UV-Vis and CIELAB Based Chemometric Characterization of Manihot esculenta Carotenoid Contents

Telma Afonso, Rodolfo Moresco, Virgilio G. Uarrota, Bruno Bachiega Navarro, Eduardo da C. Nunes, Marcelo Maraschin, Miguel Rocha

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish
JournalJournal of integrative bioinformatics
Issue number4
StatePublished - 13 Dec 2017


  • Carotenoids
  • Cassava genotypes
  • Chemometrics
  • Machine learning


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