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ranges can be plotted successively from left to right to indicate a time sequence. Any time a plotted point falls outside the three standard deviation limits, the operation is assumed to be out of control.

Many of Shewhart's followers have made refinements of this device for decision making, but the essence of this method has remained the same since originally suggested. The main idea is that unusual values for the sample statistics indicate that the underlying distribution of measurements has changed. It is a signal that the cause of this change should be sought and found. Making decisions based on this method can produce two types of errors. On the average, three times in a thousand samples the sample mean will be beyond the "three sigma limits" even though the underlying distribution has not changed. Or the plotted point will fall between the control limits even though the distribution has changed. Such errors can be anticipated, and the use of this device can be adjusted to take into account the importance of these errors.

Control charts have been used to help propel the quality revolution. Emphasis on good quality products and on continuous quality improvement has had large impact on some industries. In automotive and electronic industries the commitment to constant quality improvement has indeed made products more reliable and convenient. The food industry has benefited less from the quality emphasis for two reasons. First, it is difficult to reduce the intrinsic variability in viable biological systems. The amount of this variability is unlikely to be reducible because genetic diversity is a strength of biological systems. This often leads to a level of variability in food materials that is hard to reduce. Second, distinguishing between a successful product and a failure is not clear-cut. This makes it difficult to define and detect quality improvement. For example, if a stereo or one if its components fail, the symptom is usually obvious to most users. In a food product, the wrong amount of an ingredient may be a failure, but the result may not be detectable by the consumer. Similarly, a food manufacturer may choose to change a product. While some customers may judge the altered product a failure, others may like it better.

This brief article cannot deal with the details of developing and using control charts, but there are many texts that can assist (2). The basic underlying idea is that patterns in variation of measurements can be used to determine the state of control (or lack of it) of the qualities of food products.

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