Statistical Analysis/Design of Experiments: Application in Drying of Foods

Scientific experiments are important in research laboratories of universities and also in the engineering laboratories of industries. Experiments in processing companies are often conducted in a series of trials or tests which produce quantifiable outcomes. For continuous improvement in product/process quality, it is fundamental to understand the process behavior, the amount of variability and its impact on processes (Mason et al., 2003).

Quality and productivity with process economics are characteristic goals of industrial processes, which are expected to result in superior goods that are highly sought by consumers and that yields profit for firms that supply them. Recognition is now being given to the necessary link between scientific study of industrial drying process and the quality of dried products produced.

The stimulus for this recognition is the intense international competition among companies selling similar dehydrated products to a limited consumer group. Competition demands that a better product need to be produced within the limits of economic realities.

Improved process economics

It also has extensive applications in development of novel drying techniques. Design of Experiments (DOE) is a statistical technique introduced in the 1920s by Sir Ronald Fisher in London. His initial experiments were concerned with determining the effect of various fertilizers on different plots of land. The final condition of the crop was not only dependent on the fertilizer but also on a number of other factors (such as underlying soil condition, moisture content of the soil, etc.) of each of the respective plots.

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Fisher used DOE which could differentiate the effect of fertilizer and the effect of other factors. Since then DOE has been widely accepted and applied in many research disciplines (Antony, 2003). The applications of experimental design techniques in process development can result;

1. Improved process yields and product quality.

2. Reduced variability and closer performance to target requirements.

3. Reduced number of steps and development time.

4. Increased understanding of the relationship between key process inputs and output (s). (Montgomery, 2003) An experiment is a series of tests, called runs, in which changes are made in the input variables in order to identify the reasons for changes in the output response (Box et al, 2005).

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