Evaluation of the Mineral Composition of Fruit of 15 Date Cultivars (Phoenix dactylifera L.) Using Univariate and Multivariate Analyses

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Mohammadreza Pourghayoumi, Kamal Gholamipour Fard, Sara Farokhzadeh and Rahman Yousefi

 

Abstract

Date fruit is renowned for its rich mineral composition and is considered as an important component of the diet in many countries. Therefore, the current study aimed to explore the mineral composition and moisture contents (MC%) of 12 Iranian native cultivars and three imported cultivars, using univariate and multivariate methods. The results showed that the K content in date fruits was higher than other elements, followed by Ca and Na. The predominant micronutrient in all date fruit cultivars was Fe. The ‘Hallawi’ cultivar exhibited high and considerable concentrations for all mineral elements (Fe, Mn, Cu, Zn, Na, and K), except for Ca. The ‘Barhee’ date had the lowest Na content, making it suitable for individuals with high blood pressure. The highest positive correlations were observed between Cu-Fe, Cu-Mn, Fe-Mn, Na-Cu, Na-Fe, and K-Ca, respectively. Cluster analysis (CA) of MC% and mineral composition of fruits grouped 15 date cultivars into three and four clusters, respectively. The discriminant function analysis revealed that the accuracy of the CA in grouping date fruits was 100% for MC% and 93.3% for macro- and micro-nutrients. According to principal component analysis (PCA), the first two components explained 36.01% and 24.90% of the total variation in traits, respectively. Overall, the fruits of examined cultivars contained considerable, but highly variable, amounts of minerals. According to the study, it is suggested that the superior cultivars can be potentially used as nutritional supplements or as ingredients in functional food products.

Published on: August 16, 2023
doi: 10.17756/jfcn.2023-156
Citation: Pourghayoumi M, Gholamipour Fard K, Farokhzadeh S, Yousefi R. 2023. Evaluation of the Mineral Composition of Fruit of 15 Date Cultivars (Phoenix dactylifera L.) Using Univariate and Multivariate Analyses. J Food Chem Nanotechnol 9(3): 100-112.

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