Application of gas chromatography-ion mobility spectrometry (GC-IMS) and ultrafast gas chromatography electronic-nose (uf-GC E-nose) to distinguish four Chinese freshwater fishes at both raw and cooked status.

Abstract

The volatile organic compounds (VOCs) in four Chinese freshwater fishes (i.e., Hypophthalmichthys molitrix (H), Aristichthys nobilis (A), Lateolabrax japonicus (L), Parabramis pekinensis (P)) were separated using gas chromatography-ion mobility spectrometry (GC-IMS) and ultrafast gas chromatography electronic-nose (uf-GC E-nose). Principal component analysis (PCA) was applied to distinguish the VOCs identified from the four freshwater fishes in both raw and cooked states. Twenty compounds were identified from the spectral database of GC-IMS, including five aldehydes, eight alcohols, six ketones, and three esters. In addition, using GC E-nose, 32 compounds were isolated by the first column MTX-5, and 24 compounds were isolated by the second column MXT-1701. PCA results showed that the four fishes could be well discriminated against. The odor profiles of raw and cooked fishes were clearly different. This study demonstrated that specific signals provided from GC-IMS could differentiate freshwater fishes. GC-IMS and uf-GC E-nose could be developed further to distinguish aquatic products based on VOCs. PRACTICAL APPLICATIONS: Two new methods, gas chromatography-ion mobility spectrometry (GC-IMS) and ultrafast gas chromatography electronic-nose (uf-GC E-nose), were used to analyze the volatile organic compounds (VOCs) in four Chinese freshwater fishes at raw and cooked status. GC-IMS has the characteristics of fast detection speed and high sensitivity. The accuracy of the qualitative analysis of the compounds is better with GC-IMS (larger data volume, leading to a better in-depth statistical analysis). Uf-GC E-nose could provide a nondestructive, fast, relatively low cost, and trustworthy way for flavor analysis. According to the techniques, the established fingerprints of VOCs provided an additional tool for food analysis.