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

• Main Authors: Afonso Telma, Moresco Rodolfo, Uarrota Virgilio G., Navarro Bruno Bachiega, Nunes Eduardo da C., Maraschin Marcelo, Rocha Miguel
• Format: Article
• Language: English
• Published: De Gruyter 2017-12-01
• Series: Journal of Integrative Bioinformatics
• Subjects: carotenoids; cassava genotypes; chemometrics; cielab; machine learning
• Online Access: https://doi.org/10.1515/jib-2017-0056

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.