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Participatory processing diagnosis of boiled cassava in Benin. Understanding the drivers of trait preferences and the development of multi-user RTB product profiles, WP1

Bouniol A., Adinsi L., Honfozo F.L., Hounhouigan J.D., Akissoé N.. 2021. Cotonou : RTBfoods Project; CIRAD, 24 p..

DOI: 10.18167/DVN1/9ZWIDR

DOI: 10.18167/agritrop/00624

This study is part of the RTBfoods project WP1 outputs, essentially the step 3 of the developed methodology which deals with the quality traits of boiled cassava, collected through a participatory processing diagnosis. Six local varieties with contrasting characteristics identified within the WP1 step 2 survey were processed into boiled cassava by 6 qualified processors. Completes sets of quantitative data (raw material characteristics, yield, productivity, applied conditions for each unit operation) and qualitative data (raw material and end products evaluation) were collected in two small urban centers, Bonou and Dangbo - Benin. The varieties have been classified according their phenotypic characteristics according 3 groups having significant differences according their circumferences and weights. Processors indicated that a variety could not be evaluated or selected only according its appearance. Significant differences were also identified on their dry matter content which ranged between 18.6 and 40.0 %. Regarding the cooking step, the control of the [Qwater/Qcassava] ratio is important in the quality of the boiled cassava, and in particular its textural homogeneity, its friability and the level of stickiness. No significant varietal difference were observed as far as cooking time is concerned. The texture of the boiled cassava pieces and the behaviour of the roots during cooking appear to be related to the initial dry matter content and/or to the ability of the root to lose dry matter or to absorb water during this step. The processor's end products evaluation allowed to generate a complete set of good and bad descriptors on colour, textural, taste and flavor properties, with 13 good and 18 bad descriptors that have been useful for the step 4 of the WP1's methodology.

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