Coupling electronic nose with GC–MS improves flavor recognition and grade differentiation of Zhenjiang aromatic vinegar

电子鼻 化学 糠醛 风味 醋酸 色谱法 气相色谱-质谱法 芳香 气相色谱法 质谱法 有机化学 食品科学 生物 催化作用 神经科学
作者
Li Wang,Xingyi Huang,Chengquan Wang,Joshua Harrington Aheto,Xianhui Chang,Shanshan Yu,Xiaorui Zhang,Yu Wang
出处
期刊:Journal of Food Process Engineering [Wiley]
卷期号:44 (10) 被引量:15
标识
DOI:10.1111/jfpe.13806
摘要

Abstract In this study, the flavor volatile profiles of five grades of Zhenjiang aromatic vinegar (ZAV) were compared using a gas chromatography‐mass spectrometer (GC–MS) and electronic nose (E‐nose). A total of 55 volatile compounds were identified, with 14 volatile compounds, including furfural, acetic acid, tetramethylpyrazine, phenethyl acetate, and phenylethyl alcohol, being identified as the key volatile compounds causing significant differences between different grades of ZAV samples. Principal component analysis (PCA) analysis of E‐nose data revealed that the W5S, W1W, W2W, W1S, and W2S sensors with high signal intensity to all ZAV samples correctly categorized the five grades of ZAV samples. The correlation analysis results suggested that the response signal intensities of W5S, W1W, and W2W had significant positive correlations with the abundances of acetic acid, isobutyric acid, ethyl phenylacetate, 3‐hydroxy‐2‐butanone, 2,3‐butanedione, 2‐acetylfuran, etc., adding to the evidence that W5S, W1W, and W2W sensors were sensitive to nitrogen oxides, sulfur compounds, pyrazines, and aromatic components. Based on the findings, it is possible to conclude that E‐nose, in conjunction with chemometric pattern recognition methods, can be used to differentiate between different grades of ZAV based on volatile compounds. Practical Applications Different grades of Zhenjiang aromatic vinegar (ZAV) were distinguished by physical and chemical detection of total acid, nonvolatile acid, amino acid nitrogen and other indicators. In order to simplify the detection work, this study was to establish a rapid and feasible method for achieving ZAV grades differentiation according to their flavor volatile compounds. PCA of E‐nose data showed that the five grades of ZAV samples were well classified by the W5S, W1W, W2W, W1S, and W2S sensors. The correlation analysis results suggested that the response signal intensities of W5S, W1W, and W2W had significant positive correlations with the abundances of acetic acid, isobutyric acid, ethyl phenylacetate, 3‐hydroxy‐2‐butanone, etc., which adds to the proof that W5S, W1W, and W2W sensors were sensitive to nitrogen oxides, sulfur compounds, pyrazines and aromatic components. Therefore, E‐nose can be used as a rapid and nondestructive testing tool for quality measurements of ZAV.

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