Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

681
Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
681
Quality Control01:05

Quality Control

4.0K
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
4.0K
Quality Assurance01:19

Quality Assurance

3.7K
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
3.7K
Response Surface Methodology01:16

Response Surface Methodology

710
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
710
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

1.8K
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
1.8K
The X̄ Chart00:58

The X̄ Chart

509
The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality...
509

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Epidemiological, clinical, and obstetrical profile of women who used medicinal plants during labor and delivery: a retrospective survey in the Guelmim-Oued Noun region, Morocco.

Brazilian journal of biology = Revista brasleira de biologia·2025
Same author

Quantification of ciprofloxacin in pharmaceutical products from various brands using FT-NIR: A comparative investigation of PLS and MCR-ALS.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2023
Same author

Identification of the new progress on Pyrazole Derivatives Molecules as Antimicrobial and Antifungal Agents.

West African journal of medicine·2022
Same author

[Moroccan pharmacy students' knowledge and perceptions about pharmacovigilance].

Annales pharmaceutiques francaises·2020
Same author

Evaluation of data preprocessings for the comparison of GC-MS chemical profiles of seized cannabis samples.

Forensic science international·2020
Same author

Quantitation of active pharmaceutical ingredient through the packaging using Raman handheld spectrophotometers: A comparison study.

Talanta·2019
Same journal

[Artificial Intelligence in the Pharmaceutical Industry: Governance, Quality, and Regulatory Challenges].

Annales pharmaceutiques francaises·2026
Same journal

No evidence of significant natural environmental exposure to arsenic for Napoleon I on the island of Saint Helena (1815-1821).

Annales pharmaceutiques francaises·2026
Same journal

EFFECTS OF GLP-1 RECEPTOR AGONISTS ON PERIODONTAL TISSUE: AN INTERDISCIPLINARY EVIDENCE MAPPING - SCOPING REVIEW.

Annales pharmaceutiques francaises·2026
Same journal

Regulatory non-compliance of parallel imported medicines in Kenya and implications for public health: Evidence from Nairobi County.

Annales pharmaceutiques francaises·2026
Same journal

Prostaglandin analogues in cosmetic products formulated for eyelash lengthening.

Annales pharmaceutiques francaises·2026
Same journal

Mechanistic and thermodynamic evaluation of sodium citrate-mediated hydrotropism for the biopharmaceutical optimization of BCS Class II drug atorvastatin calcium.

Annales pharmaceutiques francaises·2026
See all related articles

Related Experiment Video

Updated: Feb 24, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

489

Multivariate statistical process control in product quality review assessment - A case study.

M Kharbach1, Y Cherrah2, Y Vander Heyden3

  • 1Pharmaceutical and toxicological analysis research team, laboratory of pharmacology and toxicology, faculty of medicine and pharmacy, university Mohammed V. Souissi, avenue Med Belarbi El Alaoui, BP 6203, 10000 Rabat, Morocco; Department of analytical chemistry and pharmaceutical technology, CePhaR, Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090 Brussels, Belgium.

Annales Pharmaceutiques Francaises
|August 12, 2017
PubMed
Summary
This summary is machine-generated.

Multivariate Statistical Process Control (MSPC) offers a more sensitive approach to Annual Product Reviews (APR) than traditional Statistical Process Control (SPC). MSPC effectively identifies process variations missed by SPC, ensuring better pharmaceutical quality control.

Keywords:
Analyse en composante principaleAnnual product reviewHotelling's T(2)Maîtrise statistique du procédéMaîtrise statistique du procédé multivariéeMultivariate statistical process controlPrincipal component analysisRevue annuelle produitStatistical process controlT(2) de Hotelling

More Related Videos

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method
09:16

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Published on: May 12, 2023

1.6K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.3K

Related Experiment Videos

Last Updated: Feb 24, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

489
Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method
09:16

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Published on: May 12, 2023

1.6K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.3K

Area of Science:

  • Pharmaceutical Manufacturing
  • Quality Control
  • Statistical Process Monitoring

Background:

  • Annual Product Review (APR) is a mandatory Good Manufacturing Practices (GMP) requirement for evaluating process consistency.
  • Conventional Statistical Process Control (SPC) using Shewhart's charts has limitations in detecting interactions between variables.
  • Multivariate Statistical Process Control (MSPC) offers an advanced method for process analysis.

Purpose of the Study:

  • To compare the effectiveness of SPC and MSPC in assessing pharmaceutical manufacturing processes during an APR.
  • To evaluate the capability of MSPC in identifying subtle process variations.
  • To demonstrate the application of MSPC using Principal Component Analysis (PCA) on real-world batch data.

Main Methods:

  • Collected data from 164 historical batches of a pharmaceutical product manufactured in Morocco.
  • Assayed six active ingredients per batch using High-Performance Liquid Chromatography (HPLC).
  • Applied both SPC and MSPC (including PCA with autoscaling and robust scaling) to the data matrix.

Main Results:

  • SPC indicated all batches were within control limits.
  • MSPC identified 4-7 batches outside the Hotelling T² 95% control limits, depending on data scaling.
  • Excluding extreme batches improved the observed process capability.

Conclusions:

  • MSPC is more sensitive than SPC for detecting subtle deviations in pharmaceutical manufacturing during APR.
  • MSPC, particularly PCA, enhances the ability to monitor process consistency and identify potential issues.
  • The findings support the use of MSPC for robust quality control in pharmaceutical production.