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

Telma Afonso, Rodolfo Moresco, VIRGILIO GAVICHO UARROTA , Bruno Bachiega Navarro, Eduardo da C. Nunes, Marcelo Maraschin, Miguel Rocha

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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
Volume14
Issue number4
DOIs
StatePublished - 13 Dec 2017

Keywords

  • Carotenoids
  • Cassava genotypes
  • Chemometrics
  • CIELAB
  • Machine learning

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