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Quality Assurance in Field andLab Work

Quality Assurance

� Laboratory experiments require a level of data quality that give confidence in the reported values.

� A Quality Assurance Program establishes statistical proceduresto identify and minimize errors.

� Quality Assurance includes Quality Control and Quality Assessment.

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Quality Control Elements

• Trained personnel

• Equipment maintenance

• Proper analytical methods

• Good Laboratory Practices

• http://en.wikipedia.org/wiki/Good_Laboratory_Practice• http://www.fda.gov/ICECI/EnforcementActions/BioresearchMonitoring/ucm135197.htm

• Good Measurement Practices• http://www.nist.gov/pml/wmd/labmetrology/upload/GMP_13_20120229.pdf

QC Elements (cont.)• Standard Operation Procedures - established or prescribed

methods to be followed routinely for the performance of designated operations or in designated situations

• Documentation

• Inspection and Validation

• Mechanism to control errors that provides adequate quality level at reasonable cost

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QA/QC How? Where?

� Other Guides:

� ASTM Standards

� EPA Guidance documents

� Manufacturer’s guidelines

� TCEQ or state environmental agency documents

Quality Assessment (QA)� Is the mechanism to verify that the system is operating

within acceptable limits by assuring that quality control elements are followed

� The quality procedures are described in a QA/QC manual

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Quality Assurance Program Elements� Sampling

� Sample Custody

� Sample preparation

� Analytical Methodology

� Calibration

� Detection Limits ( http://www.chemiasoft.com/node/58)

� Statistics in lab analysis

� Quality Control Charts (http://en.wikipedia.org/wiki/Control_chart)

Ex: Quality Control Chart/

http://environmentalqa.com/2012/07/04/quality-control-charting-in-the-laboratory/

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Sampling� Quality of data depends primarily of the sample analyzed

� Sample must be Representative of the system being evaluated

� Grab or Individual Samples are those collected at specific sites and times

� Composite samples are made of parts of grab samples so they represent averages of several sites or times

� Samples must be completely identifiable by Labeling

Field Quality Control Elements� Equipment blanks before sampling detect contamination in the

sampling equipment with clean (rinsed, evacuated) sampling containers on pure media (ex. analyte-free water, zero air)

� Field blanks are collected similarly at the end of the sampling

� Trip blanks prepared at the lab and do the trip to the field and back to evaluate contamination during transportation

� Duplicate samples to check precision of the sampling process

� Split samples are taken from a container and a part separated into another container to check analytical performance

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Sample Custody and Documentation� Documentation of how the samples are collected, preserved,

stored, transported, treated, labeled, tracked

� State names of persons at each stage of the process

� Other documentation include sample prep, analytical methods, calibration, reagents and standards, calculations, detection limits, quality control checks, data validation and reduction, and reporting

Ex: Sample Custody

Forms

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Analytical Method Elements� Adequate for the objective

� Sensitivity- ratio of the change of output to the change in input. The slope of the curve relating the input to an output representing the measurement

� Selectivity - Selectivity refers to the extent to which a method can determine particular analytes in mixtures or matrices without interferences from other components.

� Accuracy

� Precision

� Calibration requirements

� Reproducibility- is the degree of agreement between measurements or observations conducted on replicate specimens in different locations by different people

Calibration� A calibration curve is a plot of the response of the instrument to

different concentrations of the standard

� Concentrations prepared by dilution of the standard

� Frequency of calibration established, initial calibration and continuing calibrations

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Detection Limits� Detection Limit is the smallest concentration that can be

measured with a stated probability of significance

� The Method Detection Limit is measured at 98% confidence

� Instrument Detection Limit is the concentration of the analyte standard that produces a signal greater than five times the signal-to-noise ratio

� http://en.wikipedia.org/wiki/Detection_limit

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General Lab DiscussionWhat kind of information should be kept or documented in relationship to equipment performance and use?� pH Meter, Gas Chromatograph, Glassware (http://www.astm.org/Standards/E288.htm)

� Lab Refrigerator for sample storage

� DI water

� Chemicals (http://www.sciencecompany.com/Chemical-Grade-Designations-W53C665.aspx)

� LIMS – Laboratory Information Management System

� On November 6, 2006, a giant step towards achieving a long-term goal of the environmental laboratory and monitoring communities to have a national accreditation program was realized. After years of an evolving program under the auspices of the National Environmental Laboratory Accreditation Conference (NELAC) and the Institute for National Environmental Laboratory Accreditation (INELA), both Boards of Directors took action to form The NELAC Institute (TNI).http://www.nelac-institute.org/standards.php?pab=1_1#pab1_2

Statistics� Accuracy (Bias) is the degree of agreement of a measured value with the true or expected value (concentration of the standard), it is

measured as Percentage of Recovery:

%R = (Measured Value x 100)/True Value

� The assessment of Accuracy for monitors is accomplished by challenging the analyzer with at least one audit gas of known concentration

� Precision is a measure of the repeatability of the results. A precision check consists of introducing a known concentration of the pollutant into the monitor in the concentration range required. The resulting measured concentration is then compared to the known concentration.

� Precision is the degree of agreement of measurements (of the standard) performed under the same conditions, it is assessed by the standard deviation,

� Precision can also be assessed by the Confidence Limit (CL) determined by the probability that n multiple measurements (of the standard) are within a certain interval of the mean.

CL = [(t)(Ϭ)]/ n

where t is the student probability factor at the desired probability.

� Upper Control Limits (UCL) and Lower Control Limits (LCL) - defined at +/- 2 Ϭ or 3Ϭ serve to monitor that Accuracy remains under statistical control by checking that %R remains within the LCL and UCL. Precision can be monitored in a similar fashion.

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Statistics Review� Error analysis:

Measured value = true value ± error.

� If error is random, we can make replicate measurements and calculate an average.

� The calculated average is an estimate of the true mean.

Basic statistical parameters� Sample average, which is estimate of true mean η of the underlying random variable, is calculated:

� Sample variance s2, which is estimate of true variance Ϭ2 of the underlying random variable, is calculated:

� Sample standard deviation s is equal to the square root of the sample variance s2.

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Basic statistical parameters� Often more interested in the variance of the average value, or the standard error of the

mean (SEM) :

or standard deviation divided by square root of number of samples.

� The SEM is an estimate of the uncertainty involved in estimating the mean of a sample.

� In contrast, the standard deviation (s) is an estimate of the variability involved in measuring the data from which mean is calculated.

� The SEM (i.e., the uncertainty of the estimate of the mean) decreases as the sample size increases.

The normal (or Gaussian) distribution� Valid to assume that sample averages have normally distributed errors.

� Figure on left shows a probability distribution function (PDF) for a normal distribution with a mean of 0 and a standard deviation of 1.

� The area under any PDF equals unity.

� For a normal distribution, 68% of PDF lies within η±Ϭ, 95% within η±2Ϭ , and 99.7% within η±3Ϭ .

� Figure on right shows cumulative distribution function (CDF).

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Confidence Intervals for the Mean Value� Uncertainty is typically characterized by calculating confidence intervals for

the true mean:

� t = the t-distribution value. The value of t is both a function of the degrees of freedom (n-1) and the level of significance.

� For example, assume α = 0.05 to calculate a 95% CI.

�� = � ± � �

√= � ± (�)( ��)

� The value is the probability that a random variable will fall in the upper or lower tail of a probability distribution.

� For example, α = 0.05 implies that there is a 0.95 probability that a random variable will not fall in the upper or lower tail of the probability distribution.

� Statistical tables of probability distributions (e.g., normal and “student t”) list probabilities that a random variable will fall in the upper tail only.

α values for probability distributions

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α values and confidence intervals� We typically want to determine a confidence interval for which we

are 90% confident that a random variable will not fall in eithertail.

� In this case, we use an α/2 = 0.05.� Similarly, to determine 95% and 99% confidence intervals, we

would use α/2 = 0.025 and 0.005, respectively.� t α/2,n-1 can be determined in EXCEL using the function:“=TINV[α, n-1]”.http://office.microsoft.com/en-us/excel-help/tinv-HP005209317.aspx

T-table� As a statistical tool, a t-table lists critical values for two-

tailed tests. You then use these values to determine confidence values. The following t-table shows degrees of freedom for selected percentiles from the 90th to the 99th:

� http://www.dummies.com/how-to/content/statistical-tdistribution-the-147ttable148.html

� Excel has a function to obtain the t-value. =TINV(probability,deg_freedom)

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T-test� A t-test is any statistical hypothesis test in which the test statistic follows a

Student's t distribution if the null hypothesis is supported. It can be used to determine if two sets of data are significantly different from each other, and is most commonly applied when the test statistic would follow a normal distribution if the value of a scaling term in the test statistic were known. When the scaling term is unknown and is replaced by an estimate based on the data, the test statistic (under certain conditions) follows a Student's tdistribution.

� The t-statistic was introduced in 1908 by William Sealy Gosset, a chemist working for the Guinness brewery in Dublin, Ireland ("Student" was his pen name).

� http://en.wikipedia.org/wiki/Student%27s_t-test� TTEST(array1,array2,tails,type) available in Excel.

Other uses of the t-test� Compare the data sets from two labs (or lab groups) for the same

compound samples with the same technique.� Compare a parameter such as temperature from two different, but

perhaps fairly close locations.� Compare two different treatment methods.� This test is only used when both:

the two sample sizes (that is, the number, n, of participants of each group) are equal;it can be assumed that the two distributions have the same variance.