Service Manual 1001 5015 / Rev. 4
4:3
E-outlier (difference between sample and ambient temperature)
An outlier denoted by the letter E indicates that the difference between the sample
temperature and the ambient temperature is outside the set limits.
Sensitivity of outlier detection
The sensitivity for detection of outliers can be set in the instrument software, see User
Menu, Application Models®Setup®Outlier limits. For the outlier constants, it is im-
portant that a relevant value is set, so only real outliers will be indicated. If the value
selected is too low, samples that are not real outliers will be indicated as such. If the
value is set too high, some real outliers may be missed.
The sensitivity setting for C is the maximum acceptable standard deviation for each
set of sub samples. The appropriate value for this parameter, as well as the appropri-
ate values for A and B parameters, are determined when the calibration is developed.
Values for the A,B, and C constants will be recommended for AMs supplied by Foss
Analytical.
The low and high limits for D should be set according to the range for each constit-
uent in the actual AM.
The following are some questions that should be asked concerning the outlier:
•
How many of the samples give outliers?
•
In which positions are the outliers (A, B, C, D or E)?
•
What is the value of the outlier (1-5)?
•
Have they had outlier indications on that type of sample before?
•
Are the outlier indications reproducible?
•
Is the used calibration the correct one?
Conditions that will cause a sample to be flagged as an outlier
•
Scan data does not fit with the calibration
•
The sample differs from the typical samples in the calibration
•
Standard deviation between sub-sample predictions outside preset limit
•
Sample outside calibration range
Possible causes for outliers
•
Wrong sample analysed
•
Variety not included in the calibration
•
Extreme chemical composition
•
Impure sample (has too much foreign material)
•
Instrument error
•
Sample too hot or too cold (i.e. sample is frozen)
Corrective actions in the event of an outlier
•
Re-run the analysis and verify it to be a true outlier
•
Check to see if the sample meets any of the criteria mentioned in the section
“Possible causes for outliers” above. If so, take corrective measures.
•
Use reference method (Wet chemistry) instead
4.1.5
Poor Reference Data
•
Which laboratory was used?
•
Was more than one laboratory used, and if so, were there level differences?
•
Are there systematic errors lab to lab bias?
•
What reference method was used?
Summary of Contents for Infratec 1241
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