Chemometric tools pattern recognition

Nowadays, generating huge amounts of data is relatively simple. That means Data Reduction and Interpretation using multivariate statistical tools (chemometrics), such as pattern recognition, factor analysis, and principal components analysis, can be critically important to extracting useful information from the data. These subjects have been introduced in Chapters 5 and 6. 

An important aspect of our AI application is the attention paid to including well-established Fortran programs and database search methods into the decision structure of an expert system network. Only certain AI software tools (such as TIMM) effectively handle this critical aspect for the analytical instrumentation field at this time (57-60)> The ability to combine symbolic and numeric processing appears to be a major factor in development of multilevel expert systems for practical instrumentation use. Therefore, the expert systems in the EXMAT linked network access factor values and the decisions from EXMATH, an expert system with chemometric/Fortran routines which are appropriate to the nature of the instrumental data and the information needed by the analyst. Pattern recognition and correlation methods are basic capabilities in this field. 

The multivariate tools typically used for the NIR-CI analysis of pharmaceutical products fall into two main categories pattern recognition techniques and factor-based chemometric analysis methods. Pattern recognition algorithms such as spectral correlation or Euclidian distance calculations basically determine the similarity of a sample spectrum to a reference spectrum. These tools are especially useful for images where the individual pixels yield relatively unmixed spectra. These techniques can be used to quickly define spatial distributions of known materials based on external reference spectra. Alternatively, they can be used with internal references, to locate and classify regions with similar spectral response. 

On the other hand, pattern recognition tools are widely employed for processing data in the field of electronic tongues and, more generally, of artificial senses. Nowadays, a large number of chemometric techniques, which are schematized in Fig. 2.7, are available, giving the... 

Several very important accessory tools, for example for data preprocessing and variable selection, complete the chemometric pattern recognition arsenal. 

Upon pyrolysis, under both combustion conditions, the material collected from the smoke aerosols produced a unique Py-MS spectrum for each substance. In many cases, visual inspection of the spectra from simple mixtures could lead to the identification of the fuel materials. For more complex mixtures, however, extensive crunching of the data was necessary using pattern recognition techniques before judgments could be made as to what fuel mixture might have produced the smoke aerosol analyzed. The authors did demonstrate considerable success in identilying some or all of the fuel components of complex mixtures and speculated that with further experiments and refinement in the applications of chemometric techniques, this approach could become a useful tool in fire investigations. 

Among the different chemometric methods, exploratory data analysis and pattern recognition are frequently used in the area of food analysis. Exploratory data analysis is focused on the possible relationships between samples and variables, while pattern recognition studies the behavior between samples and variables [95]. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) are the methods most commonly used for exploratory analysis and pattern recognition, respectively. The importance of these statistical tools has been demonstrated by the wide number of works in the field of food science where they have been applied. The majority of the applications are related to the characterization and authentication of olive oil, animal fats, marine and vegetable oils [95], wine [97], fruit juice [98], honey [99], cheese [100,101], and so on, although other important use of statistical tools is the detection of adulterants or frauds [96,102]. 

Pattern recognition The ability to analyze and interpret patterns, oftentimes with the help of statistical analysis tools, to discriminate and/or identify analytes. See also chemometric analysis. 

The tools of chemometrics encompass not only the familiar (univariant) methods of statistics, but especially the various multivariant methods, together with a package of pattern-recognition methods for time-series analyses and all the known models for signal detection and signal processing. Chemometric methods of evaluation have now become an essential part of environmental analysis, medicine, process analysis, criminology, and a host of other fields. 

---