Multivariate calibration models selectivity

Instrument specialization Multivariate calibration models are built in order to provide selectivity for a multivariate analytical instrument, or... 

Figure 10.2 schematizes the use of spectral information for constructing multivariate calibration models. The pretreatment of the sample spectrum and variable selection processes involve constructing both identification libraries and calibration models for quantifying APIs however, the samples and spectra should suit the specific aim in each case. 

Arnold MA, Small GW, Xiang D, Qui J, Murhammer DW. Pure component selectivity analysis of multivariate calibration models from near-infrared spectra. Analytical Chemistry 2004, 76, 2583-2590. 

Preprocessing of instrument response data can be a critical step in the development of successful multivariate calibration models. Oftentimes, selection of an appropriate preprocessing technique can remove unwanted artifacts such as variable path lengths or different amounts of scatter from optical reflectance measurements. Preprocessing techniques can be applied to rows of the data matrix (by object) or columns (by variable). 

Multivariate calibration models are capable of enhancing the selectivity and reliability and thereby greatly reducing the effort of sample preparation. Some drawbacks of this approach, however, hinder the acceptance as analytical reference methods (a) calibration is sometimes heavily matrix-dependent (b) statistical evaluation is difficult or impossible (c) transfer of the calibration model to other instruments or changed experimental conditions usually is impossible (d) the process of data treatment is not evident enough. 

There are several potential disadvantages of ISE/ISFET sensors, as compared to standard analytical methods. One is chemical interference by other ions, because ion-selective electrodes are not truly specific but respond more or less to a variety of interfering ions. To overcome interference issues, various data processing methods have been used. For example, multivariate calibration models have been proposed to allow cross responses arising from primary and interfering ions to be decoupled, thus allowing accurate determination of individual ion concentrations within mixtures.In some cases, another compound can be added to suppress the interference effect. For example, Ag2S04 can be used to suppress the chloride interference 10 ... 

Selectivity. In general, selectivity of analytical multicomponent systems can be expressed qualitatively (Vessman et al. [2001]) and estimated quantitatively according to a statement of Kaiser [1972] and advanced models (Danzer [2001]). In multivariate calibration, selectivity is mostly quantified by the condition number see Eqs. (6.80)-(6.82). Unfortunately, the condition number does not consider the concentrations of the species and gives therefore only an aid to orientation of maximum expectable analytical errors. Inclusion of the concentrations of calibration standards into selectivity models makes it possible to derive multivariate limits of detection. 

QSPR models have been developed by six multivariate calibration methods as described in the previous sections. We focus on demonstration of the use of these methods but not on GC aspects. Since the number of variables is much larger than the number of observations, OLS and robust regression cannot be applied directly to the original data set. These methods could only be applied to selected variables or to linear combinations of the variables. 

The main advantage of multivariate calibration based on CLS with respect to univariate calibration is that CLS does not require selective measurements. Selectivity is obtained mathematically by solving a system of equations, without the requirement for chemical or instrumental separations that are so often needed in univariate calibration. In addition, the model can use a large number of sensors to obtain a signal-averaging effect [4], which is beneficial for the precision of the predicted concentration, making it less susceptible to the noise in the data. Finally, for the case of spectroscopic data, the Lambert Bouguer Beer s law provides a sound foundation for the predictive model. 

In multivariate calibration, selectivity is commonly used to measure the amount of signal that cannot be used for prediction because of the overlap between the signal of the analyte and the signal of the interferences [68,69]. For inverse models, such as PLS, selectivity is usually calculated for each calibration sample as... 

The concept of the PCSA method is general and this method should be applicable to many types of multivariate calibration techniques. As near-infrared and other spectroscopic methods are developed further for noninvasive in vivo clinical measurements, it is critical to understand the chemical basis of measurement selectivity. Unfortunately, calibration models generated from multivariate statistics are typically accepted without further investigation. Application of the PCSA method can help to establish the chemical or spectroscopic basis of predicted concentrations. 

Data preprocessing is important in multivariate calibration. Indeed, the relationship between even basic procedures such as centring the columns is not always clear, most investigators following conventional methods, that have been developed for some popular application but are not always appropriately transferable. Variable selection and standardisation can have a significant influence on the performance of calibration models. 

The absorbances for a user-selected IR spectral frequency range and temperatures are used to build the calibration model relating IR spectra to the solution concentration (Fig. 11). Multivariate statistics, such... 

Fig. 11 The calibration model relating IR spectra to solution concentration. The multivariate model relates IR absorbances of a selected frequency range and temperature or solvent-antisolvent ratio to solution concentration.

Of course, the reason for the improvement in the calibration model when the second term is included is that A21 serves to compensate for the absorbance due to the tyrosine since X21 is in the spectral region of a tyrosine absorption band with little interference from tryptophan. Figure 6. In general, the selection of variables for multivariate regression analysis may not be so obvious. 

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