Chemometrics modeling

So we seem to have identified a key characteristic of chemometric modeling that influences the capabilities of the models that can be achieved not nonlinearity per se, because simple nonlinearity could be accommodated by a suitable transformation of the data, but differential nonlinearity, which cannot be fixed that way. In those cases where this type of differential, or non-uniform, nonlinearity is an important characteristic of the data, then selecting those wavelengths and only those wavelengths where the data are most nearly linear will provide better models than the full-spectrum methods, which are forced to include the non-linear regions as well, are capable of. 

Thus, my interest in 2 or more factor chemometric models of your simulation is in line with this view of chemometrics. I agree with the need for better physical understanding of instrument responses as well as of the spectra themselves. I would not choose PCR/PLS or MLR to construct such physical models, however. (Fred Cahn)... 

Terrado M, Barcelo D, Tauler R (2006) Identification and distribution of contamination sources in the Ebro river basin by chemometrics modelling coupled to geographical information systems. Talanta 70 691-704... 

A total of 185 emission lines for both major and trace elements were attributed from each LIBS broadband spectrum. Then background-corrected, summed, and normalized intensities were calculated for 18 selected emission lines and 153 emission line ratios were generated. Finally, the summed intensities and ratios were used as input variables to multivariate statistical chemometric models. A total of 3100 spectra were used to generate Partial Least Squares Discriminant Analysis (PLS-DA) models and test sets. 

Varmuza, K. in Pomerantsev, A. L. (Ed.), Progress in Chemometrics Research, Vol., Nova Science Publishers, New York, 2005, pp. 67-87. Global and local chemometric models of spectra-structure relationships. 

IR instruments are available in filter-based, grating-based, and FT-based models. The usual approach is to use a full-spectrum model to ascertain the working wavelengths for a particular reaction, then to apply simpler filter instruments to the process. This works where one, two, or three discrete wavelengths may be used for the analysis. If complex, chemometric models are used, and full-spectrum instruments are needed. 

In this paper, for the first time a chemometric model based on the application of a partial-least-squares (PLS-2) method is proposed fort he resolution of a complex artificial sweetener mixture Ace-K, Sac, Apt) and Vit C have been tested to check to abilities of PLS optimized model. Also the possibility of analyzing powdered drinks (Sahlep and Tang) are shown (Fig. 33.1). 

In the worse case, where either sample temperature, pressure or reactor integrity issues make it impossible to do otherwise, it may be necessary to consider a direct in situ fiber-optic transmission or diffuse reflectance probe. However, this should be considered the position of last resort. Probe retraction devices are expensive, and an in situ probe is both vulnerable to fouling and allows for no effective sample temperature control. Having said that, the process chemical applications that normally require this configuration often have rather simple chemometric modeling development requirements, and the configuration has been used with success. 

Excessive water in samples should be removed if possible, but there are processing methodologies to remove the effects of water contamination on the chemometric models used for predictions. Water appears in a specific area of the spectrum and can be digitally removed from the spectrum during the processing stages. 

The databases of NMR data that are the basis of chemometric model development for on-line process control can be developed via several different processing approaches. 

The linear response of sample chemistry across all hydrocarbons allows spectrum-property regressions to be performed on a wide range of samples in a given chemometric model. The modeling approach can literally include individual parameter models that span a single product type (e.g. aU diesels) or span several product types (e.g. naphtha, kerosene, diesel, gas oils). Thus, the modeling is far more robust and does not have to be continually updated and thoroughly localized as is often the case for NIR or mid-lR spectroscopy. 

J. Gislason, H. Chan, and M. Sardashti, Calibration Transfer of chemometric models based on process nuclear magnetic resonance spectroscopy, Appl. Spectrosc., 55(11), 1553-1560 (2001). 

The lack of adequate software tools to develop and safely implement chemometric models in a process analytical environment, and... 

The highly empirical nature of chemometric tools can lead to customer suspicion regarding the efficacy of chemometric models and... 

In PAT, chemometric modeling efforts are seldom a static endeavor. Once an initial model is deployed, it is invariably the case that the model needs to be improved, to accommodate changing conditions or requirements, or for continuous improvement purposes. 

Ultimately, the effectiveness of a model lies in its ability to provide usefnl and timely information to the customers, which for PAT applications are often process engineers, operators and manufacturing operations personnel. Therefore, one must also consider how best to integrate the chemometric models with the existing data handling and control system. Below are several issnes in this area that are commonly experienced in PAT projects ... 

T.E. Kaltenbach, T.B. Colin and N. Redden, Chemometric modeling for process mass spectrometry applications, AT-PROCESS, 3, 43-52 (1997). 

The procedure of choice for each type of sample depends on its physical state and also on the particular purpose of the measurement. Thus, if the target chemometric models are to be applied to several steps of the process, then the simplest possible model (namely that using a single type of sample) can be expanded with new samples in order to facilitate prediction of subsequent production steps. This section discusses some major aspects of the preparation procedure for each type of sample. 

Monitoring the MMA/DMAAm reaction is challenging becanse both monomers have very similar NIR spectra, and because other interfering snbstances are present in the reaction mixtnre. The anthors prepared calibration samples gravimetrically and made the NIR measurements at reaction temperatnres. The calibration sets consisted of only five or six samples, which is considerably fewer than standard recommendations for NIR chemometric model development. The final models for MMA and DMAAm were validated with an internal validation set as well as an external reaction validation. The performance of the models is summarized in Table 15.6. This table inclndes a measurement of the standard deviation of an external GC method... 

It is vitally important that the multivariate nature of data related to a process be assessed to develop an understanding of a process and to assess quality. Process data together with appropriate chemometric models can provide information about (1) product quality inferentially from process conditions (2) process consistency (process signature, statistical process control) (3) analyzer reliability and (4) operational knowledge that can aid in scale-up and process transfers. ... 

Although it is recommended that classical designs be used whenever possible, it is common to deviate from the standard statistical designs. Eitlier it is cost prohibitive to run the required number of experiments, design points are not chemically or practically feasible, or some other factor precludes the exact use of the design. (See Appendix A for special considerations when designing experiments for inverse chemometric models.)... 

Rosengren AM, Karlsson JG, Andersson PO, Nicholls lA. Chemometric models of template— molecularly imprinted polymer binding. Anal Chem 2005 77 5700-5705. 

As the definition says, a model is a description of a real phenomenon performed by means of mathematical relationships (Box and Draper, 1987). It follows that a model is not the reality itself it is just a simplified representation of reality. Chemometric models, different from the models developed within other chemical disciplines (such as theoretical chemistry and, more generally, physical chemistry), are characterized by an elevated simplicity grade and, for this reason, their validity is often limited to restricted ranges of the whole experimental domain. 

Nevertheless, chemometric models are not developed with the aim of supporting a theory or describing a phenomenon from a general point of view, but with the aim of obtaining answers for particular real problems. Therefore, if the validity range of a model corresponds to the region of... 

Inductively coupled plasma (ICP) needs careful extraction of the relevant information to obtain satisfactory models and several chemometric studies were found. Indeed, many ICP spectroscopists have applied multivariate regression and other multivariate methods to a number of problems. An excellent review has recently been published on chemometric modelling and applications of inductively coupled plasma optical emission spectrometry (ICP-OES) [79]. 

Z. Roger, Selection of the quasi-optimal inputs in chemometric modelling by artificial neural networks analysis, Anal. Chim. Acta, 490(1-2), 2003, 31-40. 

Unfortunately, it is this writer s opinion that, considering the voluminous publications on chemometrics applications, the number of actual effective process analytical chemometrics applications in the field is much less than expected. Part of this is due to the overselling of chemometrics during its boom period, when personal computers (PCs) made these tools available to anyone who could purchase the software, even those who did not understand the methods. This resulted in misuse, failed applications, and a bad taste with many project managers (who tend to have long memories...). Part of the problem might also be due to the lack of adequate software tools to develop and safely implement chemometric models in a process analytical environment. Finally, some of the shortfall might simply be due to lack of qualified resources to develop and maintain chemometrics-based analytical methods in the field. 

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