Glöckler Johannes, Mitrovics Jan, Beeken Sara, Leja Marcis, Welearegay Tesfalem, Österlund Lars, Haick Hossam, Shani Gidi, Di Natale Corrado, Murillo Raúl, Flores-Rangel Gabriela, Bricio-Arzubide Francisco, Pinilla Raul, Vargas Rómulo, Saboya Carlos, Mizaikoff Boris, Díaz de León-Martínez Lorena
Institute of Analytical and Bioanalytical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany.
JLM Innovation GmbH, D-72070 Tübingen, Germany.
ACS Sens. 2025 Jan 24;10(1):427-438. doi: 10.1021/acssensors.4c02725. Epub 2025 Jan 8.
Gastric cancer remains a leading cause of cancer-related mortality, requiring the urgent development of innovative diagnostic tools for early detection. This study presents an integrated infrared spectroscopic electronic nose system, a novel device that combines infrared (IR) spectroscopy and electronic nose (eNose) concepts for analyzing volatile organic compounds (VOCs) in exhaled breath. This system was calibrated using relevant gas mixtures and then tested during a feasibility study involving 26 gastric cancer patients and 32 healthy controls using chemometric analyses to distinguish between exhaled breath profiles. The obtained results demonstrated that the integration of IR spectroscopy and eNose technologies significantly enhanced the accuracy of VOCs fingerprinting via principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA). Distinct differences between the study groups were revealed with an accuracy of prediction of 0.96 in exhaled breath samples. This combined system offers a high sensitivity and specificity and could potetially facilitate rapid on-site testing rendering the technology an accessible option for early screening particularly in underserved populations.
胃癌仍然是癌症相关死亡的主要原因,迫切需要开发创新的早期诊断工具。本研究提出了一种集成红外光谱电子鼻系统,这是一种将红外(IR)光谱和电子鼻(eNose)概念相结合的新型设备,用于分析呼出气体中的挥发性有机化合物(VOCs)。该系统使用相关气体混合物进行校准,然后在一项可行性研究中对26名胃癌患者和32名健康对照者进行测试,采用化学计量分析来区分呼出气体特征。获得的结果表明,红外光谱和电子鼻技术的集成通过主成分分析(PCA)和偏最小二乘判别分析(PLS-DA)显著提高了VOCs指纹识别的准确性。在呼出气体样本中,研究组之间的明显差异以0.96的预测准确率得以揭示。这种组合系统具有高灵敏度和特异性,并且有可能促进快速现场检测,使该技术成为早期筛查的一个可及选择,特别是在服务不足的人群中。