Sample acceptance criteria

The purpose of sample acceptance criteria is to confirm that the run for a test article is suitable for quantification. The corrected peak area of the sample should be within linear range of the assay the baseline should be suitable for integration the corrected peak percentage should be in a reasonable range. Again, for a purity assay, migration time is usually not a critical parameter. 

An interference check standard is a standard solution used to verify an accurate analyte response in the presence of possible interferences from other analytes present in the samples. For methods that have known interference problems arising from the matrix or that are inherent in the method, such as ICP-AES (spectral interference lines) and ICP-MS (isotope combinations with similar masses to analyte), these solutions are used in the batch. The interference check standard must be matrix matched to acid content of the samples. Acceptance criteria are set—for example, the magnitude of uncorrected background and spectral interference must not be greater than a stated value. 

The guidelines established by the Journal of Chromatography B required precision to be within 10% RSD for the high QC samples and within 20% RSD for the low QC sample. Acceptance criteria for accuracy (bias) were not specified therein [12]. 

Each analytical run should include calibration and quality control samples. Acceptance criteria should be defined in SOPs. 

When all components of a preparation process are qualified and validated separately, the largest part of the validation process is complete. The prerequisites are described in a protocol and test plans for the final validation are enclosed. In general process validation includes preparation of three consecutive batches with extended sampling. Acceptance criteria typically include no OOS, no OOT and critical IPC within specified limits. Samples may be collected fi om critical control points during the manufacmre. However, when unit operations have been validated, often only samples of the finished product (after packaging and labelling) are tested. A conclusion about the preparation process as a whole is reported in a Validation report, which has to be approved by the heads of Production, QC and Q A and afterwards will be a base for change control in relation to the process. 

However, other approaches may be equally valid. For instance, some inter-laboratory assessments utilise samples that have been spiked with a known concentration of an impurity(ies). Thus if the receiving laboratory meets the pre-determined acceptance criterion this constitutes an acceptable transfer. The transfer protocol is pre-approved by both transferring and receiving laboratory(ies) and stipulates all of the details of methodology, samples, acceptance criterion and appropriate method variability. 

In contrast to variable testing (comparison of measured values or analytical values), attribute testing means testing of product or process quality (nonconformity test, good-bad test) by samples. Important parameters are the sample size n (the number of units within the random sample) as well as the acceptance criterion naccept, both of which are determined according to the lot size, N, and the proportion of defective items, p, within the lot, namely by the related distribution function or by operational characteristics. 

Fig. 2.11. Schematic representation of a parallel tempering simulation, in which the N states of a stratified FEP calculation are run concurrently. After a predefined number of steps, iVSampie, the cells are swapped randomly across the different processors. A Metropolis-based acceptance criterion is used to determine which of the N / 2 exchanged A-states should be accepted. Pairs of boxes that fail the test are swapped back. Then additional sampling is performed until the next exchange of the replicas...

The modification factor plays a central role in a WL simulation and has several effects. First, its presence violates microscopic detailed balance because it continuously alters the state probabilities, and hence acceptance criterion. Only for g = 0 do we obtain a true Markov sampling of our system. Furthermore, we obviously cannot resolve entropy differences which are smaller than g, yet we need the modification factor to be large enough to build up the entropy estimate in a reasonable amount of simulation time. Wang and Landau s resolution of these problems was to impose a schedule on g, in which it starts at a modest value on the order of one and decreases in stages until a value very near to zero (typically in the range 10 5-10 8). In this manner, detailed balance is satisfied asymptotically toward the end of the simulation. 

In the standard HMC method two ingredients are combined to sample states from a canonical distribution efficiently. One is molecular dynamics propagation with a large time step and the other is a Metropolis-like acceptance criterion [76] based on the change of the total energy. Typically, the best sampling of the configuration space of molecular systems is achieved with a time step of about 4 fs, which corresponds to an acceptance rate of about 70% (in comparison with 40-50% for Metropolis MC of pure molecular liquids). 

Equation (10.7) implies that sampling is performed uniformly in the volume itself. The acceptance criterion for particle transfers, written here for transfer from region II to region I is... 

A very important prevalidation method requirement concerns the stability of the standards and samples, which obviously must exhibit sufficient stability for the time frames of the analyses. The acceptable criterion is that both standard(s) and sample(s) solutions should be stable within 2% for 24 h. The aged mobile phase should also give the same values of Rs, T, and N when compared to fresh mobile phase, and in addition, the results of analyses are within 2% [8],... 

The acceptance criterion for recovery data is 98-102% or 95-105% for drug preparations. In biological samples, the recovery should be 10%, and the range of the investigated concentrations is 20% of the target concentrations. For trace level analysis, the acceptance criteria are 70-120% (for below 1 ppm), 80-120% (for above 100 ppb), and 60-100% (for below 100 ppb) [2]. For impurities, the acceptance criteria are 20% (for impurity levels <0.5%) and 10% (for impurity levels >0.5%) [30], The AOAC (cited in Ref. [11]) described the recovery acceptance criteria at different concentrations, as detailed in Table 2. A statistically valid test, such as a /-test, the Doerffel-test, or the Wilcoxon-test, can be used to prove whether there is no significant difference between the result of accuracy study with the true value [29],... 

An initial calibration verification standard should be measured after calibration and before measuring any sample. A calibration verification standard is a standard solution or set of solutions used to check calibration standard levels. The concentration of the analyte should be near either the regulatory level of concern or approximately at the midpoint of the calibration range. These standards must be independent of the calibration solutions and be prepared from a stock solution with a different manufacturer or manufacturer lot identification than the calibration standards. An acceptance criterion is set, usually as a maximum allowable percentage variation (e.g., 5%, 10%). The calibration can be continually verified using either a calibration standard or the initial calibration verification standard. Acceptance criteria must be set and action taken when results fall outside the limits (i.e., stop the analysis, investigate, correct the problem and rerun samples run between the verification standards that were not limits). 

Note Tests have to be performed for each acceptance criterion. If several acceptance criteria have to be met to get overall acceptance (e.g., system suitability test relative standard deviation of standard absorbance and blank sample with no absorption), the overall functionality has to be checked. 

Sample numbers acceptance criterion (<10 ppm) acceptance criterion (<30 ppm)... 

The sa based on the SDPI is 6.0/V2.27 = 3.98%. Since the standard deviation for the example is 2.64%, which is less than. s , this sample meets the acceptance criterion. 

The sample mean for this example is 97.76%, so the upper limit for the sample RSD is 3.68%. It is recommended that the means always be rounded to the more restrictive RSD limit so that the assurance level and lower bound specifications are still met, so in this case 97.76% is rounded to 97.7%. Therefore, since the sample RSD of 2.70% is less than the critical RSD of 3.68, the acceptance criterion is met. This means that with 90% assurance, at least 95% of samples taken from the blender would pass the USP 25 content uniformity test for capsules. As mentioned in Sec. III.A., if the USP 25 tablet criterion were evaluated instead of the capsule criterion, the upper limit for the sample RSD would be 2.98% and would also pass. 

Since the sample RSD values of 2.40% for content uniformity and 3.69% for dissolution are less than the corresponding acceptance limits from the tables of 4.70% and 8.61%, both tests pass the acceptance criterion. 

Acceptance Criteria The acceptance criteria recommended by the current guidance calls for <15% for all the calibration curve standards and QCs with the exception of the LLOQ, where the acceptance criterion is increased to a 20% deviation. At least four of the six QCs must pass with <20% of the nominal value. In addition, at least one QC sample per concentration range must pass with this criterion. If additional QCs are used in a batch, at least 50% of the QCs need to be within each concentration range. 

Find the best lit that meets the initial calibration acceptance criterion (Tables 4.5, 4.6, and 4.7) program the data acquisition computer to use it for sample calculation... 

For water samples, the RPD of field duplicate results (Equation 1, Table 2.2) is a measure of total sampling and analysis precision. The RPD values are compared to the acceptance criterion specified in the SAP. A common field duplicate precision acceptance criterion for water samples is 30 percent. Field duplicate results that do not agree within 30 percent may indicate several problems as follows ... 

The evaluation of soil field duplicates is somewhat complicated because numeric acceptance criteria are not appropriate for soil. Affected mainly by sample variability, the RPD values for soil sample duplicates may vary vastly. The sampling method also affects soil field duplicate precision, as shown in Chapter 2.6.2. Because soil duplicate RPD cannot be controlled, the application of a standard, such as an acceptance criterion, for their evaluation is not reasonable or practical. 

The limit of detection, limit of quantitation, and linear dynamic range are to be determined by serial dilution of a sample. Three replicate measurements at each level are recommended, and the acceptance criterion for calibration linearity should be a prespecilied correlation coefficient (say, an r2 value of 0.995 or greater). 

In the authors laboratory, the IS response of a sample is compared to the mean IS response of the accepted calibration standards and quality controls in the same run, i.e., those that meet the acceptance criterion of accuracy and do not show other abnormality (e.g., poor chromatography). When the IS response of a sample is outside 50 % of the mean IS signal of calibration standards and quality controls, the sample will be repeated. Moreover, an investigation may be initiated for repeated abnormal IS signal and when there is a pattern or trend. This acceptance criterion was also recommended by others (e.g., [13]). Alternatively, though not reported, some compare the IS response of a sample to those of adjacent samples or to the mean IS response of all the samples in a batch. No matter what approach is used, it is important to be able to single out abnormal samples and to perform corrective actions to ensure that their reported concentrations are accurate. 

Initially, vidarabine was used as the internal standard for penciclovir in a method based on mixed-mode strong cation exchange (MCX) solid-phase extraction due to their similar properties, particularly hydrophobicity (Fig. 14a). Quite stable IS responses were obtained in a run consisted mainly of CS and QC samples with a CV of 13.02 % (Fig. 14b). However, 43 % of the CS and QC samples did not meet the acceptance criterion of accuracy. On the other hand, when penciclovir-d4 was used as the internal standard, all the CS and QC samples met the acceptance criterion in accuracy though the IS responses were more variable (the CV in IS responses was 23.81 %, Fig. 14c). 

The obtained results from both analysts are grouped together to determine whether this additive precision is acceptable or not. For example, if each analyst prepared two sample preparations API at target concentration for intermediate precision, then a total of four values are pooled together (additive precision) as stated in Table 9-3 of Assay, Precision rei < 2.0%, n > 4. In addition to an additive precision requirement, some laboratories also include an acceptance criterion (for example, absolute mean difference <2%) for mean value. For example, if analysts 1 and 2 prepare three sample preparations each, then additive precision is calculated from a total of six values (three from each analyst). In addition, the mean value obtained by analyst 1 (n = 3) is compared against the mean value obtained from analyst 2 (n = 3), in which it must pass an absolute difference (between the two means) of <2.0%. 

There is at present no generally accepted criterion for the accuracy of instruments, although tolerances for the calibration of volumetric glassware and thermometers have been published. Manufacturers claims of accuracy within 1% are diflScult to assess without knowledge of the samples tested or method used to obtain the value. It should be possible for manufacturers to specify the accuracy of calibration of many instruments. As a general rule, it seems desirable that any inaccuracy in an instrument should not contribute significantly to the total inaccuracy of the result. 

Two real-world examples are used to illustrate how to resolve this common problem encountered during the calibration of an autosampler (Chapter 9). In Table 10.1, the precision of peak area was found to be >1 % RSD, which was above the acceptance criterion of 0.5% RSD. Repeating the experiment showed similar precision results. Since a worn sampling syringe of the autosampler is the most common cause for poor precision, it was replaced. The peak area precision was found to be -0.3% RSD after this replacement. 

There may be situations during sample analysis in which the LLOQ or the ULOQ standard point is removed due to a technical error or other a priori determined acceptance criterion. The practical outcome is a truncated standard curve, that is, in the case where the LLOQ standard is revised, then the next highest level standard becomes the LLOQ for this ran. This revised standard curve should then be used in the evaluation of the study samples from that particular run, rejecting (for repeat analysis) any study samples read below the new LLOQ. In general, the study samples that fall between the revised LLOQ and the original assay LLOQ are repeated in a separate run to avoid having samples reported against two LLOQ values in the... 

The details of the assessment of stability data are under intense discussion within the scientific community. A majority of laboratories evaluate data with acceptance criteria relative to the nominal concentration of the spiked sample. The rationale for this is that it is not feasible to introduce more stringent criteria for stability evaluations than that of the assay acceptance criterion. Another common approach is to compare data against a baseline concentration (or day zero concentration) of a bulk preparation of stability samples established by repeated analysis, either during the accuracy and precision evaluations, or by other means. This evaluation then eliminates any systematic errors that may have occurred in the preparation of the stability samples. A more statistically acceptable method of stability data evaluations would be to use confidence intervals or perform trend analysis on the data [24]. In this case, when the observed concentration or response of the stability sample is beyond the lower confidence interval (as set a priori), the data indicate a lack of analyte stability under the conditions evaluated. 

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