Modern gas chromatographs are expected to deliver the highest performance levels, but actual performance can suffer because
of a number of causes that include poor sample preparation, poor injection technique, incorrect flows or temperatures or inappropriate
data handling. Measured performance levels can also vary for samples at different concentrations or containing different chemical
substances.
It is always important to keep accurate and complete records of periodic test mixture analyses, any changes to instrument
configuration, the methods used and samples run to help diagnose problems when they occur. Sometimes a problem is obvious;
often, however, something seems to be going wrong but there is no catastrophic failure. Retention times begin to drift, area
counts start to decrease or repeat results seem more scattered than before. Deciding if the changes are significant is a nontrivial
task. If there is a significant problem, then further steps can be taken to diagnose and resolve it. If the problem is insignificant,
then considerable time might be saved.
Monday Morning Syndrome
Table 1: Experimental retention times.
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Most chromatographers have encountered "Monday morning syndrome" — results obtained at the end of the previous week do not
seem to match those acquired on Monday morning. For example, consider the first two columns of Table 1, which give retention
time data for one peak in a mixture across 11 consecutive runs. The first ten runs represent data taken on a Friday at 45
min intervals, while the eleventh was the first run on Monday, after a full weekend of instrument idle time. The 14.41 min
time obtained on Monday morning is clearly different from the 14.38 min average of the 10 Friday runs. But how significant
is this difference? The Monday time lies outside the range of the Friday data, but only by 0.01 min from the longest Friday
retention time. Is there a difference, or can this result be expected as part of normal operation? A statistical approach
can help answer this question but, as we will see, additional data are required to arrive at a truly meaningful answer.
Figure 1
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A random distribution of data about an average value can be anticipated if the fluctuations in the data are caused by random
inlet pressure changes, oven temperature drift, noise-induced data-handling variations, or other system variability. Sometimes
the observed fluctuations might exhibit apparent trends, as seems to be the situation in Figure 1, where a sinusoidal trend
is evident in the retention data. Is this a real trend or is it just the human eye picking out a pattern where none exists?
Even if there is a dependency upon external conditions, the observed retention times in this instance can be considered to
be random in the sense that they will tend to group around a central average value, as long as the external causal variables
fluctuate around an average value as well.
If a large number of random retention times were to be measured, the frequencies with which each retention time occurs could
be expected to be grouped around the average value in a more-or-less bell-shaped, or Gaussian, normal distribution curve.
The set of 10 Friday measurements represents a small sampling from this hypothetical population of many values, but this is
all the data that we have. Using statistical analysis, we can infer the properties of the large hypothetical population of
retention time measurements from our sample data set. Then, we should be in a position to compare the anticipated behaviour
with other experimental data.
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