this example, 6 polymers were investigated for application in a sensor system
for the quantification of the refrigerants R22 and R134a in mixtures. By the
use of all polymers an accurate quantification of both analytes could be
performed. Additionally, 2 polymers were selected on the basis of the
sensitivity patterns for the application in a small low-cost 4l RIfS setup. By the
use of these 2 polymers, the 4l
RIfS setup could quantify R22 in the presence of R134a quite well but not vice
shown that the best selection of 2 sensors is the combination of 1 microporous
polymer with 1 polar polymer enabling a discrimination on the basis of two
interaction principles. The two interaction principles show the most different
sensitivity patterns for the two analytes, which is the common selection
criterion of sensor coatings for an analytical problem.
polymers used in this example show two different types of sorption, the
specific Langmuir sorption and the unspecific Henry sorption. For the classical
feature extraction, which uses the height of the raw signal after a definite
time of exposure to analyte, the unspecific Henry sorption is advantageous as
the immediate sensor responses allow very fast measurements. Yet, a drawback of
this common feature extraction is the extraction of only one single variable
per sensor. This limits the number of analytes to be quantified to the number
of sensors in the ideal case. This means that a 2-sensor setup can be
calibrated for 2 analytes or contaminants and the 6 sensor array setup can be
calibrated for up to 6 analytes and contaminants. Contaminants in the samples,
which do not sorb into the sensitive layers, do not interfere the determination
of the concentration of both analytes and can be ignored during the calibration.
Yet, contaminants, which sorb into the polymer layers, bias the predictions of
the analyte concentrations unless they can be considered during the calibration
process as additional analytes in combination with additional sensors.
5, the principle of a time-resolved feature extraction will be introduced.
Thereby the kinetics of sorption and desorption of analytes is exploited allowing
the extraction of a virtually unlimited number of variables per sensor. Thus,
the number of analytes, which can be quantified per sensor, is limited only
by similarities of kinetics and is not fixed by the device. The time-resolved
approach removes the limitation of the common sensor array approach of being
able to quantify simultaneously only a maximal theoretical number of analytes.
The time-resolved approach also changes dramatically the search and the rating
of polymers, which might be suitable for a specific analytical problem. The
static measurements with a single feature extraction need sensitivity patterns
as different as possible, whereas the kinetic feature extraction needs different
shapes of the sensor responses during sorption or desorption.
selection of the best combination of polymers based on the sensitivity pattern
in this example could be verified by a brute force variable selection approach.
Yet, the time-resolved measurements introduced in this work generate many
variables putting new challenges to calibration methods and variable selection techniques,
as a brute force variable selection is rendered impossible. Therefore, the
introduction of new calibration techniques combined with variable selection
methods are one of the focuses of this work.