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Related Concept Videos

Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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Related Experiment Video

Updated: Dec 7, 2025

Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders
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Quantitative Microscopy: Particle Size/Shape Characterization, Addressing Common Errors Using 'Analytics Continuum'

Devarajan Saravanan1, Prakash Muthudoss1, Praveen Khullar1

  • 1Global Development Centre, Sanofi-Synthelabo (India) Pvt. Ltd, Goa, India.

Journal of Pharmaceutical Sciences
|September 24, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an analytics continuum approach for active pharmaceutical ingredient (API) particle size and shape characterization. It enhances microscopy with NIR spectroscopy and statistical methods for reliable, quantitative material assessment.

Keywords:
Image analysisMorphologyNear-infrared spectroscopy (NIRS)Particle sizePrincipal component analysisSucrose

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Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Analytical Chemistry

Background:

  • Particle size and shape characterization of active pharmaceutical ingredients (APIs) is crucial for product development but often hindered by microscopy's non-repeatability and non-reproducibility issues.
  • Common causes for these issues include fundamental errors, segregation, human error, and challenges with sample randomness and representativeness.

Purpose of the Study:

  • To develop a robust and quantitative methodology for particle size and shape characterization of APIs.
  • To address the limitations of traditional microscopy by integrating complementary analytical techniques and statistical approaches.
  • To establish a reliable method for assessing material suitability across different batches and vendors.

Main Methods:

  • Proposed an 'analytics continuum' approach using sucrose as a model sample.
  • Integrated optical microscope particle size distribution (PSD) measurements with Near-Infrared (NIR) spectroscopy for prescreening and assessing sample randomness/representativeness.
  • Employed statistical tests for population statistics, attribute-based control charts, and bootstrap-based confidence intervals for product performance monitoring.

Main Results:

  • Demonstrated the effectiveness of the integrated approach in ensuring sample randomness and representativeness.
  • Developed a flowchart guideline, extendable to complex scenarios like APIs crystallized from different solvent systems.
  • The methodology proved capable of quantitatively assessing material suitability and identifying minor differences between API batches or vendors.

Conclusions:

  • The developed analytics continuum approach provides a quantitative and reliable method for API particle characterization.
  • This integrated strategy overcomes the limitations of traditional microscopy, improving reproducibility and representativeness.
  • The methodology significantly aids in understanding material variability and supports informed decisions in pharmaceutical development and quality control.