Empirical models and process optimization for prediction of nutritional parameters of stored cowpea variety (It96d-610k).

Abstract

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 diff erent 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 diff erent particle sizes (7.5 × 10-5 m and 9 × 10-5 m) and three diff erent 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 fi bre, and carbohydrate content were also measured alongside moisture. Signifi cant diff erences (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 fi bre (2.26%) and maximum carbohydrate (60.31%) of the stored cowpea at various conditions. Study results show that all the storage conditions had signifi cant eff ects at P <0.05.