application.In this paper,rapid and simultaneous determination of moisture and berberine in Coptidis Rhizoma(CR)and Phellodendri Chinensis Cortex(PC)was realized by near-infrared spectroscopy(NIRs)combined with global models.Competitive adaptive reweighted sampling(CARS)and successive projection algorithm(SPA)method were applied for variable selection.Principal component analysis(PCA)and partial least squares regression method(PLSR)were applied for qualitative and quantitative analysis,respectively.The characteristic variables of berberine showed similarity and consistency in distribution,providing basis for the global models.For moisture content,the global model had relative standard error of prediction set(RSEP)value of 3.04%and 2.53%for CR and PC,respectively.For berberine content,the global model had RSEP value of 5.41%and 3.97%for CR and PC,respectively.These results indicated the global models based on CARS-PLS method achieved satisfactory prediction for moisture and berberine content,improving the determination e±ciency.Furthermore,the greater range and larger number of samples enhanced the reliance of the global model.The NIRs combined with global models could be a powerful tool for quality control of CR and PC.

Improvement of NIR models for quality parameters of leech and earthworm medicines using outlier multiple diagnoses

Leeches and earthworms are the main ingredients of Shuxuetong injection compositions,whichare natural biomedicines.Near infrared(NIR)diffuse reflection spectroscopy has been used forquality assurance of Chinese medicines.In the present work,NIR spectroscopy was proposed as arapid and nondestructive technique to assess the moisture content(MC),soluble solid content(SSC)and hypoxanthine content(HXC)of leeches and earthworms.This study goal was toimprove NIR models for accurate quality control of leech and earthworm using outlier multiplediagnoses(OMD).OMD was composed of four outlier detection methods:spectrum outlier di-agnostic(MD),leverage diagnostic(LD),principal component scores diagnostic(PCSD)andfactor loading diagnostic(FLD),Conventional outlier diagnoses(MD,LD)and OMD werecompared,and the best NIR models were those based on OMD.The correlation coefficients(R)for leech were 0.9779,0.9616 and 0.9406 for MC,SSC and HXC,respectively.The values ofrelative standard error of prediction(RSEP)for leech were 2.3%,5.1%and 9.0%for MC,SSC and HXC,respectively.The values of R for earthworm were 0.9478,0.9991 and 0.9605 for MC,SSC and HXC,respectively.The values of RSEP for earthworm were 8.8%,2.4%and 12%for MC,SSC and HXC,respectively.The performance of the NIR models was certainly improved by OMD.

Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

A particle swarm optimization(PSO)-based least square support vector machine(LS-SVM)method was investigated for quantitative analysis of extraction solution of Y angxinshi tablet using near infrared(NIR)spectroscopy.The usable spectral region(5400-6200cm^(-1))was identified,then the first derivative spectra smoothed using a Savitzky-Golay filter were employed to establish calibration models.The PSO algorithm was applied to select the LS-SVM hyper-parameters(including the regularization and kernel parametens).The calibration models of total flavonoids,puerarin,salvianolic acid B and icarin were established using the optimumn hyper-parameters of LS SVM.The performance of LS SVM models were compared with partial least squares(PLS)regression,feed forward back propagation network(BPANN)and support vector machine(SVM).Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS SVM method were superior to PLS,BP-ANN and SVM.For PSO-based LS-SVM models,the determination cofficients(R2)for the calibration set were above 0.9881,and the RSEP values were controlled within 5.772%.For the validation set,the RMSEP values were close to RMSEC and less than 0.042,the RSEP values were under 8.778%,which were much lower than the PLS,BP-ANN and SVM models.The PSO-based LS SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy,which has definite practice significance and application value.

Multicellular tumor spheroids bridge the gap between two-dimensional cancer cells and solid tumors: The role of lipid metabolism and distribution

Previous studies demonstrated that three-dimensional(3D) multicellular tumor spheroids(MCTS) could more closely mimic solid tumors than two-dimensional(2D) cancer cells in terms of the spatial structure, extracellular matrix-cell interaction, and gene expression pattern. However, no study has been reported on the differences in lipid metabolism and distribution among 2D cancer cells, MCTS, and solid tumors. Here, we used Hep G2 liver cancer cell lines to establish these three cancer models. The variations of lipid profiles and spatial distribution among them were explored by using mass spectrometry-based lipidomics and matrix-assisted laser desorption/ionization mass spectrometry imaging(MSI). The results revealed that MCTS, relative to 2D cells, had more shared lipid species with solid tumors. Furthermore,MCTS contained more comparable characteristics than 2D cells to solid tumors with respect to the relative abundance of most lipid classes and mass spectra patterns. MSI data showed that 46 of 71 lipids had similar spatial distribution between solid tumors and MCTS, while lipids in 2D cells had no specific spatial distribution. Interestingly, most of detected lipid species in sphingolipids and glycerolipids preferred locating in the necrotic region to the proliferative region of solid tumors and MCTS. Taken together, our study provides the evidence of lipid metabolism and distribution demonstrating that MCTS are a more suitable in vitro model to mimic solid tumors, which may offer insights into tumor metabolism and microenvironment.