Inclusion of microbiological food safety as a novel aspect in life cycle assessments of food production.

The life cycle assessment (LCA) methodology currently covers a limited number of human health-related impact categories. Microbiological food safety is an essential aspect for the selection of an appropriate food production system and has been neglected in the LCA so far. A framework for the inclusion of a microbiological food safety indicator, expressed as disability-adjusted life year (DALY) value of the consumed food product to the human health damage category (end-point) was created, and applied in a case study model on the cooked-chilled meals as the ready-to-eat meals can be associated with the occurrence of foodborne illness cases and outbreaks. This study suggests a framework for the inclusion of microbiological risk caused by Bacillus cereus associated with the consumption of ready-to-eat meals (in Belgium) in the LCA. The results indicated that the microbiological risk of one package of the investigated ready-to-eat meal was 1.95 × 10-6 DALY, and the obtained DALY value was included as an impact category in the LCA methodology. Inclusion of other categories of food safety (including chemical safety hazards, pesticide residues, heavy metals, and mycotoxins) in LCA could be done in the same fashion.

Toward sustainable culture media: Using artificial intelligence to optimize reduced-serum formulations for cultivated meat.

When considering options for future foods, cell culture approaches are at the fore, however, culture media to support the process has been identified as a significant contributor to the overall global warming potential (GWP) and cost of cultivated meat production. To address this issue, an artificial intelligence-based approach was applied to simultaneously optimize the GWP, cost, and cell growth rate of a reduced-serum culture media formulation for a zebrafish (ZEM2S cell line) cultivated meat production system. Response surface methodology (RSM) was used to design the experiments, with seven components - IGF, FGF, TGF, PDGF, selenium, ascorbic acid, and serum - selected as independent variables, given their influence on culture media performance. Radial basis function (RBF) neural networks and genetic algorithm (GA) were applied for prediction of dependent variables, and optimization of the culture media formulation, respectively. The results indicated that the developed RBF could accurately predict the GWP, cost and growth rate, with a model efficiency of 0.98. Subsequently, the three developed RBF neural networks predictive models were used as the inputs for a multi-objective genetic algorithm, and the optimal quantities of the independent variables were determined using a multi-objective optimization algorithm. The suggested RSM + RBF + GA framework in this study could be applied to sustainably optimize serum-free media development, identifying the combination of media ingredients that balances yield, environmental impact, and cost for various cultivated meat cell lines.

Prediction of essential oil content in spearmint (Mentha spicata) via near-infrared hyperspectral imaging and chemometrics.

Spearmint (Mentha spicata L.) is grown for its essential oil (EO), which find use in food, beverage, fragrance and other industries. The current study explores the ability of near infrared hyperspectral imaging (HSI) (935 to 1720 nm) to predict, in a rapid, nondestructive manner, the essential oil content of dried spearmint (0.2 to 2.6% EO). Spectral values of spearmint samples varied considerably with spatial coordinates, and so the use of averaging the spectral values of a surface scan was warranted. Data preprocessing was done with Multiplicative Scatter Correction (MSC) or Standard Normal Variate (SNV). Selection of spectral input variables was done with Least Absolute Shrinkage and Selection Operator (LASSO), Principal Component Analysis (PCA) or Partial Least Squares (PLS). Regression was executed with linear regression (LASSO, PLS regression, PCA regression), Support Vector Machine (SVM) regression, and Multilayer Perceptron (MLP). The best prediction of EO concentration was achieved with the combination of MSC or SNV preprocessing, PLS dimension reduction, and MLP regression (1 hidden layer with 6 nodes), achieving a good prediction with a ratio of performance to deviation (RPD) of 2.84 ± 0.07, an R2 of prediction of 0.863 ± 0.008, and a RMSE of prediction of 0.219 ± 0.005% EO. These results show that NIR-HSI is a viable method for rapid, nondestructive analysis of EO concentration. Future work should explore the use of NIR in the visible spectrum, the use of HSI for determining EO in other plant materials and the potential of HSI to determine individual compounds in these solid plant/food matrices.

Hyperspectral Imaging and Chemometrics for Authentication of Extra Virgin Olive Oil: A Comparative Approach with FTIR, UV-VIS, Raman, and GC-MS.

Limited information on monitoring adulteration in extra virgin olive oil (EVOO) by hyperspectral imaging (HSI) exists. This work presents a comparative study of chemometrics for the authentication and quantification of adulteration in EVOO with cheaper edible oils using GC-MS, HSI, FTIR, Raman and UV-Vis spectroscopies. The adulteration mixtures were prepared by separately blending safflower oil, corn oil, soybean oil, canola oil, sunflower oil, and sesame oil with authentic EVOO in different concentrations (0-20%, m/m). Partial least squares-discriminant analysis (PLS-DA) and PLS regression models were then built for the classification and quantification of adulteration in olive oil, respectively. HSI, FTIR, UV-Vis, Raman, and GC-MS combined with PLS-DA achieved correct classification accuracies of 100%, 99.8%, 99.6%, 96.6%, and 93.7%, respectively, in the discrimination of authentic and adulterated olive oil. The overall PLS regression model using HSI data was the best in predicting the concentration of adulterants in olive oil with a low root mean square error of prediction (RMSEP) of 1.1%, high R2pred (0.97), and high residual predictive deviation (RPD) of 6.0. The findings suggest the potential of HSI technology as a fast and non-destructive technique to control fraud in the olive oil industry.

Life cycle assessment of edible insects (Protaetia brevitarsis seulensis larvae) as a future protein and fat source.

Because it is important to develop new sustainable sources of edible protein, insects have been recommended as a new protein source. This study applied Life Cycle Assessment (LCA) to investigate the environmental impact of small-scale edible insect production unit in South Korea. IMPACT 2002 + was applied as the baseline impact assessment (IA) methodology. The CML-IA baseline, EDIP 2003, EDP 2013, ILCD 2011 Midpoint, and ReCiPe midpoint IA methodologies were also used for LCIA methodology sensitivity analysis. The protein, fat contents, and fatty acid profile of the investigated insect (Protaetia brevitarsis seulensis larvae) were analyzed to determine its potential food application. The results revealed that the studied edible insect production system has beneficial environmental effects on various impact categories (ICs), i.e., land occupation, mineral extraction, aquatic and terrestrial ecotoxicity, due to utilization of bio-waste to feed insects. This food production system can mitigate the negative environmental effects of those ICs, but has negative environmental impact on some other ICs such as global warming potential. By managing the consumption of various inputs, edible insects can become an environmentally efficient food production system for human nutrition.

Life cycle assessment and energy comparison of aseptic ohmic heating and appertization of chopped tomatoes with juice.

The energy balance and life cycle assessment (LCA) of ohmic heating and appertization systems for processing of chopped tomatoes with juice (CTwJ) were evaluated. The data included in the study, such as processing conditions, energy consumption, and water use, were experimentally collected. The functional unit was considered to be 1 kg of packaged CTwJ. Six LCA impact assessment methodologies were evaluated for uncertainty analysis of selection of the impact assessment methodology. The energy requirement evaluation showed the highest energy consumption for appertization (156 kWh/t of product). The energy saving of the ohmic heating line compared to the appertization line is 102 kWh/t of the product (or 65% energy saving). The energy efficiencies of the appertization and ohmic heating lines are 25% and 77%, respectively. Regarding the environmental impact, CTwJ processing and packaging by appertization were higher than those of ohmic heating systems. In other words, CTwJ production by the ohmic heating system was more environmentally efficient. The tin production phase was the environmental hotspot in packaged CTwJ production by the appertization system; however, the agricultural phase of production was the hotspot in ohmic heating processing. The uncertainty analysis results indicated that the global warming potential for appertization of 1 kg of packaged CTwJ ranges from 4.13 to 4.44 kg CO2eq. In addition, the global warming potential of the ohmic heating system ranges from 2.50 to 2.54 kg CO2eq. This study highlights that ohmic heating presents a great alternative to conventional sterilization methods due to its low environmental impact and high energy efficiency.

Exergy flow of rice production system in Italy: Comparison among nine different varieties.

Exergy analysis is receiving considerable attention as an approach to be applied for making decisions toward moving to a sustainable and energy-efficient food supply chain. This study focuses on how the selection of variety affects the exergy flow of a paddy rice production system. In this regard, nine varieties of rice in Italy, the largest rice producer in Europe, were evaluated using the cumulative exergy analysis approach. Sensitivity analysis of inputs consumption and the exergy management scenarios of the most sensitive inputs are also provided in this study. The results indicated that the cumulative exergy consumption value of the investigated rice varieties ranges from 16.09 GJha-1 to 25.80 GJ ha-1. Fossil fuels and chemical fertilizer consumption were the most significant contributors to the total energy consumption in all investigated varieties. Luna variety, with the cumulative degree of perfection value of 7.96 and renewability indicator of 0.88, was identified as the most exergy-efficient variety of rice in Italy.

Measuring Circularity in Food Supply Chain Using Life Cycle Assessment; Refining Oil from Olive Kernel.

Valorization of food waste is a potential strategy toward a circular food supply chain. In this regard, measuring the circularity of food waste valorization systems is highly important to better understand multiple environmental impacts. Therefore, this study investigated the circularity of a food waste valorization system (refining oil from olive kernel) using a life cycle assessment methodology. An inventory of an industrial-based olive kernel oil production system is also provided in this study. The system boundary was the cradle to the factory gate of the production system. The results indicated that natural gas consumption was the highest contributor to most of the investigated impact categories. The global warming potential of one kg of oil produced from olive kernel was calculated to be 1.37 kg CO2eq. Moreover, the calculated damages of 1 kg oil production from olive kernel to human health, ecosystem quality, and resource depletion were 5.29 × 10-7 DALY, 0.12 PDF∙m2∙yr., and 24.40 MJ, respectively.

Hybrid multi-criteria decision-making approach to select appropriate biomass resources for biofuel production.

Biofuel generation from local biomass resources can significantly contribute to greenhouse gas mitigation and cleaner energy production. In this regard, a hybrid Multi-Criteria Decision-Making (MCDM) approach was employed to prioritize appropriate biomass resources for biofuel production. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Additive Ratio Assessment (ARAS), and Weighted Aggregates Sum Product Assessment (WASPAS) were the employed MCDM approaches. Subsequently, ranking aggregation methods, i.e., Borda, Copeland, and Rank Mean, were applied to integrate the rankings obtained from the MCDM approaches. Guilan province of Iran was selected as a case study based on its promising potential for biofuel production from first-, second-, and third-generation biofuel resources. Initially, through an in-depth review of the literature and the use of academic professors' expert opinions, ten criteria were selected as the evaluation indices of the study: 1) creating technical side jobs, 2) preserving non-renewable energy resources, 3) relative advantage of biofuel production, 4) complexity of biofuel production process, 5) cost of the biomass conversion process, 6) biomass reusability, 7) cost of biomass supply, 8) environmental impacts of biomass accumulation, 9) adaptability of the biofuel production process to the size of biomass production units and the attitude and knowledge of the producers, and 10) energy self-sufficiency of the biomass producer. Moreover, the 11 investigated potential sources of biofuel production were rice, peanut, livestock and poultry wastes, rice waste, peanut waste, tea residues and its processing wastes, olive residues and its processing wastes, livestock and poultry slaughter and farm-raised fish wastes, municipal solid waste and sewage, forest and wood farming wastes, algae and Azolla. The results indicated that "municipal solid wastes and sewage", "forest and wood farming wastes" and "livestock and poultry wastes" from the second-generation biofuels were identified as the most important biomass resources in the studied area.

Application of analytic hierarchy process to develop a weighting scheme for life cycle assessment of agricultural production.

The Impact Assessment (IA) step in Life Cycle Assessment (LCA) studies is classified into three steps of characterization, normalization and weighting. In this research, various impact categories were weighted using Analytic Hierarchy Process (AHP), as a multi criteria decision making tool. Iranian tobacco production system was the example of agricultural system. The data for LCA analysis were collected from 225 farms. The data for AHP analysis were gathered by surveying 12 LCA experts. The results indicated that on-farm emissions of CO2, CH4, N2O, NH3, NOx and SO2 were accounted for 25, 96, 93, 99, 21 and 2% of the total emissions (cradle to farm gate), respectively. The characterization indices for the impact categories of acidification, terrestrial eutrophication, global warming, phosphate resources depletion, potash resources depletion and fossil resources depletion for one ton tobacco production were calculated to be 13.87 kgSO2eq, 19.69 kgNOxeq, 1883.90 kgCO2eq, 19.69 kgNOxeq, 4.19 kgP2O5, 6.14 kgK2O, and 59,659.23 MJ, respectively. The LCA + AHP showed that the weighted factors of global warming, terrestrial eutrophication, acidification, fossil resources depletion, phosphate resources depletion and potash resources depletion were 1, 0.790, 0.518, 0.681, 0.422, and 0.263, respectively. Many agricultural systems could benefit from using the developed weighting factors for LCA studies in the agricultural sector. Based on the new developed weighted factors, the weighted indices of aforementioned impact categories for Iranian tobacco production were 0.24, 0.41, 0.19, 1.04, 0.23, and 0.20, respectively. This implies that fossil resources depletion impact category was attributed the highest negative environmental impacts of tobacco production followed by terrestrial eutrophication.

Rice single cropping or ratooning agro-system: which one is more environment-friendly?

Sustainable development of food production depends on damping the environmental impacts of agricultural production. The aim of this study was to investigate the environmental performance of rice production in single crop and ratooning (main rice + ratoon crop) agro-systems through life cycle assessment (LCA) methodology in Guilan province, Northern Iran, in 2015. The flooding irrigation regime was the dominant irrigation method for single cropping system and main crop rising in ratooning agro-system. The data were gathered through a face to face interview with 215 single crop and 115 ratoon breeder paddy farmers. The environmental risks were determined in six impact categories including global warming, terrestrial eutrophication, acidification, and depletion of fossil fuels, phosphate, and potash resources. The functional unit (FU) was set as 100 kg protein. Results indicated that the CO2, N2O, and CH4 emissions of the rice ratooning agro-system (661.44, 1.96, and 5.42 kg 100 kg-1 protein) were less than the corresponding values in the rice single cropping agro-system (1341.63, 2.88, and 9.20 kg 100 kg-1 protein, respectively). Among all the environmental impact categories, the terrestrial eutrophication had the widest negative environmental effect followed by depletion of phosphate resources in single cropping agro-system with weighted indices of 0.51 and 0.41, respectively. Moreover, the terrestrial eutrophication had the largest negative environmental effect followed by acidification in rice ratooning agro-system with weighted indices of 0.48 and 0.29, respectively. Overall, the results highlighted that the rice ratooning ago-system is more environmentally beneficial than the single cropping system, particularly in terms of depletion of fossil fuels, global warming, and depletion of phosphate and potash resources categories. This priority may be improved through adopting proper management of agronomic practices for main and ratoon rice in ratooning agro-system. Graphical abstract ᅟ.