Interference effects, analytical error

The unique aspects of speciation procedures arise from the additional specification that the procedure adopted should not disturb existing equilibrium conditions. The choice of procedure is further restricted by the fact that the total concentration of element present in a sample (e.g. Cu, Pb, Cd, Zn in water samples) is often near the detection limits of many standard analytical techniques, and modified or refined techniques are required to handle the even lower levels present in isolated sub-categories. In biological matrices, the concentrations of inorganic and organo-metallic compounds present can range from 10 3 to 10 12 mol dm 3, and at the lower levels even the determination of total element content can be greatly in error, if suitable correction is not made for interference effects which can arise from the nature of the sample. 

Risk of occurrence of the interference effect increases with an increasing number of sample components as well as with a higher ratio of the concentrations of interferent and analyte. Because a specific feature of trace analysis is the excess of some sample components accompanying the analyte, the interference effect in these circumstances can potentially lead to great analytical errors. 

If the standards contain analyte alone (as is usual), such a calibration procedure allows accurate reconstruction of the calibration dependence only when the interference effect does not exist. However, if the sample contains some interferents, the reconstruction cannot be accurate and, consequently, the analyte is determined with a systematic error (as shown in Fig. 3.3). 

However, the interference effect is stiU a very serious problem in the indirect method because sample components can change the anal3Tical signal, not only by direct influence on the reagent, but also indirectly by reaction with the analyte. Because the standard solutions are prepared and measured separately from the sample, systematic error of the analytical result can be expected in both cases unless the appropriate measures (mentioned above) are taken to eliminate interferences. 

The two common sources of analytical error are interference during the analytical measurement and contamination during sample preparation. During the analytical measurement, potential interferences include, matrix effects chemical interferences, ionization interferences, and spectral interferences. 

Figure 14-25 Simulated examples that illustrate the effect of sample-related random interferences in a scatter plot with regression analysis. A, xl and x2 are subject to only analytical errors. B, Additional random bias of the same magnitude is present, which results in a wider scatter around the line.

Some systematic instrument errors can be found and corrected by calibration. Periodic calibration of equipment is always desirable because the response of most instruments changes with time as a result of wear, corrosion, or mistreatment. Many systematic instrument errors involve interferences in which a species present in the sample affects the response of the analyte. Simple calibration does not compensate for these effects. Instead, the methods described in Section 8C-3 can be used when such interference effects exist. 

Recoveries and separation factors are useful ways to evaluate the effectiveness of a separation. They do not, however, give a direct indication of the relative error introduced by failing to remove all interferents or failing to recover all the analyte. The relative error introduced by the separation, E, is defined as... 

Separate sample blanking requires an additional analytical channel, and is therefore wasteflil of both reagents and hardware. An alternative approach that is used on several automated systems, eg, Du Pont ACA, BM-Hitachi 704, Technicon RA-1000, is that of bichromatic analysis (5) where absorbance measurements are taken at two, rather than one, wavelength. When the spectral curves for the interference material and the chromogen of the species measured differ sufficiently, this can be an effective technique for reducing blank contributions to assay error. Bichromatic analysis is effective for blanks of both the first and second type. 

The analytical response generated by an immunoassay is caused by the interaction of the analyte with the antibody. Although immunoassays have greater specificity than many other analytical procedures, they are also subject to significant interference problems. Interference is defined as any alteration in the assay signal different from the signal produced by the assay under standard conditions. Specific (cross-reactivity) and nonspecific (matrix) interferences may be major sources of immunoassay error and should be controlled to the greatest extent possible. Because of their different impacts on analyses, different approaches to minimize matrix effects and antibody cross-reactivity will be discussed separately. 

Modern-day chemical analysis can involve very complicated material samples—complicated in the sense that there can be many substances present in the sample, creating a myriad of problems with interferences when the lab worker attempts the analysis. These interferences can manifest themselves in a number of ways. The kind of interference that is most famihar is one in which substances other than the analyte generate an instrumental readout similar to the analyte, such that the interference adds to the readout of the analyte, creating an error. However, an interference can also suppress the readout for the analyte (e.g., by reacting with the analyte). An interference present in a chemical to be used as a standard (such as a primary standard) would cause an error, unless its presence and concentration were known (determinant error, or bias). Analytical chemists must deal with these problems, and chemical procedures designed to effect separations or purification are now commonplace. 

One often unsuspected source of error can arise from interference by the substances originating in the sample which are present in addition to the analyte, and which are collectively termed the matrix. The matrix components could enhance, diminish or have no effect on the measured reading, when present within the normal range of concentrations. Atomic absorption spectrophotometry is particularly susceptible to this type of interference, especially with electrothermal atomization. Flame AAS may also be affected by the flame emission or absorption spectrum, even using ac modulated hollow cathode lamp emission and detection (Faithfull, 1971b, 1975). 

Interference is defined as an effect causing a systematic deviation in the measurement of the signal when a sample is nebulized, as compared with the measure that would be obtained for a solution of equal analyte concentration in the same solvent, but in the absence of concomitants. The interference may be due to a particular concomitant or to the combined effect of several concomitants. A concomitant causing an interference is called an interferent. Interference only causes an error if not adequately corrected for during an analysis. Uncorrected interferences may lead to either enhancements or depressions. Additionally, errors may arise in analytical methods in other ways, e g. in sample pretreatment via the... 

Reducing oxide based isobaric interferences in the ICP mass spectrum via gas flow modulation was proposed by Wetzel and Hieftje. After a careful manipulation of the central chaimel gas flow to impact distinguishable frequency specific behaviour of analyte and oxide ion species and application of a Fourier transform (FT) correction method, contributions from an analyte and oxide species superimposed at a given mass can be mathematically umavelled with a degree of success. Through application of this correction method, a greater than ten-fold error at m/z 156 caused by tlie interference of Ce 0 on Gd has been effectively eliminated. ... 

One final problem that must be addressed in the interpretation of sensor-array data is the reliability of the final result. Each sensor response contains a certain degree of error, and the propagation of error for a sensor array is not trivial. This fact has important ramifications in terms of identification of an analyte in the presence of interferences, as well as in the selection of coatings for inclusion in the array. The efficacy of the array depends on the uniqueness of coating responses as colinearity increases, error in the final result is amplified and the detection limit is adversely affected. These concerns have been addressed and the effects on the analytical result from the sensor array have been described quantitatively [260,272]. 

As mentioned earlier, the matrix-related random interferences may not be independent. In this case, simple addition of the components is not correct, because a covariance term should be included. However, we can estimate the combined effect corresponding to the bracket term, which then strictly refers to the CV of the differences (CV b2-rb])- As in the case with constant standard deviations, information on the analytical components is usually available, either from duplicate sets of measurements or from quality control data, and the combined random bias term in the second bracket can then be derived by subtracting the analytical component from CV21. Systematic and random errors can then be determined, and it can be decided whether a new field method can replace an existing one. Figure 14-31 shows an example with proportional random errors around the regression line. 

Svramping The introduction of a potential interferent to both calibration standards and the solution of the analyte in order to minimize the effect of the interferent in the sample matrix. Systematic error Errors that have a known source they affect measurements in one and only one way, and can, in principle, be accounted for. Also called determinate error or bias. 

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