Qun Ma

Rapid screening and identification of target constituents using full scan-parent ions list-dynamic exclusion acquisition coupled to diagnostic product ions analysis on a hybrid LTQ-Orbitrap mass spectrometer

A highly sensitive and effective strategy for rapid screening and identification of target constituents has been developed using full scan-parent ions list-dynamic exclusion (FS-PIL-DE) acquisition coupled to diagnostic product ions (DPIs) analysis on a hybrid LTQ-Orbitrap mass spectrometer. The FS-PIL-DE was adopted as a survey scan to trigger the MS/MS acquisition of all the predictable constituents contained in traditional Chinese medicines. Additionally, DPIs analysis can provide a criterion to judge the target constituents detected into certain chemical families. Results from analyzing polymethoxylated flavonoids (PMFs) in the leaves of Citrus reticulata Blanco demonstrated that FS-PIL-DE was capable of targeting a greater number of constituents than FS, FS-PIL and FS-DE, thereby increasing the coverage of constituent screening. As a result, 135 PMFs including 81 polymethoxyflavones, 54 polymethoxyflavanones or polymethoxychalcones were identified preliminarily. And this was the first time to systematically report the presence of PMFs in the leaves of Citrus reticulata Blanco, especially for polymethoxylated flavanones and chalcones, most of which were new compounds. The results indicated that the developed FS-PIL-DE coupled to DPIs analysis methodology could be employed as a rapid, effective technique to screen and identify target constituents from TCMs extracts and other organic matter mixtures whose compounds contained can also be classified into families based on the common carbon skeletons.

Multivariate detection limits of on-line NIR model for extraction process of chlorogenic acid from Lonicera japonica

A methodology is proposed to estimate the multivariate detection limits (MDL) of on-line near-infrared (NIR) model in Chinese Herbal Medicines (CHM) system. In this paper, Lonicera japonica was used as an example, and its extraction process was monitored by on-line NIR spectroscopy. Spectra of on-line NIR could be collected by two fiber optic probes designed to transmit NIR radiation by a 2mm-flange. High performance liquid chromatography (HPLC) was used as a reference method to determine the content of chlorogenic acid in the extract solution. Multivariate calibration models were carried out including partial least squares regression (PLS) and interval partial least-squares (iPLS). The result showed improvement of model performance: compared with PLS model, the root mean square errors of prediction (RMSEP) of iPLS model decreased from 0.111mg to 0.068mg, and the R(2) parameter increased from 0.9434 to 0.9801. Furthermore, MDL values were determined by a multivariate method using the type of errors and concentration ranges. The MDL of iPLS model was about 14ppm, which confirmed that on-line NIR spectroscopy had the ability to detect trace amounts of chlorogenic acid in L. japonica. As a result, the application of on-line NIR spectroscopy for monitoring extraction process in CHM could be very encouraging and reliable.

Optimization of Parameter Selection for Partial Least Squares Model Development

In multivariate calibration using a spectral dataset, it is difficult to optimize nonsystematic parameters in a quantitative model, i.e., spectral pretreatment, latent factors and variable selection. In this study, we describe a novel and systematic approach that uses a processing trajectory to select three parameters including different spectral pretreatments, variable importance in the projection (VIP) for variable selection and latent factors in the Partial Least-Square (PLS) model. The root mean square errors of calibration (RMSEC), the root mean square errors of prediction (RMSEP), the ratio of standard error of prediction to standard deviation (RPD), and the determination coefficient of calibration (Rcal2) and validation (Rpre2) were simultaneously assessed to optimize the best modeling path. We used three different near-infrared (NIR) datasets, which illustrated that there was more than one modeling path to ensure good modeling. The PLS model optimizes modeling parameters step-by-step, but the robust model described here demonstrates better efficiency than other published papers.

NIR spectroscopy as a process analytical technology (PAT) tool for monitoring and understanding of a hydrolysis process

The use of near infrared spectroscopy was investigated as a process analytical technology to monitor the amino acids concentration profile during hydrolysis process of Cornu Bubali. A protocol was followed, including outlier selection using relationship plot of residuals versus the leverage level, calibration models using interval partial least squares and synergy interval partial least squares (SiPLS). A strategy of four robust root mean square error of predictions (RMSEP) values have been developed to assess calibration models by means of the desirability index. Furthermore, multivariate quantification limits (MQL) values of the optimum model were determined using two types of error. The SiPLS(3) models for L-proline, L-tyrosine, L-valine, L-phenylalanine and L-lysine provided excellent accuracies with RMSEP values of 0.0915 mg/mL, 0.1605 mg/mL, 0.0515 mg/mL, 0.0586 mg/mL and 0.0613 mg/mL, respectively. The MQL ranged from 90 ppm to 810 ppm, which confirmed that these models can be suitable for most applications.

Absorption and quantitative characteristics of C-H bond and O-H bond of NIR

The previous study mainly focused on the interpretation of the relationship between absorption characteristics and quantitative contribution in each near-infrared (NIR) frequency range. Furthermore, the absorption characteristics of NIR mainly cover overtones and combinations of molecular vibrations such as CH, OH, SH, and NH bonds. And it has been know that NIR wavelengths of C-H bond and O-H bond are assigned to different radio frequencies. This paper was intended to investigate the absorption characteristics of bond C-H and O-H bonds in NIR spectral range. Water and acetone which correspond to O-H and C-H bonds have been selected as typical solvents, as well as solutes. Calibration models were established using partial least square regression (PLS) and multiple linear regression (MLR). The parameter of the model were optimized by different spectral pretreatment methods. The result showed that the model set by Savitzky-Golay smooth (SG) in the spectral region of 810–1100 nm could successfully make accurate predictions. Short wave-NIR region has been discovered as optimum characteristic absorption of C-H and O-H bonds.

Stability and Degradation of Caffeoylquinic Acids under Different Storage Conditions Studied by High-Performance Liquid Chromatography with Photo Diode Array Detection and High-Performance Liquid Chromatography with Electrospray Ionization Collision-Induced Dissociation Tandem Mass Spectrometry

Caffeoylquinic acids (CQAs) are main constituents in many herbal medicines with various biological and pharmacological effects. However, CQAs will degrade or isomerize when affected by temperature, pH, light, etc. In this study, high-performance liquid chromatography with photodiode array detection (HPLC-PDA) and high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS) was utilized to study the stability and degradation of CQAs (three mono-acyl CQAs and four di-acyl CQAs) under various ordinary storage conditions (involving different temperatures, solvents, and light irradiation). The results indicated that the stability of CQAs was mainly affected by temperature and light irradiation, while solvents did not affect it in any obvious way under the conditions studied. Mono-acyl CQAs were generally much more stable than di-acyl CQAs under the same conditions. Meanwhile, the chemical structures of 30 degradation products were also characterized by HPLC-MSn, inferring that isomerization, methylation, and hydrolysis were three major degradation pathways. The result provides a meaningful clue for the storage conditions of CQAs standard substances and samples.

Monitoring As and Hg variation in An-Gong-Niu-Huang Wan (AGNH) intermediates in a pilot scale blending process using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) was used to assess the cinnabar and realgar blending of An-Gong-Niu-Huang Wan (AGNH) in a pilot-scale experiment, including the blending end-point. The blending variability of two mineral medicines, cinnabar and realgar, were measured by signal relative intensity changing rate (RICR) and moving window standard deviation (MWSD) based on LIBS. Meanwhile, relative concentration changing rate (RCCR) was obtained based on the reference method involving inductively coupled plasma optical emission spectrometry (ICP-OES). The LIBS result was consistent with ICP-OES at blending end-point determinations of both mineral medicines. Unlike the ICP-OES method, LIBS does not have an elaborate digestion procedure. LIBS is a promising and rapid technique to understand the blending process of Chinese Materia Medica (CMM) containing cinnabar and realgar. These results demonstrate the potential of LIBS in monitoring CMM pharmaceutical production.

Near-infrared for on-line determination of quality parameter of Sophora japonica L. (formula particles): From lab investigation to pilot-scale extraction process

Background: Extraction process of dried flowers of formula particles should be investigated from lab investigation to pilot-scale because of good water absorbing capacity and obscure active pharmaceutical ingredients (API) dissolution. Objective: Reliable analysis of on-line near-infrared (NIR) technique and novel application in fascinating modern, traditional Chinese medicine production (formula particles) was proved. Materials and Methods: The extraction process of Sophora japonica L. (formula particles) was used as an example, the rutin was regarded as API. On-line NIR technology was used to monitor the variation of rutin in the extraction process. High-performance liquid chromatography (HPLC) was used as a reference method to determine the content of rutin during the extraction process. The sample set was selected by Kennard-Stone (KS) algorithm. Different pretreatment methods were compared. The synergy interval partial least square (SiPLS) algorithm was applied. Chemometrics indicators and multivariate detection limits method were mutually used to assess the model. Results: According to both errors α (0.05) and β (0.05), rutin content could be detected by on-line NIR, which was more than 0.181 mg/mL. Conclusions: This work demonstrated the feasibility of NIR for on-line determination of rutin in the pilot-scale extraction process of S. japonica. L. It provided technical support for the NIR application in the extraction process of formula particles.

Quality-by-Design: Multivariate Model for Multicomponent Quantification in Refining Process of Honey.

Objective: A method for rapid analysis of the refining process of honey was developed based on near-infrared (NIR) spectroscopy. Methods: Partial least square calibration models were built for the four components after the selection of the optimal spectral pretreatment method and latent factors. Results: The models covered the samples of different temperatures and time pointstherefore the models were robust and universal. Conclusions: These results highlighted that the NIR technology could extract the information of critical process and provide essential process knowledge of the honey refining process. Abbreviation used: NIR: Near-infrared; 5-HMF: 5-hydroxymethylfurfural; RMSEP: Root mean square error of prediction; R: correlation coefficients; PRESS: prediction residual error-sum squares; TCM: Traditional Chinese medicine; HPLC: High-performance liquid chromatography; HPLC-DAD: HPLC-diode array detector; PLS: Partial least square; MSC: multiplicative scatter correction; RMSECV: Root mean square error of cross validation; RPD: Residual predictive deviation; 1D: 1st order derivative; SG: Savitzky-Golay smooth; 2D: 2nd order derivative.

A novel algorithm for blending process monitoring of Angong Niuhuang intermediate using vector operation moving block standard deviation

This study aimed to introduce a novel approach named Vector Operation Moving Block Standard Deviation (VO-MBSD) to characterize the original powder blend uniformity of Angong Nuihuang intermediate using NIR technology, including 400g Rhizoma Coptidis, 400g Radix Scutellariae, 400g Radix Curcumae and 400g Fructus Gardeniae. A novel blending evaluation method named VO-MBSD compared with Moving Block Standard Deviation (MBSD) was applied to characterize the blending of Chinese Materia Medica (CMM) original powder including Rhizoma Coptidis, Radix Scutellariae, Radix Curcumae and Fructus Gardeniae. HPLC (High Performance Liquid Chromatography) analysis demonstrated these observations perfectly. OV-MBSD is the rate of change by time, which not only represents the scalar change but also the vector change. The identification accuracy of blend uniformity and end-point via VO-MBSD was the same with classical HPLC method. This method is more accuracy than original MBSD method. Compared with classical MBSD, it is appropriate for the determination of blending end-point and could be successfully implemented as an on-line monitoring tool for blending process.