Ricardo J. N. Bettencourt da Silva

Optimization of the determination of chemical oxygen demand in wastewaters.

Chemical oxygen demand (COD) is one of the most relevant chemical parameters for the management of wastewater treatment facilities including the control of the quality of an effluent. The adequacy of decisions based on COD values relies on the quality of the measurements. Cost effective management of the minor sources of uncertainty can be applied to the analytical procedure without affecting measurement quality. This work presents a detailed assessment of the determination of COD values in wastewaters, according to ISO6060:1989 standard, which can support reduction of both measurement uncertainty and cost of analysis. This assessment includes the definition of the measurement traceability chain and the validation of the measurement procedure supported on sound and objective criteria. Detailed models of the measurement performance, including uncertainty, developed from the Differential Approach, were successfully validated by proficiency tests. The assumption of the measurement function linearity of the uncertainty propagation law was tested through the comparison with the numerical Kragten method. The gathered information supported the definition of strategies for measurement uncertainty or cost reduction. The developed models are available as electronic supplementary material, in an MS-Excel file, to be updated with the users data.

The Quality of Standards in Least Squares Calibrations

Frequently, the Least Square Regression Model (LSRM) assumption related to standards quality is either forgotten or formulated in too strict of a way, making its application unsuccessful or difficult. This work posits that the LSRM requires calibration standards with concentration ratios affected by negligible uncertainties that are achievable for standard solutions with large relative uncertainties. Criterion to test this assumption and a model to take into account the uncertainty of standards in performed quantifications are presented. The developed models were successfully tested with a combination of experimental data about interpolation uncertainty, for the determination of hexachlorobenzene by GC-ECD, with simulated values of standards concentrations.

Evaluation of sample processing and sub-sampling performance

Abstract A methodology for the estimation of sample processing and sub-sampling performance based on the comparison of the global method experimental dispersion of results with the uncertainty estimated from developed models for the subsequent analytical steps is presented. This approach is a valuable alternative to the evaluation of adequate experimental information using a classical ANOVA, since the significance of the sample processing and sub-sampling is evaluated with a higher number of degrees of freedom for the same number of experimental assays, due to the high number of degrees of freedom associated with the uncertainty estimated for the subsequent analytical steps from the combination of the involved sources of uncertainty. Considering the construction of a model to describe the performance of the analytical steps following sample processing and sub-sampling over a broad concentration range, the experimental assays involved at the evaluation of the sample processing and sub-sampling can be performed at any concentration meeting the previously validated range and several months after the development of that model once its adequacy has been proven over time. This approach, which also allows the construction of a detailed performance model for the global analytical method over a broad concentration range, was applied to the determination of pesticide residues in apples by gas-chromatography with electron capture detector. Considering that no information was available regarding the samples heterogeneity, sub-sampling performance was evaluated considering a sample representing the worst expectable homogeneity. This was accomplished by spiking just one out of the 10 halves of apples processed in each sample. The developed model for the performance of the analytical method was successfully and easily applied to routine analysis through an automated link between the information generated by the chromatograph software with a file containing the model.

Evaluation of the analytical method performance for incurred samples

Abstract A methodology for the evaluation of the performance of an analytical method for incurred samples is presented. Since this methodology is based on intra-laboratory information, it is suitable for analytical fields that lack reference materials with incurred analytes and it can be used to evaluate the analytical steps prior to the analytical portion, which are usually excluded in proficiency tests or at the certification of reference materials. This methodology can be based on tests performed on routine samples allowing the collection of information on the more relevant combinations analyte/matrix; therefore, this approach is particularly useful for analytical fields that involve a high number of analyte/matrix combinations, which are difficult to cover even considering the frequent participation in expensive proficiency tests. This approach is based on the development of a model of the performance of the analytical method based on the differential approach for the quantification of measurement uncertainty and on the comparison of recovery associated with each one of the analytical steps whose performance can vary with the analyte origin, for spiked and incurred samples. This approach was applied to the determination of pesticide residues in apples. For the analytes covered, no evidence was found that the studied sample processing and extraction steps performance for this matrix varies with the analyte origins.

Worst case uncertainty estimates for routine instrumental analysis

A methodology for the worst case measurement uncertainty estimation for analytical methods which include an instrumental quantification step, adequate for routine determinations, is presented. Although the methodology presented should be based on a careful evaluation of the analytical method, the resulting daily calculations are very simple. The methodology is based on the estimation of the maximum value for the different sources of uncertainty and requires the definition of limiting values for certain analytical parameters. The simplification of the instrumental quantification uncertainty estimation involves the use of the standard deviation obtained from control charts relating to the concentrations estimated from the calibration curves for control standards at the highest calibration level. Three levels of simplification are suggested, as alternatives to the detailed approach, which can be selected according to the proximity of the sample results to decision limits. These approaches were applied to the determination of pesticide residues in apples (CEN, EN 12393), for which the most simplified approach showed a relative expanded uncertainty of 37.2% for a confidence level of approximately 95%.

Weighted calibration with reduced number of signals by weighing factor modelling: application to the identification of explosives by ion chromatography.

The linear weighted regression model (LW) can be used to calibrate analytical instrumentation in a range of quantities (e.g. concentration or mass) wider than possible by the linear unweighted regression model, LuW (i.e. the least squares regression model), since this model can be applied when signals are not equally precise through the calibration range. If precision of signals varies within the calibration range, the regression line should be defined taking into account that more precise signals are more reliable and should count more to define regression parameters. Nevertheless, the LW requires the determination of the variation of signals precision through the calibration range. Typically, this information is collected experimentally for each calibration, requiring a large number of replicate collection of signals of calibrators. This work proposes reducing the number of signals needed to perform LW calibrations by developing models of weighing factors robust to daily variations of instrument sensibility. These models were applied to the determination of the ionic composition of the water soluble fraction of explosives. The adequacy of the developed models was tested through the analysis of control standards, certified reference materials and the ion balance of anions and cations in aqueous extracts of explosives, considering the measurement uncertainty estimated by detailed metrological models. The high success rate of the comparisons between estimated and known quantity values of reference solutions, considering results uncertainty, proves the validity of developed metrological models. The relative expanded measurement uncertainty of single determinations ranged from 1.93% to 35.7% for calibrations performed along 4 months.

Spreadsheet for designing valid least-squares calibrations: A tutorial

Instrumental methods of analysis are used to define the price of goods, the compliance of products with a regulation, or the outcome of fundamental or applied research. These methods can only play their role properly if reported information is objective and their quality is fit for the intended use. If measurement results are reported with an adequately small measurement uncertainty both of these goals are achieved. The evaluation of the measurement uncertainty can be performed by the bottom-up approach, that involves a detailed description of the measurement process, or using a pragmatic top-down approach that quantify major uncertainty components from global performance data. The bottom-up approach is not so frequently used due to the need to master the quantification of individual components responsible for random and systematic effects that affect measurement results. This work presents a tutorial that can be easily used by non-experts in the accurate evaluation of the measurement uncertainty of instrumental methods of analysis calibrated using least-squares regressions. The tutorial includes the definition of the calibration interval, the assessments of instrumental response homoscedasticity, the definition of calibrators preparation procedure required for least-squares regression model application, the assessment of instrumental response linearity and the evaluation of measurement uncertainty. The developed measurement model is only applicable in calibration ranges where signal precision is constant. A MS-Excel file is made available to allow the easy application of the tutorial. This tool can be useful for cases where top-down approaches cannot produce results with adequately low measurement uncertainty. An example of the application of this tool to the determination of nitrate in water by ion chromatography is presented.

Designing valid and optimised standard addition calibrations: Application to the determination of anions in seawater

A strategy for designing valid standard addition calibrations and for optimising their uncertainty is presented. The design of calibrations involves the development of models of the sensitivity and precision of the instrumental signal, in a wide range of analyte concentration (or any other studied quantity), and the definition of sample dilution and standard addition procedures that allow fulfilling the assumptions of the linear unweighted regression model in, typically, a smaller range of standard addition calibrations. Calibrators are prepared by diluting the sample and adding analyte with negligible uncertainty to fit in a concentration range where signals are homoscedastic. The minimisation of the uncertainty is supported on detailed measurement uncertainty models function of the calibrators preparation procedure and of analytical instrumentation performance. The number of collected signals replicates is defined by balancing their impact on the estimated expanded uncertainty, the resources needed and the target (maximum) uncertainty for the intended use of measurements. The calibration design strategy was successfully applied to the determination of the mass concentration (mg L(-1)) of Cl(-), Br(-), NO3(-) and SO4(-2) in seawater by ion chromatography. A target expanded uncertainty of 20% was defined for the determination of Cl(-), NO3(-) and SO4(-2), or 40% for the determination of the smaller mass concentration of Br(-). The developed measurement model produced reliable predictions of the measurement uncertainty from approximate concentration of the analyte in the sample, before its accurate quantification, thus proving optimisation is effective. Predictions are more prone to the variability of the measurement uncertainty estimation if based on low number of calibrators signals. The reported relative expanded uncertainty ranged from 7.1% to 49%.

Multivariate analysis of nutritional information of foodstuff of plant origin for the selection of representative matrices for the analysis of pesticide residues.

Testing safety of foodstuffs of plant origin involves the analysis of hundreds of pesticide residues. This control is only cost-effective through the use of methods validated for the analysis of many thousands of analyte/matrix combinations. Several documents propose representative matrices of groups of matrices from which the validity of the analytical method can be extrapolated to the represented matrices after summarised experimental check of within group method performance homogeneity. Those groups are based on an evolved expert consensus based on the empirical knowledge on the current analytical procedures; they are not exhaustive, they are not objectively defined and they propose a large list of representative matrices which makes their application difficult. This work proposes grouping 240 matrices, based on the nutritional composition pattern equivalence of the analytical portion right after hydration and before solvent extraction, aiming at defining groups that observe method performance homogeneity. This grouping was based on the combined outcome of three multivariate tools, namely: Principal Component Analysis, Hierarchical Cluster Analysis and K-Mean Cluster Analysis. These tools allowed the selection of eight groups for which representative matrices with average characteristics and objective criteria to test inclusion of new matrices were established. The proposed matrices groups are homogeneous to nutritional data not considered in their definition but correlated with the studied multivariate nutritional pattern. The developed grouping that must be checked with experimental test before use was tested against small deviations in food composition and for the integration of new matrices.

Conformity assessment of multicomponent materials or objects: Risk of false decisions due to measurement uncertainty – A case study of denatured alcohols

Risk of a false decision on conformity of a multicomponent material or object due to measurement uncertainty is discussed. Even if conformity assessment for each component of a material sample is successful, the total probability of a false decision (total consumers risk or producers risk) concerning the sample as a whole might still be significant. A model of the total probability of such false decisions is formulated based on the law (theorem) of total probability. It is shown that the total risk can be evaluated as a combination of the particular risks of conformity assessment of sample components. For a more complicated task, i.e. for a larger number of components of a sample under control, the total risk is greater. As a case study, the total probability of false conforming (total consumers risk) is evaluated for customs control of completely denatured alcohols, where conformity assessment is performed by comparison of chemical analytical test results with the regulatory limits.