Differentiation of ripe banana flour using mineral composition and logistic regression model

Cavendish (Musa paradisiaca L, cv cavendshii) and Dream (Musa acuminata colla. AAA, cv ‘Berangan’) banana flours were prepared from ripe fruits collected from eleven markets located in Penang, Malaysia. The mineral composition (Na, K, Ca, Mg, Cu, Fe, Mn, Zn) of the flour were analyzed by atomic absorption spectrophotometer and the data obtained were analyzed using logistic regression model. Ripe banana flours were rich source of K and a fair source of other minerals, however logistic regression model identified Mg as an indicator to discriminate between the two types of banana flour affording 100 % correct assignation. Based on this result, mineral analysis may be suggested as a method to authenticate ripe banana flour. This study also presents the usefulness of logistic regression technique for analysis and interpretation of complex data.