Convolutional neural networks for automated targeted analysis of raw gas chromatography-mass spectrometry data

Through their breath, humans exhale hundreds of volatile organic compounds (VOCs) that can reveal pathologies, including many types of cancer at early stages. Gas chromatography-mass spectrometry (GC-MS) is an analytical method used to separate and detect compounds in the mixture contained in breath samples. The identification of VOCs is based on the recognition of their specific ion patterns in GC-MS data, which requires labour-intensive and time-consuming preprocessing and analysis by domain experts. This paper explores the original idea of applying supervised machine learning, and in particular convolutional neural networks (CNNs), to learn ion patterns directly from raw GC-MS data. The method adapts to machine specific characteristics, and once trained, can quickly analyse breath samples bypassing the time-consuming preprocessing phase. The CNN classification performance is compared to those of shallow neural networks and support vector machines. All considered machine learning tools achieved high accuracy in experiments with clinical data from participants. In particular, the CNN-based approach detected the lowest number of false positives. The results indicate that the proposed method is a promising tool to improve accuracy, specificity, and in particular speed in the detection of VOCs of interest in large-scale data analysis.

I. Introduction

The typical human breath is estimated to carry over a thousand distinct volatile organic compounds (VOCs) [1] . These are the products of the metabolic processes that occur not only in the lung but, due to the blood-gas exchange, in the whole body. A breath sample contains information that describes physiological and pathological conditions, and thereby the health status of the patient [2] . Recently, the relationship among the changes in VOC patterns and different types of diseases, including breast, colorectal and lung cancers, have been presented in several research studies [3] – [5] . Breath analysis is thought to have a potential to provide a new noninvasive, fast and accurate diagnostic platform.