Empirical Models and Process Optimization for Prediction of Nutritional Parameters of Stored Cowpea Variety (IT96D-610K)

Musliu Olushola Sunmonu, Michael Mayokun Odewole, Ibukunolowa Omotola Oyeleke, Grace Olufunke Otitodun, Mobolaji Omobowale, Moses Odunayo Ogundare
2018 Acta Technologica Agriculturae  
This paper presents a study carried out to develop empirical models and process optimization for prediction of nutritional parameters of stored cowpea variety (IT96D-610K). Twelve small scale prototype silos made of two different materials (wooden and galvanised mild steel) were constructed and used in the storage of the cowpea for a 4-month period. Seven kilograms of cowpea at 9.88% moisture content admixed with DE having two different particle sizes (7.5 × 10−5 m and 9 × 10−5 m) and three
more » ... −5 m) and three different concentrations (0.0001 kg, 0.00005 kg and 0 kg) in varying combinations were loaded into each prototype silo structure. The control (zero/no concentration) was set without the use of DE in each of the wooden and galvanised mild steel structures, respectively. Temperature, relative humidity and moisture content within the storage structures were monitored. Nutritional parameters such as ash, crude protein, fat, crude fibre, and carbohydrate content were also measured alongside moisture. Significant differences (P <0.05) were observed between the control sample and treated samples. Six model equations using Essential Regression Software package were further generated to determine the relationship between input and output parameters, and were checked for adequacy and validity. The model equations developed were used to get the optimum values of output parameters which are: minimum moisture content (8.87%), minimum ash content (4.07%), maximum crude protein (22.86%), maximum fat (2.04%), maximum crude fibre (2.26%) and maximum carbohydrate (60.31%) of the stored cowpea at various conditions. Study results show that all the storage conditions had significant effects at P <0.05.
doi:10.2478/ata-2018-0026 fatcat:u3lvizlh6nfm3h3uewwjarfbg4