Analytical method references

A reference analytical method, for which much experimental evidence is at hand, shows a standard deviation Sr = 0.037 with = 50. A few tests with a purportedly better method yield s, = 0.029 for... 

Reference values, n - the component concentrations or property values for the calibration or validation samples which are measured by the reference analytical method. 

In PAT applications, MCR methods have been found to be particularly useful for scouting studies of new products and new processes, where little is known about the chemistry and process dynamics, and reliable reference analytical methods have not yet been developed. 

The test set method of validation is rather straightforward, but requires some caution. First, the test set samples must be sufficiently representative of samples that the model will be applied to in the future. Otherwise, external validation can provide misleading results - in either an optimistic or pessimistic manner. Under-representing the anticipated sample states could lead to optimistic results, while the use of sample states not represented in the calibration data could lead to pessimistic results. A practical disadvantage of the test set method is that it requires that the reference analytical method be performed on an additional set of samples beyond the original calibration samples, which can be an issue if the cost of each reference analysis is high. 

Improved reference analysis If the method assessment reveals that the main source of model error comes from error in the reference analytical method used to generate the (y) calibration data, then efforts to improve the accuracy and precision of this method could prove to be very beneficial. 

Reference Analytical method Therapeutic range Toxic range ... 

System suitability allows the determination of system performance by analysis of a defined solution prior to running the analytical batch. System suitability should test the entire analytical system, chromatographic performance as well as the sensitivity of the mass spectrometer for the compounds of interest. Some LC-MS SOPs reference analytical methods as the source of operating details for a given analysis. This works particularly well for quantitative analysis, where analytical methods include critical details on instrument parameters and special calibrations that might be required for a particular analyte. Thus, system suitability testing provides the daily [3] checking of the system. 

A CLS model can be implemented using either measured pure component spectra (K) or estimated pure component spectra (K). For most process analytical applications, the second option is most recommended because it is rare that high-quality pure component spectra of all analytes can be physically obtained. Furthermore, even if they could be obtained, they might not sufficiently represent the spectra of the pure components in the process mixture. However, the use of estimated pure component spectra requires that a series of standards of known concentration (C) for all analytes be analyzed by a reference analytical method, thus generating a series of standard spectra (Xsta). If one can manage to do this, the estimated pure component spectra (K) can be calculated ... 

There is also a practical disadvantage of the external validation method. It requires that the reference analytical method be performed on an additional set of samples, namely the external validation samples. Considering the possible high cost of the reference analytical method, and the possibility of requiring a large number of external samples to provide sufficient representation of the calibration samples, this disadvantage can be rather costly. 

If a YRES is much greater than the YRESTOt> then the sample can be considered an outlier. Such Y-sample outliers are usually caused by a large systematic error in the reference analytical method. 

One source of prediction error is the inherent accuracy and precision of the reference analytical method used. If the reference method produces erroneous values that are consistently high or low, this bias will be reflected in the prediction results. Other sources of prediction error relate to the reproducibility, stability, and repeatability of the NIR instrument. Reproducibility (precision) is validated by making repeated measurements of the same sample and removing it between runs. Small changes in conditions may occur owing to multiple insertions of a sample onto the instrument. Stability refers to similar changes that may occur over... 

The reference analytical methods for anions in wines and byproducts are summarized in Table 8.3. They use titrimetric, gravimetric approaches or selective ion electrodes, but today the most performed and extensively used approach for quality control analysis in oenology is ion chromatography. 

Table 8.3 Reference analytical methods for anions in wine and relevant by-products.

Ferreira et al evaluated the use of the traditional linear fit and MLP-ANN models for LIBS calibration. Their aim was to obtain a quantitative multi-elemental method for pollution measurement in soil under sewage sludge application. Two sets of samples, both composed of two kinds of soil, were used for calibration and validation. The target analyte concentrations were determined using a reference analytical method based on ICP-OES. The values determined by LIBS-MLP-ANN showed low prediction errors, good correlations with the concentrations determined by ICP-OES, high accuracy and precision, low limits of detection and best potential to quantify different types of soils. 

Cost-effective, non-invasive, quantitative and simultaneous determination of low level Tlnuvin 770 (0.05 to 0.4 wt.%) and Irganox 225 (0.1 to 0.45 wt.%) contents in PP pellets by NIRS in diffuse reflection mode using MLWR and PLS spectroscopic models has been reported [287]. Seven samples were used for calibration and two for validation. Spectral bands attributable to Tinuvin 770 and Irganox 225 appear at 1560 nm and 1390 nm, respectively. For Tinuvin 770 a two factor PLS model from 1500 to 1600 nm was developed for Irganox 225 a four factor PLS model in the 1360 to 1460 nm region. A quotient-term multiple linear least-squares spectroscopic model was derived that characterises analyte concentration and corrects for spectroscopic differences within the matrix due to the extruder/pelletisers. Reported standard deviations of ca. 25 ppm for Tinuvin 770 and ca. 80 ppm for Irganox 225, or relative standard errors of 0.01 wt.% for Tinuvin 770 and 0.03 wt.% for Irganox 225, approach the accuracy of the reference analytical method. 

---