Data Scrubbing is the process by which noise, outliers and mistakes are identified and eliminated. Data scrubbing is required for accurate modeling.

Using the records and spectra views, fix any meta-data errors and remove or address the chart outliers. Use the processed and normalized filters to help you find the outliers in the spectra. In the screen below, the outliers are highlighted.
Data Scrubbing_Spotting Outliers

Data scrubbing and Model Creation are an iterative process.
In order to build the best models, data scrubbing should be done until the outliers and anomalies are removed from your collection.

Once you have sufficiently scrubbed your data, it is time to create the model.

While both SCiO Lab Mobile and SCiO Lab can be used to observe data, SCiO Lab is easier to use for this purpose as the view screen is larger, and you can download your data and build models only from SCiO Lab.

Multiple views are available to observe the data:

Scan View
Single Record_Hard Cheese

 Sample View

Observing data_screen2

 Spectrum View

Observing data_screen2

Use the scan view and check for accuracy in each scan. Use the sample view to see multiple scans of the same sample at one time.  The accuracy of your attributes is critical to the success of your model.
Use the spectrum view to look for outliers and see trends.

Once you have observed your data, the next step is to scrub it.