Latent information

The latent information of the measuring sample is transferred via an energetic carrier into analytical information which is manifested by signals. Their parameters correspond to the coding (encoding) process in information systems. For the formal representation of the analytical coding the following analytical quantities are introduced ... 

The analytical process is a procedure of gaining information. At first, samples contain only latent information on the composition and structure, namely by their intrinsic properties (Malissa [1984] Eckschlager and Danzer [1994]). By interactions between the sample and the measuring system this information is transformed step by step into signals, measured results and useful chemical information. 

The specific problems discussed above emphasize that environmental chemistry poses considerably harder problems to the chemometrician than straight analytical chemistry [BRERETON, 1995]. The current state of environmental analysis often involves empirical planning of experiments and monitoring, as well as expensive and time-consuming analysis, with the result that only simple statistics are applied to the data obtained. In practice, simple comparison of data averaged in time or space with legally fixed thresholds or limits is often performed at the end of the environmental analysis process. Because environmental data contains so much information, chemometric methods should be used to extract the latent information from these data. 

In the majority of studies pollution load data result from many, often unknown entry paths of noxious substances and from interactions among one another and the environmental compartments. In brief, the scope of application of chemometric methods is to extract the latent information from environmental data. In this section the power of multivariate data analysis shall be demonstrated for specific and typical examples of environmental investigation. 

FA was used to detect the latent information within the data set. The reduced factor analytical model consists of two common factors they explain 86.6% of the data s variance. 

Multivariate methods are useful for describing depth profiles of soils and detecting latent information about data sets which could not be easily discovered by univariate analysis. 

The methodology of the CLOGP program is somewhat unique in that fragments are determined by the computer—not preset by the designer. As will become evident, this has advantages in how much valuable latent information ... 

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