Simultaneous quantification of six proteins related to liver injury using nano liquid chromatography-tandem mass spectrometry.

RATIONALE: In clinical diagnosis of liver injury, which is an important health concern, serum aminotransferase assays have been the go-to method used worldwide. However, the measurement of serum enzyme activity has limitations, including inadequate disease specificity and enzyme specificity. METHODS: With the high selectivity and specificity provided by nano liquid chromatography-tandem mass spectrometry (LC/MS/MS), this work describes a method for the simultaneous determination of six proteins in liver that can be potentially used as biomarkers for liver injury: glutamic-pyruvic transaminase 1 (GPT1), glutamic oxaloacetic transaminase 1 (GOT1), methionine adenosyl transferase 1A (MAT1A), glutathione peroxidase 1 (GPX1), cytokeratin 18 (KRT18) and apolipoprotein E (APOE). RESULTS: In validation, the method was shown to have good selectivity and sensitivity (limits of detection at pg/mL level). The analytical method revealed that, compared with normal mice, in carbon tetrachloride-induced acute liver injury mice, liver MAT1A and GPX1 were significantly lower (p < 0.01 and p < 0.05, respectively), KRT18 was significantly higher (p < 0.05) and APOE and GPT1 were marginally significantly lower (p between 0.05 and 0.1). This is the first work reporting the absolute contents of GPT1, GOT1, MAT1A, GPX1 and KRT18 proteins based on LC/MS. CONCLUSIONS: The proposed method provides a basis for establishing more specific diagnostic indicators of liver injury.

Data-Driven Tool for Cross-Run Ion Selection and Peak-Picking in Quantitative Proteomics with Data-Independent Acquisition LC-MS/MS.

Proteomics provides molecular bases of biology and disease, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a platform widely used for bottom-up proteomics. Data-independent acquisition (DIA) improves the run-to-run reproducibility of LC-MS/MS in proteomics research. However, the existing DIA data processing tools sometimes produce large deviations from true values for the peptides and proteins in quantification. Peak-picking error and incorrect ion selection are the two main causes of the deviations. We present a cross-run ion selection and peak-picking (CRISP) tool that utilizes the important advantage of run-to-run consistency of DIA and simultaneously examines the DIA data from the whole set of runs to filter out the interfering signals, instead of only looking at a single run at a time. Eight datasets acquired by mass spectrometers from different vendors with different types of mass analyzers were used to benchmark our CRISP-DIA against other currently available DIA tools. In the benchmark datasets, for analytes with large content variation among samples, CRISP-DIA generally resulted in 20 to 50% relative decrease in error rates compared to other DIA tools, at both the peptide precursor level and the protein level. CRISP-DIA detected differentially expressed proteins more efficiently, with 3.3 to 90.3% increases in the numbers of true positives and 12.3 to 35.3% decreases in the false positive rates, in some cases. In the real biological datasets, CRISP-DIA showed better consistencies of the quantification results. The advantages of assimilating DIA data in multiple runs for quantitative proteomics were demonstrated, which can significantly improve the quantification accuracy.

An integrated strategy of spectrum-effect relationship and near-infrared spectroscopy rapid evaluation based on back propagation neural network for quality control of Paeoniae Radix Alba.

The quantitative analysis of near-infrared spectroscopy in traditional Chinese medicine has still deficiencies in the selection of the measured indexes. Then Paeoniae Radix Alba is one of the famous "Eight Flavors of Zhejiang" herbs, however, it lacks the pharmacodynamic support, and cannot reflect the quality of Paeoniae Radix Alba accurately and reasonably. In this study, the spectrum-effect relationship of the anti-inflammatory activity of Paeoniae Radix Alba was established. Then based on the obtained bioactive component groups, the genetic algorithm, back propagation neural network, was combined with near-infrared spectroscopy to establish calibration models for the content of the bioactive components of Paeoniae Radix Alba. Finally, three bioactive components, paeoniflorin, 1,2,3,4,6-O-pentagalloylglucose, and benzoyl paeoniflorin, were successfully obtained. Their near-infrared spectroscopy content models were also established separately, and the validation sets results showed the coefficient of determination (R2 > 0.85), indicating that good calibration statistics were obtained for the prediction of key pharmacodynamic components. As a result, an integrated analytical method of spectrum-effect relationship combined with near-infrared spectroscopy and deep learning algorithm was first proposed to assess and control the quality of traditional Chinese medicine, which is the future development trend for the rapid inspection of traditional Chinese medicine.

Capillary electrophoresis-mass spectrometry analysis of bisphenol A and its analogues in bottled tea beverages with dynamic pH focusing.

A simple and sensitive method for the determination of bisphenol A and its analogues at the ng/mL level in bottled tea beverages is presented. This method utilized a dynamic pH junction to focus the analyte into a more concentrated zone, based on the electrophoretic mobility difference of analytes in the sample matrix and background electrolytes in capillary electrophoresis coupled to mass spectrometry (CE-MS). The optimised analyte focusing led to enhanced signal detection with average peak heights for five bisphenols of 53-170 folds higher than conventional injections. Under optimised conditions, the method showed good linearity in the range of 0.1-100 ng/mL, excellent limits of detection (0.03-0.04 ng/mL), good analyte recovery (80.3-118.1%) with acceptable relative standard deviations (<12%). The limits of quantifications were below the maximum permissible content of bisphenol A set by the European Commission for this product. This method was used to quantitatively analyse bisphenols in six different kinds of bottled tea beverages, making it a promising tool for practical applications.

Single Standard Substance for the Simultaneous Determination of Eleven Components in the Extract of Paeoniae Radix Alba (Root of Paeonia lactiflora Pall.).

Paeoniae Radix Alba (PRA), an herbal drug produced from the root of Paeonia lactiflora Pall., is widely used in many herbal medicine prescriptions/preparations. Since the pharmacological effects of PRA come from multiple chemical components, it is important to establish a method for the determination of those components in PRA extracts with simple operation and low cost, which is more suitable to evaluate the quality of PRA extracts and optimize the extraction process. This work introduced the quantitative analysis of multicomponents with a single-marker (QAMS) method for the simultaneous determination of eleven bioactive components in PRA extracts, including gallic acid, oxypaeoniflorin, catechin, albiflorin, paeoniflorin, ethyl gallate, galloylpaeoniflorin, pentagalloylglucose, benzoic acid, benzoylpaeoniflorin, and paeonol. In the QAMS method established based on high performance liquid chromatography with diode array detection, only the reference substance of paeoniflorin was needed, and the other ten components were determined based on their relative correction factors (RCFs) to paeoniflorin. Moreover, the repeatability and robustness of the RCFs were studied with different column temperatures, detection wavelengths, flow rates, column types, and instruments. In method validation, good linearity (r > 0.999), stability, repeatability (RSD < 1.9%), and accuracy (recoveries within 96.1%-105.5%) were shown. Sample analyses showed that the QAMS method was consistent with the conventional external standard method. The established method provided a comprehensive, efficient, and low-cost tool for the routine quality evaluation of PRA extracts.

Data-driven development of liquid chromatography-mass spectrometry methods for combined sample matrices.

Herbal medicine formulas (HMFs), the combinations of two or more herbal medicine (HM) ingredients required in a single prescription, are a typical kind of combined sample matrices. LC-MS is a powerful platform for the analyses of such complex samples. The optimization of separation conditions may require a lot of experiments, because multiple analytes need to be separated from a plethora of possible interfering compounds in the sample mixture containing different herbal medicines. To greatly reduce the complexity needed for the optimization of separation conditions, this work proposes a data-driven approach for the systematic development of LC-MS methods for HMFs, using six HMFs created from four HMs (Atractylodis Macrocephalae Rhizoma, Paeoniae Radix Alba, Corydalis Rhizoma and Ophiopogonis Radix) as case-studies. In this approach, the chromatographic peak parameters (like retention times) of the analytes and interfering compounds under different separation conditions were extracted from the LC-MS database of the HMs. Then data-driven models between the chromatographic peak parameters and the separation parameters were built with machine learning methods (r > 0.996 for all the compounds) and used to predict the chromatographic peaks of the analytes and interfering compounds in HMF analyses. Based on the predictions, all of the separation parameters were optimized without any previous experiments on the HMFs. In the validation experiments for the six HMFs, all of the analytes were well separated. The data-driven approach demonstrated enables systematic and rapid development of LC-MS methods for HMFs, and the separation conditions can be efficiently adjusted for different analytes.

The Application of Chemometrics for Efficiency Enhancement and Toxicity Reduction in Cancer Treatment with Combined Therapy.

Chemometrics is an important emerging discipline with unique charm formed by the intersection of mathematics, statistics, chemistry and computer science. The application of chemometrics in the field of pharmacy has injected fresh blood into the scientific research and clinical practice of medicine and has provided a sufficient scientific basis for drug analysis and content determination to solve the problem of cancer treatment with combined therapy in different ranges. This paper introduces the basic principles, advantages and disadvantages of several commonly used pattern recognition and multidimensional correction methods of chemometrics, reviews the application of chemometrics for efficiency enhancement and toxicity reduction in cancer treatment with combined therapy and summarizes its development and prospects in the future.

Enantioselective analysis in complex matrices using capillary electrophoresis-mass spectrometry: A case study of the botanical drug Corydalis Rhizoma.

Enantioselective analysis is critically important in the pharmaceutical and agricultural industries. However, most of the methods reported were developed for the analysis of pure racemates acquired from chemical synthesis or purification. Direct analysis of chiral enantiomers in complex matrices has rarely been reported. This work demonstrated capillary electrophoresis-mass spectrometry (CE-MS) for the enantioselective analysis of botanical drugs for the first time, using a widely used botanical drug, Corydalis Rhizoma, as an example. The method was used for the simultaneous enantioselective analysis of dl-tetrahydropalmatine and (RS)-tetrahydroberberine (canadine) in Corydalis Rhizoma extract. Using (2-hydroxypropyl)-ß-cyclodextrin as the chiral selector, a partial filling technique was used to avoid signal suppression and contamination of the MS detector. Post column organic modifier was used to assist with ionization in the flow through microvial CE-MS interface, therefore, organic solvents was not used in the background electrolyte. The completely aqueous background electrolyte contributed to better chiral separations. The CE-MS method established here can directly determine the analytes in their complex matrix without any pre-purification steps, while also offering high sensitivity and low operational costs (including sample, chiral selector and solvent). In the method validation process, good linearity (r > 0.993), sensitivity and accuracy (recoveries within 89.1-110.0%) were demonstrated. The CE-MS technique was shown to be able to provide good selectivity for the simultaneous chiral separation of multiple pairs of enantiomers in complex matrices.

Development and validation of in-line near-infrared spectroscopy based analytical method for commercial production of a botanical drug product.

Near-infrared (NIR) spectroscopy is one of the most successful pharmaceutical process analytical tools. For botanical drug products (BDPs), many studies have applied NIR spectroscopy for rapid analysis of botanical raw materials, extracts and formulations. However, the real-time process analysis reported for BDPs was still mainly conducted on lab- or pilot- scale equipment, where some essential conditions of the materials and process parameters can be easily controlled for NIR spectral measurement. Due to the chemical and physical characteristics of the commercial production of BDPs, it is challenging to develop in-line NIR methods with enough robustness for industrial-scale application. This is the first reported case study of the development and validation of the in-line NIR method for the commercial production of BDPs, taking Compound Danshen Dripping Pill (CDDP) as an example. An in-line NIR method was developed for simultaneous measurement of the three critical quality attributes, i.e. the relative density, the moisture content and the content of 3,4-dihydroxyphenyl lactic acid (danshensu, a key active compound), during the extract concentration process. The NIR spectra and sample collection lasted for three years (67 production batches) to cover the variability of raw materials and process conditions. NIR calibration models were established respectively, with determination coefficients (r2) of 0.9905, root mean square errors of prediction (RMSEP) of 0.004 for the relative density in the range of 1.042-1.184; r2 of 0.9870, RMSEP of 1.1% for the moisture content in the range of 50.8-83.0%; r2 of 0.9870, RMSEP of 0.461 mg/g for danshensu in the range of 2.563-8.869 mg/g. Then, all the method validation parameters (accuracy, precision, range, specificity, linearity, robustness, detection and quantitation limits) were discussed according to the characteristics of the commercial production of BDPs. The NIR method development and validation strategy proposed may also be applied in the future for the commercial production of other BDPs.

[Medication regularities of traditional Chinese medicine formulas for six kinds of pains by data-mining approach].

To study and compare the medication regularities of traditional Chinese medicine formulas(TCMFs) for the six kinds of pains,namely abdominal pain,headache,epigastric pain,hypochondriac pain,heartache and backache,using a data-mining approach,in order to provide reference for relevant studies for the compatibility mechanism and new compound development of related TCMFs. A total of 2 443 TCMFs for pains were collected from the Dictionary of Chinese Medicine Prescriptions,and analyzed using the Apriori algorithm based on three indicators,namely confidence,lift and support,so as to study pivotal traditional Chinese medicines(TCMs) for each pain and their compatibility regularities. The results showed that deficiency tonifying medicines(such as Glycyrrhizae Radix et Rhizoma and Angelicae Sinensis Radix),Qi-regulating medicines(like Aucklandiae Radix) and blood circulation promoting medicines(like Chuanxiong Rhizoma) were commonly used TCMs for pains. However,there were many differences between drugs for various kinds of pains. For example,Magnoliae Officinalis Cortex was used frequently for abdominal pain and epigastric pain,while Saposhnikoviae Radix was used frequently for headache. The latent association rules with significant lift included Carthami Flos → Angelicae Sinensis Radix for abdominal pain,Astragali Radix → Glycyrrhizae Radix et Rhizoma for headache,Hordei Fructus Germinatus → Citri Reticulatae Pericarpium for epigastric pain,Gentianae Radix et Rhizoma → Bupleuri Radix for hypochondriac pain,and Caryophylli Flos → Moschus for backache. This study showed that based on the TCMFs from the Dictionary of Chinese Medicine Prescriptions,the data-mining approach can reveal the differences and similarities in the use of TCMs for the six kinds of pains,and discover the latent composition regularities of relevant TCMs.

Advancement in the chemical analysis of Paeoniae Radix (Shaoyao).

Paeoniae Radix Alba (baishao or white peony root) and Paeoniae Radix Rubra (chishao or red peony root) are two highly valuable traditional Chinese medicines (TCMs) usually indicated for painful conditions, menstrual disorders and viral infections. These two TCMs are collectively referred to as shaoyao (Paeoniae Radix) due to their close origins and similar chemical compositions. Modern research indicates that monoterpene glycosides, polyphenols and paeonols are the three main types of compounds related to the pharmacological activities of Paeoniae Radix. This review summarizes recent advances in the chemical analysis of Paeoniae Radix and the related traditional Chinese medicine formulas/preparations, including methods used for sample pretreatment, qualitative analysis, quantitative analysis and biological sample analysis. More than 120 papers are discussed in this review, focusing on the chemical analysis of Paeoniae Radix, and various analytical techniques (such as HPLC, LC-MS, IR, near IR and quantitative NMR), as well as their advantages/disadvantages, are described. It is our hope that this paper can provide necessary information for improving the quality evaluation methods currently available for Paeoniae Radix and offer a scientific basis for the future in-depth study of the pharmacokinetics and pharmacodynamics of Paeoniae Radix.

Rapid HPLC Analytical Method Development for Herbal Medicine Formulae Based on Retention Rules Acquired from the Constituting Herbs.

Herbal medicine (HM) formulae are the combinations of two or more types of constituting herbs. This study has proposed a novel approach to efficiently develop HPLC methods for HM formulae, which take advantage of the mutual retention rules between HM formulae and their constituting herbs. An HM formula composed of two herbs, Radix Salviae Miltiorrhizae and Rhizoma Chuanxiong, was taken as a case study. Based on design of experiments and stepwise multiple linear regression, models relating the analytical parameters to the chromatographic parameters were built (correlation coefficients >0.9870) for chemical compounds in the two herbs. These models representing the retention rules were utilized to predict the elution profile of the formula. The analytical parameters were numerically optimized to ensure adequate separation of the analytes. In validation experiments, satisfactory separations were achieved without any pre-experiments on the formula. The approach can significantly increase the HPLC method development efficiency for HM formulae.

[Rapid determination of nine components in the first extraction process of Xingnaojing injection by using ultraviolet spectroscopy].

In this study, an analytical method based on ultraviolet spectroscopy was established for the rapid determination of nine components including isophorone, 4-methylene-isophorone, curcumenone, curcumenol, curdione, curzerenone, furanodienone, curcumol and germacrone in the first extraction process of Xingnaojing injection. 166 distillate samples of Gardeniae Fructus and Radix Curcumae were collected in the first extraction process of Xingnaojing injection. The ultraviolet spectra of these samples were collected, and the contents of the nine components in these samples were determined by high performance liquid chromatography. Least squares support vector machine and radial basis function artificial neural network were used to establish the multivariate calibration models between the ultraviolet spectra and the contents of the nine components. The results showed that the established ultraviolet spectrum analysis method can determine the contents of the nine components in the distillates accurately, with root mean square error of prediction of 0.068, 0.147, 0.215, 0.319, 1.01, 1.27, 0.764, 0.147, 0.610 mg•L⁻¹, respectively. This proposed method is a rapid, simple and low-cost tool for the monitoring and endpoint determination of the extraction process of Xingnaojing injection to reduce quality defects and variations.

Single standard substance for the determination of nine volatile components in the distillate of Fructus Gardeniae and Radix Curcumae (an intermediate of Xingnaojing Injection).

Xingnaojing Injection is a traditional Chinese medicine extensively used for stroke and cerebral ischemia. For better in-process quality control of Xingnaojing Injection, a method for the analysis of its intermediate (i.e., the distillate of Fructus Gardeniae and Radix Curcumae) is needed to monitor and optimize the hydrodistillation extraction process. In this work, nine major volatile components in the intermediate were identified: isophorone, 4-methylene-isophorone, curcumenone, curcumenol, curdione, curzerenone, furanodienone, curcumol, and germacrone. A quantitative analysis of multi-component with a single-marker method based on high-performance liquid chromatography with diode array detection was developed for the simultaneous determination of the nine components. In this method, only curdione was needed as the reference substance, and the other eight components were determined using their relative correction factors to curdione. In the method validation, good linearity (r > 0.9999), sensitivity, repeatability, and accuracy (recoveries within 95.3-105.4%) were shown. The repeatability and robustness of the relative correction factors were studied with different column temperatures, flow rates, detection wavelengths, columns, and instruments. In sample analyses, consistent results between the proposed method and the external standard method were shown. The proposed method provides a comprehensive and low-cost tool for the quality assessment of the intermediate of Xingnaojing Injection.

Statistical Model Based HPLC Analytical Method Adjustment Strategy to Adapt to Different Sets of Analytes in Complicated Samples.

INTRODUCTION: On account of the complicated compositions of the products like traditional Chinese medicines (TCMs) and functional foods, it is a common practice to determine different sets of analytes in the same product for different purposes. OBJECTIVE: To efficiently develop the corresponding HPLC methods, a statistical model based analytical method adjustment (SMB-AMA) strategy was proposed. METHODS: In this strategy, the HPLC data acquired with design of experiments methodology were efficiently utilised to build the retention models for all the analytes and interferences shown in the chromatograms with multivariate statistical modelling methods. According to the set of analytes under research, Monte-Carlo simulations were conducted based on these retention models to estimate the probability of achieving adequate separations between all the analytes and their interferences. Then the analytical parameters were mathematically optimised to the point giving a high value of this probability to compose a robust HPLC method. Radix Angelica Sinensis (RAS) and its TCM formula with Folium Epimedii (FE) were taken as the complicated samples for case studies. RESULTS: The retention models for the compounds in RAS and FE were built independently with correlation coefficients all above 0.9799. The analytical parameters were tactfully adjusted to adapt to six cases of different sets of analytes and different sample matrices. In the validation experiments using the adjusted analytical parameters, satisfactory separations were acquired. CONCLUSION: The results demonstrated that the SMB-AMA strategy was able to develop HPLC methods rationally and rapidly in the adaption of different sets of analytes. Copyright © 2017 John Wiley & Sons, Ltd.

Root Cause Analysis of Quality Defects Using HPLC-MS Fingerprint Knowledgebase for Batch-to-batch Quality Control of Herbal Drugs.

INTRODUCTION: The batch-to-batch quality consistency of herbal drugs has always been an important issue. OBJECTIVES: To propose a methodology for batch-to-batch quality control based on HPLC-MS fingerprints and process knowledgebase. METHODS: The extraction process of Compound E-jiao Oral Liquid was taken as a case study. After establishing the HPLC-MS fingerprint analysis method, the fingerprints of the extract solutions produced under normal and abnormal operation conditions were obtained. Multivariate statistical models were built for fault detection and a discriminant analysis model was built using the probabilistic discriminant partial-least-squares method for fault diagnosis. RESULTS: Based on multivariate statistical analysis, process knowledge was acquired and the cause-effect relationship between process deviations and quality defects was revealed. The quality defects were detected successfully by multivariate statistical control charts and the type of process deviations were diagnosed correctly by discriminant analysis. CONCLUSION: This work has demonstrated the benefits of combining HPLC-MS fingerprints, process knowledge and multivariate analysis for the quality control of herbal drugs.

Rapid process development of chromatographic process using direct analysis in real time mass spectrometry as a process analytical technology tool.

The concept of quality by design (QbD) is widely applied in the process development of pharmaceuticals. However, the additional cost and time have caused some resistance about QbD implementation. To show a possible solution, this work proposed a rapid process development method, which used direct analysis in real time mass spectrometry (DART-MS) as a process analytical technology (PAT) tool for studying the chromatographic process of Ginkgo biloba L., as an example. The breakthrough curves were fast determined by DART-MS at-line. A high correlation coefficient of 0.9520 was found between the concentrations of ginkgolide A determined by DART-MS and HPLC. Based on the PAT tool, the impacts of process parameters on the adsorption capacity were discovered rapidly, which showed a decreased adsorption capacity with the increase of the flow rate. This work has shown the feasibility and advantages of integrating PAT into QbD implementation for rapid process development.

Quality by design for herbal drugs: a feedforward control strategy and an approach to define the acceptable ranges of critical quality attributes.

INTRODUCTION: The concept of quality by design (QbD) has been widely accepted and applied in the pharmaceutical manufacturing industry. There are still two key issues to be addressed in the implementation of QbD for herbal drugs. The first issue is the quality variation of herbal raw materials and the second issue is the difficulty in defining the acceptable ranges of critical quality attributes (CQAs). OBJECTIVE: To propose a feedforward control strategy and a method for defining the acceptable ranges of CQAs for the two issues. METHODS: In the case study of the ethanol precipitation process of Danshen (Radix Salvia miltiorrhiza) injection, regression models linking input material attributes and process parameters to CQAs were built first and an optimisation model for calculating the best process parameters according to the input materials was established. Then, the feasible material space was defined and the acceptable ranges of CQAs for the previous process were determined. RESULTS: In the case study, satisfactory regression models were built with cross-validated regression coefficients (Q(2) ) all above 91 %. The feedforward control strategy was applied successfully to compensate the quality variation of the input materials, which was able to control the CQAs in the 90-110 % ranges of the desired values. In addition, the feasible material space for the ethanol precipitation process was built successfully, which showed the acceptable ranges of the CQAs for the concentration process. CONCLUSION: The proposed methodology can help to promote the implementation of QbD for herbal drugs.

An approach to optimize the batch mixing process for improving the quality consistency of the products made from traditional Chinese medicines.

The efficacy of traditional Chinese medicine (TCM) is based on the combined effects of its constituents. Variation in chemical composition between batches of TCM has always been the deterring factor in achieving consistency in efficacy. The batch mixing process can significantly reduce the batch-to-batch quality variation in TCM extracts by mixing them in a well-designed proportion. However, reducing the quality variation without sacrificing too much of the production efficiency is one of the challenges. Accordingly, an innovative and practical batch mixing method aimed at providing acceptable efficiency for industrial production of TCM products is proposed in this work, which uses a minimum number of batches of extracts to meet the content limits. The important factors affecting the utilization ratio of the extracts (URE) were studied by simulations. The results have shown that URE was affected by the correlation between the contents of constituents, and URE decreased with the increase in the number of targets and the relative standard deviations of the contents. URE could be increased by increasing the number of storage tanks. The results have provided a reference for designing the batch mixing process. The proposed method has possible application value in reducing the quality variation in TCM and providing acceptable production efficiency simultaneously.

[Feedforward control strategy and its application in quality improvement of ethanol precipitation process of danhong injection].

In this work, a feedforward control strategy basing on the concept of quality by design was established for the manufacturing process of traditional Chinese medicine to reduce the impact of the quality variation of raw materials on drug. In the research, the ethanol precipitation process of Danhong injection was taken as an application case of the method established. Box-Behnken design of experiments was conducted. Mathematical models relating the attributes of the concentrate, the process parameters and the quality of the supernatants produced were established. Then an optimization model for calculating the best process parameters basing on the attributes of the concentrate was built. The quality of the supernatants produced by ethanol precipitation with optimized and non-optimized process parameters were compared. The results showed that using the feedforward control strategy for process parameters optimization can control the quality of the supernatants effectively. The feedforward control strategy proposed can enhance the batch-to-batch consistency of the supernatants produced by ethanol precipitation.