HPPI-TOFMS, which consisted of a vacuum ultraviolet (VUV) lamp-based HPPI ion source and an orthogonal acceleration time-of-flight (TOF) mass analyzer, was used to detect and analyze the breath samples. A commercial VUV-Kr lamp with a photon energy of 10.6 eV was adopted in this platform. Most VOCs with an ionization potential lower than 10.6 eV were ionized in the ionization region directly [32 (link)]. Breath samples were directly introduced through a 250 μm i.d. 0.60 m long stainless-steel capillary. The HPPI ion source works in soft HPPI ionization mode, which will produce mostly radical cations (M+) by ionization reaction as M + hγ → M+ + e. Then, the ion transmission system effectively transferred these ions from the ion source into the orthogonal acceleration, reflection TOFMS mass analyzer. The TOFMS signals were recorded by a 400 ps time-to-digital conversion rate at 25 kHz, and all the mass spectra were accumulated for 60 s. Thus, it takes 1 min for one sample to go through a detection. A spectrogram with 31,666 data pairs was extracted from each exhaled breath sample. Based on the flight time and m/z calibration on the standard gas with nine compounds at a concentration of 1 ppmv, the timeline of flight can be transferred as m/z, which is in the range of (0, 350). The TOFMS signals were positively correlated with the concentration of the VOC ions. The detection limit is down to 0.015 ppbv (parts per billion by volume) for aliphatic and aromatic hydrocarbons [28 (link)]. The gas-phase breath sample was directly inhaled into the ionization region through a 250 μm i.d. 0.60 m long capillary from the sampling bag. The TOF signals were recorded by a time-to-digital converter, and all the mass spectra were accumulated for 60 s. Mass spectrum peaks with m/z < 350 were detected by HPPI-TOFMS for each exhaled breath sample. The noise-reducing and base-line correction were implemented via anti-symmetric wavelet transformation, which was achieved by Python package pywavelets [33 (link)]. To transfer the discrete signal of mass spectra data to standard breathomics data, we calculate the area of the strongest peak in the range of [x − 0.1, x + 0.1) as the feature of VOC with m/z close to x. In this study, 1500 breathomics data were detected for machine learning (ML) model construction in the ions m/z range of [20, 320) with an interval of 0.2. A statistical analysis based feature selection was executed to avoid model over-fitting, in which the features without significant difference (p > 0.05) were excluded before model training.
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