Maseko, Kayise Hypercia ORCID: https://orcid.org/0000-0002-9926-8714, Regnier, Thierry ORCID: https://orcid.org/0000-0001-7762-9905, Anyasi, Tonna Ashim, Du Plessis, Belinda ORCID: https://orcid.org/0000-0003-4147-9313, Da Silva, Laura Suzzanne, Kutu, Funso Raphael ORCID: https://orcid.org/0000-0002-8162-0329 and Wokadala, Obiro Cuthbert ORCID: https://orcid.org/0000-0002-0292-400X (2021) Discrimination of Musa banana genomic and sub-genomic groups based on multi-elemental fingerprints and chemometrics. Journal of Food Composition and Analysis, 106:104334. ISSN 0889-1575 (Print), 1096-0481 (Online) (doi:10.1016/j.jfca.2021.104334)

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Abstract

The potential of unripe banana flour multi-elemental fingerprints for classifying banana genomic and subgenomic groups was assessed using chemometrics. The elemental concentration of N, P, K, Mg, Ca, Zn, Cu, Mn, Fe, and B in unripe banana flour from 33 banana varieties belonging to four genome groups and 11 subgenome groups were determined using Flame-atomic Absorption spectrometry and colorimetry. Principal component analysis (PCA) combined with linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural network (ANN) was applied for classification with an 80:20 split between the calibration and verification sets (157 and 39 samples, respectively). The elements K, N, and Mg presented the highest mean concentrations of 1273 mg/100 g, 424 mg/100 g, and 132 mg/100 g, respectively. The classification model verification set samples were successfully classified based on their genome groups (100 % accuracy) and subgenome groups (78.95–100% accuracy) for PCA-LDA, PCA-ANN, and PCA-SVM models. The results demonstrate that multi-elemental fingerprinting combined with chemometrics can be employed as an effective and feasible method for classification of Musa genomic and sub-genomic groups.

Item Type:	Article
Uncontrolled Keywords:	Unripe banana flour, elements, banana sub-genome groups, banana varieties, banana genome groups, principal component analysis, linear discriminant analysis, support vector machine, artificial neural networks
Subjects:	S Agriculture > S Agriculture (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > Natural Resources Institute Faculty of Engineering & Science > Natural Resources Institute > Food & Markets Department Faculty of Engineering & Science > Natural Resources Institute > Postharvest Science and Technology Research Group Faculty of Engineering & Science > Natural Resources Institute > Centre for Food Systems Research Faculty of Engineering & Science > Natural Resources Institute > Centre for Food Systems Research > Food Systems & Nutrition
Last Modified:	27 Nov 2024 14:51
URI:	http://gala.gre.ac.uk/id/eprint/42669

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