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DESCRIPTIONLaboratory QA/QC. An Overview. Definitions (1). Quality Assurance: QA is defined as the overall program that ensures the final results reported by the laboratory are correct. QA is a broad plan for maintaining quality in all aspects of a program. QA establishes the need for quality control. - PowerPoint PPT Presentation
Quality Assurance: QA is defined as the overall program that ensures the final results reported by the laboratory are correct.QA is a broad plan for maintaining quality in all aspects of a program.QA establishes the need for quality control.
Quality Control: The measures that must be included during each laboratory procedure to verify that the test is working properly. QC refers to routine technical activities with the purpose to control error.QC can be considered as the HOW of the QA process.QC is applicable to field, lab and office procedures (administration).
Quality Assessment - quality assessment (also known as proficiency testing) is a means to determine the quality of the results generated by the laboratory. Quality assessment is a challenge to the effectiveness of the QA and QC programs. Quality Assessment may be external or internal.
QC aims at simply ensuring that the results generated by the test are correct. However, QA is concerned with much more. It checks whether the right test is carried out on the right sample, and that the right result and right interpretation is delivered to the right person at the right time
We need QA to:Understand data reliability;Quantify areas of analytical uncertainty; andStandardize measurement to allow for repeatable and comparable data across time and place.
Quality Assurance (QA)broad program plan establishes the need for QC
Quality Controls (QC) individual checks and balancesthe How to of QA
Quality control is applicable in all aspects of a soil, plant and water sampling project including:Field data collection and samplingLaboratory analysis and processing Data evaluation and assessment Reporting and project documentation
QC provides steps to ensure lab data will meet defined standards of quality with a standard level of confidence
In most cases field QC (soil, water and plant sampling) is out of laboratory control;QC is particularly critical in field data collection;Often the most costly aspect of any project and the most limiting factor is field sampling;Data is never reproducible under the exact same condition or setting; Therefore, field sampling QA is also needed to assure that best possible (most reliable) set of data is obtained.
7.5 cm coreIrrigation projectRepresenting 10 ha
Laboratory data analysis, data measurement, and data acquisition:Chain of custody formsEquipment calibrationStorage practicesAnalytical methodsHolding timesLimit of detection (LODs), previously known as MDLs.
Educational background and training of personnel;Condition of the samples;Controls used in the test runs;Reagents quality;Maintenance status of equipment;Interpretation of the results;Recording of results; andReporting of results.
True value: This is an ideal concept which practically cannot be achieved.Accepted true value: The value approximating the true value, the difference between the two values should be negligible (not statistically significant).Error: The discrepancy between the result of a measurement and the true (or accepted true value).
Input data required: Such as standards used, calibration values, and values of physical constants;Inherent characteristics of the quantity being measured;Instruments used: Accuracy, repeatability;Observer unreliability: Reading errors, blunders, equipment selection, analysis and computation errors;
Environment: Any external influences affecting the measurement; andTheory assumed: Validity of mathematical methods and approximations.
An error that varies in an unpredictable manner, in magnitude and sign, when a large number of measurements of the same quantity are made under effectively identical conditions. Random errors create a characteristic spread of results for any test method and cannot be accounted for by applying corrections. Random errors are difficult to eliminate, but repetition reduces the influences of random errors.
Examples of random errors include: errors in pipetting; changes in incubation period; or the time used for extraction/centrifuging. Random errors can be minimized by training, supervision and adherence to standard operating procedures (SOPs).
An error that, in the course of a number of measurements of the same value of a given quantity, remains constant when measurements are made under the same conditions, or varies according to a definite law when conditions change. Systematic errors create a characteristic bias in the test results and can be accounted for by applying a correction. Systematic errors may be induced by factors such as variations in incubation temperature, change in the reagent batch or modifications in testing methodology.
Internal Quality Control:Controllable by those responsible for performing the laboratory analysis.External Quality Control: A set of measures established for and conducted by people outside the analytical laboratory (lab auditors, regional or national laboratories, accreditation process, etc).
Internal Quality Control:Equipment calibrationProper training and certification of practitionersProper sampling techniques Proper data documentation
IQC samples comprises either In-house prepared aliquot of known values, orInternational standards with values within significant ranges for the element to be measured.
External quality control:Performance auditsSplit sample analysisReplicate (duplicate) sample analysis
Successful data collection and analysis is dependant upon The PARCC Parameters: PrecisionAccuracyRepresentativenessCompletenessComparability
The key concepts of QA/QC are the PARCC Parameters the WHY of the QA
Precision -degree of agreement there is between repeated measurements of the same characteristiccan be biased meaning there is a consistent error in the resultsAccuracy - measures how close data results are to a true or expected value does not allow for bias
accuracy = (average value) (true value)precision represents repeatabilitybias represents amount of errorlow bias and high precision = statistical accuracy
Representativeness - extent to which measurements actually represent the true environmental condition or population at the time a sample was collected.Representative data should result in repeatable data Does this represent this??
Comparability -the extent to which data can be compared between sample locations or periods of time within a project, or between projects Will similar data from these sites be Comparable ??
Quality Assurance (QA)broad program plan establishes the need for QC Quality Controls (QC) standardized tests and methodsthe HOW of QA
An internal quality control program depends on the use of internal quality control (IQC) samples, and using statistical analysis methods for interpretation.
A Shewhart Control Chart depend on the use of IQC samples and is developed in the following manner:Put up the IQC specimen for at least 20 or more sample runs and record down the readings;Calculate the mean (x) and standard deviations (Sd);Make a plot with the sample run on the x-axis, and concentration readings on the y axis.
Draw the following lines across the y-axis: mean, -3, -2, -1, 1, 2, and 3 Sd;Plot concentration reading obtained for the IQC specimen for subsequent sample runs.Major events such as changes in the reagent batch and/or instruments used should also be recorded on the chart.
A Shewhart control chart consists of:Points representing a statistic (e.g., a mean, range, or proportion) of measurements of a quality characteristic in samples analyzed at different times [the data];The mean of this statistic using all the samples is calculated (e.g., the mean of the means, mean of the ranges, or mean of the proportions);
A center line is drawn at the value of the mean of the statistic;The standard error (e.g., standard deviation) of the statistic is also calculated using all the samples; andUpper and lower control limits (sometimes called "natural process limits"), indicating the threshold at which the process output is considered statistically 'unlikely' are drawn typically at 3 Sd from the center line.
The formulation of Westgard rules were based on statistical methods. Westgard rules are commonly used to analyze data in Shewhart Control charts. Westgard rules are used to define specific performance limits for a particular analysis and can be use to detect both random and systematic errors.
There are six commonly used Westgard rules of which three are warning rules and the other three are mandatory rules. The violation of warning rules should trigger a review of test procedures, reagent performance and equipment calibration. The violation of mandatory rules should result in the rejection of the obtained results.
+3 sd-3 sd+2 sd-2 sd-1 sd+1 sdSample readingTarget valueSample run
Warning 12SD : It is violated if the IQC value exceeds the mean by 2SD. It is an event likely to occur normally in less than 5% of cases.Warning 22SD : It detects systematic errors and is violated when two consecutive IQC values exceed the mean on the same side of the mean by 2SD.Warning 41SD : It is violated if four consecutive IQC values exceed the sam