Artificial Neural Network Modeling of Hot-air Drying Kinetics of Mango Kernel

750-758Large quantities of mango seeds are generated as waste during extraction of mango pulp. The mango kernels are nutritionally rich and can be used as food in the form of flour and starch. Present study was undertaken to investigate the effect of blanching and convective drying air temperature of 50, 60 and 70°C on drying characteristics of mango kernel in splitted and shredded form. The drying characteristics of prepared samples were studied in terms of moisture ratio, drying time, and effective moisture diffusivity. The colour parameters (‘L’, ‘a', ‘b’) of dried samples, were also estimated separately. Drying kinetics (moisture ratio vs drying time) of mango kernels modelled using three transfer functions (Tansig, Logsig and Purelin) of Artificial Neural Network (ANN). A reduction in the total drying time was observed with decrease in size of kernel but with rise in drying air temperature. The splitted and shredded kernels took about 450 to 840 min and 210 to 600 min respectively to be dried to final moisture content of 9 ± 1% (d.b.). Blanching did not show any significant influence on drying time. The drying process of mango kernels for all the conditions was observed to follow the falling rate. Modeling of drying kinetics of mango kernels was carried out using experimental results through artificial neural network. Results showed that the developed ANN model using logsig transfer function could predict the moisture ratio with high coefficient of determination (R2 = 0.99) and low root mean square error (0.01) within the range of tested operating conditions. The established ANN model can be used for online prediction of moisture content of splitted and shredded mango kernels during hot air drying process which has relevance to the food and pharmaceutical industry to produce dried mango kernels at desired moisture content

Artificial Neural Network Modeling of Hot-air Drying Kinetics of Mango Kernel

Large quantities of mango seeds are generated as waste during extraction of mango pulp. The mango kernels are nutritionally rich and can be used as food in the form of flour and starch. Present study was undertaken to investigate the effect of blanching and convective drying air temperature of 50, 60 and 70°C on drying characteristics of mango kernel in splitted and shredded form. The drying characteristics of prepared samples were studied in terms of moisture ratio, drying time, and effective moisture diffusivity. The colour  parameters (‘L’, ‘a', ‘b’) of dried samples, were also estimated separately. Drying kinetics (moisture ratio vs drying time) of mango kernels modelled using three transfer functions (Tansig, Logsig and Purelin) of Artificial Neural Network (ANN). A reduction in the total drying time was observed with decrease in size of kernel but with rise in drying air temperature. The splitted and shredded kernels took about 450 to 840 min and 210 to 600 min respectively to be dried to final moisture content of 9 ± 1% (d.b.). Blanching did not show any significant influence on drying time. The drying process of mango kernels for all the conditions was observed to follow the falling rate. Modeling of drying kinetics of mango kernels was carried out using experimental results through artificial neural network. Results showed that the developed ANN model using logsig transfer function could predict the moisture ratio with high coefficient of determination (R2 = 0.99) and low root mean square error (0.01) within the range of tested operating conditions. The established ANN model can be used for online prediction of moisture content of splitted and shredded mango kernels during hot air drying process which has relevance to the food and pharmaceutical industry to produce dried mango kernels at desired moisture content

Effect of Storage on Quality of Stone Apple Ready-to-Serve Beverage

The present study is an effort to explore the possibility of effective utilization of the raw stone apple (Aegle marmelos correa), an indigenous fruit rich in nutritional as well as medicinal qualities. As the keeping quality of the whole fruit is very less, improvement in the post harvest processing and other relevant aspects need to be studied. Value added nutraceutical ready-to-serve (RTS) have been prepared. The process parameters have been standardized with respect to biochemical, microbial and sensory acceptance. Storage experiments for RTS with treatments (control, preservative and ginger) were conducted for 8 months and quality analysis was carried out to assess the storability. The ready to serve beverage with 13% pulp, 14Brix and 0.3% acidity was considered standard based on organoleptic evaluation. The RTS blended with ginger juice fetched higher sensory acceptability. Five hundred numbers of bottles of RTS of 200 ml. capacity each could be prepared from 25 kg of stone apple. During storage of the beverage all the biochemical qualities changed with storage period irrespective ofthe treatments. The total sugar, pH and ascorbic acid ofthe RTS got reduced with storage period where as an increasing trend was observed in acidity and TSS. However, the changes were maximum in control samples. The results suggest the use of ginger juice as a source of natural preservative. After four months of storage, there was an indication of presence of total mould count and total bacterial count in the stored samples, but the population was well within the safe limits till the end of a storage period of eight months

Effect of Drying and Storage on Quality of Betel Leaves

Due to low keeping quality, betel leaves worth millions of rupees go as waste every year. If proper drying methods are scientifically standardized, the leaves can be processed at garden level and the grower can earn more profit. Therefore, the effect of different drying methods namely, sun drying, shade drying, solar drying and mechanical drying, on the quality characteristics of the leaves was evaluated. It was found that shade drying took maximum time for drying and was followed by solar drying, hot air drying (40°C), sun drying and hot air drying (50°C). However, maximum nutrient could be preserved in shade drying. The study was also carried out on storage of fresh betel leaves using traditional packaging, polyethylene packaging and ventilated polyethylene packaging. Before storing, the leaves were subjected to chemical treatment (5 ppm benzyl adenine, 8 h) and heat treatment (45°C, 1 h). It was observed that the shelf life of the leaves stored by various methods ranged between 12 and 21 days in winter and 4 to 12 days in summer. The leaves subjected to heat treatment in traditional packaging showed better performance

Hybrid bat-grasshopper and bat-modified multiverse optimization for solar photovoltaics maximum power generation

A hybrid BAT with Grasshopper (GH) algorithm and BAT-MMVO (Modified Multiverse Optimization) are exhibited for harvesting maximum power from photovoltaics (PV) using the Xilinx System Generator (XSG) implanted controller. Using a hybrid BAT-GH and BAT-MMVO algorithm, the proposed implanted controller finds the best switching pulse for the boost converter. The implanted controller, switching schemes, and the Photovoltaic (PV) supported boost converter were built using the XSG domain. The hardware implementation of the best two cases were done using a microcontroller in a smaller scale. This aims to gather the maximum amount of power by a PV array for solar irradiation and cell temperature under varied environmental situations. The PV structure in the XSG domain is used to construct the system model for prediction. The major emphasis of this work is to keep the difference of actual power and reference power as minimum. Finally, the implanted controller's performance is compared to that of other existing hybrid controllers. The performance of the proposed algorithm is found to yield good results in terms of power extraction. The theoretical and experimental results are presented. The computational efforts for the implementation of the algorithm are found to be less complex when compared to other existing methods

Enhancement of thermal and techno-economic performance and prediction of drying kinetics of paddy dried in solar bubble dryer

The development and performance evaluation of a solar Bubble drier (SBD) for drying agricultural produce are presented in this study. In order to evaluate the solar bubble dryer's performance in terms of drying characteristics and end-product economics, it was compared to the solar tunnel dryer (STD). A solar tunnel dryer is a structure with a tunnel-like shape that is covered in UV-stabilized polythene sheet so that industrial and agricultural items can be dried off. While there was no load, the maximum temperatures inside the solar bubble dryer and solar tunnel dryer were 56.25 and 49.30 °C, respectively. When there was a full load, the maximum temperatures inside the dryer were 49.55 and 33.20 °C, respectively. For solar bubble dryers, the average final moisture content ranged from 13.07±0.335 % to 18.74±0.716 % (w.b.), while for solar tunnel dryers, it ranged from 13.60±0.575 % to 20.60±0.751 % (w.b.). The mean drying rate also varied depending on the drying air temperature and air flow mode, ranging from 0.081±0.020 to 0.006±0.005 kg/kg dm-h for solar bubble dryers and 0.056±0.025 to 0.005±0.002 kg/kg dm-h for solar tunnel dryers. The thermal efficiency of the developed dryer was found to be 58.39% for solar bubble dryers and 48.09% for solar tunnel dryers, which are significantly higher than that of other general Sun drying (36%). However, the drying rate varied between 0.081 to 0.006 g of water evaporated per g of dry matter per hour when solar bubble dryer was adopted and 0.014 to 0.007 g for solar tunnel dryer. Economic analysis showed that the SBD and STD had payback periods of 3.23 and 2.54 years, respectively. However, the cost of drying came lower for the SBD than the STD, which were Rs. 1.51 per kg and Rs. 1.76 per kg