Biodegradable Film from Maize, Rice and Potato Starch for Food Packaging: Development and Characterization

In this study, the PLA (v) andPLA+maize, PLA+rice, PLA+potato starch based biodegradable films along with reagents BPO (benzyl peroxide) and GMA (Glycidylmethacrylate) were developed using commercial extrusion blown molding method. The haze %, tensile strength, OTR, WVTR parameters of the developed films varied from 16.38–87.75%, 56.49-29.25 MPa, 318.25-176.85 cc/m2/day and 175-111.14 gm/m2/day respectively. The addition of rice, maize, potato starch decreased Tg from 65.26 to 60.46, 59.71 and 60.93 ºC; Tm from 146.81 to 143.01-150.16, 143.25-150.56 and 144.37-150.56 ºC with a slight increase in Tc from 106.45 to 107.23, 107.70 and 110.14 ºC respectively. The suitability of capsicum in modified atmospheric packaging was studied using the developed biodegradable films. Capsicum was stored under modified atmosphere packaging in the developed films namely, virgin PLA, PLA-rice (10%), PLA-potato (10%), PLA-maize(20%) of size 28×16 cm2 having packaging area for the transmission of gas of 0.0896 m2 with a fill weight of 0.35 kg. Polymeric film made up of LDPEof 60 μm was used for comparison. The MA packages maintained ≤7% PLW and higher firmness of samples as compared to the unpackaged capsicum. MA packages preserved the quality of capsicum up to 12 and 24 days at 25 ºC and 8 ºC, respectively as compared to unpackaged capsicum having shelf life of 4 and 9 days at 25 ºC and 8 ºC, respectively

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 PM<inf>2·5</inf> 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. Funding: Bill & Melinda Gates Foundation

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

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

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

Variability of extreme weather events and its impact on crop yield in Bundelkhand Agroclimatic zone of Madhya Pradesh

The Bundelkhand Agroclimatic Zone of Madhya Pradesh has witnessed many extreme weather events in recent decades like excessive hotness, dryness, coldness and number of consecutive drought years. Drought and water scarcity are the major resource limiting factors of this zone. There was sharp increase in numbers of hot days during last decade (2001-10) in Chhatarpur and Datia districts. The numbers of heavy rainfall days sharply decreased at Tikamgarh and Chhatarpur districts while frost days increased in Datia during last decade. The micro level variability of drought was much higher than the temporal scale variability. The occurrence of drought at micro level in the recent decade was much higher. The frequent occurrence of drought during recent past had increased the soybean and paddy yield variability in this zone. This paper attempts to present impact of variability of extreme weather events on paddy and soybean yield and also rural livelihood. The paddy and soybean yield were normally affected by number of heavy rainy days and number of rainy days. The number of heavy rainy days greater than equal to 6 days in Tikamgarh, 7 days in Chhatarpur per year were may be required for sustainable paddy production. It is observed that in those districts where the temporal variation in number of rainy days is decreasing, the decrease in number of rainy days below 5 days per year was crucial for sustainable yield. Whereas in the districts where little temporal variation in number of rainy days observed, a particular number of heavy rainy days is not necessary for adequate crop yield. In Datia and Chhatarpur district, the animal discomfort days increased over the decades.</jats:p