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20172
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Subject: Post Harvest Technology × End date: 2017 × Start date: 2017 × Type: Thesis × Thesis Type: Ph.D ×
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    ThesisItemOpen Access
    Development of microwave assisted leaching based integrated oil and protein extraction technology and its kinetics for black soybean (Glycine max L.)
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-02) Eknath, Kate Adinath; Singh, Anupama
    The exploration of the new plant based sources of the edible oil and their commercial utilization is one of the prominent step to meet the peculiar demand of edible oil in the country. There are hundreds of the oil bearing underutilized crops in Indian origin, but their low oil content is the major barrier to fit in to ongoing oil production technology. Microwave assisted extraction (MAE) is an important emerging ecofriendly oil extraction technology having potential to overcome the limitations of conventional methods. The present investigation aims to develop an efficient and cost effective, bio-solvent based MAE technology for low oil content crops like black soybean by standardizing the unit operations involved in the extraction process. The bio based solvent was selected by understanding the dielectric behavior in microwave environment. To understand the insight in to the MAE phenomenon mathematical modelling of extraction kinetics was also studied. The present research has been undertaken to standardize and optimize the microwave assisted oil extraction technology for black soybean. Through the preliminary experiments, the solvent (isopropanol), particle size (< 1mm) and soaking time (20 min) were standardized and taken as constant parameters in final experiments. Four independent variables having five levels of each i.e microwave power (160, 320, 480, 640 and 800 % W), exposure time (60,120,180,240 and 300 s), solvent: material ratio (1:1, 2:1, 3:1, 4:1 and 5:1) and pH (2, 3, 4, 5 and 6) were selected as independent parameters. Optimization of the independent variables was done based on oil yield, Refractive index, specific gravity, FFA, saponification value and peroxide value. Multiple response optimization of independent parameters was done using Design Expert 9.0.3. The comparative studies of standardized MAE with conventional methods was also carried out based on oil yield, physico-chemical properties, phenolics and antioxidant properties of the extracted oil. The mass transfer kinetics of extraction process was analyzed through modified Fick’s law and assumption based two stage mass transfer model. Studied revealed that there was decrease in dielectric constants of the solvents with increase in microwave power as well as exposure time. The optimized combination of independent variables was found to be 480 W Microwave power, 170 s exposure time, 2.16:1 Solvent: material ratio and pH 5. At optimized condition, MAE of black soybean flour gave the maximum oil yield of 93.56 % with maximum retention of oil quality like 0.755 mg KOH/g of FFA, 189.25 mg KOH/g of saponification value, 0.89 meq O2/kg of peroxide value, 107.70 meq I/g and light yellow in colour (62.32). The crude oil yield obtained with optimum condition of MAE (93.56%) was higher than the conventional mechanical extraction (56.50%) and n-hexane based solvent extraction (92.00%) with significant reduction in extraction time up 5 min. The oil extracted at optimized MAE conditions was rich with phenolic compounds (Total phenolic content (5.023 mg GAE/g oil) and proanthocyanidins Content (2.92 mg CE/g oil)), antioxidant activity i.e ABTS (28.88 mg TE/g oil) and DPPH (85.19 mg TE/g oil) than conventional mechanical expression, n-hexane solvent extraction and soxhlet extraction. The properties of the crude oil extracted from black soybean at optimized condition of MAE highly resembled to the properties of yellow soybean oil and also met the standards suggested by “FSSAI standards, 2009”. Both the selected mass transfer models should the high goodness of fit (R2>0.98) to the MAE extraction yield. Among the washing and diffusion, washing was the predominant stage in case of microwave assisted extraction and rate of mass transfer was very high (few hundred times than diffusion stage) for this stage. Significant effect of all the independent parameters was observed on the all responses except refractive index (RI) at its linear level while no any significant effect was found at interactive level as counter acting effect of solvent: material ration and pH than that of microwave power and exposure time.
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    ThesisItemOpen Access
    Optimization of process parameters and artificial neural network modelling for high velocity hot air drying of chicken meat and quality evaluation of dried meat product
    (G.B. Pant University of Agriculture and Technology, Pantnagar (Uttarakhand), 2017-01) Akhtar, Javeed; Omre, P.K.
    Chicken meat is a good source of high quality protein with all essential amino acids and low in cholesterol and it is also wonderful supply of minerals, tocopherol, fat-soluble vitamins, thiamine, and nicotinic acid. It is an important provider of essential polyunsaturated fatty acids, especially the omega (n)-3 fatty acids. The amounts of these important fatty acids can be increased more easily in chicken meat than in other livestock meats. Drying characteristics of chicken meat were studied in high velocity hot air dryer The experiments work was conducted under three different sizes (1 ,1.5 and 2 cm3), five different temperatures (45, 55, 65, 75, and 85ºC), five different air velocities (2.5, 3.5, 4.5, 5.5 and 6.5 m/s). The five empirical models namely page’s, Logarithmic, Exponential, two term exponential and Henderson and Pabis were fitted to experimental moisture ratio data. Performance of five drying models was evaluated on the basis of coefficient of determination (R2), standard error estimation (SEE) and root mean square error (RMSE). Page’s model gave better prediction for moisture ratio. Artificial neural network model was fitted in drying experimental data and ANN modelling was performed using two hidden layers (1 and 2) and ten neurons (2, 4, 6, 8 and 10). The best architectures of ANN were found to be two hidden layers with eight neurons in first hidden layer and ten neurons in second hidden layer(4-8-10-1) for moisture content, two hidden layers with four neurons in first hidden layer and four neurons in second hidden layer (4-4-4-1) for drying rate, and two hidden layers with eight neurons in first hidden layer and eight neurons in second hidden layer(4-8-8-1) for moisture ratio. Both protein and fat content decreased with increasing temperature and samples size. Harness increased with increasing temperature and it decreased with increasing sample size. On the basis of quality characteristics, the optimum drying conditions suggested for dehydration of chicken meat were 55ºC drying air temperature and 5.5 m/s air velocity and sample size 1 cm3. Throughout storage period, vacuum packaging showed superior quality of chicken meat samples as compared to conventional packaging method.