Ultimately, this oven-free approach simplifies maintenance requirements because we need to tend shorter supply lines. Then, by requiring the system to be a series of plug-and-play modules, we turn the maintenance visit into one measured in times of five minutes or less rather than hours.
Hydrogen carrier gas and short, high-resolution capillary columns cut the time of analysis, while detection system options (flame ionization for hydrocarbons, thermal conductivity for atmospheric components, flame photometric for sulfur compounds, and halogen-specific for environmentally significant components), coupled with NeSSI-smart valve injection, give us an extremely flexible base for analyzing nearly anything on-line in real time.
The smaller form factor, better speed and lower maintenance demands show how the NeSSI concept has migrated to the instrument side to give us the fundamental basis for true control of a process. Now, let's turn our attention to software developments that NeSSI is fostering.
ELIMINATING RETENTION TIME VARIABILITY
A series of chromatograms will clearly show that retention times change as a function of time. This is even more evident when comparing instrument to instrument or before and after column maintenance (Figure 3a).
It's not practical in a process setting to manually manipulate the chromatographic conditions to keep retention times constant. This leads to the first role for software: to adjust for retention time drift. A number of software techniques can address the problem; all use a multivariate correlation algorithm to bring the retention times under sufficient control, even when matching results from more than one chromatograph.
Figure 3b shows that such software, in this case LineUp, allows us to generate consistent run-to-run results for more than one chromatograph and over a long period of time. This capability can impact maintenance — by reducing the time spent on instrument calibration. For instance, taking three similar chromatographs (same nominal method, column and detectors) fresh off the production line and applying the alignment technology, we should be able to superimpose the results, as shown in Figure 4.
With alignment technology bundled into the method, the GC will generate very consistent results as long as mechanical problems, column degradation or a process upset don't intervene. So, we can get accurate, representative results every time, even if we must change a column, a detector or even an entire instrument. We also can monitor how hard the software must work to keep retention time under control — affording us a mechanism to predict and prioritize when a hardware problem must be addressed.
The effect on maintenance efficiency is clear. Industry analyses, e.g., Reference 2, indicate that a vast majority of trips to the analyzer location prove unnecessary and that the most common culprit in chromatographic instrumentation is the lack of retention time fidelity. Intelligent deployment of software not only eliminates the unneeded callouts but also frees the system to identify true problems with a significantly higher success rate.
The NeSSI concept strikes again, giving us true plug-and-play (not plug and work hard to recalibrate) capabilities. This leads to the second role of software: dealing with the flood of data.
AUTOMATING DATA INTERPRETATION
With our fast GC appliance in place, we approach the speed of spectroscopy in generating multivariable data streams. With retention time alignment in place, we can use the same software that we rely on in spectroscopy to interpret the GC trace.
While chromatographic data are complex and multivariate, most process monitoring and control strategies are fairly simple and often can be expressed in binary terms (i.e., good/bad, steady state/upset, consistent/changing, etc.). Therefore, the goal is to interpret either the raw chromatographic data or the tabular results into a few important quality parameters. This is done with classification or regression models.
Figure 5 shows an automated interpretation, via Pirouette software, of detailed hydrocarbon analysis (DHA) of winter-grade gasoline, to see if it falls within the acceptable envelope for release. The DHA table generates a matrix separating hydrocarbons both by carbon number and compound type. A chemometric analysis maps a point that scores every run and compares it to a library. Here, a single point represents each chromatogram; points close to one another are relatively similar. This approach allows us to create a confidence limit around, in this case, "good winter-grade gasoline" and instantly detect if a sample is out-of-specification.