Training needs analysis application

The focus of this chapter has been on proactive application of these analytical methods such as safety audits, development of procedures, training needs analysis, and equipment design. However, many of these methods can also be used in a retrospective mode, and this issue deserves further attention in its own right. Chapter 6 describes analytical methods for accident investigations and data collection. 

One commercially available sensor array analysis system, offered by Mosaic Industries [51], is Rhino , a microprocessor-based instrument with an array composed of discrete, resistive gas sensors. An artificial neural network processes sensor inputs and relates them to patterns established by training the instrument with gas components and mixtures of interest for a specific application. In principle, each system is customized for an application by the choice of sensors and the gas detection needs. Potential applications for this system are limited by the availability of suitable sensors and the complexity needed for discrimination. 

Conducting a needs analysis is one of the most important points when developing a successful training program. Too often trainers pull programs off of the shelf and try to adapt them to their specific application. This sometimes falls short because of the generic application for which the program was developed. 

Partitioning methods occasionally struggle to provide the accuracy associated with more powerful, albeit less informative techniques such as machine learning and statistical approaches. For this reason, there is a continuing need for the application of more accurate and informative classification techniques to QSAR analysis. The goal of a classifier is to produce a model that can separate new, untested compounds into classes with a training set of already classified compounds. 

Consequently, using conventional descriptive analysis methodologies, which would require training panellists on all our applications to answer short-term requests, did not seem practically realistic. Moreover, our needs in terms of sensory desalption were primarily to highlight the main differences between our products, rather than to get a very fine sensory description of each product. From this perspective, conventional sensory descriptive techniques did not appear to be the optimal choice. 

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