Artificial intelligence techniques classification

For homogeneous NDT data and repeatable inspection conditions successful automated interpretation systems can relatively easily be developed. They usually use standard techniques from statistical classification or artificial intelligence. Design of successful automated interpretation systems for heterogeneous data coming form non-repeatable, small volume inspections with little a-priori information about the pieces or constructions to be inspected is far more difficult. This paper presents an approach which can be used to develop such systems. 

Yet, when one considers the number of chemicals which is perhaps in excess of 10 million, whereas the largest of the databases of mass spectra contain some 200000 chemicals, the need to employ other techniques is obvious. In this way, and coexisting with the development of databases some software packages use the concept of artificial intelligence methods to determine chemical structures from their mass spectra. In a number of cases these techniques based upon programmable criteria, chemical classification of the unknown, rarity of mass etc. appear to work well, but are limited to particular classes of compounds. The problem lies in the fact that unknowns are more often totally unknown (such as in environmental samples). 

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