Analytical instruments that can measure small amounts of chemicals in complicated biological samples are often useful as diagnostic tools. However, it can be challenging to optimize these sensors using actual clinical samples, given the heterogeneous background and composition of the test materials. Here we use gas chromatography–
differential mobility spectrometry (GC/DMS) to analyze the chemical content of human exhaled breath condensate (EBC). Ultimately, this
system can be used for non-invasive disease diagnostics. Many parameters can be adjusted within this instrument
system, and we implemented a factorial design-of-experiments to
systematically test several combinations of parameter settings while concurrently
analyzing effects and interactions.
We examined four parameters that affect sensitivity and detection for our instrument, requiring a 24 factorial design. We optimized sensor function using EBC samples spiked with acetone, a known clinical biomarker in breath. Two outputs were recorded for each experiment combination: number of chemicals detected, and the amplitude of acetone signal. Our goal is to find the best parameter combination that yields the highest acetone peak while also preserving the largest number of other chemical peaks in the spectra. By optimizing the system, we can conduct further clinical experiments with our sensor more efficiently and accurately.
