Genomic and subgenomic group discrimination between 100 Indian banana (Musa) accessions using ripe banana pulp multi-elemental fingerprints and chemometrics.

Abstract

Worldwide, there are over 1000 banana types which are classified in various subgenomic and genomic groups. Distinguishing between the banana types, their genomic and subgenomic groups has been a challenge due to different identities and nomenclature used in different regions of the world. The present study assessed the efficacy of multi-elemental fingerprinting combined with chemometrics to distinguish between genomic and subgenomic groups within 100 Indian banana (Musa) accessions based on ripe banana pulp elemental concentrations. The concentrations of B, Ca, Fe, Mg, Mn K, Zn, Na, and P were analyzed using Inductively Coupled Plasma- Optical Emission Spectroscopy (ICP-OES). Multi-elemental fingerprints plus chemometrics were done using principal component analysis (PCA) then combined with linear discriminant analysis (PCA-LDA), support vector machine (PCA-SVM), and artificial neural network (PCA-ANN) for classification analysis with an 80:20 split between the calibration and verification sets (with total of 300 specimens). The PCA-SVM model was the most effective in classification when applied to the verification set subgenomic and genomic groups data, with accuracies of 83.7% and 100.0% respectively. These results demonstrated that ripe banana pulp multi-elemental fingerprints combined with chemometrics can discriminate between genomic and sub-genomic groups for Indian banana (Musa) accessions.