Neural Network based Predictors for Evaporation Estimation at Jabalpur in Central India

319-328Free water evaporation is an imperative parameter for estimation of crop water requirement, and irrigation scheduling. This study aims to evaluate different techniques to estimate evaporation with weather parameters inputs. Multilayer Perception (MLR), Radial Basis Function (RBF) based neural network, traditional statistical Linear Regression (LR) approach and conventional empirical methods of Linacre and Christianson were used to estimate the evaporation at Jabalpur station situated under Kymore Plateau and Satpura Hills Agro-climatic Zone of Madhya Pradesh in the Central India. The weather parameters considered for estimation of evaporation are temperature, humidity, sunshine hours and wind speed. Results indicate that MLP and RBF based models with input of all selected weather parameters is able to estimate evaporation much precisely than LR and empirical approaches. It was found that higher accuracy may be obtained with multiple weather data input and low accuracy with only temperature input. It was observed that with temperature used as input the performance accuracy reduces in estimating evaporation with the selected models. However, neural network approach seems to produce better results as compared to statistical and empirical approach. The neural network based model RBF found more efficient in estimation of evaporation as compared to MLP. This study suggests that evaporation can be estimated by RBF model of a station, where there is no standard instrument available for its observation

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

Molecular Diversity Assessment in Selected Accessions of White Seeded Sesame (Sesamum indicum L.) using SSR Markers

315-321Fifty sesame accessions with 10 simple sequence repeat (SSR) markers were used for their molecular characterization and assessment of genetic diversity. It was observed through this study that the accessions have enough genetic variability at molecular levels. Thirty five alleles with mean polymorphism information content of 0.42 resulted from the molecular studies very explicitly indicate the superiority of SSR primers in assessment of genetic diversity. These primer bands size varied from 200 to 400 bp. The number of alleles per locus in selected accessions varied from 3 to 6 and heterozygosity per primer ranged from 0.00 to 0.40. The pair wise genetic similarity varied from 0.44 to 0.86. A closure view of dendrogram identified two major clusters, indicating high genetic resemblance among sesame accessions. Hence, under the study here, diversity assessment through SSR markers was proved to be stronger tools for discriminating Sesamum indicum accessions

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM2.5 air pollution, 1990-2019 : an analysis of data from the Global Burden of Disease Study 2019

Background Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM2.5 originating from ambient and household air pollution.Methods We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM2.5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure-response curve from the extracted relative risk estimates using the MR-BRT (meta-regression-Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM2.5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM2.5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals.Findings In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM2.5 exposure, with an estimated 3.78 (95% uncertainty interval 2.68-4.83) deaths per 100 000 population and 167 (117-223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13.4% (9.49-17.5) of deaths and 13.6% (9.73-17.9) of DALYs due to type 2 diabetes were contributed by ambient PM2.5, and 6.50% (4.22-9.53) of deaths and 5.92% (3.81-8.64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South America. Since 1990, the attributable burden has increased by 50%, driven largely by population growth and ageing. Globally, the impact of reductions in household air pollution was largely offset by increased ambient PM2.5.Interpretation Air pollution is a major risk factor for diabetes. We estimated that about a fifth of the global burden of type 2 diabetes is attributable PM2.5 pollution. Air pollution mitigation therefore might have an essential role in reducing the global disease burden resulting from type 2 diabetes. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.Peer reviewe

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM2.5 air pollution, 1990-2019 : An analysis of data from the Global Burden of Disease Study 2019

Background Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM2·5 originating from ambient and household air pollution. Methods We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South America. Since 1990, the attributable burden has increased by 50%, driven largely by population growth and ageing. Globally, the impact of reductions in household air pollution was largely offset by increased ambient PM2·5. Interpretation Air pollution is a major risk factor for diabetes. We estimated that about a fifth of the global burden of type 2 diabetes is attributable PM2·5 pollution. Air pollution mitigation therefore might have an essential role in reducing the global disease burden resulting from type 2 diabetes

Solar dryer with thermal energy storage systems for drying agricultural food products: A review

Developing efficient and cost effective solar dryer with thermal energy storage system for continuous drying of agricultural food products at steady state and moderate temperature (40-75 °C) has become potentially a viable substitute for fossil fuel in much of the developing world. Solar energy storage can reduce the time between energy supply and energy demand, thereby playing a vital role in energy conservation. The rural and urban populations, depend mainly, on non-commercial fuels to meet their energy needs. Solar drying is one possible solution but its acceptance has been limited partially due to some barriers. A great deal of experimental work over the last few decades has already demonstrated that agricultural products can be satisfactorily dehydrated using solar energy. Various designs of small-scale solar dryers having thermal energy storage have been developed in the recent past, mainly for drying agricultural food products. Therefore, in this review paper, an attempt has been taken to summarize the past and current research in the field of thermal energy storage technology in materials as sensible and latent heat in solar dryers for drying of agricultural food products. With the storage unit, agricultural food materials can be dried at late evening, while late evening drying was not possible with a normal solar dryer. So that, solar dryer with storage unit is very beneficial for the humans and as well as for the energy conservation.Solar energy Thermal energy storage Solar dryer Phase change material Latent heat Sensible heat

Review of solar dryers with latent heat storage systems for agricultural products

Drying of agricultural food products is one of the most attractive and cost-effective application of solar energy as it becomes a potentially viable substitute for fuel-wood in much of the developing world. The intermittent nature of the solar energy, which is the main source of energy in solar drying, is indeed one of the major shortcomings of the solar drying system can be alleviated by storing excess energy during the peak time and use it in off sun hours or when the energy availability is inadequate. Developing efficient and inexpensive energy storage devices in solar dryers is as important as developing new sources of energy and reduce the time between energy supply and energy demand, thereby playing a vital role in energy conservation. It improves the energy systems by smoothening the output and thus increasing the reliability. Therefore, in this paper, an attempt has been taken to summarize the investigation of the solar drying system incorporating with phase change materials (PCMs) for drying agricultural food products.Solar dryer Latent heat storage systems Phase change material Solar energy

Impact of Pretreatment and Drying Methods on Quality Attributes of Onion Shreds

Experiments were conducted on dry untreated onion shreds (2 mm thickness) or treated with salt (5 % solution) and potassium metabisulphite (0.5 % solution) in convective drier at 50 °C ((46±4) % relative humidity (RH)), 55 °C ((35±4) % RH), 60 °C ((28±4) % RH) and 65 °C ((20±4) % RH), heat pump-assisted convective drier at 35 °C ((32±2) % RH), 40 °C ((26±2) % RH), 45 °C ((19±2) % RH) and 50 °C ((15±2) % RH) and microwave-assisted convective drier at four microwave power levels, i.e. 120, 240, 360 and 480 W. The quality parameters of the dried onion shreds, namely rehydration ratio, colour diff erence, pyruvic and ascorbic acid contents and sensory scores were evaluated. The quality of dehydrated onion shreds was observed to be comparatively better when treated in heat pump drier at 50 °C, followed by that in microwave-assisted convective drier at 240 W and 50 °C, and last in convective drier at 60 °C. The onion shreds pretreated with potassium metabisulphite retained better colour of the dried product irrespective of drying methods. Therefore, heat pump drying may be recommended as one of the best drying methods for onion shreds, because it maintains the fi nal product quality, which has practical importance for the food industry

Physicochemical and microbiological characteristics of ginger paste (cv. Suprabha) during storage in different packaging and temperature conditions

Shelf life quality studies of ginger paste have been carried out in three packaging materials [metalized poly-propylene (MPP), polyethylene terepthalate and high density polyethylene] at two storage temperatures [room temperature (25°C) & cold room (CT) (5°C)]. The pH, total soluble solids (TSS), total solids (TS), acidity, water activity (aw), colour and microbial load were evaluated at 15 days interval for 120 days. There was no significant change in pH, acidity, TS and TSS of the paste with package types and storage temperatures, whereas a significant change in the total colour difference (7.406 ± 0.484 to 12.468 ± 1.288) was observed. After 120 days of storage, the minimum total bacterial count value of 4.33 ± 0.58 × 105 cfu/g and total mould count value of 0.9 ± 0.1 × 105 cfu/g were observed for samples in MPP packs stored in CT. Considering all the parameters viz. the change in colour, safety of food and nutritional quality, ginger paste can be stored in MPP pouches at 5°C temperature for 120 days

Optimization of the Ohmic Heating Parameters for Pasteurization of Mango Pulp using Response Surface Methodology

1087-1097The present investigation optimized the ohmic heating variables influencing the quality parameters of mango pulp during pasteurization. Three independent variables such as voltage gradients (10–20 V/cm) of ohmic heating, pulp temperature (60–80℃) and pulp concentration (6–14 °Brix) were experimented using Box-Behnken experimental design of response surface methodology. The models generated for the quality parameters as responses such as ascorbic acid, total phenol content, overall acceptability, mold load and bacterial load were tested for their validity using ANOVA. The optimum conditions for pasteurization of mango pulp through ohmic heating were found to be 19.5 V/cm, 75°C and 9.96 °Brix respectively for voltage gradient, pulp temperature and pulp concentration. The values of corresponding quality parameters of ascorbic acid content, total phenolics content, overall acceptability, yeast/mold load and bacterial load were estimated to be 129.39 mg/100g dm, 288.006 mg GAE/100 g dm, 7.40, 11.01 cfu/ml and 162.299 cfu/ml respectively. The results were experimentally verified within a deviation of ±0.5%. Structural variations and functional compounds of fresh, conventionally heated and ohmic heated samples at optimum conditions were analyzed through SEM and FTIR respectively. The ohmic heated sample was found superior over conventionally heated sample with maximum retention of quality parameters. Further, the rheological analysis depicted the closeness of ohmic heated (optimum) sample with the fresh sample