Comparison Of Calibration Methods Research Articles

Analysis of Ethanol in Beer Using Gas Chromatography: A Side-by-Side Comparison of Calibration Methods

An important objective of any analytical chemistry course is for students to generate and interpret data from the analysis of complex, real-world samples in order to assess the effectiveness of the analysis method, including the calibration. In this laboratory exercise, students directly compare calibration methods (external standards and standard addition, both with and without an internal standard) for the determination of ethanol in beer using an affordable benchtop gas chromatograph. The experiment allows students to evaluate the advantages and disadvantages of each method (including accuracy, precision, and sample preparation) using their own data.

Comparison of Calibration Methods Used for Rietveld Quantitative Phase Analysis of Cement and Fly Ash

The overestimated contents of crystalline phases in polycrystalline powder samples containing amorphous phases must be calibrated when they are done by using Rietveld phase analysis (RPA) for quantitative X-ray diffraction patterns of samples. It has been indicated that there are two methods for this calibration are verified with nice accuracy involving internal standard method (ISM) and G-factor method (GFM) that have been applied to the RPA of both cement and fly ash that contain amorphous mine phases. However, the consistency between these two methods in quantitative determination of phase contents in silicate materials has not yet been discussed. Therefore, the accuracy and consistency between them in quantifying standard mixtures, cement and fly ash were evaluated experimentally. Results indicate that (1) both ISM and GFM gained the very nice accuracy and consistency for the ternary standard mixture, and also the good consistency for cement clinker and fly ash; (2) relatively, GFM is suitable for systems with any content of amorphous phases while ISM is preferably suitable for the system with much more amorphous phase contents, e.g. more than 50% in weight (denoted as wt.% henceforth), by adding the suggested 10wt.% of Al2O3 as internal standard;(3) ISM is not suggested to be used for quantifying the system containing less than 10w.t % of amorphous phases, and the wisely higher addition of internal standard, such as 20%, should be employed when it applied to quantifying the system with amorphous phases from 10wt.% to 50 wt.% in content.

КАЛИБРОВКА СКОРИНГОВОЙ МОДЕЛИС УЧЕТОМ ЦЕНЗУРИРОВАННЫХ ДАННЫХ

In connection with the introduction of the Basel II Agreement and IFRS 9, the question of a more accurate assessment of bank credit risk is becoming increasingly important. In accordance with this provision, banks independently calculate the credit risk assessment, which is most often based on a historical sample in the form of a scoring model. The problem that arises when building a model is the evaluation of credit agreements that they stop functioning before the date on which the model forecast was built, that is, these loans are eliminated from the observation before the end date of the study. These loans are called censored, and in the context of the study there is censorship on the right. At the same time, the influence of such credit agreements on the level of bank default is significant, and, therefore, the value that serves as the basis for calibrating the scoring model also influences the value of the calibration coefficient. The purpose of this study is to solve the problem of accounting for censored data when calibrating a scoring model at the validation stage. The article discusses various ways of accounting for censored data, namely, 1) accounting for censored loans as “good”, 2) excluding censored loans from the sample, 3) Kaplan-Meier method, 4) weighting method. At the same time, attention is paid to the currently relevant issue of several estimates of the share of defaults itself, taking into account censored contracts, and the use of censored data when adjusting the model risk assessment during the validation of the scoring model. In this article, for each of the censored data accounting methods, the influence of the calibration coefficient on the ratio of the model number of defaults to the actual is analyzed, for which three methods of model calibration are considered: Linear calibration values from the probabilities, Linear calibration values from the odds, Logarithmic calibration values from the odds. According to the results of the research, the conclusion is drawn on the dependence of the method of accounting for censored data on the policy of a credit institution. A regional retail bank experience is taken to bring an example for calculation. According to the results of the study, it is concluded that the method of accounting for censored data depends on the policy of a credit institution: for organizations with a low-risk appetite, it is necessary to use the method of eliminating censored loans, for organizations with a high-risk appetite, consider censored loans as “good”, and to obtain more accurate forecast with adequate risk appetite use the methods of weighing censored data and Kaplan-Meyer. Further studies in the field will consider censored data not only at the stage of validation of the scoring model, but also at the initial stage of its construction. Keywords credit risk, commercial bank, probability of default, a scoring model, validation of a scoring model, censored data, methods for the accounting of censored data, calibration coefficient, calibration methods, risk appetite.

Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk

BackgroundPrior to implementing predictive models in novel settings, analyses of calibration and clinical usefulness remain as important as discrimination, but they are not frequently discussed. Calibration is a model’s reflection of actual outcome prevalence in its predictions. Clinical usefulness refers to the utilities, costs, and harms of using a predictive model in practice. A decision analytic approach to calibrating and selecting an optimal intervention threshold may help maximize the impact of readmission risk and other preventive interventions. ObjectivesTo select a pragmatic means of calibrating predictive models that requires a minimum amount of validation data and that performs well in practice. To evaluate the impact of miscalibration on utility and cost via clinical usefulness analyses. Materials and methodsObservational, retrospective cohort study with electronic health record data from 120,000 inpatient admissions at an urban, academic center in Manhattan. The primary outcome was thirty-day readmission for three causes: all-cause, congestive heart failure, and chronic coronary atherosclerotic disease. Predictive modeling was performed via L1-regularized logistic regression. Calibration methods were compared including Platt Scaling, Logistic Calibration, and Prevalence Adjustment. Performance of predictive modeling and calibration was assessed via discrimination (c-statistic), calibration (Spiegelhalter Z-statistic, Root Mean Square Error [RMSE] of binned predictions, Sanders and Murphy Resolutions of the Brier Score, Calibration Slope and Intercept), and clinical usefulness (utility terms represented as costs). The amount of validation data necessary to apply each calibration algorithm was also assessed. ResultsC-statistics by diagnosis ranged from 0.7 for all-cause readmission to 0.86 (0.78–0.93) for congestive heart failure. Logistic Calibration and Platt Scaling performed best and this difference required analyzing multiple metrics of calibration simultaneously, in particular Calibration Slopes and Intercepts. Clinical usefulness analyses provided optimal risk thresholds, which varied by reason for readmission, outcome prevalence, and calibration algorithm. Utility analyses also suggested maximum tolerable intervention costs, e.g., $1720 for all-cause readmissions based on a published cost of readmission of $11,862. ConclusionsChoice of calibration method depends on availability of validation data and on performance. Improperly calibrated models may contribute to higher costs of intervention as measured via clinical usefulness. Decision-makers must understand underlying utilities or costs inherent in the use-case at hand to assess usefulness and will obtain the optimal risk threshold to trigger intervention with intervention cost limits as a result.

Electrocapillary measurements by drop-time technique: Comparison of calibration methods

This paper considers the drop-time technique in electrocapillary measurements and focuses on calibration methods required to convert the drop-time t of a mercury electrode into the value of the interfacial tension γ. The relationships between t and γ, described in the literature, are reviewed and six calibration methods identified. On the basis of high-precision drop-time electrocapillary measurements carried out in 0.1moll−1 KCl aqueous solution, with γ values known from the absolute measurement method, the performance of the calibration methods, expressed in terms of measurement bias and uncertainty, is evaluated and compared. Better performance is observed for the methods that involve a nonlinear model such as γ=αtβ fitted to calibration data, instead of a single-point linear calibration commonly used. The measurement uncertainty U⩽0.12mNm−1 evaluated with these advanced calibration approaches corresponds to the level of accuracy achieved with the classical capillary electrometer.

A comparison of calibration methods and system configurations of underwater stereo-video systems for applications in marine ecology

Stereo-video systems are used by marine ecologists to count and measure the size of aquatic organisms.Most marine ecologists are not experts in underwater photogrammetry and are not necessarily aware of someof the key design and implementation issues that influence the accuracy and precision of the measurements.Two factors that can influence the measurement accuracy and precision of a stereo-video system are the cali-bration strategy and the physical orientation of the system. We compare the accuracy and precision of lengthmeasurements made by systems calibrated using two regularly used calibration approaches. We also comparelength measurements recorded from cameras with base separations of 150 mm, 400 mm, and 800 mm. Athree-dimensional (3D), network calibration method using a purpose built calibration cube was used to deter-mine key calibration and camera orientation parameters (e.g., base separation, focal length, and lens distor-tions) with much improved precision in comparison to a two-dimensional method using either an A3 or A4planar calibration pattern. As a result, measurements made with a 3D cube displayed improved accuracy andprecision compared to either the A3 or A4 planar calibration pattern across a range of typical operational dis-tances. The distance between the cameras on the base bar (base separation) affects the accuracy and precisionof measurements with the 800 mm system producing length estimates of improved accuracy and precisionthan the systems with narrower baselines. The 150 mm system resulted in measurements with poor precisionand accuracy, especially for measurements made at distances greater than six meters.

Expansion of the Scope of AOAC First Action Method 2012.25--Single-Laboratory Validation of Triphenylmethane Dye and Leuco Metabolite Analysis in Shrimp, Tilapia, Catfish, and Salmon by LC-MS/MS.

Prior to conducting a collaborative study of AOAC First Action 2012.25 LC-MS/MS analytical method for the determination of residues of three triphenylmethane dyes (malachite green, crystal violet, and brilliant green) and their metabolites (leucomalachite green and leucocrystal violet) in seafood, a single-laboratory validation of method 2012.25 was performed to expand the scope of the method to other seafood matrixes including salmon, catfish, tilapia, and shrimp. The validation included the analysis of fortified and incurred residues over multiple weeks to assess analyte stability in matrix at -80°C, a comparison of calibration methods over the range 0.25 to 4 μg/kg, study of matrix effects for analyte quantification, and qualitative identification of targeted analytes. Method accuracy ranged from 88 to 112% with 13% RSD or less for samples fortified at 0.5, 1.0, and 2.0 μg/kg. Analyte identification and determination limits were determined by procedures recommended both by the U. S. Food and Drug Administration and the European Commission. Method detection limits and decision limits ranged from 0.05 to 0.24 μg/kg and 0.08 to 0.54 μg/kg, respectively. AOAC First Action Method 2012.25 with an extracted matrix calibration curve and internal standard correction is suitable for the determination of triphenylmethane dyes and leuco metabolites in salmon, catfish, tilapia, and shrimp by LC-MS/MS at a residue determination level of 0.5 μg/kg or below.

Heat flux measurements for use in physiological and clothing research

Scientists use passive heat flow meters to measure body heat exchanges with the environment. In recent years, several such sensors have been developed and concerns about their proper calibration have been addressed. However, calibration methods have differed in the geometry of the heated device as well as in the heat transfer mechanism. Therefore, a comparison of calibration methods is needed in order to understand the obtained differences in calibration lines. We chose three commercially available heat flux sensors and placed them on four different heated devices: a hot plate, double hot plate, nude cylinder and a cylinder covered with a spacer material. We found differences between the calibration line of the manufacturer and our own measurements, especially when forced convection was involved as the main heat transfer mechanism. The results showed clearly that the calibration method should be chosen according to the intended purpose of use. In addition, we recommend use a thin, light heat flux sensor with good thermal conduction in human subject studies.

Comparison of calibration methods for the quantification of Basmati and non-Basmati rice using microsatellite analysis

The accurate quantification of non-Basmati rice in Basmati rice is central to the successful prosecution of adulteration, where non-Basmati rice has been substituted for Basmati rice. The current method and three alternatives of constructing calibration curves for the measurement of non-Basmati rice in Basmati rice using microsatellite analysis were investigated. The methods compared involved power regression, linear regression (with and without log10 transformation) and hyperbolic regression of the ratio of Basmati to non-Basmati peak areas. Assessments were made using error uncertainty, standard error at the agreed limit of adulteration, and 95% confidence intervals for five example data sets. The linear regression of the ratio of peak areas to the ratio of content proportions was found to give the most precise calibration and thus enhanced quantification of the level of adulteration of Basmati rice with non-Basmati rice.

Sucrose Content Determination of Sugar Beets by near Infrared Reflectance Spectroscopy. Comparison of Calibration Methods and Calibration Transfer

The legal method (polarimetric measurement) for the determination of sucrose content since 1964 uses lead acetate. Because heavy metals are polluting, a law could forbid their use in the near future. Near infrared (NIR) spectroscopy is a suitable alternative method to replace it. For two years, 2412 samples of beet brei were analysed by NIR spectroscopy. In this article, spectral pre-processing and regression methods were compared in order to obtain an accurate prediction of sugar content. Analyse of variance and Fisher's tests were calculated to compare models (bias and Standard Error of Prediction corrected for bias) in terms of statistical significance. The model developed with spectra pre-treated by standard normal variate and second derivative gave the best results. The standard error of prediction of the ratio sucrose content/fresh beet weight was low (0.11 g / 100 g). The second part of this study shows that updating of the spectral database was possible. This makes it possible to take into account new variabilities of beet. For an industrial application, calibration transfer has to be studied. Sugar beet spectra or generic standard spectra were used on two NIR instruments. A simple linear regression wavelength by wavelength gives good results for the standardisation, demonstrating a possible use of the model on different instruments. In conclusion, the replacement of the polarimetric method by NIR spectroscopy was feasible.

Comparison of Calibration Methods with and without Feature Selection for the Analysis of HPLC Data

A comparison of two multivariate calibration methods, partial least squares (PLS) and principal component regression (PCR), applied to high-performance liquid chromatography (HPLC) data, is presented for the resolution of a pesticide mixture. The data set showed both coeluted peaks and overlapped absorption spectra. Besides, there is an additional overlapping between the signal of the mobile phase and that of some pesticide. Multivariate calibration models were evaluated using different criteria to choose the optimum number of latent variables. It is shown that PLS yields the best predictive models. Furthermore, two methods for selecting regions were applied with the goal to achieve an improved prediction ability in the present multicomponent determination by HPLC-DAD (diode array detector) with PLS. The selection of regions associated with a large correlation to the concentration and with large values in loading-weighs (from PLS) were considered. It is concluded that feature selection can also improve the multivariate calibration results using chromatographic data.

Using near infrared reflectance spectroscopy to estimate the nutritive value of senescing annual ryegrass (Lolium rigidum): a comparison of calibration methods

Summary. The suitability of near infrared reflectance (NIR) spectroscopy for predicting the concentration of several quality traits in samples of annual ryegrass (Lolium rigidum Gaud.) herbage was assessed in 2 separate experiments. In the first experiment, NIR calibration equations were developed for 6 traits (water-soluble carbohydrates, dry matter digestibility, neutral detergent solubles, neutral detergent solubles digestibility, neutral detergent fibre digestibility and nitrogen) using 4 calibration methods. No significant differences were found in the accuracy of NIR equations developed using either stepwise multiple linear regression (SMLR) or partial least squares regression (PLS) techniques when the equations were used to predict the concentration of constituents in those samples not used during the calibration process. The process of removing samples identified by the computer as spectral outliers was found to improve those statistics that related NIR data to the reference data of the samples used during calibration development (i.e. improved the goodness of fit of the regressions). However, when the resulting equations were used on all of the samples there was no improvement in the accuracy of the prediction of composition, and the estimates were less accurate for 2 of the equations. In the second experiment, plant part-specific equations (leaf blade, stem and leaf sheath) were developed. The specific equations were found to be no more accurate than those developed using a subset of all samples when used to analyse samples of the same plant part. However, using equations developed on either stem or leaf sheath samples to predict the composition of leaf blade samples led to inaccurate estimates of composition, illustrating the potential for error when NIR calibration equations are used on dissimilar samples. The similarity of the NIR estimates of decline in nutritive value and those obtained using reference analyses was illustrated by plotting the actual and predicted decline in nutritive value. The results of the experiments in this paper illustrate the need to monitor the accuracy of any NIR prediction of nutritive value. Striving for very low standard errors of calibration either by eliminating outliers or by limiting the plant tissues used during calibration did not lead to more accurate predictions of the composition of samples other than those used during the calibration process.

Statistical comparison of calibration methods in multicomponent analysis

Spectrophotometric multicomponent analysis is considerd on the basis of inverse multivariate calibration with linear methods (ordinary least squares, principal component, ridge and partial least squares regression) and with the non-linear methods ACE and the non-linear partial least squares. The performance of the different methods is compared by paired F-tests. As an estimate of the error variance the residual mean sum of squares in the analysis of variance table is used. The comparison is demonstrated for the infrared spectrometric analysis of the hydroxyl group content of brown coal measured in diffuse reflectance. Although the error variances among the calibration methods differ gradually, the differences are much less pronounced at statistical level.

Comparison of calibration methods in quantitative diffuse reflectance infrared spectroscopy

Four different calibration methods were used for quantitative analysis of quartz and calcite in atmospheric aerosols by diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS): (A) conventional calibration with one measurement on each standard (single calibration); (B) calibration with an internal standard; (C) calibration with parallel ( n = 4) measurements on each standard (multiple calibration); (D) multiple calibration followed by reference reflectance correction. The accuracy and the precision of the methods were compared and it was found that by using method D the reliability of the conventional pellet preparation transmission technique can be achieved.

Comparison of methods for calibration of total and partial pressure gauges

For precise measurements of most data required for vacuum system characterization and process control (i.e. gas flow rates, gas amounts and densities, fluences), gauge calibration in terms of pressure is a rather indirect method. A more direct way is to calibrate vacuum systems by means of gas quantities. In this paper two techniques are discussed: the chemical decomposition technique (CDT) and the pressure times volume (P x V) technique, developed and regularly used at Imre Brody Research Center and at Seibersdorf, respectively. Results from an intercomparison of these two methods are given. For CDT, small amounts of CO 2 (in the 0.1 mg range) were decomposed under vacuum at about 1000 K. For P x V Calibrations, known quantities of CO 2 were determined inside a 27 cm 3 chamber by the readings of a capacitance diaphragm gauge. The calibration data agree within ±5% possible systematic errors are discussed.