This paper introduces the genetic algorithm-adaptive neuro-fuzzy inference system (GA-ANFIS), as a novel nonlinear feature selection method. This hybrid technique combines genetic algorithms (GAs) as powerful optimization methods with ANFIS as a robust nonlinear statistical method. Multivariate image analysis whose descriptors achieved from bidimensional images coupled to principal component analysis (PCA) and the most significant principal components (PCs) were extracted. Eigen value ranking (EV), correlation ranking (CR), GA-partial least square (GA-PLS) as well the method proposed in this work were used to select the most relevant set of PCs as inputs for ANFIS to assess electron capture detector responses of 207 polychlorinated biphenyls. The results indicated that GA-ANFIS is superior over the others in both selecting the most relevant set of PCs and correlating the inputs (PCs) with the detector responses. The best model was statistically validated for its predictive power using cross-validation, applicability domain and Y-scrambling evaluation procedures. Moreover, the superiority of this model obtained from pixels of chemical structures over the nonlinear one obtained from original molecular descriptors in a previous work indicates that the image analysis is a powerful tool in quantitative structure-(chromatographic) property relationship (QSPR) studies.