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      Discrimination and characterization of the volatile profiles of five Fu brick teas from different manufacturing regions by using HS–SPME/GC–MS and HS–GC–IMS

      research-article
      a , b , c , , a , 1 , d , a , a , a , a , c , b , e , b , e , ∗∗ , a , c , ∗∗∗
      Current Research in Food Science
      Elsevier
      Fu brick tea, Aroma characteristics, Multivariate statistical analysis, Sensory characteristics, FBT, Fu brick tea, HS–GC–IMS, headspace gas chromatography–ion mobility spectrometry, HS–SPME–GC–MS, headspace solid-phase microextraction gas chromatography mass spectrometry, VOCs, volatile organic compounds, PCA, principal component analysis, PLS-DA, partial least squares–discriminant analysis, HCA, hierarchical cluster analysis, VIP, variable importance in the projection

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          Abstract

          Although aroma is one of the most essential factors determining the quality of Fu brick tea (FBT), the aroma profiles of FBTs from different manufacturing areas are rarely investigated. The aroma profiles of FBTs manufactured in five typical provinces of China were comprehensively analyzed on the basis of headspace gas chromatography–ion mobility spectrometry (HS–GC–IMS), headspace solid-phase microextraction gas chromatography–mass spectrometry (HS–SPME–GC–MS), sensory evaluation, odor activity value (OAV), and relative odor activity value (ROAV). HS–GC–IMS and HS–SPME–GC–MS identified 63 and 93 volatile organic compounds (VOCs), respectively. Multivariate statistical analysis indicated that the FBTs from different production regions had remarkably varied aromas. HS–SPME–GC–MS revealed that 27 VOCs (OAV >1) contributed to the overall aroma of the samples, of which 15 key differential compounds can effectively distinguish the aroma profiles of different FBTs. FBT from Shaanxi manifested a strong floral and fruity aroma; that from Hunan had a floral, grassy, and pine-woody aroma; that from Guizhou presented a grassy and herbal aroma; that from Guangxi exhibited a sweet, floral, and minty aroma; and that from Zhejiang possessed various fruit flavors and floral fragrance. OAV analysis identified the biomarkers responsible for the variation in the aroma characteristics of diverse FBTs. These biomarkers included linalool, 6-methyl-5-hepten-2-one, α-ionone, hexanal, and ethyl hexanoate. Sensory evaluation demonstrated that the infusion color and aroma of FBT samples from different provinces also greatly varied. Network correlation analysis revealed that Aspergillus and Eurotium were the crucial microorganisms for the metabolism and formation of VOCs. These findings provide new insight into the VOCs and fragrance features of FBTs produced in different regions of China.

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          Highlights

          • Volatiles in Fu brick tea from five provinces of China were comprehensively analyzed.

          • A total of 63 and 93 VOCs were identified by GC-IMS and GC-MS, respectively.

          • Aroma profiles of Fu brick tea from five regions were greatly different.

          • 15 key volatiles were proposed to discriminate Fu brick tea from different regions.

          • The correlations between the key VOCs and fungal community were analyzed.

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          Most cited references46

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          Tea aroma formation

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            Recent studies of the volatile compounds in tea

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              Characteristic fingerprints and volatile flavor compound variations in Liuyang Douchi during fermentation via HS-GC-IMS and HS-SPME-GC-MS

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                Author and article information

                Contributors
                Journal
                Curr Res Food Sci
                Curr Res Food Sci
                Current Research in Food Science
                Elsevier
                2665-9271
                25 September 2022
                2022
                25 September 2022
                : 5
                : 1788-1807
                Affiliations
                [a ]College of Food Science and Technology, Hunan Agricultural University, Changsha, 410128, China
                [b ]Key Laboratory of Ministry of Education for Tea Science, College of Horticulture, Hunan Agricultural University, Changsha, 410128, China
                [c ]Hunan Province Key Laboratory of Food Science and Biotechnology, Changsha, 410128, China
                [d ]College of Animal Science and Technology, Hunan Agricultural University, Changsha, 410128, China
                [e ]National Research Center of Engineering Technology for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha, 410128, China
                Author notes
                []Corresponding author. College of Food Science and Technology, Hunan Agricultural University, Changsha 410128, China. yuxiao_89@ 123456163.com xiaoyuhn@ 123456hotmail.com
                [∗∗ ]Corresponding author. Key Laboratory of Ministry of Education for Tea Science, College of Horticulture, Hunan Agricultural University, Changsha 410128, China. zhonghua-liu@ 123456hunau.edu.cn
                [∗∗∗ ]Corresponding author. College of Food Science and Technology, Hunan Agricultural University, Changsha 410128, China. wangyuanliang@ 123456hunau.edu.cn
                [1]

                Yuxin Huang is the co-first author.

                Article
                S2665-9271(22)00165-4
                10.1016/j.crfs.2022.09.024
                9576573
                36268133
                75d837f5-7bcd-4760-aeb6-3b71b83cd38c
                © 2022 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 3 August 2022
                : 17 September 2022
                : 20 September 2022
                Categories
                Research Article

                fu brick tea,aroma characteristics,multivariate statistical analysis,sensory characteristics,fbt, fu brick tea,hs–gc–ims, headspace gas chromatography–ion mobility spectrometry,hs–spme–gc–ms, headspace solid-phase microextraction gas chromatography mass spectrometry,vocs, volatile organic compounds,pca, principal component analysis,pls-da, partial least squares–discriminant analysis,hca, hierarchical cluster analysis,vip, variable importance in the projection

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