Cocoa bean fingerprinting via correlation networks
Abstract Cocoa products have a remarkable chemical and sensory complexity. However, in contrast to other fermentation processes in the food industry, cocoa bean fermentation is left essentially uncontrolled and is devoid of standardization. Questions of food authenticity and food quality are hence p...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-01-01
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| Series: | npj Science of Food |
| Online Access: | https://doi.org/10.1038/s41538-021-00120-4 |
| _version_ | 1819281472812482560 |
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| author | Santhust Kumar Roy N. D’Souza Marcello Corno Matthias S. Ullrich Nikolai Kuhnert Marc-Thorsten Hütt |
| author_facet | Santhust Kumar Roy N. D’Souza Marcello Corno Matthias S. Ullrich Nikolai Kuhnert Marc-Thorsten Hütt |
| author_sort | Santhust Kumar |
| collection | DOAJ |
| description | Abstract Cocoa products have a remarkable chemical and sensory complexity. However, in contrast to other fermentation processes in the food industry, cocoa bean fermentation is left essentially uncontrolled and is devoid of standardization. Questions of food authenticity and food quality are hence particularly challenging for cocoa. Here we provide an illustration how network science can support food fingerprinting and food authenticity research. Using a large dataset of 140 cocoa samples comprising three cocoa fermentation/processing stages and eight countries, we obtain correlation networks between the cocoa samples by computing measures of pairwise correlation from their liquid chromatography-mass spectrometry (LC-MS) profiles. We find that the topology of correlation networks derived from untargeted LC-MS profiles is indicative of the fermentation and processing stage as well as the origin country of cocoa samples. Progressively increasing the correlation threshold firstly reveals network clusters based on processing stage and later country-based clusters. We present both, qualitative and quantitative evidence through network visualization, network statistics and concepts from machine learning. In our view, this network-based approach for classifying mass spectrometry data has broad applicability beyond cocoa. |
| first_indexed | 2024-12-24T01:00:14Z |
| format | Article |
| id | doaj.art-98b973c66f204a80be65a43452a3da10 |
| institution | Directory Open Access Journal |
| issn | 2396-8370 |
| language | English |
| last_indexed | 2024-12-24T01:00:14Z |
| publishDate | 2022-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Science of Food |
| spelling | doaj.art-98b973c66f204a80be65a43452a3da102022-12-21T17:23:24ZengNature Portfolionpj Science of Food2396-83702022-01-01611910.1038/s41538-021-00120-4Cocoa bean fingerprinting via correlation networksSanthust Kumar0Roy N. D’Souza1Marcello Corno2Matthias S. Ullrich3Nikolai Kuhnert4Marc-Thorsten Hütt5Department of Life Sciences and Chemistry, Jacobs University BremenDepartment of Life Sciences and Chemistry, Jacobs University BremenBarry Callebaut AG, WestparkDepartment of Life Sciences and Chemistry, Jacobs University BremenDepartment of Life Sciences and Chemistry, Jacobs University BremenDepartment of Life Sciences and Chemistry, Jacobs University BremenAbstract Cocoa products have a remarkable chemical and sensory complexity. However, in contrast to other fermentation processes in the food industry, cocoa bean fermentation is left essentially uncontrolled and is devoid of standardization. Questions of food authenticity and food quality are hence particularly challenging for cocoa. Here we provide an illustration how network science can support food fingerprinting and food authenticity research. Using a large dataset of 140 cocoa samples comprising three cocoa fermentation/processing stages and eight countries, we obtain correlation networks between the cocoa samples by computing measures of pairwise correlation from their liquid chromatography-mass spectrometry (LC-MS) profiles. We find that the topology of correlation networks derived from untargeted LC-MS profiles is indicative of the fermentation and processing stage as well as the origin country of cocoa samples. Progressively increasing the correlation threshold firstly reveals network clusters based on processing stage and later country-based clusters. We present both, qualitative and quantitative evidence through network visualization, network statistics and concepts from machine learning. In our view, this network-based approach for classifying mass spectrometry data has broad applicability beyond cocoa.https://doi.org/10.1038/s41538-021-00120-4 |
| spellingShingle | Santhust Kumar Roy N. D’Souza Marcello Corno Matthias S. Ullrich Nikolai Kuhnert Marc-Thorsten Hütt Cocoa bean fingerprinting via correlation networks npj Science of Food |
| title | Cocoa bean fingerprinting via correlation networks |
| title_full | Cocoa bean fingerprinting via correlation networks |
| title_fullStr | Cocoa bean fingerprinting via correlation networks |
| title_full_unstemmed | Cocoa bean fingerprinting via correlation networks |
| title_short | Cocoa bean fingerprinting via correlation networks |
| title_sort | cocoa bean fingerprinting via correlation networks |
| url | https://doi.org/10.1038/s41538-021-00120-4 |
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