Xiaolei Fang

Dr. Xiaolei Fang


Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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20 August 2023 in Foods
Verified
Journal Article
Open access
PDF file
XFXiaolei Fang
Xiaolei Fang
Yanping ChenYanping Chen
Yanping Chen
JGJie Gao
Jie Gao
ZRZimu Run
Zimu Run
HCHe Chen
He Chen
YLYuan Liu
Yuan Liu
The volatiles in coffee play an important part in the overall flavor profile. In this study, GC–TOF/MS and GC×GC–TOF/MS were used to detect the volatile organic compounds (VOCs) in coffee samples of three different brands at three states (bean, powder, and brew). The differences between the two methods in characterizing VOCs were analyzed using the Venn diagram and PCA (principal component analysis). The important aroma-contributing compounds were further compared and analyzed. The results of the venn diagrams of different coffee samples showed that most VOCs existed in 2–3 kinds of coffee. The PCA of VOCs in different coffee samples showed that the VOCs detected by GC–TOF/MS could distinguish the coffee samples in the different states. GC×GC–TOF/MS was suitable for the further identification and differentiation of the different brands of coffee samples. In addition, pyridine, pyrrole, alcohols, and phenols greatly contributed to distinguishing coffee in three states, and alcohols greatly contributed to distinguishing the three brands of coffee.

23 September 2022 in Molecules
Verified
Journal Article
Open access
PDF file
Yanping ChenYanping Chen
Yanping Chen
HCHe Chen
He Chen
DCDandan Cui
Dandan Cui
XFXiaolei Fang
Xiaolei Fang
JGJie Gao
Jie Gao
YLYuan Liu
Yuan Liu
The flavor of coffee can be affected by the preparation parameters. In this investigation, the flavor profiles of three coffee brands under three conditions (bean, powder, and brew) were analyzed by gas chromatography—ion mobility spectrometry (GC-IMS) and the electronic nose (E-nose). The flavor results were further studied using multiple factor analysis (MFA). A total of 117 peaks were identified in all coffee samples by GC-IMS, and the principal component analysis (PCA) showed these coffee samples could be grouped and separated. A total of 37 volatile organic compounds (VOCs) were selected as biomarkers to distinguish coffee samples, including 5 aldehydes, 10 ketones, 8 alcohols, 2 acids, 4 esters, 5 furans, and 3 other compounds. The comparison between E-nose and GC-IMS data using partial least squares regression (PLSR) and MFA showed GC-IMS could present very close sample spaces. Compared with E-nose, GC–IMS could not only be used to classify coffee samples in a very short time but also provide VOC bio-markers to discriminate coffee samples.