scholarly journals Non-Automatic Weighing Instruments Calibration: Parametric Methods for Outliers Assessments

2020 ◽  
Vol 13 (5) ◽  
pp. 157-160
Author(s):  
José Daniel Hernández-Vásquez ◽  
◽  
Cristian Pedraza-Yepes ◽  
Ronald Barrios-Castillo ◽  
Mauro Castañeda- Escorcia ◽  
...  
Keyword(s):  
Experimental Data ◽  
Quality Control ◽  
Mass Measurement ◽  
Statistical Treatment ◽  
Industrial Sector ◽  
Industrial Processes ◽  
Metrological Reliability ◽  
Parametric Methods ◽  
Expanded Uncertainty ◽  
Experimental Calibration

In the experimental calibration processes, several error of measure can be obtained. Usually, these values are called as outliers. The outliers impact in the instruments’ metrological reliability and, consequently, in the industrial processes’ quality control. In specific terms, for mass metrology, the outliers are extremely critic, due to sensibility of technology used for this industry, i.e.: non-automatic weighing instruments (scales). The EURAMET/cg-18/v.02 is the international standard that defined procedure and tests for to assessment the metrological reliability of scales, however, a statistical treatment for outliers is not defined. In order to propose robust alternatives for outliers analyses, this work to evaluate parametric techniques for the elimination of outliers in the calibration of a Bernalo non-automatic weighing instruments (Capacity: 30.000 kg; Resolution: 0.001 kg). Three methods were applied in the analysis of experimental data: Dixon, Grubbs and Chauvenet. The results confirmed a reduction in the expanded uncertainty associated to mass measurement (k=2) up to: 17.6% (Dixon); 19.9% (Grubbs) and 35.2% (Chauvenet). According to the results and considering the contribution of work to applied mass metrology, the authors propose the inclusion of analyzes and evaluation of outliers in the EURAMET/cg-18/v.02. Thus, it is expected an increase in metrological reliability in mass measurement processes at the scientific and industrial sector.

Development and Certification of Formononetin Reference Material for Quality Control of Functional Foods and Botanical Supplements

2019 ◽  
Vol 15 (5) ◽  
pp. 553-559
Author(s):  
Ningbo Gong ◽  
Baoxi Zhang ◽  
Kun Hu ◽  
Zhaolin Gao ◽  
Guanhua Du ◽  
...  
Keyword(s):  
Quality Control ◽  
Reference Material ◽  
Functional Foods ◽  
Certified Reference Materials ◽  
Coulometric Titration ◽  
Pharmaceutical Formulations ◽  
Uncertainty Estimation ◽  
Expanded Uncertainty ◽  
Scanning Calorimetry ◽  
Botanical Supplements

Background: Formononetin is a common soy isoflavonoid that can be found abundantly in many natural plants. Previous studies have shown that formononetin possesses a variety of activities which can be applied for various medicinal purposes. Certified Reference Materials (CRMs) play a fundamental role in the food, traditional medicine and dietary supplement fields, and can be used for method validation, uncertainty estimation, as well as quality control. Methods: The purity of formononetin was determined by Differential Scanning Calorimetry (DSC), Coulometric Titration (CT) and Mass Balance (MB) methods. Results: This paper reports the sample preparation methodology, homogeneity and stability studies, value assignment, and uncertainty estimation of a new certified reference material of formononetin. DSC, CT and MB methods proved to be sufficiently reliable and accurate for the certification purpose. The purity of the formononetin CRM was therefore found to be 99.40% ± 0.24 % (k = 2) based on the combined value assignments and the expanded uncertainty. Conclusion: This CRM will be a reliable standard for the validation of the analytical methods and for quality assurance/quality control of formononetin and formononetin-related traditional herbs, food products, dietary supplements and pharmaceutical formulations.

Analyzing Herbicide Interactions: A Statistical Treatment of Colby's Method

1988 ◽  
Vol 2 (3) ◽  
pp. 304-309 ◽  
Author(s):  
Jerry L. Flint ◽  
Paul L. Cornelius ◽  
Michael Barrett
Keyword(s):  
Experimental Data ◽  
Statistical Tests ◽  
Statistical Treatment ◽  
Measurement Scale ◽  
Growth Data ◽  
Significance Tests ◽  
Anova Model ◽  
Heterogeneity Of Variance ◽  
Original Measurement ◽  
Data Heterogeneity

A model and a proposed method for testing herbicide interactions were modified from an analysis of variance (ANOVA) model for a 2 by 2 factorial experiment. Statistical tests for either synergism, antagonism, or additivity of herbicide combinations were developed through transforming growth data to logarithms followed by significance tests of 2 by 2 contrasts of the form μii- μi0- μ0i+ μ00with respect to the log-transformed data. Using actual experimental data, heterogeneity of variance was less severe on the log scale compared to the original measurement scale. An expedient SAS(R)program for obtaining the desired significance tests was developed.

scholarly journals Some important details on Richard growth model

2019 ◽  
Vol 23 (Suppl. 6) ◽  
pp. 1901-1908
Author(s):  
Mehmet Gurcan ◽  
Arzu Demirelli
Keyword(s):  
Experimental Data ◽  
Growth Models ◽  
Theoretical Distribution ◽  
Applied Statistics ◽  
Link Function ◽  
Parametric Methods ◽  
Location Parameters ◽  
High Flexibility ◽  
Two Parameters ◽  
Wrapped Distribution

The distribution of the data is very important in all of the parametric methods used in the applied statistics. More clearly, if the experimental data fit well to the theoretical distribution, the results will be more efficient in parametric methods. The adaptability of experimental data to a theoretical distribution depends on the flexibility of the theoretical distribution used. If the flexibility of the theoretical distribution is sufficient, it can be used easily for experimental data. Most of the theoretical distributions have shape and location parameters. However, these two parameters are not always sufficient for the distribution adapt to the experimental data. Therefore, theoretical distributions with high flexibility in parametric methods are needed. Obtaining the new theoretical distributions that provide this feature is important for the literature. In this study, a new probability distribution has been obtained via Richard link function which has been high flexibility. In the introduction, important information is given related to growth models and Richard growth curve. Later, some details about the Richard distribution and wrapped distribution have been given.

scholarly journals Modelling of Molasses Fermentation for Bioethanol Production: A Comparative Investigation of Monod and Andrews Models Accuracy Assessment

Biomolecules ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 308
Author(s):  
Hamid Zentou ◽  
Zurina Zainal Abidin ◽  
Robiah Yunus ◽  
Dayang Awang Biak ◽  
Mustapha Zouanti ◽  
...  
Keyword(s):  
Experimental Data ◽  
Batch Fermentation ◽  
Accuracy Assessment ◽  
Industrial Sector ◽  
Comparative Investigation ◽  
Parameters Estimation ◽  
Monod Model ◽  
Kinetics Parameters ◽  
Initial Substrate ◽  
Good Agreement

Modelling has recently become a key tool to promote the bioethanol industry and to optimise the fermentation process to be easily integrated into the industrial sector. In this context, this study aims at investigating the applicability of two mathematical models (Andrews and Monod) for molasses fermentation. The kinetics parameters for Monod and Andrews were estimated from experimental data using Matlab and OriginLab software. The models were simulated and compared with another set of experimental data that was not used for parameters’ estimation. The results of modelling showed that μmax = 0.179 1/h and Ks = 11.37 g.L−1 for the Monod model, whereas μmax = 0.508 1/h, Ks = 47.53 g.L−1 and Ki = 181.01 g.L−1 for the Andrews model, which are too close to the values reported in previous studies. The validation of both models showed that the Monod model is more suitable for batch fermentation modelling at a low concentration, where the highest R squared was observed at S0 = 75 g.L−1 with an R squared equal to 0.99956, 0.99954, and 0.99859 for the biomass, substrate, and product concentrations, respectively. In contrast, the Andrews model was more accurate at a high initial substrate concentration and the model data showed a good agreement compared to the experimental data of batch fermentation at S0 = 225 g.L−1, which was reflected in a high R squared with values 0.99795, 0.99903, and 0.99962 for the biomass, substrate, and product concentrations respectively.

Sign in / Sign up

Export Citation Format

Share Document