Estimation Of Measurement Uncertainty Research Articles

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.

On thresholds for controlling negative particle (PM2.5) readings in air quality reporting

Ambient PM2.5 (particles less than 2.5 μm in diameter) is monitored in many countries including Australia. Occasionally PM2.5 instruments may report negative measurements, although in realty the ambient air can never contain negative amounts of particles. Some negative readings are caused by instrument faults or procedural errors, thus can be simply invalidated from air quality reporting. There are occasions, however, when negative readings occur due to other factors including technological or procedural limitations. Treatment of such negative data requires consideration of factors such as measurement uncertainty, instrument noise and risk for significant bias in air quality reporting. There is very limited documentation on handling negative PM2.5 data in the literature. This paper demonstrates how a threshold is determined for controlling negative hourly PM2.5 readings in the New South Wales (NSW) air quality data system. The investigation involved a review of thresholds used in different data systems and an assessment of instrument measurement uncertainties, zero air test data and impacts on key reporting statistics when applying different thresholds to historical datasets. The results show that a threshold of −10.0 μg/m3 appears optimal for controlling negative PM2.5 data in public reporting. This choice is consistent with the measurement uncertainty estimates and the zero air test data statistics calculated for the NSW Air Quality Monitoring Network, and is expected not to have significant impacts on key compliance reporting statistics such as data availability and annual average pollution levels. The analysis can be useful for air quality monitoring in other Australian jurisdictions or wider context.

Evaluating the uncertainty of a virtual power quality disturbance generator and its use in power quality classifier evaluation

This paper presents the metrological evaluation of a proposed virtual power quality (PQ) disturbance generator. It explains in detail the calibration and measurement uncertainty estimation procedures for the RMS voltage and frequency of the generator, presents the results, and discusses their implications. The results indicate that the measurement uncertainty of the generator is satisfactory for all reference points, making it suitable for generation of PQ signals for research purposes, particularly in the development, testing, and improvement of PQ event classifiers. Additionally, the paper introduces a virtual power quality disturbance classifier for real-time classification of PQ events and verifies its accuracy through testing using the proposed PQ disturbance generator.

Perspective: Hidden biases in isotope delta results and the need for comprehensive reporting.

Measurements of stable-isotope composition on an isotope-delta scale can be subject to bias between laboratories or over time within a single laboratory. This bias can arise not just from differences in method protocol but also from changes in reporting guidelines, or even to the isotope-delta scales themselves. Without a clear description of method protocols, including all sample preparation steps, instrumental parameters and settings, data processing including calibration of results and estimation of measurement uncertainty, the traceability and comparability of isotope-delta values cannot be assured as bias(es) may remain hidden. To address this need, there are now clear guidelines published by IUPAC for reporting isotope-delta values for the "light" elements hydrogen, carbon, nitrogen, oxygen and sulfur.1 We recommend that authors and reviewers adhere to those guidelines when preparing and reviewing future publications.

IMU-assisted robotic structured light sensing with featureless registration under uncertainties for pipeline inspection

Laser profilometry and structured light sensors are being increasingly deployed for pipeline inspection as they provide the operator with a precise 3D map that can enable visual detection and direct insight into the integrity of the pipe. The focus of the presented paper is the design of an integrated robotic structured light sensing system used to improve the performance of 3D defect reconstruction for pipeline inspection while accommodating the uncertainty seen in a real-world environment. Point cloud registration of the consecutive 3D frames is a key factor in building this 3D map; therefore, a comprehensive featureless registration approach is proposed first, which is proven more efficient than conventional feature-based registration algorithms. Wheel odometry from the developed robotic platform and inertial measurements are integrated into the registration algorithm to enhance the 3D reconstruction performance for sensor stabilization. An intensity-based threshold searching method is further applied to retrieve the reconstructed defect size. Lastly, the uncertainties of the structured light sensing are investigated for the total reconstruction uncertainty and estimated measurement uncertainty to be quantified in order to illustrate the measurement precision. The efficacy of the proposed algorithms are supported by experimental results of pipeline inspection.

Measurement of Thermal Conductivity and Thermal Diffusivity of Porcine and Bovine Kidney Tissues at Supraphysiological Temperatures up to 93 °C.

This experimental study aimed to characterize the thermal properties of ex vivo porcine and bovine kidney tissues in steady-state heat transfer conditions in a wider thermal interval (23.2-92.8 °C) compared to previous investigations limited to 45 °C. Thermal properties, namely thermal conductivity (k) and thermal diffusivity (α), were measured in a temperature-controlled environment using a dual-needle probe connected to a commercial thermal property analyzer, using the transient hot-wire technique. The estimation of measurement uncertainty was performed along with the assessment of regression models describing the trend of measured quantities as a function of temperature to be used in simulations involving heat transfer in kidney tissue. A direct comparison of the thermal properties of the same tissue from two different species, i.e., porcine and bovine kidney tissues, with the same experimental transient hot-wire technique, was conducted to provide indications on the possible inter-species variabilities of k and α at different selected temperatures. Exponential fitting curves were selected to interpolate the measured values for both porcine and bovine kidney tissues, for both k and α. The results show that the k and α values of the tissues remained rather constant from room temperature up to the onset of water evaporation, and a more marked increase was observed afterward. Indeed, at the highest investigated temperatures, i.e., 90.0-92.8 °C, the average k values were subject to 1.2- and 1.3-fold increases, compared to their nominal values at room temperature, in porcine and bovine kidney tissue, respectively. Moreover, at 90.0-92.8 °C, 1.4- and 1.2-fold increases in the average values of α, compared to baseline values, were observed for porcine and bovine kidney tissue, respectively. No statistically significant differences were found between the thermal properties of porcine and bovine kidney tissues at the same selected tissue temperatures despite their anatomical and structural differences. The provided quantitative values and best-fit regression models can be used to enhance the accuracy of the prediction capability of numerical models of thermal therapies. Furthermore, this study may provide insights into the refinement of protocols for the realization of tissue-mimicking phantoms and the choice of tissue models for bioheat transfer studies in experimental laboratories.

Characterisation of Conventional 87Sr/86Sr Isotope Ratios in Cement, Limestone and Slate Reference Materials Based on an Interlaboratory Comparison Study

An interlaboratory comparison (ILC) was organised to characterise 87Sr/86Sr isotope ratios in geological and industrial reference materials by applying the so‐called conventional method for determining 87Sr/86Sr isotope ratios. Four cements (VDZ 100a, VDZ 200a, VDZ 300a, IAG OPC‐1), one limestone (IAG CGL ML‐3) and one slate (IAG OU‐6) reference materials were selected, covering a wide range of naturally occurring Sr isotopic signatures. Thirteen laboratories received aliquots of these six reference materials together with a detailed technical protocol. The consensus values for the six reference materials and their associated measurement uncertainties were obtained by applying a Gaussian, linear mixed effects model fitted to all the measurement results. By combining the consensus values and their uncertainties with an uncertainty contribution for potential heterogeneity, reference values ranging from 0.708134 mol mol‐1 to 0.729778 mol mol‐1 were obtained with relative expanded uncertainties of ≤ 0.007 %. This study represents an ILC on conventional 87Sr/86Sr isotope ratios, within which metrological principles were considered and the compatibility of measurement results obtained by MC‐ICP‐MS and by MC‐TIMS is demonstrated. The materials characterised in this study can be used as reference materials for validation and quality control purposes and to estimate measurement uncertainties in conventional 87Sr/86Sr isotope ratio measurement.

Development, validation, and measurement uncertainty estimation of automated micro‐solid‐phase extraction coupled with liquid chromatography‐triple quadrupole mass spectrometry for the quantitative determination of anti‐tuberculosis drugs in milk samples

AbstractMilk is one of the most widely consumed animal‐derived food due to its nutritional properties. It is mainly derived from animals, which are being treated with anti‐tuberculosis (anti‐TB) drugs for disease treatment and prevention, hence, these may excrete from animals through milk, thus, pose a threat to consumers. An analytical method based on automated micro‐solid phase extraction coupled to liquid chromatography‐triple quadrupole mass spectrometry was developed for the analysis of anti‐TB drugs in milk samples. The developed method was validated and estimated measurement uncertainty for its applicability to routine analysis. The method linearity was in the range of 25–1000 ng/ml with a coefficient of determination between 0.994 and 0.999. The limit of detection and limit of quantification were in the range of 1.342–9.606 and 4.469–31.98 ng/ml, respectively for anti‐TB drugs. The percent recovery was found to be in the range of 82.29%–111.84%, and the expanded uncertainty of the proposed and validated method was observed to be in the range of 2.94%–28.1%. The present method has been able to analyze 16 anti‐TB drugs in different milk samples and was found to be quick, rapid, high‐throughput, cost‐effective, precise and accurate, and useful for routine testing of anti‐tuberculosis drugs in milk samples.