Critical process parameter identification of manufacturing processes of Astragali Radix extract with a weighted determination coefficient method

Sun, Min-Fei; Yang, Jingyi; Cao, Wen; Shao, Jingyuan; Wang, Guoxiang; Qu, Haibin; Huang, Weichao; Gong, Xingchu

doi:10.1016/j.chmed.2019.11.001

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…El coeficiente de determinación (R2) (Ecuación 5), que representa una relación entre la covarianza de las variables y las desviaciones típicas de cada variable, tiende a uno para un ajuste lineal perfecto, así que entre más cercano a la unidad representa un ajuste mucho mejor, una explicación mejor entre las variables independientes y dependientes. (Stumpp, et al, 2009, Min-Fei, et al, 2020):…”

Section: Resultados Y Discusiónunclassified

Métodos de análisis estadístico con datos dispersos

Bautista

2020

Ingeniería y Región

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En este artículo se presentan las principales características de algunas metodologías para derivar modelos de predicción o clasificación cuando se tienen muchos datos. Dentro de estos se describen las redes neuronales, bosques aleatorios, árboles de decisión, árboles altamente aleatorizados (extra trees) y método de regresión asistida por patrón de contraste (CPXR). Técnicas que han sido utilizadas para derivar modelos de predicción de retención de agua, clasificación de imágenes, clasificación de suelos, determinación de las principales variables que intervienen en un proceso.

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Section: Resultados Y Discusiónunclassified

Métodos de análisis estadístico con datos dispersos

Bautista

2020

Ingeniería y Región

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“…The mathematical relationship among the CPPs and the evaluation indicators was built by the quadratic multiple regression analysis conducted with Equation (6). Before establishing mathematical models among CPPs and evaluation indicators, the purity of HSYA and total flavonoids was subjected to square root arcsine transformation.…”

Section: Process Modeling Of Ethanol Precipitationmentioning

confidence: 99%

“…[2]. In recent years, to understand ethanol precipitation, numerous researchers have studied the process, mainly focusing on the optimization of process parameters [3][4][5][6], process monitoring technology [7][8][9], ethanol precipitation equipment [10,11], sediment morphology [12,13], etc. For evaluating the interactions among factors and establishing a robust process operation space, combinations of Quality by Design (QbD) were used to optimize EPP parameters, such as QbD with a design space approach optimizing the EPP of Panax notoginseng for Xuesaitong injection [14] and QbD with membrane dispersion ethanol precipitation equipment optimizing the EPP of Astragali radix [11].…”

Section: Introductionmentioning

confidence: 99%

Design Space Calculation and Continuous Improvement Considering a Noise Parameter: A Case Study of Ethanol Precipitation Process Optimization for Carthami Flos Extract

Tai

Gong

2021

Separations

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The optimization of process parameters in the pharmaceutical industry is often carried out according to the Quality by Design (QbD) concept. QbD also emphasizes that continuous improvement should be performed in life cycle management. Process parameters that are difficult to control in actual production can be regarded as noise parameters. In this study, based on the QbD concept, the ethanol precipitation process of Carthami Flos extract was optimized, considering a noise parameter. The density of the concentrated extract, ethanol concentration, the volume ratio of ethanol to concentrated extract, stirring time after ethanol addition, and refrigeration temperature were selected as critical process parameters (CPPs), using a definitive screening design. The mathematical models among CPPs and evaluation indicators were established. Considering that the refrigeration temperature of industrial ethanol precipitation is often difficult to control with seasonal changes, refrigeration temperature was treated as a noise parameter. A calculation method for the design space in the presence of the noise parameter was proposed. The design space was calculated according to the probability of reaching the standards of evaluation indicators. Controlling parameters within the design space was expected to reduce the influence of noise parameter fluctuations on the quality of the ethanol precipitation supernatant. With more data obtained, the design space was updated. In industry, it is also recommended to adopt a similar idea: that is, continuing to collect industrial data and regularly updating mathematical models, which can further update the design space and make it more stable and reliable.

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“…Though process conditions were xed, the experimental results were quite different, indicating that the material attributes signi cantly affected the performance of Astragali radix ethanol precipitation process. The correlation analysis of material attributes was carried out to nd attributes with similar trends [34]. The Pearson coe cients are shown in Table 4.…”

Section: Materials Attributesmentioning

confidence: 99%

Optimization of membrane dispersion ethanol precipitation process with a set of temperature control improved equipment

Pan

Tai

et al. 2020

Preprint

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Background Ethanol precipitation is an important separation and purification process in the traditional Chinese medicine (TCM) industry. However, ethanol precipitation process suffers from loss of active ingredients, low batch-to-batch consistency of supernatant composition, and long standing time. These problems are mainly caused by low efficient equipment and imperfect control strategy. Methods In the present study, a membrane dispersion ethanol precipitation device which can achieve rapid cooling was developed for Astragali radix ethanol precipitation. Ethanol precipitation process was optimized according to Quality by design concept. To identify critical material attributes (CMAs), ten batches of Astragali radix was used to prepare concentrates. Definitive screening design was used to investigate the relationships among critical process parameters, CMAs, and process critical quality attributes (CQAs). Results Calycosin-7-O-β-D-glucoside content, the sucrose content, and the electrical conductivity were found to be CMAs after the correlation analysis and stepwise regression modelling. Quadratic models were developed and design space was calculated according to the probability of attaining process CQA standards. A material quality control strategy was proposed. High quality and low quality Astragali radix concentrates can be discriminated by the inequalities. Low quality Astragali radix concentrates should not be released for ethanol precipitation process directly. Verification experiment results indicated accurate models and reliable design space. Conclusions The temperature control method and control strategy in this study are effective and promising for ethanol precipitation process of other TCMs or foods.

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Critical process parameter identification of manufacturing processes of Astragali Radix extract with a weighted determination coefficient method

Cited by 9 publications

References 19 publications

Métodos de análisis estadístico con datos dispersos

Métodos de análisis estadístico con datos dispersos

Design Space Calculation and Continuous Improvement Considering a Noise Parameter: A Case Study of Ethanol Precipitation Process Optimization for Carthami Flos Extract

Optimization of membrane dispersion ethanol precipitation process with a set of temperature control improved equipment

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