Estimation Of Measurement Uncertainty Research Articles

A novel Zende’s-TOPSIS method towards estimation of measurement uncertainty in hole diameters

The ‘Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)’ is one of the best methods for ‘Multi-Criteria Decision-Making’ and ‘Multi-Objective Optimization’. The traditional TOPSIS method determines the best alternative under fixed conditions. However, it cannot determine the best upper limit and lowest limit values. This work explains the detailed methodology of the newly developed Zende’s-TOPSIS method which was used to estimate the measurement uncertainty in hole diameters. Four identical holes and one center hole in an industrial component were measured to investigate measurement uncertainty. According to the experimental results, Zende’s-TOPSIS method performed better than the traditional TOPSIS method. The percentage improvement in the Zende’s-TOPSIS method over the traditional TOPSIS method ranges from 0.0209% to 0.3053%. Using Zende’s-TOPSIS method, the percentage maximum measurement uncertainty for four identical holes varies from 0.8067% to 1.0222%, whereas for the center hole, it varies from 0.5261% to 0.5576%. Similarly, the percentage minimum measurement uncertainty for four identical holes varies from 0.3839% to 0.6406%, whereas for the center hole, it varies from 0.4014% to 0.4041%. The proposed method is also capable of estimating the machined tolerances of the component, which ranges from 18.0772 mm to 18.1708 mm for four identical holes and 49.2215 mm to 49.2572 mm for the center hole. The proposed method can solve various ‘Multi-Objective Optimization’ problems.

The estimation of measurement uncertainty of serum free light chain assays at Atellica NEPH630

The estimation of measurement uncertainty of serum free light chain assays at Atellica NEPH630

An uncertainty methodology for solar occultation flux measurements: ammonia emissions from livestock production

Abstract. Ammonia (NH3) emissions can negatively affect ecosystems and human health, so they should be monitored and mitigated. This study presents methodology for the estimation of uncertainties in NH3 emissions measurements using the solar occultation flux (SOF) method. The reactive nature of NH3 makes its measurement challenging, but SOF offers a reliable open-path passive method which utilizes solar spectrum data, thereby avoiding gas adsorption within the instrument. To compute NH3 gas fluxes, horizontal and vertical wind speed profiles, as well as plume height estimates and spatially resolved column measurements, are integrated. A unique aspect of this work is the first-time description of plume height estimations derived from ground and column NH3 concentration measurements aimed at uncertainty reduction. Initial validation tests indicated measurement errors between −31 % and +14 % on average, which was slightly larger than the estimated expanded uncertainty ranging from ± 12 % to ± 17 %. Application of the methodology to assess emission rates from farms of various sizes showed uncertainties between ± 21 % and ± 37 %, generally influenced by systematic wind uncertainties and random errors. The method demonstrates the capacity to measure NH3 emissions from both small (∼ 0.5–1 kg h−1) and large (∼ 100 kg h−1) sources in high-density farming areas. Generally, the SOF method provided an expanded uncertainty below 30 % in measuring NH3 emissions from livestock production, which could be further improved by adhering to best application practices. This paper's findings offer the potential for broader applications, such as measuring NH3 fluxes from fertilized fields and in the oil and gas sector. However, these applications would require further research to adapt and refine the methodologies for these specific contexts.

Nonparametric estimation of measurement uncertainty arising from sampling

BackgroundIncreasingly, measurement uncertainty has been used by pure and applied analytical chemistry to ensure decision-making in commercial transactions and technical-scientific applications. Until recently, it was considered that measurement uncertainty boiled down to analytical uncertainty; however, over the last two decades, uncertainty arising from sampling has also been considered. However, the second version of the EURACHEM guide, published in 2019, assumes that the frequency distribution is approximately normal or can be normalized through logarithmic transformations, without treating data that deviate from the normality. ResultsHere, six examples (four from Eurachem guide) were treated by classical ANOVA and submitted to an innovative nonparametric approach for estimating the uncertainty contribution arising from sampling. Based on bootstrapping method, confidence intervals were used to guarantee metrological compatibility between the uncertainty ratios arising from the results of the traditional parametric tests and the unprecedented proposed nonparametric methodology. Significance and noveltyThe present study proposed an innovative methodology for covering this gap in the literature based on nonparametric statistics (NONPANOVA) using the median absolute deviation concepts. Supplementary material based on Excel spreadsheets was developed, assisting users in the statistical treatment of their real examples.

Validation, measurement uncertainty estimation and evaluation of UHPLC greenness for simultaneous determination of metoprolol tartrate and hydrochlorothiazide in binary tablet

A novel green ultra high performance liquid chromatography (UHPLC) method was validated and estimated the measurement uncertainty for simultaneous determination of metoprolol tartrate (MET) and hydrochlorothiazide (HCT) in binary tablet. A Zorbax® SB-C18 column with isocratic elution (flow rate 0.9 mL min−1) at 25°C was used. Analytes and HCT degradation product were separated in approximately 1 min using acetonitrile:water:triethylamine (17:83:0.2, v/v) as mobile phase. Analytical curves were linear with (R 2 > 0.99 for MET and HCT) with detection limits of 2.42 and 1.05 µg mL−1 for MET and HCT, respectively. Precision measurements showed RSD% from 0.39 to 1.2% and method accuracy by recovery tests was 100 ± 2%. Measurement uncertainties using Eurachem procedure were 100.1 ± 2.8% and 100.3 ± 2.4% for MET and HCT, respectively. This UHPLC method was accurate, precise, linear and selective, making it suitable for pharmaceutical quality control. It also proved eco-friendly compared to other reported methods.

Stability, homogeneity and measurement uncertainty estimation of PVP-I solutions for the application on oro and nasopharynx against SARS-CoV-2

Aqueous solution containing different concentration (0.5, 0.6 and 1.0%) (w/v) of Polyvinyl pyrrolodon-Iodine (PVP-I) complex, a well-known antiseptic; is prepared and the stability and homogeneity of these solution is assessed as per the ICH Guidelines and International Harmonized Protocol respectively. The solutions were found to be sufficiently homogeneous and stable for a year at 25 °C (60%RH). Measurement uncertainty of the prepared PVP-I solutions were estimated by identifying possible sources of uncertainty using Ishikawa diagram and preparing uncertainty budget based on scope of calibration laboratory. The stable and homogenized PVP-I solution is to be used in a clinical trial for the application on oro and nasopharynx against novel SARS-CoV-2 Virus.

Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm

Abstract. The processes responsible for methane (CH4) emissions from boreal wetlands are complex; hence, their model representation is complicated by a large number of parameters and parameter uncertainties. The arctic-enabled dynamic global vegetation model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator) is one such model that allows quantification and understanding of the natural wetland CH4 fluxes at various scales, ranging from local to regional and global, but with several uncertainties. The model contains detailed descriptions of the CH4 production, oxidation, and transport controlled by several process parameters. Complexities in the underlying environmental processes, warming-driven alternative paths of meteorological phenomena, and changes in hydrological and vegetation conditions highlight the need for a calibrated and optimised version of LPJ-GUESS. In this study, we formulated the parameter calibration as a Bayesian problem, using knowledge of reasonable parameters values as priors. We then used an adaptive Metropolis–Hastings (MH)-based Markov chain Monte Carlo (MCMC) algorithm to improve predictions of CH4 emission by LPJ-GUESS and to quantify uncertainties. Application of this method on uncertain parameters allows for a greater search of their posterior distribution, leading to a more complete characterisation of the posterior distribution with a reduced risk of the sample impoverishment that can occur when using other optimisation methods. For assimilation, the analysis used flux measurement data gathered during the period from 2005 to 2014 from the Siikaneva wetlands in Southern Finland with an estimation of measurement uncertainties. The data are used to constrain the processes behind the CH4 dynamics, and the posterior covariance structures are used to explain how the parameters and the processes are related. To further support the conclusions, the CH4 flux and the other component fluxes associated with the flux are examined. The results demonstrate the robustness of MCMC methods to quantitatively assess the interrelationship between objective function choices, parameter identifiability, and data support. The experiment using real observations from Siikaneva resulted in a reduction in the root-mean-square error (RMSE), from 0.044 to 0.023 gC m−2 d−1, and a 93.89 % reduction in the cost function value. As a part of this work, knowledge about how CH4 data can constrain the parameters and processes is derived. Although the optimisation is performed based on a single site's flux data from Siikaneva, the algorithm is useful for larger-scale multi-site studies for a more robust calibration of LPJ-GUESS and similar models, and the results can highlight where model improvements are needed.

Estimation and usefulness of measurement uncertainty from sampling at different spatial scales: microns to kilometres

Uncertainty of measurement values (MU) is crucial to their reliable geochemical interpretation. MU can be estimated using the Duplicate Method, which requires the taking of a small proportion of duplicated samples, and can be applied at any spatial scale. The distance between the duplicated samples is selected to reflect the effect of analyte heterogeneity on the measurement result (i.e. estimated concentration) within each sampling target, at the particular scale of investigation. Three published case studies, at different spatial scales, are used to explain how the Duplicate Method can be applied to estimate MU. They also illustrate how MU can be used to improve geochemical interpretation and validate measurement procedures (that include sampling) by judging their fitness for purpose. At the kilometre scale, measurements from the GEMAS survey of agricultural soils across Europe are used to estimate their MU for the first time. The MU for 53 elements range from an uncertainty factor of 1.01 to over 10. The MU contributes more that 20% to the total variance for 8 of the 53 elements, showing that the measurement procedure was not fit for purpose in those cases. At the micron scale, measurements of oxygen isotopes in candidate quartz reference materials had MU that was dominated by its sampling component, caused by sometimes unacceptable heterogeneity. A third case study of Pb in soils at 12 UK sites showed that the Duplicate Method can also be used to quantify the heterogeneity (as factor 1.03 to 2.4), and that it can indicate different possible sources of an element.

Agar diffusion microbiological assay measurement using a smartphone device and its measurement uncertainty using the bootstrapping method

Usually, inhibition zone sizes in agar diffusion microbiological assay of antibiotics are measured using regular callipers. In this study, we proposed the use of a smartphone app for measuring inhibition zone sizes. The smartphone app allows for standardized measurements with a reference scale, which provides measured values comparable to those obtained using regular callipers. The main uncertainty source associated with antibiotic potency is inhibition zone size variability. Here, we suggest using the bootstrapping method, a resampling technique, to estimate measurement uncertainty without complex calculations. This simplifies the process and yields comparable results to the usual measurement uncertainty calculations. Both improvements were successfully applied to determine the potency of gentamicin, linezolid, and cefazolin. The results showed that smartphone-based measurements and the bootstrapping approach are valuable tools in agar diffusion microbiological assays.

When bias becomes part of imprecision: how to use analytical performance specifications to determine acceptability of lot-lot variation and other sources of possibly unacceptable bias.

ISO 15189 requires laboratories to estimate the uncertainty of their quantitative measurements and to maintain them within relevant performance specifications. Furthermore, it refers to ISO TS 20914 for instructions on how to estimate the uncertainty and what to take into consideration when communicating uncertainty of measurement with requesting clinicians. These instructions include the responsibility of laboratories to verify that bias is not larger than medically significant. If estimated to be larger than acceptable, such bias first needs to be eliminated or (temporarily) corrected for. In the latter case, the uncertainty of such correction becomes part of the estimation of the total measurement uncertainty. If small enough to be acceptable, bias becomes part of the long term within laboratory random variation. Sources of possible bias are (not limited to) changes in reagent or calibrator lot variation or calibration itself. In this paper we clarify how the rationale and mathematics from an EFLM WG ISO/A position paper on allowable between reagent lot variation can be applied to calculate whether bias can be accepted to become part of long-term imprecision. The central point of this rationale is to prevent the risk that requesting clinicians confuse changes in bias with changes in the steady state of their patients.

Use of the Monte Carlo method for the estimation of measurement uncertainty in chemical analysis systems with intensive mathematical treatment

Use of the Monte Carlo method for the estimation of measurement uncertainty in chemical analysis systems with intensive mathematical treatment

Application of analytical quality by design (AQbD) for stability-indicating method development for quantification of nevirapine and its degradation products

Development of stability-indicating method by liquid chromatography are always a challenge for analytical researchers. A traditional way is by varying one chromatographic parameter at a time assessing the responses from each test. Recent international guidelines encourage the implementation of systematic approaches in analytical development, such as Analytical Quality by Design (AQbD). The aim of this paper was to develop, using the AQbD approach, an efficient and selective stability-indicating method by high performance liquid chromatography for nevirapine and its related substances. The Analytical Target Profile (ATP) and the Critical Method Attributes (CMA) were determined. Through the risk assessment studies, the high-risk analytical conditions were considered as Critical Method Parameters (CMP) and they were screened and optimized by Design of Experiment (DoE). The Method Operable Design Region (MODR) was defined using Monte Carlo Simulation. A control strategy was established by the definition of the system suitability of the analytical procedure. Optimal chromatographic conditions were achieved using an isocratic elution consisting of a mixture of 5 mM ammonium formate buffer pH 5.4/acetonitrile/methanol (60:20:20), with a flow rate of 1.0 mL/min, oven temperature of 30 °C, injection volume of 5 µL and an ACE C18 250 mm x 4,6 mm x 5 µm chromatographic column. Finally, analytical validation and forced degradation studies confirmed the performance of the analytical method. From the estimation of measurement uncertainty, strategies were outlined so that the method is fit for purpose throughout its life cycle. Additionally, guard bands were defined, changing the specification for nevirapine quantification from 90 % to 100 % to the acceptance zone of 92.1–107.9 %. The AQbD method development provided strong justifications for the selection of important parameters for nevirapine method during regulatory filling. Furthermore, with the pass/fail decision rule using guard band, restricted acceptance zones were defined, reducing the risk to the consumer of taking a drug product whose batch was inadequately approved.

FUNDAMENTAL ASPECTS OF METROLOGICAL SUPPORT IN IoT

The application of intelligent sensors, network technologies, and machine learning in IoT and industry is increas- ingly widespread as a part of the development and implementation of Industry 4.0, Industry 5.0, and Smart City. It is necessary to review the fundamental principles of metrological support for production. This includes calibration, estimation of measurement uncertainty, traceability, and processing of large data sets to reproduce and compare the results of measurements of physical quan- tities remotely. Modern smart sensors are cost-effective, which makes traditional sensor calibration methods increasingly uneco- nomical. The utilization of advanced networking technologies, along with machine learning, complicates the pre-processing of measured values. Therefore, new solutions are required when it comes to implementing digital metrology. In this article, a metrological framework for the full life cycle of measured data in IoT is presented. It ensures transparency, comparability, consistent quality and reliability of measured data, processing methods and results. The OPC-UA digital data com- munication standard is considered, which provides a single interface for exchanging digital data with devices from different manu- facturers or via different protocols. The syntax of a machine-readable representation of SI units and derived quantities as well as the structure of the sensor network metadata model are also described. Special emphasis is placed on dynamic calibration of sen- sors, determining measurement uncertainty in sensor networks, and implementing digital calibration certificates in IoT and industry.

A precision sound pressure level measurement system

Noise pollution affects human and wildlife. It is in the public interest to reduce noise levels. Measures to be taken to reduce the noise are normally expensive and must be based on facts and reliable measurements of noise. Therefore, sound pressure level measurements are key evidence to determine whether there is a breach of noise limits. The sound pressure measurement method should comply with the metrological principles concerning validation, measurement traceability and estimation of measurement uncertainty as stated in ISO/IEC Directives, Part 2, Principles and rules for the structure and drafting of ISO and IEC document. For this reason, a precision sound pressure measurement system has been developed at the National Research Council Canada. The system utilizes the latest advances in digitization and is metrologically transparent, facilitating the evaluation of measurement uncertainties. In this paper, the design and implementation of the system based on IEC 61672-1, Electroacoustics - Sound level meters - Part 1: Specifications is described. The technical challenges, such as truncation, are discussed. Closed-form solutions are obtained for typical sinusoidal sound signals. Experiments were conducted for the validation of the system. The measurement results are presented showing that the discrepancy between theoretical calculations and measurements is only 1.6 ppm.

Comparison between top-down and bottom-up approaches in the estimation of measurement uncertainty in Bisphenol A analysis by HPLC-FLD

Comparison between top-down and bottom-up approaches in the estimation of measurement uncertainty in Bisphenol A analysis by HPLC-FLD

Baldur: Bayesian Hierarchical Modeling for Label-Free Proteomics with Gamma Regressing Mean-Variance Trends

Label-free proteomics is a fast-growing methodology to infer abundances in mass spectrometry proteomics. Extensive research has focused on spectral quantification and peptide identification. However, research toward modeling and understanding quantitative proteomics data is scarce. Here we propose a Bayesian hierarchical decision model (Baldur) to test for differences in means between conditions for proteins, peptides, and post-translational modifications. We developed a Bayesian regression model to characterize local mean-variance trends in data, to estimate measurement uncertainty and hyperparameters for the decision model. A key contribution is the development of a new gamma regression model that describes the mean-variance dependency as a mixture of a common and a latent trend—allowing for localized trend estimates. We then evaluate the performance of Baldur, limma-trend, and t test on six benchmark datasets: five total proteomics and one post-translational modification dataset. We find that Baldur drastically improves the decision in noisier post-translational modification data over limma-trend and t test. In addition, we see significant improvements using Baldur over the other methods in the total proteomics datasets. Finally, we analyzed Baldur’s performance when increasing the number of replicates and found that the method always increases precision with sample size, while showing robust control of the false positive rate. We conclude that our model vastly improves over popular data analysis methods (limma-trend and t test) in several spike-in datasets by achieving a high true positive detection rate, while greatly reducing the false-positive rate.

New framework for nanoindentation curve fitting and measurement uncertainty estimation

Uncertainty quantification is a vital component of any measurement process and is indispensable for comparing results obtained by different methods, instruments, or laboratories. The processing of the measured data often relies on fitting the data by a given function. Common methods such as ordinary nonlinear least squares are not capable of treating general uncertainties and correlations in both dependent and independent variables. A new computation method for nonlinear curve fitting to data with a general covariance structure is introduced. This method is applied to the Oliver-Pharr analysis of unloading curves and differences between different regression methods are addressed. Numerical simulations show that the new method yields parameter estimates in agreement with other methods for simple covariance structures. The obtained uncertainty estimates are in agreement with Monte Carlo studies.

Estimation of Measurement Uncertainty for Nonylphenol Compounds in Industrial Wastewater

This study focuses on quantifying the impact of various factors on nonylphenol analysis and identifying the key contributors affecting the analytical outcomes. We calculated the measurement uncertainty arising from the analysis process using GC-MS, aiming to pinpoint uncertainty sources, quantify them numerically and then calculated the overall uncertainty through uncertainty synthesis. The results revealed that factors such as calibration curve construction, internal standard injection, and reproducibility through repeated testing significantly influenced the synthetic standard uncertainty. Negligible effects were observed during processes, involving concentration and filling the standard volume post-purification, as well as during sample collection. The relative expanded uncertainty for nonylphenol concentration was within 4.6%. The uncertainty contributions from each step were as follows: sample collection, calibration curve construction, injection of internal standards, filling of the 1 mL standard volume, and reproducibility from repeated testing accounted for 0.0%, 92.7%, 4.0%, 0.1%, and 3.2% of the total uncertainty, respectively.

Characterization, traceability, and uncertainty estimation of reference solar panel module measurements using pulsed solar simulators and reference solar cells

This paper presents the design, characterization, and traceability of reference solar panel modules for determining the performance of photovoltaic (PV) modules at standard test conditions (STC). The research introduces an advanced experimental system based on a class AAA pulsed solar simulator to measure the radiometric, electrical performances, and efficiency of PV modules. I-V/P characteristics of three PV modules at different STCs and the associated uncertainty budget of the system were estimated. I-V characteristic and associated parameters including Isc, Voc, Pmax, FF, and efficiency were measured. The radiometric and electrical traceability were discussed, and the relative expanded combined uncertainties were concluded to be 1.62 % (Isc), 0.42 % (Voc), 2.05 % (Pmax), and 2.5% (η), with a coverage factor k = 2. Reference solar panel modules were also used on-site to test the performance of large PV panels, and the results are reported.

Estimation of measurement uncertainty for the determination of loss on drying of biologicals

Scientific relevance. GOST ISO/IEC 17025-2019 requires testing laboratories to evaluate the measurement uncertainty of their results. Estimating the uncertainty of analytical methods intended for biologicals is a challenging task that requires time, effort, and a special approach. Measurement uncertainty estimation is of particular interest in the case of measuring loss on drying (LOD) for biologicals, since LOD testing procedures involve analysing measurements of a physical value, i.e. mass.Aim. This study aimed to estimate the measurement uncertainty of LOD determination in biological medicinal products.Materials and methods. The study examined a powdered active substance intended for a Bifidobacterium product (test sample). The authors conducted the LOD test in accordance with the State Pharmacopoeia of the Russian Federation (OFS.1.2.1.0010.15). Statistical processing of the results was performed using Microsoft Excel. To estimate the measurement uncertainty, the authors employed the bottom-up approach or used the standard deviation from testing results.Results. The authors identified the uncertainty components that affected the LOD determination results. When calculated using the bottom-up approach, the expanded uncertainty was 0.34% (coverage factor, k=2; approximate confidence level, 95%). In particular, the largest contributor to the expanded uncertainty was the uncertainty of measuring the mass of weighing bottles containing dried test samples (0.147%), whereas the smallest contributor was the uncertainty of weighing empty bottles (0.003%). When calculated using the standard deviation, the uncertainty of two parallel measurements amounted to 0.32%.Conclusions. Both approaches to calculating LOD measurement uncertainty yield comparable results. According to the uncertainty budget analysis, the uncertainty of measuring the mass of weighing bottles with dried test samples is the major contributor to the test result. For this reason, the conditions of sample preparation should be carefully controlled. The study results confirm that the LOD measurement uncertainty can be calculated using the standard deviation. Testing laboratory teams may benefit from the methods for identifying the factors influencing LOD test results and the methods for calculating the uncertainty of measurement described in this study.