Probiotic-loaded seed mucilage-based edible coatings for fresh pistachio fruit preservation: an experimental and modeling study.

In this study, Lallemantia royleana mucilage (LRM) based edible coating containing 1.5 × 108 and 3 × 109 CFU/mL Lacticaseibacillus casei XN18 (Lbc1.5 and Lbc3) was designed to improve the quality and shelf-life of fresh pistachio. The fresh pistachios were coated with LRM + Lbc and their physicochemical, microbial, and sensory properties were evaluated after 1-, 5-, 15-, 25-, and 35-day storage at 4 °C. By the end of storage day, in comparison to control, the presence of probiotic isolate in the edible coating (particularly LRM + Lbc3) led to a marked decrease in fungal growth (3.1 vs. 5.8 Log CFU/g), weight loss (6.7 vs. 8.1%), and fat oxidation (0.19 vs. 0.98 meq O2/kg), and preserved total chlorophylls (8.1 vs. 5.85 mg/kg) and phenols (31.5 vs. 20.32 mg GAE/100 g), and antioxidant activity (38.95 vs. 15.18%) of samples during storage period. Furthermore, LRM + Lbc3-coated samples had a probiotic number above the recommended level (6.85-9.29 log CFU/g) throughout storage. The pistachios coated with probiotic-enriched edible coatings were greatly accepted by panelists. In the next section, Gaussian Process Regression (GPR) was used for predicting some parameters including: weight loss, TSS, Fat content, PV, Soluble carbohydrate content, Viability, Total phenolic compounds, Antioxidant activity, Mold and yeast, Total chlorophylls, Total carotenoids, Color, Odor and Overall acceptance. The results indicated that, there is a good agreement between the actual and predicted data by GPR model and it can be used for similar situation to decrease the cost of laboratory tests and increase the respond of analysis.

Assessing the protection mechanisms on Enterobacter aerogenes ATCC 13048 by potentially probiotic strain Lacticaseibacillus casei XN18: An experimental and modeling study.

In this study, Lacticaseibacillus casei XN18 had a remarkable resistant to simulated gastrointestinal conditions, hydrophobicity (38.60%), auto-aggregation (29.80%), co-aggregation (21.10%), adhesion (9.50%), anti-adhesion (24.40-36.90%), antioxidant activity (46.47%), cholesterol assimilation (41.10%), and antimicrobial effect on some pathogenic microorganisms. The modified double layer method, and Enterobacter aerogenes (inhibition zone (IZ) = 9.10 mm) and Listeria monocytogenes (IZ = 14.60 mm) were the most sensitive and resistant pathogens to the probiotic strain. The Lb. casei was sensitive to ciprofloxacin (IZ = 23 mm) and nitrofurantoin (IZ = 25.10 mm), semi-sensitive to imipenem (IZ = 18.80 mm), erythromycin (IZ = 16.90 mm), and chloramphenicol (IZ = 17.90 mm), and resistant to ampicillin (IZ = 9.60 mm) and nalidixic acid (IZ = 9.90 mm). The Lb. casei showed no haemolytic and DNase properties, and it could therefore be used for health-promoting purposes. In the next section, multilayer perceptron (MLP) neural network (NN) and gaussian process regression (GPR) models with k-fold cross validation method were used for predicting the rate of probiotic viability based on three levels of pH and time. The results showed that GPR has the lowest error. The mean absolute percentage error (MAPE), root mean absolute error (RMSE) and coefficient of determination (R2) for GPR and MLP models were 1.49 ± 0.40, 0.21 ± 0.03, 0.98 ± 0.05 and 6.66 ± 0.98, 0.83 ± 0.23 0.82 ± 0.09, respectively. So, the GPR model can be reliably used as a useful method to predict the probiotic viability in similar cases.

Eco-energy and environmental evaluation of cantaloupe production by life cycle assessment method.

Environmental crises and resource depletion have adversely affected the food security around the world. Food security in the future can be guaranteed by sustainable agriculture that respects the environment. So, it is necessary to decrease the energy consumption of resources for agricultural productions to achieve the maximum sustainability. For agricultural productions, environmental and energy issues are completely interrelated, and a comprehensive evaluation is necessary to manage them in all productions. In this study, energy, environmental, and economic indicators in cantaloupe production were studied. The studied energy indices included energy efficiency, energy productivity, net energy gain, and energy intensity. Life cycle method based on ISO 14040 standard was used to evaluate the environmental impacts. This method includes goal statement, identification of inputs and outputs, and a system for assessing and interpreting the environmental impacts of various agricultural productions. Also, for economic analysis, the average prices of inputs and outputs and also net return (NR), gross return (GR), and profit-to-cost ratio were used. The results showed that nitrogen fertilizer (32.28%) and diesel fuel (30.52%) had the highest and cantaloupe seeds (0.39%), and oil consumption in tractor engines (0.43%) had the lowest share of energy consumption, respectively. Energy efficiency, energy productivity, energy intensity, and net energy gain were estimated 0.56, 0.70 kg MJ-1, 1.41 MJ kg-1, and - 11,775.86 MJ ha-1, respectively. The results of the present status of environmental impacts showed that the most effective factor in climate change is direct emissions from the diesel fuel. Also, indirect emissions from phosphorus and urea fertilizers had the highest effect on ecosystem quality. Various machine operations such as primary and secondary plowing, spraying, and transportation were the main causes of high diesel fuel consumption. Economic analysis showed that the profit-to-cost ratio and the productivity values were calculated about 1.6 and 7.27, respectively, which means that for every dollar spent in cantaloupe farms, it produced 7.27 kg of cantaloupe production. The variable costs were estimated at 1154.5 and fixed cost was 1487 $ha-1. Among the variable costs, transportation and fuel costs were the highest with 64.3%. Decreasing the diesel fuel consumption by using appropriate farm management methods and using the reduce tillage methods can play an effective role in reducing the consumption of this input and improving the energy, environmental, and economic indicators in cantaloupe production.

Energy-economic-environmental cycle evaluation comparing two polyethylene and polycarbonate plastic greenhouses in cucumber production (from production to packaging and distribution).

Excessive consumption and improper management of inputs would lead to environmental damages, as well as decreased economic benefits. Thus, a thorough examination of the entire production process from the viewpoint of energy flow, economic profit, and environmental effects can identify hotspots and facilitate input management. Accordingly, in the current investigation, energy, economic and environmental aspects of greenhouse cucumber production systems were measured by life cycle assessment (LCA) technique and cumulative exergy demand (CExD) analysis by considering different greenhouse structures. Furthermore, the data envelopment analysis (DEA) approach was used to determine the efficiency of manufacturing units and optimal consumption pattern. The information required was acquired through interviews and questionnaires with 35 greenhouse owners, and consultation with greenhouse enterprises in the Khuzestan province of Iran. Based on the findings, energy consumed was 6626.45 MJton-1 in Sc1, and 6410.32 MJton-1 in Sc 3. The findings of benchmarking revealed that boosting the efficiency of the crop production process can lower input energy by 14.80%. The energy consumption for the construction of the first and second type of greenhouses was calculated to be 14,811.13 and 17,541.73 MJ (1000 m2)-1, respectively. With regard to the production variable costs, chemical fertilizers and labor had the largest contributions to the total expenses, at 7.6 (15.41%) and 7.87 $tonne-1(15.94%), respectively. In the evaluation of the energy and economic indicators, the combined indicator of Energy Intensiveness for the first and second types of greenhouse systems was found to be 80.26 and 77.07 MJ$-1, respectively, indicating higher energy-economic productivity of the first type of system. Based on LCA results, direct emissions due to input consumption (air: carbon dioxide (CO2), and nitrogen oxides (NOx); soil: mercury (Hg), copper (Cu), and lead (Pb)), and indirect emissions induced by chemical fertilizers, greenhouse structures, and chemical pesticides production are the environmental hotspots.

Application of classic and soft computing for modeling yield and environmental final impact in vegetable production (a case study: transplanting onion in Isfahan province, Iran).

This study aimed to develop a precision model between inputs and yield, and also between inputs (indirect emissions) and environmental final index (EFI) in onion farms through regression models (classic computing) and artificial intelligence models (soft computing). Required data were collected through direct measurement and questionnaire. To this end, 85 and 70 questionnaires were distributed among onion farmers in Fereydan and Falavarjan regions (Isfahan province, center of Iran), respectively. In the Fereydan region, the total energy input, onion yield, and water use efficiency (WUE) were obtained as 239496 MJ.ha-1, 97658 kg.ha-1, and 9.08 kg.m-3, respectively, while for Falavarjan region, these were obtained as 232221 MJ.ha-1, 94485 kg.ha-1, and 10.8 kg m-3, respectively. Electricity and diesel fuel were the most widely used inputs in the study areas. Based on the results related to the environmental indices, EFI was obtained as 547.38 and 363.54 pPt.t-1 for Fereydan and Falavarjan regions, respectively. The contribution of direct (such as CO2 and NH3) and indirect emissions (especially electricity) to the total EFI was 74 and 26% in Fereydan and 63 and 37% in Falavarjan region, respectively. Results related to the Cobb-Douglas regression model (CDR) showed that the effects of seed, manure, and labor on the onion yield were significant at 1% level of confidence. However, despite meeting the regression assumptions, the CDR model has predicted the yield and EFI with lower accuracy and higher error compared to artificial neural network models (ANNs), multi-layer perceptron (MLP), and adaptive neuro-fuzzy inference system (ANFIS). Soft computing (artificial intelligence) modeling showed that the ANFIS model (Grid Partitioning (GP)) has higher computational speed an lower error compared to multi-layer perceptron (MLP) models. Therefore, the comparison of the best GP and MLP models showed that the root-mean-square-error (RMSE) was obtained as 10.649 and 52.321 kg.ha-1 for yield and 25.08 and 40.94 pPt.ha-1 for EFI, respectively.

Impact of energy flow optimization on the mitigation of environmental consequences and costs in greenhouse cucumber production.

Cucumber, as a major cultivated plant in Iran, takes up more than 80% of greenhouse production. So, improving the production efficiency can save energy, reduce the environmental consequences and production costs, and also create a lot of job opportunities. The present study aimed to investigate the impact of energy flow optimization on the environmental impacts and costs in greenhouse cucumber production by data envelopment analysis (DEA) and life cycle assessment (LCA) methods. For environmental assessment, human health damage, ecosystem damage, and resource scarcity were considered and analyzed in the form of 8, 12, and 2 impact categories, respectively, using the ReCiPe2016 (H) method on endpoint level. Required data were collected through questionnaire from 30 cucumber growers. The results of the current status of environmental impacts showed the major contribution of fuels (natural gas and diesel) in all three main impact categories. Based on DEA analysis, 19 greenhouses (63%) were identified as inefficient units with a mean efficiency score of 0.70 (TE = 0.7). The highest inefficiency was related to the manure (72%), electricity (71.7%), and fuel (59%), respectively. Electricity and manure showed the highest saving potential, while the highest contribution to total energy saving was related to fuel (87%). Environmental analysis of optimized greenhouses showed that efficient use of inputs can reduce all environmental impacts between 7 and 80%, which highlights the importance of efficient resource management in this study. Also, production costs were decreased by about 67 $ tonne-1 (196 to 129 $) after the optimization of the units.

Data supporting midpoint-weighting life cycle assessment and energy forms of cumulative exergy demand for horticultural crops.

With an increasing demand of horticultural crops, it is critical to examine environmental damages and exergy impacts and evaluate their potential in producing sustainable products of agricultural systems. As such, environmental midpoints of five dominated horticultural products, namely, hazelnut, watermelon, tea, kiwifruit, and citrus, are scrutinized using life cycle assessment approach in Guilan province, Iran. Each crop is considered under a separate scenario and 10 tons of yield is determined as the functional unit. ReCiPe2016, as a new approach, is used for computation of 17 midpoints. Moreover, a weighting analysis is undertaken to find the share of each input in environmental damages with dimensionless notation. In the second part of this paper, cumulative exergy demand (CExD) is applied for evaluation of energy forms in each scenario. Data are presented under two sectors in the main article. The first part is midpoint results of each crop and the second part depicts energy forms of CExD with input rate in each category. Besides, the supplementary files contain raw material of each input, midpoint physical rate, share of each input to contribute midpoint, raw data of weighted damages and share of each input in total weighted damages.

Chemical footprint of pesticides used in citrus orchards based on canopy deposition and off-target losses.

The application of chemical pesticides is one of the main practices in citrus orchards. But during this operation, a considerable amount of sprayed volume is emitted to off-target areas using air-blast sprayers. The present study aimed to investigate pesticides' toxicological impacts in citrus orchards through determining the proportion of pesticides in different areas (air, soil, and canopy), which facilitate toxicity assessment of pesticides in citrus orchards. In this study, human toxicity and freshwater ecotoxicity impacts were assessed using USEtox 2.1 modeling approach for five active ingredients used in citrus orchards in the south of Iran. Different spraying scenarios were defined based on two types of nozzles (Hallow-cone and Teejet full-cone) and three levels of pressure (30, 40, and 50 bar) in two orchards with different row spacing. Results showed that only 26-37% of spray solution is deposited on the target with much loss to areas between tree canopies on the row. Scenario 1 (Hallow-cone nozzle with spraying pressure as 30 bar) shows the highest spraying efficiency in the both orchards (37% and 34% for Tangerine and Lemon orchards, respectively). Air emissions were obtained around 17 and 18% for hollow-cone and Teejet full-cone nozzles, respectively. The final inventory was obtained considering evaporation rate of active ingredients from soil surface and leaves. Based on the results obtained from toxicological impact assessment, Thiacloprid and Carbendazim had the highest negative environmental impacts on human health and freshwater aquatic ecosystem, respectively. Based on the results, soil emissions were identified as more critical than air emissions in terms of environmental consequences. It can be attributed to the higher characterization factor and deposition on the soil in comparison to the air. The present study provided well-founded information on the environmental performance of production systems by estimating the relevant emissions of pesticides to different compartments and determined the human and freshwater toxicity impact profiles, which assist decision-makers and LCA-practitioners to have a better perspective about pesticides behavior in receiving compartment.