These are two NIR on-line instrument to control the production in the two lines of a Flour Mill Company.
With this two instruments all the production can be controlled for certain parameters as Moisture, Protein and Ash.
If you want to see how the product pass trough the window, you can see the post:
To get a representative sample for validation and calibration, we have to acquire the sample physically from a point near the sample window and in the moment that we acquire the spectrum. This way we will get the maximun correlation between the spectra and the lab data for the sample.
These instruments must be installed in a place free of turbulences, so we can see how the sample pass trough the window without any gaps of product flow. Anyway we can train the Model to discard samples where there are disturbances in the samples, or when there is no product flowing. All this sample will be outliers, with high Mahalanobis distance values, and will not count in the batch statistics.
We can use global, local, or discriminant models, to predict the samples. The last one (discriminant) is quite interesting, in cases we have quite a lot of different products. We can train the model to distinguish wich sample is flowing and you will get the name of the product at the same time that the chemical values from a particular equation for each product (this takes a lot of chemometrics involve).
LOCAL calibrations can give accurate results and are really easy to maintain.
I´m testing these two types of models and I will came back to this post as soon as I get values from some validation sets I´m waiting.