ABSTRACT
Development process of rice analogues by utilising the broken rice (BRF) and broken pigeonpea dhal (BPDF) flours together with water and sodium alginate as binding agent through extrusion was carried out. Two variable viz., BPDF (20, 30 and 40%) and moisture content (25, 30 and 35%) were controlled in the study to produce rice analogue resembling the raw rice. The optimum combination of flour mixture established for 30% BPDF and 30% water content with highest desirability of 0.855. The optimum combination had highest crude protein, carbohydrate and ash contents of 12.70, 71.72 and 0.99%, respectively. The colour values L*, a* and b* were found to be 68.30, 4.62 and 25.91, respectively. The pasting temperature and peak viscosity were 78.68 °C and 23,173.3 cP. The physico-chemical and pasting properties can be modified by altering the different constituents for specific quality requirements.
ABSTRACT
Fortified rice analogues were developed utilising the broken-rice fortified with selected micronutrients like iron, folic acid and Vitamin A. The purpose of this study was to investigate the feasibility of fortifying rice analogues with micronutrients and retention after extrusion and cooking. Cold extruder operated at 55 rpm screw speed and 1.5 kg/h feed rate was used for the study. The composite flour prepared using broken-rice flour, sodium alginate (1%), water (30%) and micronutrient mix was extruded through rice shaped die at barrel temperature of 60 °C. The level of fortifying nutrient ready mix (FNRM) was statistically optimised based on retention of nutrients after extrusion and cooking. The retention results for iron was observed to be 73.3 to 91.3 per cent after cooking whereas folic acid and Vitamin A being sensitive to processing and culinary operations were 44.2 to 60.4 and 10.1 to 12.4 per cent, respectively. Statistical optimisation resulted 150 per cent of FNRM could supply nutrient levels nearing the standards with the desirability of 0.835. The production cost was calculated as Rs.53.50 per kg whereas, increase in the cost of raw rice mixed with fortified analogues @ 1:50 ratio was about Rs.1.00 per kg with benefit-cost ratio of 1.22:1.
ABSTRACT
The present investigation was focused to compare chitosan based nano-adsorbents (CZnO and CTiO2) for efficient treatment of dairy industry wastewater using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models. The nano-adsorbents were synthesized using chemical precipitation method and characterized by using scanning electron microscope with elemental detection sensor (SEM-EDS) and atomic force microscope (AFM). Maximum %RBOD (96.71 and 87.56%) and %RCOD (90.48 and 82.10%) for CZnO and CTiO2 nano-adsorbents were obtained at adsorbent dosage of 1.25 mg/L, initial biological oxygen demand (BOD) and chemical oxygen demand (COD) concentration of 100 and 200 mg/L, pH of 7.0 and 2.00, contact time of 100 and 60 min, respectively. The results obtained for both the nano-adsorbents were subject to RSM and ANN models for determination of goodness of fit in terms of sum of square errors (SSE), root mean square error (RMSE), R2 and Adj. R2, respectively. The well trained ANN model was found superior over RSM in prediction of the treatment effect. Hence, the developed CZnO and CTiO2 nano-adsorbents could be effectively used for dairy industry wastewater treatment.
