Dataset Information

Uncertainty analysis of a non-automatic weighing instrument from calibration data on scales according to the SIM guide.

ABSTRACT: This research was motivated by technical-economic challenges imposed by mass metrology, specifically, in matters concerning calibration methods of non-automatic type weighing instruments (i.e.: digital scales). In order to contextualize the problem detected, in the industry there are different processes of mass measurement that are controlled by digital scales, such as: mass of liquids, chemicals, food, body mass of a person. In these processes, the scale is used in the following four conditions for mass measurement: (i) ascending and descending load, returned to zero; (ii) ascending and descending load, without the need to return to zero; (iii) only with ascending load and (iv) only with descending load. In this context and, maintaining the principles for the calibration of a measurement instrument in which it must be carried out under the same operating conditions as the instrument, metrology laboratories must knowing the metrological reliability (i.e.: errors and uncertainties) for each situation. This is exactly the main motivation for the development of the research. Thus, the experimental data obtained in a research laboratory under controlled environmental conditions allowed obtaining a minimum expanded uncertainty associated with the mass measurement of 0.0012 kg (k=2; confidence level: 95%).

SUBMITTER: Roca-Gomez G

PROVIDER: S-EPMC7593520 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Uncertainty analysis of a non-automatic weighing instrument from calibration data on scales according to the SIM guide.

Roca-Gómez Germán G Ospino-López Ulises U Pedraza-Yepes Cristian Antonio CA Higuera-Cobos Oscar Fabián OF Hernández-Vásquez José Daniel JD

Data in brief 20201020

This research was motivated by technical-economic challenges imposed by mass metrology, specifically, in matters concerning calibration methods of non-automatic type weighing instruments (<i>i.e.</i>: digital scales). In order to contextualize the problem detected, in the industry there are different processes of mass measurement that are controlled by digital scales, such as: mass of liquids, chemicals, food, body mass of a person. In these processes, the scale is used in the following four con ...[more]

PMID: 33145381

Similar Datasets

Project description:High-throughput experimentation has revolutionized data-driven experimental sciences and opened the door to the application of machine learning techniques. Nevertheless, the quality of any data analysis strongly depends on the quality of the data and specifically the degree to which random effects in the experimental data-generating process are quantified and accounted for. Accordingly calibration, i.e. the quantitative association between observed quantities and measurement responses, is a core element of many workflows in experimental sciences. Particularly in life sciences, univariate calibration, often involving non-linear saturation effects, must be performed to extract quantitative information from measured data. At the same time, the estimation of uncertainty is inseparably connected to quantitative experimentation. Adequate calibration models that describe not only the input/output relationship in a measurement system but also its inherent measurement noise are required. Due to its mathematical nature, statistically robust calibration modeling remains a challenge for many practitioners, at the same time being extremely beneficial for machine learning applications. In this work, we present a bottom-up conceptual and computational approach that solves many problems of understanding and implementing non-linear, empirical calibration modeling for quantification of analytes and process modeling. The methodology is first applied to the optical measurement of biomass concentrations in a high-throughput cultivation system, then to the quantification of glucose by an automated enzymatic assay. We implemented the conceptual framework in two Python packages, calibr8 and murefi, with which we demonstrate how to make uncertainty quantification for various calibration tasks more accessible. Our software packages enable more reproducible and automatable data analysis routines compared to commonly observed workflows in life sciences. Subsequently, we combine the previously established calibration models with a hierarchical Monod-like ordinary differential equation model of microbial growth to describe multiple replicates of Corynebacterium glutamicum batch cultures. Key process model parameters are learned by both maximum likelihood estimation and Bayesian inference, highlighting the flexibility of the statistical and computational framework.

Project description:The management of biological invasions is a worldwide conservation priority. Unfortunately, decision-making on optimal invasion management can be impeded by lack of information about the biological processes that determine invader success (i.e. biological uncertainty) or by uncertainty about the effectiveness of candidate interventions (i.e. operational uncertainty). Concurrent assessment of both sources of uncertainty within the same framework can help to optimize control decisions.Here, we present a Value of Information (VoI) framework to simultaneously analyse the effects of biological and operational uncertainties on management outcomes. We demonstrate this approach with a case study: minimizing the long-term population growth of musk thistle Carduus nutans, a widespread invasive plant, using several insects as biological control agents, including Trichosirocalus horridus, Rhinocyllus conicus and Urophora solstitialis.The ranking of biocontrol agents was sensitive to differences in the target weed's demography and also to differences in the effectiveness of the different biocontrol agents. This finding suggests that accounting for both biological and operational uncertainties is valuable when making management recommendations for invasion control. Furthermore, our VoI analyses show that reduction of all uncertainties across all combinations of demographic model and biocontrol effectiveness explored in the current study would lead, on average, to a 15.6% reduction in musk thistle population growth rate. The specific growth reduction that would be observed in any instance would depend on how the uncertainties actually resolve. Resolving biological uncertainty (across demographic model combinations) or operational uncertainty (across biocontrol effectiveness combinations) alone would reduce expected population growth rate by 8.5% and 10.5% respectively.Synthesis and applications. Our study demonstrates that intervention rank is determined both by biological processes in the targeted invasive populations and by intervention effectiveness. Ignoring either biological uncertainty or operational uncertainty may result in a suboptimal recommendation. Therefore, it is important to simultaneously acknowledge both sources of uncertainty during the decision-making process in invasion management. The framework presented here can accommodate diverse data sources and modelling approaches, and has wide applicability to guide invasive species management and conservation efforts.

Dataset Information

Uncertainty analysis of a non-automatic weighing instrument from calibration data on scales according to the SIM guide.

Publications

Uncertainty analysis of a non-automatic weighing instrument from calibration data on scales according to the SIM guide.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets