Fuzzy C-means clustering for chromatographic fingerprints analysis: A gas chromatography-mass spectrometry case study

Fuzzy C-means clustering (FCM) is proposed as a promising method for the clustering of chromatographic fingerprints of complex samples, such as essential oils. As an example, secondary metabolites of 14 citrus leaves samples are extracted and analyzed by gas chromatography-mass spectrometry (GC-MS). The obtained chromatographic fingerprints are divided to desired number of chromatographic regions. Owing to the fact that chromatographic problems, such as elution time shift and peak overlap can significantly affect the clustering results, therefore, each chromatographic region is analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to address these problems. Then, the resolved elution profiles are used to make a new data matrix based on peak areas of pure components to cluster by FCM. The FCM clustering parameters (i.e., fuzziness coefficient and number of cluster) are optimized by two different methods of partial least squares (PLS) as a conventional method and minimization of FCM objective function as our new idea. The results showed that minimization of FCM objective function is an easier and better way to optimize FCM clustering parameters. Then, the optimized FCM clustering algorithm is used to cluster samples and variables to figure out the similarities and dissimilarities among samples and to find discriminant secondary metabolites in each cluster (chemotype). Finally, the FCM clustering results are compared with those of principal component analysis (PCA), hierarchical cluster analysis (HCA) and Kohonon maps. The results confirmed the outperformance of FCM over the frequently used clustering algorithms.