End-user preferences for pounded yam and implications for food product profile development

Pounded yam is a popular food in Nigeria. This study reports end-user preferences for pounded yam and implications for trait evaluation by breeding programme. The study was carried out in two pounded yam-consuming regions in Nigeria: south-east and south-west. Multistage sampling technique was used to collect information from users along food chain. This involved market, individual, key informant interviews and focus group discussions. Responses of participants were used to develop product profile of pounded yam from raw material (yam) to final product. Key user-preferred quality traits for pounded yam in both regions were colour and textural quality followed by taste and aroma which are lesser attributes. There were regional differences in ranking of these quality attributes but no gender difference. This information will be useful in determining food quality indicators that can be used to select breeding lines for preferred quality traits in pounded yam.     

Cover Image,Volume 98, Issue 5

The cover image, by Oluyinka A. Olukosi et al., is based on the Research Article Peptide supplementation to nutrient‐adequate diets enhanced internal egg quality during storage in hens at peak production, DOI: 10.1002/jsfa.8661 . Photo Credit: Oluyinka A. Olukosi, Scotland's Rural College

     

An improved particle swarm optimiser based on swarm success rate for global optimisation problems

Inertia weight is one of the control parameters that influences the performance of particle swarm optimisation (PSO) in the course of solving global optimisation problems, by striking a balance between exploration and exploitation. Among many inertia weight strategies that have been proposed in literature are chaotic descending inertia weight (CDIW) and chaotic random inertia weight (CRIW). These two strategies have been claimed to perform better than linear descending inertia weight (LDIW) and random inertia weight (RIW). Despite these successes, a closer look at their results reveals that the common problem of premature convergence associated with PSO algorithm still lingers. Motivated by the better performances of CDIW and CRIW, this paper proposed two new inertia weight strategies namely: swarm success rate descending inertia weight (SSRDIW) and swarm success rate random inertia weight (SSRRIW). These two strategies use swarm success rates as a feedback parameter. Efforts were made using the proposed inertia weight strategies with PSO to further improve the effectiveness of the algorithm in terms of convergence speed, global search ability and improved solution accuracy. The proposed PSO variants, SSRDIW_PSO and SSRRIW_PSO were validated using several benchmark unconstrained global optimisation test problems and their performances compared with LDIW-PSO, CDIW-PSO, RIW-PSO, CRIW-PSO and some other existing PSO variants. Empirical results showed that the proposed variants are more efficient.