Quality Assessment
Quality Assessment - Related Videos
05:00
Using a Chemical Biopsy for Graft Quality Assessment
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Cited by 11 •
2020
The protocol presents utilization of the chemical biopsy approach followed by comprehensive metabolomic and lipidomic analysis for quality assessment of kidney grafts allocated for transplantation.
06:26
Rapid Assessment of Membrane Protein Quality by Fluorescent Size Exclusion Chromatography
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Cited by 2 •
2023
The present protocol describes a procedure to perform fluorescent size exclusion chromatography (FSEC) on membrane proteins to assess their quality for downstream functional and structural analysis. Representative FSEC results collected for several G-protein coupled receptors (GPCRs) under detergent-solubilized and detergent-free conditions are presented.
13:09
Assessment of Boron Doped Diamond Electrode Quality and Application to In Situ Modification of Local pH by Water Electrolysis
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Cited by 5 •
2016
A protocol is described for the characterization of the key electrochemical parameters of a boron doped diamond (BDD) electrode and subsequent application for in situ pH generation experiments.
05:07
Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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2025
This protocol allows initial quality control for RNA-seq experiments for wet-lab biologists with limited bioinformatics experience.
10:39
Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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2025
This paper outlines the protocol for qualitatively and quantitatively validating a university service-learning instrument, following the parsimony criterion to create the most robust and optimal version possible. The Delphi qualitative validation method and Robust Unweighted Least Squares Exploratory Factor Analysis, as a quantitative item optimization method, are utilized for this purpose.
FAQs
Calibration establishes how measurements are related to known expectations, while reference materials provide a comparison point for evaluating analytical performance. Used together, they help reveal whether reported chemical values are accurate or systematically biased. This evidence is especially important when results must support identity, purity, reaction monitoring, or other decisions about a material.
Accuracy describes whether a result agrees with the expected value, whereas precision describes consistency among repeated measurements. Sensitivity concerns the ability of the analytical measurement to respond to the quantity being examined. Reviewing these characteristics against acceptance criteria helps distinguish systematic bias from unacceptable variability and determines whether the data are suitable for their intended purpose.
Blanks help identify contamination or signals introduced by the analytical system rather than by the sample. Replicate analyses show how consistently the procedure produces results under repeated measurement. Considering both types of evidence helps laboratories separate sample-related findings from procedural problems and detect variability that could weaken confidence in chemical conclusions.
A typical workflow applies a validated measurement procedure, calibrates the analytical system, and evaluates results with reference materials, blanks, and replicate analyses. The resulting data are examined for accuracy, precision, sensitivity, contamination, and bias. Finally, comparison with defined acceptance criteria indicates whether the measurements meet the requirements for their intended use.
Chemistry laboratories apply these practices when testing identity or purity, monitoring a reaction, evaluating pharmaceutical materials, or analyzing environmental samples. They also support routine laboratory quality control by checking whether measurements remain reliable and reproducible. The appropriate focus depends on the decision, such as confirming a material, tracking a process, or judging analytical data.
Findings should be interpreted against predefined acceptance criteria rather than viewed as isolated measurements. Evidence of bias, contamination, or excessive variability signals that results may not be reliable or fit for purpose. Identifying these problems early allows researchers and manufacturers to address weaknesses before they influence scientific conclusions, reported results, or manufacturing decisions.