Because of their direct impact on patients, medical supply lines are under strict regulations and have to be monitored in terms of purity on a regular basis. State-of-the-art measurement solutions do not allow for continuous bedside monitoring. The aim of the presented project is to provide a compact multispecies monitoring system based on the latest quantum cascade laser technologies.

Medical gas supply lines in hospitals are subject to strict regulations.
Besides the establishment of modern management techniques, continuous
monitoring of the distribution lines could avoid the fatal accidents that
unfortunately still happen to patients, especially after maintenance is performed on
older buildings (Weller et al., 2007; Cornwell et al., 2016). Concentration
limits are defined for every gas line by local or national regulations, such as the
European Pharmacopeia. The most important impurities are CO

In this context, sensors based on quantum cascade laser technology, which have proven their applicability in demanding conditions (Anscombe, 2011) spanning from the environmental monitoring sector to process monitoring and control applications (Röpcke et al., 2012), may offer a suitable solution.

The present study introduces a new approach to the bedside purity monitoring of medical gases in hospitals. The three main supply lines to check are the oxygen line, the nitrogen protoxide line and the compressed air line.

As a first step, a single-species monitor focusing on SO

Two sketches, one for the sensor assembly and the other for the optical subsystem of the Q-MACS Trace compact sensor configuration, are presented in Fig. 1.

The general sensor assembly with electronics, optics

As a laser absorption spectrometer based on the direct absorption technique,
the Q-MACS Trace compact SO

The acquired detector signal with the SO

The acquired signal is analyzed with respect to the well-known molecular
parameters by means of the Levenberg–Marquardt algorithm, which ensures
nearly calibration-free measurements. In terms of the introduced SO

The gas dilution system consisted of two units (MF1; MKS Instruments, Inc., Andover, MA, USA) of mass flow controllers and a diaphragm pump (Thomas 7011Z DC; Gardner Denver, Milwaukee, WI, USA). One mass flow controller provides a range of 0 to 200 sccm and another one comes with a range of 0 to 10 slm. This set of flow controllers allows for reasonable accuracy in the dilution of gas standards over a very wide range of dilution ratios with the selection of the appropriate settings on the mass flow controllers. The distinct concentration steps, which were alternatingly tuned as a continuous flow through the measurement cell, are reflected in Table 1.

A list of the dilution steps, the resulting SO

This dilution system provides the test atmospheres for the analyzers that are undergoing testing.

The pressure in the sample cell is monitored via an HPS Series 902 piezo
transducer (MKS Instruments, Inc., Andover, MA, USA), which provides a range of application of 0 to 1300 mbar.
Using this pressure gauge, the pressure in the cell was maintained at a
fixed value of

The statistical methods used to evaluate the quantitative performance
factors are presented in this chapter. Since no alternative concept for the
detection of SO

The linearity factor is assessed by linear regression with the calibration concentration as the independent variable and the analyzer response as the dependent variable. The calibration model is given in Eq. (1):

The form of the regression model to be fitted is expressed in Eq. (4):

The concentration values were calculated from the estimated calibration curve using the following formula:

A test for the departure from linearity is carried out by comparing the residual
sum of squares to a chi-square distribution with

The response time of the analyzers to a step change in the analyte concentration was calculated by determining the total change in the response due to the step change (either an increase or a decrease) in concentration, and then determining the point in time at which 95 % of that change was achieved. Both the rise and fall times were determined. Using data taken at intervals of 1 s, the calculation is carried out by Eq. (7):

The point in time at which this response occurs is determined by inspecting the response time data, and the response time is calculated according to Eq. (9):

The detection limit (LOD) was defined as the smallest true concentration at which the analyzers expected the response to exceed the calibration curve at zero concentration by 3 times the standard deviation of the analyzer zero reading. The LOD is then determined by applying Eq. (10):

The statistical procedures for assessing the zero and span drift were similar to those used to assess the interrupted sampling. The zero (span) drift was calculated as the arithmetic difference between the zero and span values respectively obtained before and after the sampling of the source emissions. During this test, no estimate of the precision of the zero and span drift values was made.

A Q-MACS Trace analyzer prototype was tested for the most highly sensitive
online monitoring of SO

The linearity results for the Q-MACS Trace setup with a 1 s acquisition time.

In Table 2, the linear equations for the system configuration developed from these data are shown.

The statistical results of the linearity test.

The quite low value for the regression coefficient in the linearity tests is
caused by the rather high uncertainty that the chosen dilution system provides
for the N

The results of the linear fit for the measured values at 0 and 1.67 % of the range.

For the linear fit, the concentration values are weighted with the uncertainties of the dilution system to respect the deviation of the finally settled concentrations from the targeted ones.

The detection limit determined according to the SO

Table 3 shows the detection limits for each configuration of the Q-MACS
Trace analyzer tested, determined from the detection limit procedure
described in the previous section. Figure 4
visualizes the results of the linear fit valid for 0 to
1.67 % of the range. The calculated detection limit for 1 s of acquisition time
is 22.99 ppb, corresponding to a density of

The response time for the sensor based on a step change in the analyte
concentration was determined to about 14

The trends in the SO

Figure 5 shows the
trend in the SO

The zero and span data taken at the start and end of the linearity test are shown in Table 4.

The data used to assess the zero and span drift of the Q-MACS Trace compact
SO

The observed drift values are shown in Table 5 as the differences between the pretest and the posttest concentration measurements. Furthermore, Table 5 also presents the zero and span drifts as a percent of the span gas concentrations. The zero drift for the tested sensor was less than 0.04 % of the respective span gas concentration. The span drift was less than 0.22 % of the respective span concentration.

The results of the zero and span drift of the SO

The noise behavior of the Q-MACS Trace system is characterized by means
of an Allan variance analysis. In Fig. 6, the
resulting Allan–Werle plot, as introduced by Werle as a powerful tool in the
characterization of noise performance from laser-based sensors (Werle et
al., 1993), is shown for a 3 h run of the sensor at a constant SO

An Allan–Werle plot from 3 hours of continuous measurement by the Q-MACS SO

The developed sensor module was applied in a measurement campaign at Ancona
Hospital in Italy (Ospedali Riuniti di Ancona). In this campaign,
benchmarking was performed
against an established FTIR-based measurement system: the Loccioni GIGAS
10M. The QCLAS sensor system was configured to support sulfur
dioxide (SO

A comparison of the SO

In Fig. 7, the results of a continuous
measurement of SO

The test results, which are summarized in Table 6, confirm that the Q-MACS Trace analyzer provides a linear response over wide operating ranges. The compact prototype configuration, as used in this preliminary study, provides very good results with respect to sensitivity, selectivity and stability.

The results from the performance analysis for the Q-MACS Trace SO

The system is rugged and portable, and the necessary setup time is minimal. The fast sensor response times and measurement stability allowed for the verification testing to proceed smoothly. Its design incorporates a sample probe and a sample conditioning system, making it adaptable to a wide range of measurement applications.

Although the aim of 15 ppb and below for the limit of detection was not
achieved with this Q-MACS Trace compact configuration, the positive
results show that it is possible to design a system that will fulfill this
specification. Recently, long path cells with more than 150 m of optical
path length have become available. Unfortunately, such a cell would not allow
for the
maintaining or even the further optimization of the compactness of the resulting sensor
system. Moreover, the much higher volume of such a cell would lead to a
significant and unwanted increase in the response time. A more practical
approach is the selection of stronger absorption lines, which could be
possible depending on the gas matrix to be analyzed in the specific
application. Developments for the further optimization of the gas handling, e.g.
through the integration of an active pressure control, and the extension
of the analytical methods are currently in progress. Applying these drafted
optimizations in a future configuration will allow for a decrease in the achievable
detection limit by approximately a factor of 5; SO

During an on-site measurement campaign, it was possible to convincingly
demonstrate the applicability of QCL-based concentration monitoring
solutions in hospital gas supply lines. With respect to the monitored
CH

In pending development steps, the single-QCL source will be exchanged by a QCL array to address several molecular species in parallel while maintaining the compactness of the system. First introduced by Lee et al. (2009), QCL arrays will now make the step from the lab to the market as their technological relevance and applicability has already been demonstrated (Geiser et al., 2016).

Research data are available upon request to the authors.

The authors declare that they have no conflict of interest.

This research was made possible thanks to funding from the European Union Seventh Framework Programme (FP7, 2007–2013) under grant agreement no. 17884 through the collaborative integrated project MIRIFISENS.

A special thank you also goes to the plasma diagnostic group at the Leibniz
Institute for Plasma Science and Technology in Greifswald for the professional
support during the final verification tests of the sensor configuration
(