A comprehensive and comparative GC–MS metabolomics study of non-volatiles in Tanzanian grown mango, pineapple, jackfruit, baobab and tamarind fruits. (15th December 2016)

• Record Type: Journal Article
• Authors: Khakimov, Bekzod; Mongi, Richard J.; Sørensen, Klavs M.; Ndabikunze, Bernadette K.; Chove, Bernard E.; Engelsen, Søren Balling
• Is Part Of: Food chemistry. Volume 213(2016)
• Page Start: 691
• Page End: 699
• Publication Date: 2016-12-15
• Subjects: Tanzanian fruits -- Jackfruit -- Mango -- Pineapple -- Baobab -- Tamarind -- GC–MS -- Metabolomics -- PARAFAC2 -- Trimethylsilyl cyanide; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2016.07.005
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: GC–MS methodology developed for fruit pulp metabolomics. 45 non-volatile metabolites identified from five tropical fruits. PARAFAC2 enabled deconvolution 92 peaks across all samples. Metabolite data clustered fruits according to their relationships in phylogenetic tree. Abstract: Tropical fruits contribute significantly to the total fruit intake worldwide. However, their metabolomes have not yet been investigated comprehensively, as most previous studies revealed only volatile and bulk compositions. This study compares non-volatile metabolites of five fruits grown in Tanzania. A new methodology is developed for broad-spectrum GC–MS metabolomics in fruits using a new derivatization and a two dimensional peak deconvolution techniques. A total of 92 peaks were detected from fruits of which 45 were identified. Jackfruits contained the highest amount of carbohydrates, while baobab contained the highest amount of fatty acids. The highest content of organic acids was detected in tamarind. Principal component analysis revealed insights into metabolic differences and similarities, while hierarchical cluster analysis correctly grouped the fruits according to their relationships in plants' phylogenetic tree. The developed methodology could potentially be applied in large-scale studies on fruit quality, authenticity/variety, optimization of post-harvest processing and storage.