Nutritional and Antioxidant Profile of Two Traditional Foods From Eastern Cameroon.
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| Title: | Nutritional and Antioxidant Profile of Two Traditional Foods From Eastern Cameroon. |
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| Authors: | Kamdem Bemmo, Ulrich Landry1 (AUTHOR) ulrichbemmo@yahoo.com, Bindzi, Jean Marcel1 (AUTHOR), Kenfack Momo, Chancel Hector2 (AUTHOR), Damndja Ngaha, Wilfred3 (AUTHOR), Mbaissoubo Massaba, Agnes1 (AUTHOR), Tene Tambo, Stephano4 (AUTHOR), Ngoufack Zambou, François4 (AUTHOR), Omara, Timothy (AUTHOR) |
| Source: | Journal of Food Quality. 11/29/2025, Vol. 2025, p1-12. 12p. |
| Subjects: | Nutritional assessment, Antioxidants, Fermented foods, Ethnic foods, Iron deficiency, Public health |
| Geographic Terms: | Cameroon |
| Abstract: | This work aimed to assess the nutritional and antioxidant profiles of two traditional foods widely consumed in the Eastern Region of Cameroon (ERC). An ethnofood survey highlighted that the ERC has a great diversity of traditional recipes, among which Koko and Mbol, prepared with Beilschmiedia anacardioides and Gnetum africanum, respectively, are the most consumed. Koko and Mbol contained 67.46% and 75.29% of carbohydrates, 20.21% and 13.43% of fat, 423.68 mg/100 g and 81.19 mg/100 g of iron, 0.918 ± 0.12 and 0.622 ± 0.27 mg GAE/100 g of phenolic components and 0.541 ± 0.21 and 0.332 ± 0.23 mg CE/100 g of flavonoids, respectively. These results indicate that both foods can contribute substantially to dietary energy and micronutrient intake and therefore combat anaemia, caused by iron deficiency in the population of ERC. Consequently, promoting the consumption of these traditional foods, which can be inserted into the official food composition table of Cameroon, might support nutritional strategies aimed at improving public health in the region. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Food Quality is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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