No abstract

With 1.6 million deaths per year, lung cancer is one of the leading causes of death worldwide. One reason for this high number is the absence of a preventive medical examination method. Many diagnoses occur in a late cancer stage with a low survival rate. An early detection could significantly decrease the mortality. In recent decades, certain substances in human breath have been linked to certain diseases. Different studies show that it is possible to distinguish between lung cancer patients and a healthy control group by analyzing the volatile organic compounds (VOCs) in their breath. We developed a sensor based on photoacoustic spectroscopy for six of the most relevant VOCs linked to lung cancer. As a radiation source, the sensor uses an optical-parametric oscillator (OPO) in a wavelength region from 3.2 µm to 3.5 µm. The limits of detection for a single substance range between 5 ppb and 142 ppb. We also measured high resolution absorption spectra of the biomarkers compared to the data currently available from the National Institute of Standards and Technology (NIST) database, which is the basis of any selective spectroscopic detection. Future lung cancer screening devices could be based on the further development of this sensor.

We applied a multivariate analysis (MVA) to spectroscopic data of gas mixtures in the mid-IR in order to calculate the concentrations of the single components which exhibit strongly overlapping absorption spectra. This is a common challenge in broadband spectroscopy. Photoacoustic (PA) measurements of different volatile organic compounds (VOCs) in the wavelength region of 3250 nm to 3550 nm served as the exemplary detection technique. Partial least squares regression (PLS) was used to calculate concentrations from the PA spectra. After calibration, the PLS model was able to determine concentrations of single VOCs with a relative accuracy of 2.60%.