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Reproducibility validation

Figure 1. The process of model testing/validation. (Reproduced with permission from Ref. 2.)... Figure 1. The process of model testing/validation. (Reproduced with permission from Ref. 2.)...
Method Validation. Reproducibility of the method was determined by analyzing one beer sample 10 times. Table 1 shows that the method provides very good reproducibility, with coefficients of variations for monitored aldehydes below 5.5%, except for (E)-2-nonenal. The higher coefficient of variation for (E)-2-nonenal may be due to extremely low levels of this aldehyde in the analyzed beer. [Pg.116]

Figure 3 Elements of the prediction model. In order for an alternative method to predict toxicity in vivo, there must be a way to convert the results of the alternative method into a correct prediction of toxicity in vivo. The description of howto perform this conversion is called the prediction model. The conversion process followed must be input into the conversion algorithm that will lead to a prediction of toxicity as an output. A prediction model must define each of the data types available from the alternative method, an algorithm useful for converting the results of the alternative method into a prediction of toxicity, and the chemical classes, product categories, and physical forms for which the prediction model is valid. Reproduced from Toxicology In Vitro 10 479-501, 1996, Bruner, L. Proctor Gamble. Figure 3 Elements of the prediction model. In order for an alternative method to predict toxicity in vivo, there must be a way to convert the results of the alternative method into a correct prediction of toxicity in vivo. The description of howto perform this conversion is called the prediction model. The conversion process followed must be input into the conversion algorithm that will lead to a prediction of toxicity as an output. A prediction model must define each of the data types available from the alternative method, an algorithm useful for converting the results of the alternative method into a prediction of toxicity, and the chemical classes, product categories, and physical forms for which the prediction model is valid. Reproduced from Toxicology In Vitro 10 479-501, 1996, Bruner, L. Proctor Gamble.
Assay Development and Validation. Reproducibility of this ELISA assay was determined based on a set of clomazone standards that were run on different plates on the same day (intra-assay) and on different days (inter-assay). The intra-assay coefficient of variation of the standards changed from 1.5 at the highest clomazone concentration (250 ppb) to 22 at the lowest concentration of 1.4 ppb. The coefficient of variation(CV) at clomazone rate of 12.5 ppb was 10. Similar values were obtained for the inter-assay variability, with the CV of the 1.4 ppb concentration being 22.5, and the CV for the 250 ppb concentration being 2.7. The CV for the 10 ppb concentration of clomazone was about 5 between tests. Analysis of the data for this range of clomazone concentrations indicates that there is good correlation (r =0.97) between the log of the concentration of clomazone and percent inhibition in the assay when the linear regression equation was used. Based on these results, the limit of the test s sensitivity was defined as 2 ppb (10 ppb in soil) and the limit of detection was set at 1 ppb. [Pg.173]

Valid, reproducible results can also be derived from ATR studies [80], and more recently both transmission and ATR data have been compared for routine FTIR analysis [81]. The method of choice will be dictated by the relative concentrations of comonomers and consequent strength of absorption bands, and also by local needs and the availability and suitability of sample-preparation procedures and spectrometers [82]. In many circumstances, reproducibility and precision may be of greater priority than absolute accuracy. FT-Raman [83] and NIR now open up the opportunity for simpler, efficient, more direct measurements on granular product [83] (see Figure 4.4(e)-4.4(h)). [Pg.80]

For strip-like cracks, both internal and surface-breaking, UTDefect has been well validated against experiments (6, 8, 9). To illustrate this a few comparisons are reproduced here. [Pg.158]

In the nonclassical ion controversy discussed in Chapter 9, there was never any question on either side of the debate about the validity of the observed data, only about their interpretation. Had any of the experimental data been questioned or found to be incorrect, this would have been soon found out because so many people repeated and rechecked the data. This is the strength of science (in contrast to politics, economics, etc.), i.e., that we deal with reproducible experimental observation and data. Nevertheless, interpretation can still result in heated discussions or controversies, but science eventually will sort these out based on new results and data. [Pg.250]

Condensed from Fundamentals, American Society of Heating, Refrigerating and Air-Conditioning Engineers, 1967 and 1972. Reproduced by permission. Tbe validity of many standard reference tables bas been critically reviewed by Jancso, Pupezin, and van Hook, J. Fhys. Chem., 74 (1970) 2984. Tbis source is recommended for further study. Tbe notation 4.949.-8, 3.607.-I-9, etc., means 4.949 x 10 , 3.607 x 10, etc. [Pg.348]

An inventory of existing test methods, together with an appreciation of the scientific validity, sensitivity, specificity and reproducibility of these methods. [Pg.24]

Having demonstrated that our simulation reproduces the neutron data reasonably well, we may critically evaluate the models used to interpret the data. For the models to be analytically tractable, it is generally assumed that the center-of-mass and internal motions are decoupled so that the total intermediate scattering function can be written as a product of the expression for the center-of-mass motion and that for the internal motions. We have confirmed the validity of the decoupling assumption over a wide range of Q (data not shown). In the next two sections we take a closer look at our simulation to see to what extent the dynamics is consistent with models used to describe the dynamics. We discuss the motion of the center of mass in the next section and the internal dynamics of the hydrocarbon chains in Section IV.F. [Pg.485]

Validation of a force field is typically done by showing how accurately it reproduces reference data, which may or may not have been used in the actual parameterization. Since different force fields employ different sets of reference data, it is difficult to compare their accuracy directly. Indeed there is no single best force field, each has its advantages and disadvantages. They perform best for the type of compounds used in the parameterization, but may give questionable results for other systems. Table 2.6 gives some typical accuracies for AH( that can be obtained with the MM2 force field. [Pg.45]

The design of the system must take into account possible variation of critical control parameters that could affect performance. The maximum performance of the process should be defined by a reasonable safety margin. In order to comply with cGMP guidelines, established validation protocols, and parameters should allow the process to achieve reproducible purity and yield under stressed conditions. This implies that the industrial SMB system must be stressed to simulate worst-case conditions for process validation. [Pg.278]

Field-induced exciton-breaking rate. To check the validity of this hypothesis, we have to demonstrate that we can reproduce the measured singlet exciton population quenching using the following equation ... [Pg.455]

Nevertheless, the overall body of work remains valid for much of today s larger boiler plants and consequently the McCoy recommendations for very high pressure and supercritical boilers, industrial boiler FW quality, and boiler salines are also reproduced in this chapter (see Tables 12.16 to 12.18). [Pg.566]

Example 2. Equilibrium constants of the reaction of twenty substituted dinitromethanes with formaldehyde have been measured (57) in the range 10-50°C. The isokinetic relationship is valid for only nine of them, as revealed in a preliminary graphical treatment using the plot of log Kjo versus log Kio( 163) the pertinent values of logK are reproduced in Table I. The values of x = T" were transformed according to eq. (36a) with... [Pg.445]

At sufficiently low strain, most polymer materials exhibit a linear viscoelastic response and, once the appropriate strain amplitude has been determined through a preliminary strain sweep test, valid frequency sweep tests can be performed. Filled mbber compounds however hardly exhibit a linear viscoelastic response when submitted to harmonic strains and the current practice consists in testing such materials at the lowest permitted strain for satisfactory reproducibility an approach that obviously provides apparent material properties, at best. From a fundamental point of view, for instance in terms of material sciences, such measurements have a limited meaning because theoretical relationships that relate material structure to properties have so far been established only in the linear viscoelastic domain. Nevertheless, experience proves that apparent test results can be well reproducible and related to a number of other viscoelastic effects, including certain processing phenomena. [Pg.820]

Reproducibility of experiments indicates whether measurements are reliable or not under GMP regulations this is used in the systems suitability and the method validation settings. [Pg.13]

Of all the requirements that have to be fulfilled by a manufacturer, starting with responsibilities and reporting relationships, warehousing practices, service contract policies, airhandUng equipment, etc., only a few of those will be touched upon here that directly relate to the analytical laboratory. Key phrases are underlined or are in italics Acceptance Criteria, Accuracy, Baseline, Calibration, Concentration range. Control samples. Data Clean-Up, Deviation, Error propagation. Error recovery. Interference, Linearity, Noise, Numerical artifact. Precision, Recovery, Reliability, Repeatability, Reproducibility, Ruggedness, Selectivity, Specifications, System Suitability, Validation. [Pg.138]


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See also in sourсe #XX -- [ Pg.303 ]

See also in sourсe #XX -- [ Pg.563 , Pg.565 , Pg.566 ]




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