Acevedo Cristhian Manuel Durán, Gómez Jeniffer Katerine Carrillo, Bautista Gómez Gustavo Adolfo, Carrero Carrero José Luis, Ramírez Rogelio Flores
Multisensory Systems and Pattern Recognition Research Group, Faculty of Engineering and Architecture, University of Pamplona (UP), Pamplona 543050, Colombia.
Chemical Engineering Group, Faculty of Engineering and Architecture, University of Pamplona (UP), Pamplona 543050, Colombia.
Cancers (Basel). 2025 Aug 23;17(17):2742. doi: 10.3390/cancers17172742.
Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, emphasizing the urgent need for early, non-invasive, and accessible diagnostic tools. This study aimed to evaluate the effectiveness of a microelectromechanical systems (MEMS)-based electronic nose (E-nose) in combination with gas chromatography-mass spectrometry (GC-MS) for CRC detection through sweat volatile organic compounds (VOCs).
A total of 136 sweat samples were collected from 68 volunteer participants. Samples were processed using solid-phase microextraction (SPME) and analyzed by GC-MS, while a custom-designed E-nose system comprising 14 gas sensors captured real-time VOC profiles. Data were analyzed using multivariate statistical techniques, including PCA and PLS-DA, and classified with machine learning algorithms (LDA, LR, SVM, k-NN).
GC-MS analysis revealed statistically significant differences between CRC patients and healthy controls (COs). Cross-validation showed that the highest classification accuracy for GC-MS data was 81% with the k-NN classifier, whereas E-nose data achieved up to 97% accuracy using the LDA classifier.
Sweat volatilome analysis, supported by advanced data processing and complementary use of E-nose technology and GC-MS, demonstrates strong potential as a reliable, non-invasive approach for early CRC detection.
结直肠癌（CRC）仍是全球癌症相关死亡的主要原因之一，这凸显了对早期、非侵入性且易于获得的诊断工具的迫切需求。本研究旨在评估基于微机电系统（MEMS）的电子鼻（E-nose）结合气相色谱-质谱联用仪（GC-MS）通过汗液挥发性有机化合物（VOCs）检测CRC的有效性。
从68名志愿者参与者中总共收集了136份汗液样本。样本采用固相微萃取（SPME）进行处理，并通过GC-MS进行分析，同时一个由14个气体传感器组成的定制E-nose系统采集实时VOC图谱。数据采用多元统计技术进行分析，包括主成分分析（PCA）和偏最小二乘判别分析（PLS-DA），并使用机器学习算法（线性判别分析（LDA）、逻辑回归（LR）、支持向量机（SVM）、k近邻算法（k-NN））进行分类。
GC-MS分析显示CRC患者与健康对照者（COs）之间存在统计学上的显著差异。交叉验证表明，使用k-NN分类器时，GC-MS数据的最高分类准确率为81%，而使用LDA分类器时，E-nose数据的准确率高达97%。
在先进的数据处理以及E-nose技术和GC-MS的互补使用支持下，汗液挥发物分析显示出作为一种可靠的、非侵入性的早期CRC检测方法的强大潜力。
