Validating a Measurement Device and Setting a Critical Limit

Six Sigma – iSixSigma Forums Operations Manufacturing Validating a Measurement Device and Setting a Critical Limit

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    Hello everyone,

    I work at a food factory where large batches of product are being filled in small containers. To ensure a food safe product, controlling the pH of each batch is critical. Currently every batch gets measured, however this is a very labour intensive process which requires an additional operator.

    The business has decided that we should move to inline pH measurement devices. The scope of the project is to mount two inline pH measurement devices in the tank where the batch is stored. When measuring under these conditions however, we know that there is an inaccuracy. I am looking to make a validation protocol, to determine how much this inaccuracy is so I can set a critical limit on the measurent device.

    1) the pH inline measurement devices have a standard maximum inaccuracy of pH 0.1
    2) the additional inaccuracy due to storing conditions is different per recipe.

    The data I can generate to make this validation are the following:

    1) measurements of inline pH probe 1 when the batch is finished and stored in the buffertank prior to filling.
    2) measurements of inline pH probe 2 when the batch is finished and stored in the buffertank prior to filling.
    3) measurements of each batch under the right measurement conditions

    Does anyone have a suggestion how I can determine the critical limit for the pH inline measurement devices, supported by statistical analysis to make up for the measurement inaccuracy?

    Any help will be highly appreciated.



    I would suggest taking multiple measurements of each sample and conduct a gage linearity and bias study against the known standard. Realistically it sounds like you are trying to work out the bias in your measurement system. You can check this bias if you can get your hands on a known standard from the pH measurement supplier maybe or any other chemistry based department in your workplace. Once you have the bias locked down you can then set a standard for each recipe.


    Joel Mason


    I believe your scenario can fit into a traditional Gauge R&R experimental structure. In your case, I believe you can treat your probes as “operators” in a traditional Gauge R&R sense. You have two probes that are going to be exposed to the exact same batch each time. I would think of your batch as your “parts” in a traditional Gauge R&R sense. It sounds like you have some engineering experience that tells you that your measurement uncertainty will change with the batch (which would be like a linearity problem as Daniel mentioned). How many batches can you afford to mix for this measurement validation? In a traditional Gauge R&R with 2 operators, in my industry we would want 10 parts (or batches in your case) that would span the entire specification range of whatever characteristic it was that we are measuring and we would want 3 replicates (that means each operator measuring each part 3 times in a blind and randomized fashion).

    And Daniel is correct – a traditional Gauge R&R will not expose you to linearity and bias concerns. Linearity is a changing bias across the operational range of the measurement system. Bias is, well, bias – it’s an “offset” of the true value. In order to do that study, as Daniel indicates, you would have to have some samples of known pH for which to expose your probes.

    Everything I’ve said assumes you are interested in quantifying the uncertainty of your measurement system, not setting control limits around the food production process itself. In my industry, we typically want to quantify the measurement uncertainty first, and we then perform a capability study once we understand the uncertainty due to the measurement system and have ensured we have a capable measurement system for discriminating good from bad. That is because in a capability study (where control limits will be calculated as a result), it has variation due to the process and variation due to the measurement system all thrown together. Best wishes.

    If you want to discuss this further, indicate that in a response and perhaps we can connect by other means.

    John Deere

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