Uncovering the differences in flavor volatiles of different colored foxtail millets based on gas chromatography-ion migration spectrometry and chemometrics

Wengang Jin; Wenqiang Cai; Shibo Zhao; Ruichang Gao; Pengfei Jiang

doi:10.1016/j.crfs.2023.100585

Uncovering the differences in flavor volatiles of different colored foxtail millets based on gas chromatography-ion migration spectrometry and chemometrics

Curr Res Food Sci. 2023 Sep 9:7:100585. doi: 10.1016/j.crfs.2023.100585. eCollection 2023.

Authors

Wengang Jin^{1

2}, Wenqiang Cai³, Shibo Zhao^{1

2}, Ruichang Gao^{1

4}, Pengfei Jiang³

Affiliations

¹ Qinba State Key Laboratory of Biological Resource and Ecological Environment (Incubation), School of Bioscience and Technology, Shaanxi University of Technology, Hanzhong, Shaanxi, 723001, China.
² Collaborative Innovation Center of Bio-Resource in Qinba Mountain Area, Shaanxi Province Key Laboratory of Bio-resources, Hanzhong, Shaanxi, 723001, China.
³ School of Food Science and Technology, Dalian Polytechnic University, Dalian, Liaoning, 116034, China.
⁴ School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, China.

Abstract

The differences of volatile organic compounds in commercially available foxtail millets with different colors (black, green, white and yellow) were assayed through gas chromatography-ion migration spectrometry (GC-IMS) to explore their volatile flavor characteristics. Fifty-five volatile components were found in various colored foxtail millets, including 25 kinds of aldehydes (accounting for 39.19-48.69%), 10 ketones (25.36-32.37%), 15 alcohols (20.19-24.11%), 2 ethers (2.29-2.45%), 2 furans (1.49-2.95%) and 1 ester (0.27-0.39%). Aldehydes, alcohols and ketones were the chief volatiles in different colored foxtail millet, followed by furans, esters and ethers. These identified volatile flavor components in various colored foxtail millets obtained by GC-IMS could be well distinguished by principal components and cluster analysis. Meanwhile, a stable prediction model was fitted via partial least squares-discriminant analysis (PLS-DA), in which 17 kinds of differentially volatile components were screened out based on variable importance in projection (VIP>1). These findings might provide certain information for understanding the flavor traits of colored foxtail millets in future.

Keywords: Colored foxtail millet; Partial least squares-discriminant analysis; Similarity analysis; Volatile flavor components.