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All Journal Journal of Natural Sciences and Mathematics Research Journal of Biotechnology and Natural Science
Nugroho, Imam Bagus
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Immunology & microbiology Materials Science & Nanotechnology Physics

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Establishing Geographical Indicator of Fermented Cacao Beans Using Microbiome Fingerprinting Nugroho, Imam Bagus; Siregar, Abdul Rahman
Journal of Biotechnology and Natural Science Vol. 4 No. 1 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jbns.v4i1.10775

Abstract

Geographical indication is an essential label for industrial products. Herein, we aimed to explore a method for establishing geographical indications based on microbial diversity data. We collected two groups of datasets available on the public server of the European Nucleotide Archive. These datasets contain 12 (twelve) NGS-generated reads (amplicon sequencing metagenomes) of fermented cacao beans from Brazil and Mexico. We extracted the microbiome profile using bioinformatic tools in the SHAMAN server. We analyzed further using Principal Component Analysis, Clustering (Ward’s Method of Hierarchical Agglomerative Clustering), and UMAP (Uniform Manifold Approximation and Projection) combined with KNN (K-Nearest Neighbor). We discovered differences in microbial diversity and unique taxa in the fermented cacao beans from Brazil and Mexico. Lactic acid bacteria (LAB), such as Liquorilactobacillus, Tatumella, Leuconostoc, Companilactobacillus, and Limosilactobacillus, are unique genera in samples from Mexico, while Bacillus is a unique genus found in samples from Brazil.  We have demonstrated the separation of the microbiome profiles between fermented cacao beans from Brazil and Mexico using PCA, clustering analysis and UMAP-KNN. We have successfully developed the proof of concept in establishing geographical indicators based on microbial diversity data or microbiome profiles. In the future, we will extend this research to analyze samples from Indonesia and establish a microbial diversity database of Indonesian fermented cacao. This database is essential for the authentication assay of Indonesian fermented cacao and for developing fine cacao and specialty products.
Kombucha origin clustering based on 16S metabarcoding datasets analysis Nugroho, Imam Bagus; Darmawan Ari Nugroho; Abdul Rahman Siregar
Journal of Natural Sciences and Mathematics Research Vol. 11 No. 2 (2025): December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/jnsmr.v11i2.23949

Abstract

Consumers of fermented products increasingly demand detailed information on product ingredients, quality, health benefits, and origin. Herein, we have chosen kombucha as a model for a fermented product. This study aims to establish the origin information of kombucha using clustering analysis of 16S metabarcoding datasets. We have downloaded and analysed datasets of kombucha 16S metabarcoding originating from 5 distinct places: Brazil, the United States, the United Kingdom, Turkey, and Thailand. We randomly selected datasets from the collection (n = 32) and analyzed them on the SHAMAN server to develop an initial microbiome profile. We implemented hierarchical agglomerative Clustering and found that Ward's method and the Chao distance produced the best cluster tree, which consistently separates kombucha into five distinct clades, reflecting their origin. We have extended our examination to include more datasets (n=13) to build the final cluster tree (total n = 45). We have also assessed the uncertainty of the final Clustering by pvclust in R. The pvclust cluster tree is comparable in topology to the final cluster tree built using Ward's method and Chao distance. The pvclust cluster tree features stable clades that are highly supported by AU (Approximately Unbiased) values (p-value ≥ 95%). Each kombucha was also placed correctly and consistently according to its respective origin. We have successfully conducted analyses and demonstrated that a simple clustering method, combining Ward's method and the Chao distance, is the most effective for classifying kombucha by origin using a 16S metabarcoding dataset.
Co-Authors Abdul Rahman Siregar Abdul Rahman Siregar, Abdul Rahman Nugroho, Darmawan Ari