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The use of the unified pH concept, [Formula: see text] , applicable to aqueous and non-aqueous solutions, which allows interpreting and comparison of the acidity of different types of solutions, requires reliable and objective determination. The [Formula: see text] can be determined by a single differential potentiometry measurement referenced to an aqueous reference buffer or by a ladder of differential potentiometric measurements that allows minimisation of inconsistencies of various determinations. This work describes and assesses bottom-up evaluations of the uncertainty of these measurements, where uncertainty components are combined by the Monte Carlo Method (MCM) or Taylor Series Approximation (TSM). The MCM allows a detailed simulation of the measurements, including an iterative process involving in minimising ladder deviations. On the other hand, the TSM requires the approximate determination of minimisation uncertainty. The uncertainty evaluation was successfully applied to measuring aqueous buffers with pH of 2.00, 4.00, 7.00, and 10.00, with a standard uncertainty of 0.01. The reference and estimated values from both approaches are metrologically compatible for a 95% confidence level even when a negligible contribution of liquid junction potential uncertainty is assumed. The MCM estimated pH values with an expanded uncertainty, for the 95% confidence level, between 0.26 and 0.51, depending on the pH value and ladder inconsistencies. The minimisation uncertainty is negligible or responsible for up to 87% of the measurement uncertainty. The TSM quantified measurement uncertainties on average only 0.05 units larger than the MCM estimated ones. Additional experimental tests should be performed to test these uncertainty models for analysis performed in other laboratories and on non-aqueous solutions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Many instrumental methods of analysis require the daily collection of calibrator signals to calibrate their response. The quality of quantifications based on these calibrations depends on calibrators quality, instrumental signal performance and regression model fitness. Linear Ordinary Least Squares (LOLS), Linear Weighted Least Squares (LWLS) or Linear Bivariate Least Squares (LBLS) regression models can be used to calibrate and evaluate the uncertainty from instrumental quantifications, but require the fulfilment of some assumptions, namely, constant signal variance (LOLS), high calibrators quality (LOLS and LWLS) and linear variation of instrumental signal with calibrator values. The LBLS is flexible regarding calibrator values uncertainty and correlation but requires the determination of calibrator values and signals covariances. This work developed a computational tool for the bottom-up evaluation of global instrumental quantifications uncertainty which simulates calibrator values correlations from entered calibrators preparation procedure and simulates calibrators and samples signals precision from prior precision data, allowing accurate uncertainty evaluation from a few replicate signals of the daily calibration. The used signal precision models were built from previously observed repeatability variation throughout the calibration interval adjusted to daily precision condition from a residual standard deviation adjustment factor. This approach was implemented in a user-friendly MS-Excel file and was successfully applied to the analysis of As, Cd, Ni and Pb in marine sediment extracts by Absorption Spectroscopy. Evaluations were tested by the metrological compatibility of estimated and reference values of control standards for confidence levels of 95% and 99%. The success rates of the compatibility tests were statistically equivalent to the confidence level (p-value>0.01)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The decision on the fitness of a measurement for its intended use and the interpretation of an analytical result requires the assessment of the measurement uncertainty. Frequently, the determination of analytes in complex matrices involves demanding sample preparations in which analyte losses are observed. These losses should be considered when reporting the results, which can be corrected for low recovery by taking the mean recovery observed in the analysis of reference items (e.g. spiked samples) or, alternatively, by subjecting calibrators to the same pre-treatment performed on the samples. In these cases, neat (NC) or adjusted (AC) calibrators are used, respectively. The way analyte losses are handled impacts on the measurement uncertainty. The top-down evaluation of the measurement uncertainty involves combining precision, trueness and additional uncertainty components. The trueness component is quantified by pooling various analyte recovery determinations. This work assesses and compares the uncertainty of polycyclic aromatic hydrocarbons (PAHs) measurements in water based on HPLC-FD calibrations with NC or AC. The trueness component is estimated by pooling mean recoveries observed from the analysis of different spiked samples to which mean recovery uncertainty and degrees of freedom are used to estimate a weighted mean recovery and respective uncertainty. The performance of measurements based on NC and AC are associated with equivalent uncertainty except when large analyte losses are observed, namely in the determination of Naphtalene. In this case, the processing of AC reduces the expanded relative uncertainty from 9.9% to 3.5%. The evaluated expanded uncertainty ranged from 3.5% to 12% of the measured value., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Recently, rapid microbiological methods (RMM) have often been used to determinate the potency of antibiotic drugs. Since all the standard and sample preparations are assayed into the same analytical conditions, it is expected that the correlations among the inhibitions zone sizes are not negligible. However, the procedures adopted in uncertainty estimations do not consider the correlation of data. The aim of this work was to study the impact of the correlation of data in the measurement uncertainty and, consequently, in the risk of false conformity decisions. RMM for the determination of the potency of cephalosporin antibiotics in pharmaceutical products were performed using an agar diffusion method. The shared analytical effects on inhibition resulted in correlation of data, which significantly decreased the combined measurement uncertainties, and therefore, the risk of false conformity decisions. Due to the lognormal distribution of potency values, measurement uncertainties were reported as a multiplicative uncertainty factor (U F ). A MS-Excel spreadsheet is provided as supplementary material and may be used to estimate the measurement uncertainty and the risk of false conformity decisions for results obtained from RMM., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Risks of a false decision on conformity of the chemical composition of a multicomponent material or object due to measurement uncertainty are defined using the Bayesian approach. Even if the conformity assessment for each particular component of a material is successful, the total probability of a false decision (total consumer's risk or producer's risk) concerning the material as a whole might still be significant. This is related to the specific batch, lot, sample, environmental compartment, or other item of material or object (specific consumer's and producer's risks), or to a population of these items (global consumer's and producer's risks). A model of the total probability of such false decisions for cases of independent actual ('true') concentrations or contents of the components and the corresponding measurement results is formulated based on the law of total probability. It is shown that the total risk can be evaluated as a combination of the particular risks in the conformity assessment of components of the item. For a more complicated task, i.e. for a larger number of components under control, the total risk is greater. When the actual values of the components' concentrations or contents, as well as the measurement results, are correlated, they are modelled by multivariate distributions. Then, a total global risk of a false decision on the material conformity is evaluated by the calculation of integrals of corresponding joint probability density function. A total specific risk can be evaluated as the joint posterior cumulative function of actual property values of a specific item lying outside the multivariate specification (tolerance) domain when the vector of measured values obtained for the item is inside this domain. The effect of correlation on the risk is not easily predictable. Examples of the evaluation of risks are provided for conformity assessment of denatured alcohols, total suspended particulate matter in ambient air, a cold/flu medication, and a PtRh alloy. [ABSTRACT FROM AUTHOR]

Abstract The comparison of pH measurements in seawater collected at different locations or occasions, is meaningful if the same measurand (i.e. the quantity intended to be measured) is determined, if adequate measurement procedures are used, including the selection of calibrators, and if the measurement uncertainty is known. Depending on the purpose of this evaluation, the measurement uncertainty should be smaller than a defined target value. The measured pH should have a sound physical-chemical meaning to allow the adequate assessment of its impacts. In the present procedure TRIS-TRIS HCl solutions, of different molality ratios, prepared in artificial seawater with reference values estimated by primary measurements, were used to obtain proper calibrators for the pH meter used for the analysis of seawater samples. This work presents the uncertainty evaluation of pH measurements in seawater, performed by potentiometry using a combination glass electrode, from the interpolation uncertainty evaluated by the Least Squares Regression Model and by Monte Carlo Simulations of measured potentials and reference values. The uncertainty evaluation was critically assessed. The developed algorithms were implemented in a user-friendly MS-Excel file available as Electronic Supplementary Material. Seawater pH was measured with an expanded uncertainty of 0.019 enabling discriminating differences of pH of two samples larger than 0.029. Graphical abstract fx1 Highlights • pH in seawater was measured using high ionic strength reference buffer solutions. • Uncertainty was evaluated by Least Square Regression Model and Monte Carlo Method. • Seawater samples' pH was measured with expanded uncertainty of 0.019 (k = 95%). • The recorded signals of the samples' measurement are the major source of uncertainty. • Calibration with low ionic strength commercial buffers leads to a pH difference > 0.1. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • [HCO 3− ] and [CO 3 2−] in seawater were evaluated with associated uncertainties. • Three seawater samples from the Portuguese Coast were analyzed for pH and TA. • Total Alkalinity (TA) of seawater samples was measured with u ′ ≈ 1.5 %. • Equivalence point (EP) detection is one of the major sources of uncertainty. • Replicate measurements reduce the impact of EP detection on uncertainty. Abstract Alkalinity and pH are chemical parameters that, once measured in seawater, allow the calculation of the concentration of the carbonate system species CO 3 2−, HCO 3 −, H 2 CO 3 and CO 2 which tend to be in equilibrium. Hence, these are key parameters used in oceanic carbon cycle models. The interpretation of the measured values depends on their respective uncertainties. This work presents a bottom-up evaluation of the uncertainty associated with measurements of total alkalinity, carbonate and bicarbonate concentrations in seawater samples by acid-base titration, with potentiometric detection, using as titrant a hydrochloric acid solution, 0.01 mol L−1 HCl in 0.67 mol L−1 NaCl, in order to approach working conditions to those of real seawater, with average ionic strength I = 0.67 mol L−1. The precision of the endpoint identification is estimated by the difference between the standard deviation of measurements repeatability and the combination of the repeatability of volumetric operations. Endpoint identification is the major uncertainty component that can be reduced by estimating alkalinity and concentration of the titrant solution, using a larger number of replicates. Total alkalinity was evaluated with a relative standard uncertainty of 1.5 % from triplicate measurements enabling distinction of relative differences larger than 6.3 % on a pair of seawater samples. A validated spreadsheet for estimating seawater alkalinity, carbonate and bicarbonate equivalent concentrations with respective uncertainty is made available as Electronic Supplementary Material. [ABSTRACT FROM AUTHOR]

The identification of trace levels of compounds in complex matrices by conventional low-resolution gas chromatography hyphenated with mass spectrometry is based in the comparison of retention times and abundance ratios of characteristic mass spectrum fragments of analyte peaks from calibrators with sample peaks. Statistically sound criteria for the comparison of these parameters were developed based on the normal distribution of retention times and the simulation of possible non-normal distribution of correlated abundances ratios. The confidence level used to set the statistical maximum and minimum limits of parameters defines the true positive rates of identifications. The false positive rate of identification was estimated from worst-case signal noise models. The estimated true and false positive identifications rate from one retention time and two correlated ratios of three fragments abundances were combined using simple Bayes' statistics to estimate the probability of compound identification being correct designated examination uncertainty. Models of the variation of examination uncertainty with analyte quantity allowed the estimation of the Limit of Examination as the lowest quantity that produced “Extremely strong” evidences of compound presence. User friendly MS-Excel files are made available to allow the easy application of developed approach in routine and research laboratories. The developed approach was successfully applied to the identification of chlorpyrifos-methyl and malathion in QuEChERS method extracts of vegetables with high water content for which the estimated Limit of Examination is 0.14 mg kg −1 and 0.23 mg kg −1 respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 − , NO 3 - and SO 4 - 2 in seawater by ion chromatography. A target expanded uncertainty of 20 % was defined for the determination of Cl − , NO 3 - and SO 4 - 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 %. [ABSTRACT FROM AUTHOR]

Highlights: [•] Reliable linear weighted calibrations, based in reduced number of signal, are performed. [•] Simplification of weighted calibrations is based in prior models of weighting factors. [•] Weighted calibrations are applied to the identification of inorganic explosives by IC. [•] Identification of explosives is based in ion balance of aqueous extract of explosive. [•] The high success rate of developed metrological models proves their validity. [Copyright &y& Elsevier]

The knowledge of the mass of particulate matter in air, its chemical composition and emission sources is of relevance for taking decisions concerning air quality management in urban areas. The interpretation of these data is a function of the quality of the measurement results expressed by their uncertainties. This study aimed at developing models of the performance of the determination of anions in the water-soluble fraction of atmospheric aerosols, capable of determining, separately, the contribution of aerosols sampling, extraction of water-soluble fraction of atmospheric aerosols and quantification, by ion chromatography, of anions in the extract. The sampling procedure was assessed from the dispersion of results of duplicate parallel sampling after subtracting the analytical component of this dispersion. These models are used to evaluate the adequacy of the measurement procedure for the determination of urban aerosol composition and to support strategies for reducing measurement uncertainty or cost of analysis. The method performance was studied for the following ranges considering extract dilution up to five times: 0.23–8μgm−3 for chloride and nitrate, and 0.093–3.25μgm−3 for sulphate. Measurements are fit for the analysis of urban aerosols since the relative expanded measurement uncertainty is smaller than a maximum value of 40%. The percentage contribution of the uncertainty components varies with the analyte and its mass concentration, the major components being 24–93% for the extraction, 43–59% for sampling, 0.2–28% for the interpolation of the sample signal in the calibration curve and 4–8% for air volume measurement. The typical composition of analysed air is: (1.12±0.26) μgm−3, (1.02±0.30) μgm−3 and (0.76±0.22) μgm−3 of chloride, nitrate and sulphate in the water soluble fraction of aerosol, respectively, for a confidence level of approximately 95% considering a coverage factor of 2. [ABSTRACT FROM AUTHOR]

Abstract: 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 , in an MS-Excel file, to be updated with the user''s data. [Copyright &y& Elsevier]

Abstract: The analysis of organic contaminants or residues in biological samples is frequently affected by the presence of compounds producing interfering instrumental signals. This feature is responsible for the higher complexity and cost of these analyses and/or by a significant reduction of the number of studied analytes in a multi-analyte method. This work presents a methodology to estimate the impact of the interfering compounds on the quality of the analysis of complex samples, based on separative instrumental methods of analysis, aiming at supporting the inclusion of analytes affected by interfering compounds in the list of compounds analysed in the studied samples. The proposed methodology involves the study of the magnitude of the signal produced by the interfering compounds in the analysed matrix, and is applicable to analytical systems affected by interfering compounds with varying concentration in the studied matrix. The proposed methodology is based on the comparison of the signals from a representative number of examples of the studied matrix, in order to estimate the impact of the presence of such compounds on the measurement quality. The treatment of the chromatographic signals necessary to collect these data can be easily performed considering algorithms of subtraction of chromatographic signals available in most of the analytical instrumentation software. The subtraction of the interfering compounds signal from the sample signal allows the compensation of the interfering effect irrespective of the relative magnitude of the interfering and analyte signals, supporting the applicability of the same model of the method performance for a broader concentration range. The quantification of the measurement uncertainty was performed using the differential approach, which allows the estimation of the contribution of the presence of the interfering compounds to the quality of the measurement. The proposed methodology was successfully applied to the analysis of pesticide residues in spiked oranges considering the quantification of the oranges ethyl acetate extract by gas-chromatography with electron capture detector. The application of the proposed methodology to the analysis of this fruit using the studied chromatographic system, allowed the quantification of an increased number of analytes in the samples. The magnitude of the measurement uncertainty estimated by the proposed methodology is fit for the purpose of monitoring pesticide residues in oranges since, frequently, the difference between the maximum residue level and the best estimation of the sample content is larger than the respective uncertainty. The proposed methodology can be useful in other analytical fields and/or instrumental methods of analysis. [Copyright &y& Elsevier]

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. [Copyright &y& Elsevier]

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. [Copyright &y& Elsevier]

The assessment of long-term trends in river water composition is hampered by river composition heterogeneity, and sampling and sample analysis uncertainty. This work describes a novel methodology for the reliable detection of small river composition trends by taking all relevant uncertainty components into account. The methodology was applied to study the variation of nutrients concentration of Tagus river estuary in the extremely dry 2017 year. Mean nutrient concentrations were determined with an uncertainty that combines sampling and sample analysis uncertainty by the Monte Carlo Method. The nutrient concentration variation observed in two occasions is meaningful if the difference of mean concentrations is metrologically different from zero for a 95% confidence level. The observed meaningful NO 2 increase, and SiO 2 and NO x variations is justified by dissolved oxygen reduction, decreased freshwater input and algal productivity variations. The developed tool can be applied to the assessment of other composition trends in rivers. Unlabelled Image • Methodology for distinguishing small composition trends of a large river area • Concentration variations determined with sampling and sample analysis uncertainty • Variation of nutrients concentrations in Tagus river estuary in the very dry 2017 • NO 2 , SiO 2 and NOx variations justified by physicochemical and biological processes [ABSTRACT FROM AUTHOR]

Abstract Many measurements in chemistry are affected by matrix effects responsible for larger deviation between results from the analysis of various matrices than observed from the replicate analysis of the same matrix. The identification of cases where matrix effects are relevant is useful to know if measurement robustness to matrix effects can significantly reduce the measurement uncertainty, e.g. by performing time-consuming standard addition calibrations or additional matrix clean-up. This work presents a methodology to estimate the percentage contribution of matrix effects to the measurement uncertainty by comparing the intermediate precision estimated from the analysis of a sample with the dispersion of analyte recovery observed form the analyses of samples with different matrices. The measurement model was divide in two intervals of the studied quantity: Interval I between the limit of detection and two times the limit of quantification, where the absolute measurement uncertainty is constant, and Interval II above or equal to two times the limit of quantification where the relative measurement uncertainty is constant. The division of measurements scope in these intervals allowed the comparison of information collected at different values of the studied quantity. The developed methodology was successfully applied to the analysis of total Cr, Cu, Li, Mn and Zn using a procedure developed by OSPAR and acid extractable Ni and Pb according to the EPA3051A standard in marine sediments. Matrix effects are responsible for 7.7 to 79% of the global uncertainty. For Interval II, the relative expanded uncertainty ranged from 12% to 25% and is fit for the environmental monitoring of sediments according to the way the performance of laboratories is assessed in QUASIMEME proficiency tests. The developed measurement uncertainty models produced results compatible with reference values of one Certified Reference Material and 65 proficient test samples independent of the ones used to estimate the measurement uncertainty. The used data and performed calculations are presented in a user-friendly MS-Excel spreadsheet, available as Electronic Supplementary Material, that can be used for other determinations. Graphical abstract fx1 Highlights • Matrix effects studied from precision observed using the same or different matrices. • The analytical range is divided in intervals below or above two times the LOQ. • The methodology was successful for the determination of metals in marine sediments. • The uncertainty from matrix effects is responsible for 7.7 to 79% of the uncertainty. • Expanded relative uncertainties (12 to 25%) are fit for environmental monitoring. [ABSTRACT FROM AUTHOR]

Abstract: The most popular procedures for the detection of residues of accelerants in fire debris are the ones published by the American Society for Testing and Materials (ASTM E1412-07 and E1618-10). The most critical stages of these tests are the conservation of fire debris from the sampling to the laboratory, the extraction of residues of accelerants from the debris to the activated charcoal strips (ACS) and from those to the final solvent, as well as the analysis of sample extract by gas chromatography–mass spectrometry (GC–MS) and the interpretation of the instrumental signal. This work proposes a strategy for checking the quality of the sample conservation, the accelerant residues transference to final solvent and GC–MS analysis, using internal standard additions. It is used internal standards ranging from a highly volatile compound for checking debris conservation to low volatile compound for checking GC–MS repeatability. The developed quality control (QC) parameters are not affected by GC–MS sensitivity variation and, specifically, the GC–MS performance control is not affected by ACS adsorption saturation that may mask test performance deviations. The proposed QC procedure proved to be adequate to check GC–MS repeatability, ACS extraction and sample conservation since: (1) standard additions are affected by negligible uncertainty and (2) observed dispersion of QC parameters are fit for its intended use. [Copyright &y& Elsevier]

The monitoring of microplastics in the environment is crucial to determine the relevance and trends of this contamination necessary to plan environmental protection policies. Monitoring data reliability is essential for this purpose. This work describes a methodology for the reliable identification of the most abundant polymer types in aquatic sediments (polyethylene, PE, polypropylene, PP, polyethylene terephthalate, PET, and polystyrene, PS) by micro-ATR-FTIR. Identifications with true and false result rates greater than 95% and lower than 5% are performed, respectively. The analysis is based on defining spectra requirements regarding characteristic and interfering bands intensity and selecting optimal assessed wavenumber range, signal processing, and algorithm to quantify the match/agreement between particle and reference spectra. It is also defined the minimum match value, P 5»P, for reliable identifications. Examinations are performed in two stages where in the first stage PE and PP, PE&PP, are distinguished from other microplastics by taking the [4000–500] cm−1 spectra and various Match Methods and P 5»P depending on the polymer type. PE and PP are distinguished by quantifying weighted or unweighted Pearson correlation coefficients against a reference spectrum in the [3000–2800] cm−1 range. The defined P 5»P are above the 0.6 value considered in many references that do not quantify identification uncertainty. The MS-Excel files used in method development and validation are made available as Supplementary Material being applicable to other spectral techniques and analytical fields. [Display omitted] • Flowchart for the reliable identification of microplastics by micro-ATR-FTIR. • Identification steps supported on different signal, and match algorithm and threshold. • Identifications with true and false positive rates greater and lower than 95% and 5%. • Two stages identification of PP and PE from full and narrower IR spectral range. • Methodology successfully applied to microplastics from aquatic sediments. [ABSTRACT FROM AUTHOR]

Many chemical analyses involve a complex sample preparation, and some, based on an instrumental method of analysis such as spectrometric or chromatographic methods, are affected by matrix effects. The objective interpretation of the results of these analyses performed in the framework of a research or a conformity assessment requires quantifying the measurement uncertainty. This work presents a novel methodology for the bottom-up modelling of the performance of complex analytical operations, such as sample digestion or extraction, by the Monte Carlo simulation of their performance independently of the performance of the other analytical steps. The simulation of between-days precision of complex sample preparation and mean measurement error observed from the analysis of various reference materials and their combination with models of instrumental quantification performance allow the detailed modelling of the measurement uncertainty. The developed methodology adapts to the complex distribution of observed measurement performance data avoiding the under evaluation of the measurement uncertainty by assuming the normal distribution of systematic and random effects. The developed methodology was successfully applied to the determination of total or acid-extractable As (following OSPAR or EPA 3051A methods, respectively) in sediments where measurement trueness was assessed from the analysis of one Certified Reference Material and two spiked samples. The evaluated uncertainty is fit for environmental monitoring considering performance criteria defined for Quasimeme proficiency tests. The developed measurement models were successfully cross-validated by randomly extracting data from the validation set subsequently used to check the compatibility between estimated and reference values for 95% or 99% confidence level. The observed success rate of these assessments is compatible with the confidence level of the tests. [Display omitted] • Monte-Carlo simulation of isolated sample preparation precision and analyte recovery. • Simulated multimodal between-days component of the relative measurement error. • Mean recovery from the analysis of a CRM and two spiked samples with native analyte. • Fit for purpose uncertainty of total & acid-extractable As determinations in sediment. • Cross-validated uncertainty from randomly selected reference material analyses. [ABSTRACT FROM AUTHOR]

The assessment of microplastic contamination in an environmental compartment involves identifying and counting microplastics in a representative fraction of the compartment. Microplastics can be identified by μFTIR spectroscopy where spectra are manually examined for characteristic polymer bands or by an automatic comparison of particle spectrum with reference spectra of polymers. The automatic spectra comparison can involve calculating a correlation coefficient, CC, between particle and reference spectra where a minimum correlation above which identification is adequately reliable should be defined. Correlation can be calculated from original or transformed signals, such as taking the first derivative, and by using unweighted or weighted CC. Weighted CC can highlight the spectral features more relevant to distinguish polymers. This work describes a methodology for setting the minimum CC, P 5»P, associated with a true positive result rate, TP , of 95% and for checking if this threshold allows identifications with a false positive result rate, FP , not greater than 5%. This methodology was successfully applied to the use of various CC determined from original or transformed spectra for the identification of polyethylene, PE, and polypropylene, PP, microplastics in river sediments by μFTIR. The analytical portions of sediments were digested with H 2 O 2 and microplastics separated from the remaining particles by density using a saturated NaCl solution. Pearson's, Spearman's and Alternative unweighted and weighted correlation coefficients were studied. The P 5»P was estimated by the Bootstrap method that resamples spectra CC between a reference material and microparticle of the same polymer collected from the environment. This resampling allows simulating CC distribution required to estimate its 5th percentile (i.e. P 5»P). The FP was estimated from the probability of a particle not from the same polymer type of the reference material producing a CC greater than P 5»P. Some unweighted and weighted CC determined from original or transformed spectra were successfully used to identify PE or PP particles in river sediments. More particle spectra need to be collected to ensure performance is assessed from a representative diversity of aged polymers with different additives. The spreadsheets used for CC calculations and Bootstrap simulations are made available and can be used for the validation of the identification of other polymer types by μFTIR or ATR-FTIR spectroscopy. Image 1 • Microplastic identification by μFTIR from (un)weighted correlation coefficients, CC. • Minimum CC estimated by the Bootstrap method as the 5th percentile of observed CC. • Quantified probability of non-polymer producing a CC greater than the minimum CC, FP. • Unweighted Pearson's and Spearman's CC allow identifications with FP < 5%. • Validated polypropylene identification in microplastics from Mondego river sediments. [ABSTRACT FROM AUTHOR]

Abstract The cumulative standard addition method allows the calibration of an instrument affected by matrix effects when a small sample volume is available. Recently, it was developed and validated a metrologically sound procedure to estimate the uncertainty of these measurements based on the modelling of the uncertainty of the extrapolation of the calibration curve by the linear least squares regression model. However, this procedure is only applicable when the uncertainty of cumulative sample dilutions and analyte mass additions are negligible given the uncertainty of the total solution volume (v) times the instrumental signal (I) (i.e. v ∙ I). This work developed a measurement uncertainty model, not limited by this assumption of the quality of calibrators preparation, based on Monte Carlo simulations. This method was successfully applied to the voltammetric measurements of uric acid in human serum, using a working nanocarbon electrode modified with Cu-nanocarbon-lignin, since the uncertainty model adapts to the uncertainty of cumulative volume additions. The validated procedure was checked through the analysis of spiked physiological serum samples and human serum samples, by assessing the metrological compatibility between estimated and reference values. The measurements are reported with an expanded uncertainty not larger than a target value of 0.56 mg dL−1. The used spreadsheet is made available as supplementary material. Graphical abstract Image 1 Highlights • Cumulative standard addition method (CSAM) allows analysis of small sample volumes. • Calibration involves regression of added analyte mass vs. signal times total volume. • Monte Carlo Method (MCM) allows flexible evaluation of the measurement uncertainty. • CSAM was successfully applied to amperometric determination of uric acid in serum. • Working nanocarbon electrode modified with Cu and lignin was used in the amperometry. [ABSTRACT FROM AUTHOR]

Abstract For most titrations based on the visual detection of the end-point this operation is an important uncertainty component. Well-known models are available to estimate the uncertainty of gravimetric and volumetric steps involved in these titrations, but how the uncertainty from end-point detection should be estimated is not harmonized yet. This work presents a methodology to quantify the uncertainty from the detection of the end-point of a titration when a blank test is performed to subtract the excess of volume used to obtain the colour change that identifies the end of titration. This methodology was applied to the determination of water total hardness by EDTA titration. The repeatability of the end-point detection was estimated by the difference between repeatability obtained from replicate titrations of the same solution and the combined repeatability of the measurements of the analytical portion and titrant volumes. It was assessed if determinations are affected by relevant systematic effects from the replicate analysis of a standard solution. This methodology allowed the determination of the percentage contribution of all uncertainty components to the measurement uncertainty, including the detection of the end-point of the titration, necessary for the optimization of the uncertainty and cost of the analysis. The uncertainty components where combined using three methods to assess their applicability to this evaluation: the law of propagation of the uncertainty, the numerical Kragten method and the numerical Monte Carlo Method. Since the assumption of the first two methods is valid, the three methodologies for combining uncertainty components produced equivalent results. The end-point detection is responsible for 2% to 22% of the global uncertainty and the determinations of water total hardness are associated with an expanded relative uncertainty ranging from 1.5% to 18% that is fit for the monitoring of drinking waters taking performance requirements of the legislation. Highlights • A model for the detailed assessment of the uncertainty from titrations was developed. • Uncertainty from visual match of colours between blank and sample test was estimated. • Detailed models of the variation of the uncertainty with hardness were developed. • The visual detection of the end-point is responsible for 2 to 22% of the uncertainty. • The uncertainty of the hardness determination is adequate for drinking water analysis. [ABSTRACT FROM AUTHOR]

Sea cucumbers are indicators of metal contamination in sea bottoms due to their low mobility and feeding behaviour. Comparing contaminations of specimens from different locations, habitats, and/or organs allows understanding of contamination processes and differences. However, the interpretation of these data is affected by the variability of contamination levels in specimens, the uncertainty of tissue analyses, and the complex correlation of mass fractions estimated by using the same calibration of the used instrumental method of analysis. This work presents a novel tool for the sound comparison of contamination levels of biota where all mentioned factors are considered to produce reliable and undisputable information on the studied system. The Monte Carlo simulation of uncertainty components, affecting the determination of mean contamination levels observed in selected types of tissues, allowed simulating mean contamination differences and determining if these are meaningful. This tool was used to assess the levels of Cd, Cu, Ni and Pb of animals collected in different locations of Sesimbra-Portugal. It was concluded that specimens that selectively consume macroalgae have larger contamination levels than animals feeding on sediment. The gut is the most contaminated organ suggesting intake from feeding is dominant. Three of the analysed animals have Pb mass fractions larger than a maximum admissible value for human consumption of 3 mg kg−1 with a probability larger than 2.5%. [Display omitted] • Pb, Cd, Ni & Cu levels in sea cucumber from different origins with known uncertainty. • Contamination of organs from different species, habitats, feeding habits & locations. • Monte Carlo Simulation of mean concentration variability and analysis uncertainty. • Comparison by simulating mean contamination differences for 95% confidence level. • Animals feeding from macroalgae are more contaminated than those fed from sediments. [ABSTRACT FROM AUTHOR]

A tutorial and spreadsheet for the validation and bottom-up uncertainty evaluation of quantifications performed by instrumental methods of analysis based on linear weighted calibrations is presented. The developed tool automatically assesses if calibrator values uncertainty is negligible given instrumental signal precision, assesses signal homoscedasticity by the Levene's test, guides the selection of weighting factors and evaluates the fitness of the regression model to define the calibration curve. The spreadsheet allows the use of the linear weighted regression model without the need for collecting many replicate signals of calibrators and sample by taking previously developed detailed models of signal precision variation in the calibration interval after adjustments to the daily precision conditions. This tool was successfully applied to the determination of the mass concentration of Cd, Pb, As, Hg, Co, V and Ni in a nasal spray by ICP-MS after samples dilution and acidification. The developed uncertainty models were checked through the analysis of nasal sprays after spiking with known analyte concentration levels. The metrological compatibility between estimated and reference analyte levels for 95% or 99% confidence level supports uncertainty model adequacy. The spiked samples were quantified from many replicate signals but uncertainty evaluation from duplicate calibrator and sample signals was assessed by randomly selecting calibrators and sample signals and by numerically defining a minimum acceptable success rate of the compatibility tests. The developed model was proven adequate to quantify the uncertainty of the studied measurements. Image 1 • Tutorial and spreadsheet for validating and using Linear Weighted Calibrations. • Calibrators quality is checked and the selection of weighting factors is guided. • Regression model can be used with no replicate signals by using prior precision model. • Bottom-up uncertainty of elemental impurities determination in nasal spray by ICP-MS. • Cross-validation of uncertainty models from random selection of signals combination. [ABSTRACT FROM AUTHOR]

The detection of composition or pollution trends of vast environmental water areas, from a river, lake or sea, requires the determination of the mean concentration of the studied component in the studied area at defined depth in, at least, two occasions. Mean concentration estimates of a large area are robust to system heterogeneity and, if expressed with uncertainty, allow assessing if observed trends are meaningful or can be attributed to the measurement process. Mean concentration values and respective uncertainty are more accurately determined if various samples are collected from the studied area and if samples coordinates are considered. The spatial representation of concentration variation and the subsequent randomization of this model, given coordinates and samples analysis uncertainty, allows an improved characterization of studied area and the optimization of the sampling process. Recently, this evaluation methodology was described and implemented in a user-friendly MS-Excel file. This tool was upgraded to allow determinations close to zero concentration and "bottom-up" uncertainty evaluations of collected samples analysis. Since concentrations cannot be negative, this prior knowledge is merged with the original measurements in a Bayesian uncertainty evaluation that improves studied area description and sampling modelling. The Bayesian assessment avoids the underestimation of concentrations distribution by assuming that negative concentrations are impossible. This tool was successfully applied to the determination of reactive phosphate concentration in a vast ocean area of the Portuguese coast. The new version of the developed tool is made available as Supplementary Material. Image 1 • Determination of the mean concentration of phosphate in a vast ocean area. • The mean concentration uncertainty allows distinguishing small concentration trends. • Uncertainty evaluated by Monte Carlo simulation of spatial composition variability. • GPS coordinates and sample analysis uncertainty are considered in the modelling. [ABSTRACT FROM AUTHOR]

Pharmaceutical products as well as active pharmaceutical ingredients (APIs) are checked for levels of elemental contaminants to guarantee medicines administration will not involve the consumption of level of contaminants greater than their maximum admissible intake. However, the conformity decision is affected by the measurement uncertainty function of analytical steps performance, used standards quality and how measurement performance is assessed during method validation. When an ingredient is considered conform, since the measured concentration is lower than the maximum limit, the risk of a false acceptance depends on how close the measured concentration is from the limit and on the measurement uncertainty. The analytical methods used for pharmaceutical analysis should be validated by ICH and USP recommendations, in order to prove measurements are fit for purpose. The validation must also be economically feasible and have an acceptable duration. This work discusses how to evaluate the uncertainty of elemental analysis in pharmaceutical ingredients from data collected during the validation of the analytical method by following ICH guidelines and USP chapters. A top-down uncertainty evaluation based on results from the analysis of a model API intermediate, with the native analyte after spiking at three concentration levels, where analyses are performed by two analysts in two different days, is presented. The impact of the correlation of some uncertainty components of collected results on the uncertainty evaluation is discussed and considered in the calculations. The developed measurement model was checked by a cross-validation procedure where some validation data was randomly removed and used for an independent model control. The developed uncertainty evaluation methodology was successfully applied to the analysis of Pd in a model API intermediate by ICP-MS after a micro-wave assisted acid digestion, where the risk of a false acceptance of the pharmaceuticals is determined. The measurement performance data and used spreadsheet are made available as Supplementary Material. Image 1 • Top-down uncertainty evaluation adapted to a common method validation design. • Uncertainty components correlation from tests performed in the same day is considered. • Uncertainty model was cross validated by randomly moving data to test quality control. • First uncertainty evaluation of elemental impurities quantification in pharmaceuticals. • Uncertainty is used to quantify the risk of a false acceptance of an intermediate of API. [ABSTRACT FROM AUTHOR]