Discrimination of artificial strawberry aroma by electronic nose based on nanocomposites

An electronic nose containing an array of gas sensors with interdigitated electrodes coated with nanocomposites based on polyaniline (Pani) combined with carboxylated multiwalled carbon nanotubes doped with different acids was used to discriminate artificial strawberry aromas. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate if the electronic nose was able to distinguish between different strawberry aromas. In addition, a nonlinear projection of the interactive document map technique (IDMAP) was applied to evaluate the distinction ability of the gas sensor array. The sensing layers showed tubular characteristics for Pani/MWCNT_COOH morphology and the group's representative doped state. The main and most common compound identified by gas chromatography in strawberry aromas was ethyl butyrate. The gas sensors showed high sensitivity (>6.55 mV/ppm), low limit of detection (LD <0.31 ppm), limit of quantification (LQ <5.43 ppm), short response time (<63 s) and recovery time (<10.15 s) for different artificial strawberry aromas. The results demonstrate that the electronic nose could differentiate among the artificial strawberry aromas. The volatile profile of strawberry aromas was distinguished into distinct groups using both LDA and PCA analysis. The use of IDMAP permitted the identification of the major contributors to the distinguishing ability of gas sensors with a high silhouette coefficient (0.91). The sensor array was efficient at aroma discrimination, demonstrating excellent potential for food analysis in industrial applications.