Estimation of measurement uncertainty for the quantification of protein by ID-LC-MS/MS.

The emergence of mass spectrometry (MS)-based methods to quantify proteins for clinical applications has led to the need for accurate and consistent measurements. To meet the clinical needs of MS-based protein results, it is important that the results are traceable to higher-order standards and methods and have defined uncertainty values. Therefore, we outline a comprehensive approach for the estimation of measurement uncertainty of a MS-based procedure for the quantification of a protein biomarker. Using a bottom-up approach, which is the model outlined in the "Guide to the Expression of Uncertainty of Measurement" (GUM), we evaluated the uncertainty components of a MS-based measurement procedure for a protein biomarker in a complex matrix. The cause-and-effect diagram of the procedure is used to identify each uncertainty component, and statistical equations are derived to determine the overall combined uncertainty. Evaluation of the uncertainty components not only enables the calculation of the measurement uncertainty but can also be used to determine if the procedure needs improvement. To demonstrate the use of the bottom-up approach, the overall combined uncertainty is estimated for the National Institute of Standards and Technology (NIST) candidate reference measurement procedure for albumin in human urine. The results of the uncertainty approach are applied to the determination of uncertainty for the certified value for albumin in candidate NIST Standard Reference Material® (SRM) 3666. This study provides a framework for measurement uncertainty estimation of a MS-based protein procedure by identifying the uncertainty components of the procedure to derive the overall combined uncertainty.

Estimation of a Minimum Allowable Structural Strength Based on Uncertainty in Material Test Data.

Three types of uncertainties exist in the estimation of the minimum fracture strength of a full-scale component or structure size. The first, to be called the "model selection uncertainty," is in selecting a statistical distribution that best fits the laboratory test data. The second, to be called the "laboratory-scale strength uncertainty," is in estimating model parameters of a specific distribution from which the minimum failure strength of a material at a certain confidence level is estimated using the laboratory test data. To extrapolate the laboratory-scale strength prediction to that of a full-scale component, a third uncertainty exists that can be called the "full-scale strength uncertainty." In this paper, we develop a three-step approach to estimating the minimum strength of a full-scale component using two metrics: One metric is based on six goodness-of-fit and parameter-estimation-method criteria, and the second metric is based on the uncertainty quantification of the so-called A-basis design allowable (99 % coverage at 95 % level of confidence) of the full-scale component. The three steps of our approach are: (1) Find the "best" model for the sample data from a list of five candidates, namely, normal, two-parameter Weibull, three-parameter Weibull, two-parameter lognormal, and three-parameter lognormal. (2) For each model, estimate (2a) the parameters of that model with uncertainty using the sample data, and (2b) the minimum strength at the laboratory scale at 95 % level of confidence. (3) Introduce the concept of "coverage" and estimate the fullscale allowable minimum strength of the component at 95 % level of confidence for two types of coverages commonly used in the aerospace industry, namely, 99 % (A-basis for critical parts) and 90 % (B-basis for less critical parts). This uncertainty-based approach is novel in all three steps: In step-1 we use a composite goodness-of-fit metric to rank and select the "best" distribution, in step-2 we introduce uncertainty quantification in estimating the parameters of each distribution, and in step-3 we introduce the concept of an uncertainty metric based on the estimates of the upper and lower tolerance limits of the so-called A-basis design allowable minimum strength. To illustrate the applicability of this uncertainty-based approach to a diverse group of data, we present results of our analysis for six sets of laboratory failure strength data from four engineering materials. A discussion of the significance and limitations of this approach and some concluding remarks are included.

Uncertainty in multi-scale fatigue life modeling and a new approach to estimating frequency of in-service inspection of aging components.

Uncertainty in modeling the fatigue life of a full-scale component using experimental data at microscopic (Level 1), specimen (Level 2), and full-size (Level 3) scales, is addressed by applying statistical theory of prediction intervals, and that of tolerance intervals based on the concept of coverage, p. Using a nonlinear least squares fit algorithm and the physical assumption that the one-sided Lower Tolerance Limit (LTL), at 95% confidence level, of the fatigue life, i.e., the minimum cycles-to-failure, minNf, of a full-scale component, cannot be negative as the lack or "Failure" of coverage (Fp), defined as 1 - p, approaches zero, we develop a new fatigue life model, where the minimum cycles-to-failure, minNf, at extremely low "Failure" of coverage, Fp, can be estimated. Since the concept of coverage is closely related to that of an inspection strategy, and if one assumes that the predominent cause of failure of a full-size component is due to the "Failure" of inspection or coverage, it is reasonable to equate the quantity, Fp, to a Failure Probability, FP, thereby leading to a new approach of estimating the frequency of in-service inspection of a full-size component. To illustrate this approach, we include a numerical example using the published data of the fatigue of an AISI 4340 steel (N.E. Dowling, Journal of Testing and Evaluation, ASTM, Vol. 1(4) (1973), 271-287) and a linear least squares fit to generate the necessary uncertainties for performing a dynamic risk analysis, where a graphical plot of an estimate of risk with uncertainty vs. a predicted most likely date of a high consequence failure event becomes available. In addition, a nonlinear least squares logistic function fit of the fatigue data yields a prediction of the statistical distribution of both the ultimate strength and the endurance limit.

Planetary Boundary Layer Modeling and Standard Provisions for Supertall Building Design.

According to recent results of planetary boundary layer research relevant to the design of tall buildings subjected to large-scale synoptic storm winds, for elevations of up to at least 1 km, the longitudinal mean wind speeds are monotonically increasing with height. It is shown that, for this reason, to avoid the possible unconservative design of supertall buildings significantly affected aerodynamically by neighboring buildings, an explicit derogation from the ASCE 7 standard specification of the gradient heights zg is necessary for buildings with heights greater than zg .

Development of two fine particulate matter standard reference materials (<4 µm and <10 µm) for the determination of organic and inorganic constituents.

Two new Standard Reference Materials (SRMs), SRM 2786 Fine Particulate Matter (<4 µm) and SRM 2787 Fine Particulate Matter (<10 µm) have been developed in support of the US Environmental Protection Agency's National Ambient Air Quality Standards for particulate matter (PM). These materials have been characterized for the mass fractions of selected polycyclic aromatic hydrocarbons (PAHs), nitrated PAHs, brominated diphenyl ether (BDE) congeners, hexabromocyclododecane (HBCD) isomers, sugars, polychlorinated dibenzo-p-dioxin (PCDD) and dibenzofuran (PCDF) congeners, and inorganic constituents, as well as particle-size characteristics. These materials are the first Certified Reference Materials available to support measurements of both organic and inorganic constituents in fine PM. In addition, values for PAHs are available for RM 8785 Air Particulate Matter on Filter Media. As such, these SRMs will be useful as quality control samples for ensuring compatibility of results among PM monitoring studies and will fill a void to assess the accuracy of analytical methods used in these studies. Graphical Abstract Removal of PM from filter for the preparation of SRM 2786 Fine Particulate Matter.

Development of urine standard reference materials for metabolites of organic chemicals including polycyclic aromatic hydrocarbons, phthalates, phenols, parabens, and volatile organic compounds.

Two new Standard Reference Materials (SRMs), SRM 3672 Organic Contaminants in Smokers' Urine (Frozen) and SRM 3673 Organic Contaminants in Non-Smokers' Urine (Frozen), have been developed in support of studies for assessment of human exposure to select organic environmental contaminants. Collaborations among three organizations resulted in certified values for 11 hydroxylated polycyclic aromatic hydrocarbons (OH-PAHs) and reference values for 11 phthalate metabolites, 8 environmental phenols and parabens, and 24 volatile organic compound (VOC) metabolites. Reference values are also available for creatinine and the free forms of caffeine, theobromine, ibuprofen, nicotine, cotinine, and 3-hydroxycotinine. These are the first urine Certified Reference Materials characterized for metabolites of organic environmental contaminants. Noteworthy, the mass fractions of the environmental organic contaminants in the two SRMs are within the ranges reported in population survey studies such as the National Health and Nutrition Examination Survey (NHANES) and the Canadian Health Measures Survey (CHMS). These SRMs will be useful as quality control samples for ensuring compatibility of results among population survey studies and will fill a void to assess the accuracy of analytical methods used in studies monitoring human exposure to these organic environmental contaminants.

Retrospective Analysis of NIST Standard Reference Material 1450, Fibrous Glass Board, for Thermal Insulation Measurements.

Thermal conductivity data acquired previously for the establishment of Standard Reference Material (SRM) 1450, Fibrous Glass Board, as well as subsequent renewals 1450a, 1450b, 1450c, and 1450d, are re-analyzed collectively and as individual data sets. Additional data sets for proto-1450 material lots are also included in the analysis. The data cover 36 years of activity by the National Institute of Standards and Technology (NIST) in developing and providing thermal insulation SRMs, specifically high-density molded fibrous-glass board, to the public. Collectively, the data sets cover two nominal thicknesses of 13 mm and 25 mm, bulk densities from 60 kg·m(-3) to 180 kg·m(-3), and mean temperatures from 100 K to 340 K. The analysis repetitively fits six models to the individual data sets. The most general form of the nested set of multilinear models used is given in the following equation: [Formula: see text]where λ(ρ,T) is the predicted thermal conductivity (W·m(-1)·K(-1)), ρ is the bulk density (kg·m(-3)), T is the mean temperature (K) and ai (for i = 1, 2, … 6) are the regression coefficients. The least squares fit results for each model across all data sets are analyzed using both graphical and analytic techniques. The prevailing generic model for the majority of data sets is the bilinear model in ρ and T. [Formula: see text] One data set supports the inclusion of a cubic temperature term and two data sets with low-temperature data support the inclusion of an exponential term in T to improve the model predictions. Physical interpretations of the model function terms are described. Recommendations for future renewals of SRM 1450 are provided. An Addendum provides historical background on the origin of this SRM and the influence of the SRM on external measurement programs.

Breakfast cereal sampling study for nutritional elements.

The National Institute of Standards and Technology (NIST) has established a Dietary Supplement Laboratory Quality Assurance Program (DSQAP) in collaboration with the National Institutes of Health Office of Dietary Supplements (NIH-ODS). The DSQAP invites laboratories twice annually to participate in interlaboratory studies where participants elect to measure concentrations of nutritional and/or toxic elements as well as active and/or marker compounds. One of these studies was designed to determine the effects of material granularity and sample processing techniques on measurement variability (precision) as well as to provide participating laboratories information on their performance relative to the NIST assigned values (bias) and to the other participants (concordance). Participants were asked to determine the mass fractions of Ca, Fe, and Zn, in mg/kg, in six breakfast cereal samples. Cereal samples consisted of three ground materials (homogenized wheat, wheat, and rice), two flake materials (wheat and rice) and a partially crushed material (a wheat/rice mixture). In general, approximately 25% of the laboratories processed and analyzed the suite of six cereal materials with adequate to exemplary measurement precision. Over half of the laboratories (60%) experienced measurement issues related to only a particular type of cereal matrix or for only a single element. A small number (15%) of laboratories experienced significant sample processing or measurement problems. Future studies planned by the DSQAP may be designed to use commercial products to aid laboratories with their sampling and analytical techniques.

Milk and serum standard reference materials for monitoring organic contaminants in human samples.

Four new Standard Reference Materials (SRMs) have been developed to assist in the quality assurance of chemical contaminant measurements required for human biomonitoring studies, SRM 1953 Organic Contaminants in Non-Fortified Human Milk, SRM 1954 Organic Contaminants in Fortified Human Milk, SRM 1957 Organic Contaminants in Non-Fortified Human Serum, and SRM 1958 Organic Contaminants in Fortified Human Serum. These materials were developed as part of a collaboration between the National Institute of Standards and Technology (NIST) and the Centers for Disease Control and Prevention (CDC) with both agencies contributing data used in the certification of mass fraction values for a wide range of organic contaminants including polychlorinated biphenyl (PCB) congeners, chlorinated pesticides, polybrominated diphenyl ether (PBDE) congeners, and polychlorinated dibenzo-p-dioxin (PCDD) and dibenzofuran (PCDF) congeners. The certified mass fractions of the organic contaminants in unfortified samples, SRM 1953 and SRM 1957, ranged from 12 ng/kg to 2200 ng/kg with the exception of 4,4'-DDE in SRM 1953 at 7400 ng/kg with expanded uncertainties generally <14 %. This agreement suggests that there were no significant biases existing among the multiple methods used for analysis.

Excitation-emission matrix fluorescence spectroscopy for natural organic matter characterization: a quantitative evaluation of calibration and spectral correction procedures.

The influence of different data collection procedures and of wavelength-dependent instrumental biases on fluorescence excitation-emission matrix (EEM) spectral analysis of aqueous organic matter samples was investigated. Particular attention was given to fluorescence contours (spectral shape) and peak fluorescence intensities. Instrumental bias was evaluated by independently applying excitation and emission correction factors to the raw excitation and emission data, respectively. The peak fluorescence intensities of representative natural organic matter and tryptophan were significantly influenced by the application of excitation and emission spectral correction factors and by the manner in which the raw data was collected. Humification and fluorescence indices were also influenced by emission correction factors but were independent of reference (excitation) intensity normalization or correction. EEM surface contours were dependent on normalization of the fluorescence intensity to the reference intensity but were not influenced by either excitation or emission spectral correction factors. Authors should be explicit in how excitation and emission spectral correction procedures are implemented in their investigations, which will help to facilitate intra-laboratory comparisons and data sharing.