4.2 Bias, Standards and Standardization


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1 4.2 Bias, Standards and Standardization bias and accuracy, estimation of bias origin of bias and the uncertainty in reference values quantifying by mass, chemical reactions, and physical methods standard reference materials primary, secondary and working standards methods of standardization: direct calculation, direct calibration, standard addition, internal standard, and calibration graphs 4.2 : 1/17
2 Bias and Accuracy Bias is defined as the difference between the "true" value and the mean of the pdf of the measurement. An accurate measurement is one that is more or less free of bias. An accurate measurement can have experimental error. However, the precision of the measurement affects our ability to estimate the mean of the pdf. It is often impossible to determine accuracy because the true value is unknown. To avoid this dilemma, true values are replaced by reference values. a) a real, although unknown, value such as Avogadro's number b) an assigned or agreedupon value such as standard reference materials from NIST c) a value based on a definition followed by a series of experiments, such as the length of a meter. Only type (b) can be used to determine bias. 4.2 : 2/17
3 Mathematical Definition of Bias Let x be a measurement and R be a reference value. The total error is (xr) which can be written as, (xr) = (xμ) + (μr) where (xμ) is the random error and (μr) is the bias. Accuracy implies that μ = R. For linear equations, the dependent random variable has a bias which is the sum of the individual biases. S = aa + bb + cc μ S = aμ A + bμ B + cμ C μ S S = a(μ A A) + b(μ B B) + c(μ C C) Note that bias can be signed, thus it is possible for biases to cancel. If the individual measurements are free of bias, the linear dependent random variable is free of bias. 4.2 : 3/17
4 Estimation of Bias The ttest for one average can be used to identify the presence of bias. As an example consider the following data for the spectrophotometric determination of beryllium [John Mandel, The Statistical Analysis of Experimental Data, Interscience, New York, 1964, p. 123]. The sample was a NIST standard known to contain mg beryllium. {3.167,3.177,3.177,3.169,3.173,3.177,3.177,3.177,3.171,3.169} avg = mg; std.dev. of data = t calc = = 4.33 ttable ( 0.95,9) = : 4/17 Since t calc > t table, the measurement is biased. To estimate the range of the true bias, compute the tinterval about the bias mean. B = x R ts μb = B ± = ± = ± N μ B
5 Origin of Bias The most common source of bias for an inexperienced analyst is an error in understanding, or not following the procedure. A onetime procedural error is caught by analyzing replicates. A systematic procedural error is caught by analyzing standards. For an experienced analyst the most common sources of bias are uncalibrated instrumentation, impure reagents, and the application of a familiar technique to a new type of sample. Classification of bias into constant or proportional categories can often lead to identification of the error. Consider a spectrophotometric calibration curve. A constant error appears as a nonzero intercept. It might be due to an impurity in a buffer or an instrumental offset. A proportional error appears as an incorrect slope. It might be due to an impurity in the sample, or an incorrectly calibrated photometric scale. 4.2 : 5/17
6 Uncertainty in Reference Values The reference value is determined using two or more methods. The methods are chosen such that the expected sources of bias will be different for each method. The values are then compared with the t test for two averages. If they are statistically indistinguishable it is doubtful that bias is influencing any of the values. Standards are also designed for specific methods of measurement, e.g. atomic absorption or ICP mass spectrometry. This helps ensure that the instrumentation is not biasing the result. Standards are prepared in matrices which are as close as possible to that found in the samples. For example, it is possible to purchase from NIST salmon containing known levels of mercury. Reference values will be provided with an upper bound for the total uncertainty: precision + bias. The analysis method will also be described in sufficient detail to minimize bias originating from changes to the procedure by the analyst. 4.2 : 6/17
7 Quantifying by Mass Large amounts of pure substances are expressed in moles, where 1 mole = ± molecules. The number of moles is related to mass by the expression, moles = mass/molecular mass. The mole depends upon both the definition of atomic mass and the kilogram. Sample purity is a concern if a high level of accuracy is required. For example, an analytical balance with ClassS weights can determine g to an accuracy of g. A measurement limited by the balance would require a sample at least % pure. At this level of accuracy sampletosample variations in isotope abundance can produce an error. The key mole standard for much of analytical chemistry is silver with an average mass of ± g/mol. Silver has two important properties. (1) It has an isotope distribution which is virtually independent of mineral source. (2) It can be used to standardize HCl, reducing reagents, and precipitating reagents. 4.2 : 7/17
8 Quantifying by Other Methods If a substance is impure or otherwise unweighable, it can often be quantified by a specific reaction and identification of the equivalence point. analyte + standard products Equivalence point methods are not useful for low concentrations, or the reaction may not be sufficiently specific. The method is ultimately based on weighing the standard. Physical methods are preferred for small concentrations of analyte. As an example, Beer's Law relates concentration to sample absorbance. To use Beer's Law, the molar absorptivity needs to be known. The value of molar absorptivity is ultimately based on weighing a standard sample of the analyte. In this case the standard must be the compound of interest. This can be a severe restriction. 4.2 : 8/17
9 NIST Standard Reference Materials The National Institute for Standards and Technology (NIST) provides many services to the analytical chemistry community. calibration services for many types of instruments physical standards such as mass and length generation and archiving of standard reference data such as atomic masses standard reference materials (SRMs) that can be used to calibrate instrumentation There are four major considerations involved in certifying an SRM. homogeneity  every subportion of a lot has to be statistically representative of the whole stability  the reference value should not change with time handing procedures  special handling such as temperature certified values  the stated uncertainties include possible sources of bias as well as the propagation of precision 4.2 : 9/17
10 Standards and Certified Reagents A primary standard is a solid weighable compound which is used in equivalence point techniques. It has the following characteristics. the purity must be known and higher than the required accuracy of the analysis  usually 99.9% is sufficient it must be of known composition and react quantitatively with the analyte the composition should not change upon drying it and its salts should be soluble in water the molecular mass should be high Certified reagents have a specified purity and come with a listing of known contaminants. The ACS provides specifications for hundreds of reagents, and companies manufacture reagents to these specifications. This link shows the scope of ACS involvement in the certification activity: : 10/17
11 Example Reagents from Sigma standard base sodium carbonate produced by the thermal decomposition of sodium oxalate; sodium oxalate, ACS certified reagent, 99.5% tris(hydroxymethyl) aminomethane (THAM or TRIS), primary standard, 99.9% standard acid potassium hydrogen phthalate, primary standard, 99.95% standard reductant sodium oxalate, ACS certified reagent, 99.5% arseneous acid, As 2 O 3, primary standard, 99.95% standard oxidant potassium dichromate, ACS certified reagent, 99.0% potassium bromate, primary standard, 99.8% standard precipitant sodium chloride, ACS certified reagent, 99.0% silver nitrate, ACS certified reagent, 99.0% 4.2 : 11/17
12 Secondary and Working Standards Secondary standards are compounds that do not have all of the attributes of a primary standard but are still useful in equivalence point techniques or physical methods. sodium hydroxide cannot be quantified by weighing and reacts with CO 2 in air and glass surfaces  requires repetitive standardization potassium permanganate is so reactive it needs repetitive standardization thiosulfate is eaten by airborne bacteria Working standards have a low or unknown accuracy, but a stable response. Such standards are used to monitor instrumental drift, where the response is used to normalize timedependent signals. 4.2 : 12/17
13 Direct Calculation This method of standardization is used when there is a known mathematical relationship between the measured response and the concentration. R = kc where R is the measured response, k is the known proportionality constant, and C is the analyte concentration. A good example is the titration of an unknown acid with a known base, ml base ml = acid M M base acid where, ml acid and M base are known and ml base is measured. one measurement per sample the standard and the analyte are different compounds cost effective for any number of samples 4.2 : 13/17
14 Direct Calibration This method of standardization is used when the measured response is known to be proportional to concentration, but the proportionality constant is unknown. R = kc An example would be spectrophotometry using Beer's law, A = εlc where A is absorption, ε is the molar absorptivity, l is the pathlength, and C is the molar concentration of analyte. The value of l is known, but ε needs to be determined in a separate experiment. two measurements are required  standard and unknown the standard and unknown are the same compound cost effective when many determinations of the same compound need to be made 4.2 : 14/17
15 Standard Addition This method of standardization is used when the proportionality constant is unknown and matrix dependent. The analyte concentration is determined by a twostep process. (1) Run the unknown by itself. R u = kc u (2) Run the unknown plus a small, known amount of analyte called the "spike." R u+s = k(c u + C s ) The concentration of the unknown is obtained by solving the two equations. R C u u = Cs R R u+ s two measurements are required the standard and analyte are the same compound not cost effective with many samples (it would be preferable to dilute into a solvent where a direct calibration could be used) u 4.2 : 15/17
16 Internal Standardization This is a form of standardization used when the proportionality constant is time dependent. The temporal dependence is eliminated by using a ratio of responses measured simultaneously. The second compound must have the same temporal response as the analyte. R u = k u f(t)c u R s = k s f(t)c s First, known concentrations of both the analyte and standard are used to determine the timeindependent ratio, K = k u f(t)/k s f(t) = k u /k s. Then the unknown and standard are measured simultaneously using the known value K. R u RK = s C C s u four measurements are required the standard and analyte are different compounds internal standards can be inefficient unless there is an easy way to make the simultaneous measurements 4.2 : 16/17
17 Calibration Graph This is a form of standardization used when the mathematical relationship between the measured response and concentration is unknown. R = f(c) A series of standards are prepared that are spread evenly over the range of concentrations expected for the unknown samples. When the functional form of f(c) is known but the equation constants are not, the calibration graph points can be curvefit to obtain them. Once the constants are known, a direct calculation can be used. When the functional form of f(c) is unknown, the density of standards has to be sufficient that interpolation between points yields a valid value. requires many measurements the standard and analyte are the same compound only cost effective if many samples are analyzed 4.2 : 17/17
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