Repeatability chemometrics

The most reliable approach would be an exhaustive search among all possible variable subsets. Since each variable could enter the model or be omitted, this would be 2m - 1 possible models for a total number of m available regressor variables. For 10 variables, there are about 1000 possible models, for 20 about one million, and for 30 variables one ends up with more than one billion possibilities—and we are still not in the range for m that is standard in chemometrics. Since the goal is best possible prediction performance, one would also have to evaluate each model in an appropriate way (see Section 4.2). This makes clear that an expensive evaluation scheme like repeated double CV is not feasible within variable selection, and thus mostly only fit-criteria (AIC, BIC, adjusted R2, etc.) or fast evaluation schemes (leave-one-out CV) are used for this purpose. It is essential to use performance criteria that consider the number of used variables for instance simply R2 is not appropriate because this measure usually increases with increasing number of variables. 

Wahbi et al. [32] used a spectrophotometric method for the determination of omeprazole in pharmaceutical formulations. The compensation method and other chemometric methods (derivative, orthogonal function, and difference spectrophotometry) have been applied to the direct determination of omeprazole in its pharmaceutical preparations. The method has been validated the limits of detection was 3.3 x 10 2 /ig/ml. The repeatability of the method was found to be 0.3-0.5%. The linearity range is 0.5-3.5 /ig/ml. The method has been applied to the determination of omeprazole in its gastro-resistant formulation. The difference spectrophotometric (AA) method is unaffected by the presence of acid induced degradation products, and can be used as a stability-indicating assay method. 

The discipline of chemometrics can play an important role in the effective application of infrared imaging to agri-food materials. In a univariate approach, a researcher extracts a specific observable (e.g. frequency or band height) from a set of spectra as a series of scalar quantities. The process is then repeated for additional observables. In many cases, this approach is sufficient to meet the researcher s needs.49 However, the task of extracting the most useful information possible from an infrared image dataset, which may contain thousands of spectra measured at hundreds or even thousands of individual frequencies, often requires a different strategy. It simply may not be possible for the researcher to manually examine and extract information from each of these spectra. The researcher may, therefore, choose to employ a chemometric method instead. Chemometric methods can reduce a large dataset... 

Our experience in learning chemometrics and teaching it to others has demonstrated repeatedly that people learn new techniques by using them to solve interesting problems. For this reason, many of the contributing authors to this book have chosen to illustrate their chemometric methods with examples using... 

It is generally known that the repeatability of CE analyses is not optimal due to irreproducible flow rates (37). Therefore, it is recommended to align the corresponding peaks in the different electropherograms before chemometric data analysis (exploration or classification) is started. This alignment results in a data matrix, where the signals of the corresponding peaks of the... 

The ultimate development in the field of sample preparation is to eliminate it completely, that is, to make a chemical measurement directly without any sample pretreatment. This has been achieved with the application of chemometric near-infrared methods to direct analysis of pharmaceutical tablets and other pharmaceutical solids (74-77). Chemometrics is the use of mathematical and statistical correlation techniques to process instrumental data. Using these techniques, relatively raw analytical data can be converted to specific quantitative information. These methods have been most often used to treat near-infrared (NIR) data, but they can be applied to any instrumental measurement. Multiple linear regression or principal-component analysis is applied to direct absorbance spectra or to the mathematical derivatives of the spectra to define a calibration curve. These methods are considered secondary methods and must be calibrated using data from a primary method such as HPLC, and the calibration material must be manufactured using an equivalent process to the subject test material. However, once the calibration is done, it does not need to be repeated before each analysis. 

Precision of a chemometric method refers to the reproducibility of the method. For quantitative chemometric methods, it is important to test both the instrument and method precision. Instrument precision is done by repeating measurements on the same sample method precision is the closeness of replicate sample measurements while intermediate precision can be evaluated by running the same samples with different analysts on different days. 

P. Filzmoser, B. Liebmann, K. Varmuza, Repeated double cross validation, J Chemometr,... 

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