| Autor: |
Afonso Telma, Moresco Rodolfo, Uarrota Virgilio G., Navarro Bruno Bachiega, Nunes Eduardo da C., Maraschin Marcelo, Rocha Miguel |
| Jazyk: |
angličtina |
| Rok vydání: |
2017 |
| Předmět: |
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| Zdroj: |
Journal of Integrative Bioinformatics, Vol 14, Iss 4, Pp 336-4 (2017) |
| Druh dokumentu: |
article |
| ISSN: |
1613-4516 |
| DOI: |
10.1515/jib-2017-0056 |
| Popis: |
Vitamin A deficiency is a prevalent health problem in many areas of the world, where cassava genotypes with high pro-vitamin A content have been identified as a strategy to address this issue. In this study, we found a positive correlation between the color of the root pulp and the total carotenoid contents and, importantly, showed how CIELAB color measurements can be used as a non-destructive and fast technique to quantify the amount of carotenoids in cassava root samples, as opposed to traditional methods. We trained several machine learning models using UV-visible spectrophotometry data, CIELAB data and a low-level data fusion of the two. Best performance models were obtained for the total carotenoids contents calculated using the UV-visible dataset as input, with R2 values above 90 %. Using CIELAB and fusion data, values around 60 % and above 90 % were found. Importantly, these results demonstrated how data fusion can lead to a better model performance for prediction when comparing to the use of a single data source. Considering all these findings, the use of colorimetric data associated with UV-visible and HPLC data through statistical and machine learning methods is a reliable way of predicting the content of total carotenoids in cassava root samples. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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