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Updated: Jan 18, 2026
Fruit Volatile Analysis Using an Electronic Nose
Published on: March 30, 2012
Colorectal Cancer Detection Through Sweat Volatilome Using an Electronic Nose System and GC-MS Analysis
Cristhian Manuel Durán Acevedo1, Jeniffer Katerine Carrillo Gómez1,2, Gustavo Adolfo Bautista Gómez1
1Multisensory Systems and Pattern Recognition Research Group, Faculty of Engineering and Architecture, University of Pamplona (UP), Pamplona 543050, Colombia.
Background:
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).
Methods:
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).
Results:
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.
Conclusions:
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.
