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Accuracy and Precision

Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. Highly accurate...

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

Data Validation

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:

Contaminants and Errors

Contaminants and Errors

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.
Another key consideration is determining the appropriate number of samples required to...

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Statistical Analysis: Overview

Statistical Analysis: Overview

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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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

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Prediction of Precision for Purity Methods.

Izydor Apostol¹, Richard Wu¹, Mee Ko¹

¹Amgen, Attribute Sciences, Thousand Oaks California 91362.

Journal of Pharmaceutical Sciences

|January 25, 2020

Summary

A new model accurately predicts biopharmaceutical purity measurement precision, reducing the need for extensive data. This method streamlines precision assessment for the pharmaceutical industry.

Keywords:

HPLC bioinformatics biotechnology liquid chromatography mathematical model(s)separation science

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Area of Science:

Pharmaceutical analysis
Analytical chemistry
Biopharmaceutical quality control

Background:

Biopharmaceutical purity analysis is crucial for the pharmaceutical industry.
Assessing method capability and measurement uncertainty under real-world conditions remains challenging.
Conventional methods for assessing method precision often require large datasets and are time-consuming.

Purpose of the Study:

To refine and apply the Uncertainty Based on Current Information (UBCI) model for predicting purity measurement precision.
To compare the UBCI model's predicted precision with measured variability from various purity methods.
To demonstrate the utility of the UBCI model for streamlining precision assessments.

Main Methods:

The Uncertainty Based on Current Information (UBCI) model was applied to predict measurement precision.
Measured method variability was determined from large datasets (hundreds to thousands of measurements) for different purity methods.
Predicted precision values were statistically compared against measured variability.

Main Results:

The UBCI model's predicted precision showed excellent agreement with measured variability.
A high coefficient of determination (R² = 0.94) was achieved, validating the model's predictive capability.
The model successfully predicted precision across diverse biopharmaceutical purity methods.

Conclusions:

The UBCI model offers a reliable and efficient alternative to conventional, data-intensive methods for assessing measurement precision.
This approach enables faster and more streamlined method validation and quality control in the pharmaceutical industry.
Leveraging the UBCI model allows for accurate precision assessment using significantly smaller datasets, potentially even a single experiment.