# Taking the Measure of Mixing Technologies and Practices J. Peter Clark | February 2014, Volume 68, No.2

#### PROCESSING

Mixing in all its permutations remains a common and critical unit operation in food processing. Representative examples include mixing liquid sweeteners into water-based beverages, mixing solid ingredients into soups and sauces, mixing solid ingredients into seasoning bases, and mixing carbon dioxide into carbonated beverages.

In all cases, energy is required to ensure homogeneous mixtures. How much energy? How is it best delivered? How long should it take to achieve good mixing? And, most critically, how does one know that the desired results have been achieved?

These are some of the important questions encountered. They are more difficult to answer than might be supposed.

The last question actually has the simplest answer, but applying the answer is not always easy. Since the overall objective of any mixing operation is to achieve consistent results, the way to measure consistency is statistically.  Specifically, the standard deviation of a composition is the accepted measure of consistency among samples of supposedly the same population, such as individual bags from a batch of dry seasoning mix or individual bottles from a batch of beverage mix.

Much mixing is done in batches because formulation often involves addition of individual components in a recipe. Theoretically, one wants the standard deviation of the measured amount of each component to be small, but, in practice, one or two ingredients are used to represent the composition of the complete mixture. This may be because it is hard to analyze for every ingredient because not all are equally significant and because it is costly to do so much analysis.

In many dry solid mixtures, salt is a relatively large component. It is relatively easy to analyze, so the standard deviation of salt content is a good measure of mixing effectiveness. Some operations take a single sample after mixing for a set time as a quality check. This is relatively meaningless as a measure of consistency. All it really indicates is whether all the components were added in about the correct amounts. As such, it is a useful check.

To determine mixing time in a given mixer for a given formula, multiple samples must be taken from different locations in the mixer at various elapsed time periods. It requires about 10 samples to calculate a meaningful standard deviation. When the standard deviation stabilizes at a low value, the optimum mix time is identified. In solids mixing, it is actually possible to overmix, so an optimum time exists. Unfortunately, the optimum time may change in different equipment, with different formulas, and with different batch sizes.

As I have pointed out before, it is frequently tempting for operations to overfill dry mixers in a misguided effort to increase production. Because all mixing relies on bulk convection of the components, some free volume in a mixer is necessary. Overfilling can lead to poor mixing or excessively long mix times. Many dry mixers are horizontal vessels with one or more shafts and paddles or ribbons to move the contents. Such mixers should be filled to just over the shaft, with the ends of the ribbon or paddles just showing. This is typically about 70% of the theoretical volume.

To perform a mix time determination, the mixer should be loaded with the full recipe, run for a short period, and stopped (for safety reasons). Then samples may be taken with a sample thief (a device that allows recovery of small samples from below the surface) and the samples analyzed for a representative component. The standard deviation is calculated for each time period and plotted against time. There are mathematical manipulations of the statistical results that may linearize the curve, but the concept is the same.

For an ongoing operation, the standard deviation can be calculated for any population that is assumed to be the same. For example, in one case, bags of seasoning mix that contained salt and antioxidants were analyzed from several different batches. If the mixing operation were consistent, one would expect the standard deviation within batches to be the same from batch to batch. In this case, they were not. The consequence could be serious, because there are federal regulatory limitations on the allowable antioxidant concentrations in the final product. If the salt concentrations are not consistent, it is reasonable to assume that the antioxidant concentrations are not either.