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Microbial communities of a variety of cheeses and comparison between core and rind region of cheesesopen access

Authors
Choi J.Lee S.I.Rackerby B.Goddik L.Frojen R.Ha S.-D.Kim J.H.Park S.H.
Issue Date
May-2020
Publisher
Elsevier Inc.
Keywords
cheese; Illumina MiSeq; microbiome
Citation
Journal of Dairy Science, v.103, no.5, pp 4026 - 4042
Pages
17
Journal Title
Journal of Dairy Science
Volume
103
Number
5
Start Page
4026
End Page
4042
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/38891
DOI
10.3168/jds.2019-17455
ISSN
0022-0302
1525-3198
Abstract
Understanding the microbial community of cheese is important in the dairy industry, as the microbiota contributes to the safety, quality, and physicochemical and sensory properties of cheese. In this study, the microbial compositions of different cheeses (Cheddar, provolone, and Swiss cheese) and cheese locations (core, rind, and mixed) collected from the Arbuthnot Dairy Center at Oregon State University were analyzed using 16S rRNA gene amplicon sequencing with the Illumina MiSeq platform (Illumina, San Diego, CA). A total of 225 operational taxonomic units were identified from the 4,675,187 sequencing reads generated. Streptococcus was observed to be the most abundant organism in provolone (72 to 85%) and Swiss (60 to 67%), whereas Lactococcus spp. were found to dominate Cheddar cheese (27 to 76%). Species richness varied significantly by cheese. According to alpha diversity analysis, porter-soaked Cheddar cheese exhibited the highest microbial richness, whereas smoked provolone cheese showed the lowest. Rind regions of each cheese changed color through smoking and soaking for the beverage process. In addition, the microbial diversity of the rind region was higher than the core region because smoking and soaking processes directly contacted the rind region of each cheese. The microbial communities of the samples clustered by cheese, indicated that, within a given type of cheese, microbial compositions were very similar. Moreover, 34 operational taxonomic units were identified as biomarkers for different types of cheese through the linear discriminant analysis effect size method. Last, both carbohydrate and AA metabolites comprised more than 40% of the total functional annotated genes from 9 varieties of cheese samples. This study provides insight into the microbial composition of different types of cheese, as well as various locations within a cheese, which is applicable to its safety and sensory quality. © 2020 American Dairy Science Association
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대학원 (식품생명공학과)
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