**4.2. Materials and Methods**

4.2.1. Refining Oil from Olive Kernel

The olive kernel oil production company investigated in this study is located in Iran. Olives
(*Olea europaea* L.) were grown in Iran, and the uses of olive fruits in the studied region
are: (i) raw material for extra virgin olive oil production, (ii) inside dishes (pitted table olive),
and (iii) raw material for pickling. Solid-liquid olive pomace and olive mill wastewater are
the two major by-products of the extra virgin olive oil production system (Batuecas et al.,
2019). The kernel must be separated from the olive fruit in the second and third
abovementioned olive fruit applications. Therefore, olive kernel is a common source of
waste in olive fruit processing systems (Figure 4.2). Olive kernel can be used to produce
oil. The characterization of olive kernel oil was described by Moghaddam et al. (2012).

The industrial olive kernel oil production process is shown in Figure 4.3. Olive kernels are transported to the processing plant, and the factory is located in the olive oil production/processing area. A small amount of olive pulp is stuck to kernels because it cannot be completely separated from the olive kernels in olive processing (Figure 4.2b).

The received kernels are washed to remove impurities. Then, they are crushed to ease
the release of the oil and are subsequently mixed. Afterward, the liquid (including oil and
water) is separated through a decanter. In the next step, the oil is separated from the
water and the olive kernel oil is extracted by a separator. Natural gas is consumed to heat
the water in the boiler at a working temperature of 60 to 70 °C. Its circulation in the
decanter's double-walled jacket heats the dough (the crushed olive kernel, oil, and water).

Mixer blades of the decanter provide a uniform spread of heat throughout the dough.

Heating the dough contributes more efficiently to separating the three phases of oil, water,
and pulp through centrifuging at 4000–4200 rpm. Moreover, the remaining pulp needs to be warmed to flow easily through the discharge mono pump of the decanter. Warm water is also added to the oil entering the separator in order to maximize the oil extraction rate. At the final step, a centrifuge rotating at a high rotational speed of 7000–7200 rpm separates the warm water and the olive kernel oil.

<figure><img src="image_17.png" /><figcaption>**Figure 4.2:** (a) different parts of an olive fruit (Martins and Kiritsakis, 2017; Kiritsakis et al., 2020), (b) the parts of the olive which were used to produce oil in the case study. Note: In this study, the term “kernel” refers to “kernel, pit, and pulp stuck to kernel”</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>**Figure 4.3:** Olive oil production line with a daily production capacity of 100 kg</figcaption></figure>

### 4-2.2. Measuring Circularity Using LCA

In this study, LCA methodology was applied to measure the circularity of an olive waste valorization system. Based on the review by Espadas-Aldana et al. (2019) on olive oil LCA studies, the functional units (FUs) are defined as the quantity of olive oil produced in kg, and the energy content of the produced oil in MJ. Therefore, 1 kg of oil produced from olive kernel and 100 MJ energy generation were selected as the FUs for this LCA analysis to compare the environmental consequences associated with oil production from olive
kernel with other vegetable-based oil production systems. The heating value (energy equivalent) of olive kernel oil was considered to be the same as for olive oil at 34.5 MJ/L (Cappelletti et al., 2014). The system boundary of this LCA study was the cradle-to-factory gate olive kernel oil production.

The cradle-to-gate environmental impact for olive kernel oil production was evaluated using LCA. In this regard, the main primary inventory data for olive kernel production in the considered factory are shown in Table 1. It should be noted that the input and output amounts were measured and recorded at the factory level, not through surveys or interviews. The experiments were performed in 2019. Experiments were conducted three times and the mean values are reported (Table 4.1). The olive kernel was not included as an input for the LCA analysis, and it was considered as a burden-free input; this is because it is a by-product/waste of a food production system. Therefore, the environmental impacts of wastewater from the olive kernel production system are not included in the system boundary. In other words, the assumption of not considering the wastewater treatment is due to the fact that olive kernel was not included as an input for the LCA analysis, as it is actually a by-product/waste of an olive processing system. Packaging is also excluded from the system boundary. The emitted pollutants were divided into off-site and on-site emissions. The emitted pollutants in the off-site phase (production of input materials) were adapted from the Ecoinvent 3.0 database using SimaPro 9.0.0.49 software. The datasets applied for calculation of emissions from off-site operations are shown in Appendix C7.5, Table A1. The on-site emissions of natural gas consumption (see Appendix C4-2) were calculated and added to the on-site emission
section using SimaPro software. The inventory of emissions for refining one kilogram oil from olive kernel is provided at https://doi.org/10.3390/foods10030590.

<table><caption>**Table 4.1:** Main primary inventory data for the olive kernel oil production system</caption><thead><tr><th rowspan="2">Inputs and Outputs</th><th rowspan="2">Unit</th><th colspan="2">Quantity</th></tr><tr><th>Per 1 kg Produced Oil</th><th>Per 100 MJ Produced Oil</th></tr></thead><tbody><tr><td>—Olive kernel</td><td>kg</td><td>47.72</td><td>150.83</td></tr><tr><td>—Water consumption</td><td>m<sup>3</sup></td><td>0.05</td><td>0.14</td></tr><tr><td>—Natural gas</td><td>m<sup>3</sup></td><td>0.52</td><td>1.65</td></tr><tr><td>—Electricity</td><td>kWh</td><td>0.10</td><td>0.31</td></tr><tr><td>—Human labor</td><td>h</td><td>0.16</td><td>0.50</td></tr><tr><td>—Transportation of the produced oil</td><td>ton × km</td><td>0.34</td><td>1.07</td></tr><tr><td colspan="4">Outputs</td></tr><tr><td>—Olive kernel oil</td><td></td><td>1 kg</td><td>100 MJ</td></tr><tr><td>—Efficiency (oil/olive kernel)</td><td>%</td><td>2.09</td><td>2.09</td></tr></tbody></table>

In the third step of the LCA study, IMPACT 2002+ was employed as the impact assessment (IA) methodology due to its hybrid application of IMPACT 2002, Eco-Indicator 99, CML, and IPCC, all of which cover various impact and damage categories. This impact assessment (IA) methodology evaluates the environmental impacts based on the 15 impact categories, and also divides the impact categories into four damage categories: human health, ecosystem quality, GWP, and resource depletion. The human health damage category is represented as disability-adjusted life year (DALY), ecosystem quality as PDF·m²·yr., GWP as kg CO₂eq, and resource depletion as MJ. The fourth and last step of conducting an LCA study is the interpretation of the LCA results, which are explained in the Results and Discussion section. The fourth step of an LCA study includes
the determination of hotspots, the specification of areas with a potential for improvement,
and recommendations (Sundaram et al., 2015). A detailed explanation of this IA
methodology can be found in Jolliet et al. (2003). A flowchart of the utilization of LCA for
measuring the circularity of olive kernel oil production systems is demonstrated in Figure
4.4. Edraw Max (ver. 9.1, 2018; Sheung Wan, Hong Kong, China) software was used for
the representation of graphical items.

There are several sources of uncertainty in an LCA study (Zargar-Ershadi et al., 2021). It
is important to take into account the uncertainty of LCA results which can be due to a lack
of accuracy in the collected data (Vásquez-Ibarra et al., 2020), the initial assumption
(Fathollahi et al., 2018), the allocation method (Cherubini et al., 2018), the selection of
functional units (Noya et al., 2018), the determination of on-site emissions (Rezaei et al.,
2021), and the type of IA methodology (Paramesh et al., 2018). Therefore, to test the
consistency of the LCA results obtained in this study, a quantitative uncertainty analysis
was conducted to evaluate the effect of IA methodology selection on the final LCA results.
In this regard, the GWP impact category was selected for comparison, as it is the mutual
impact category among the investigated IA methodologies. The selected impact
assessment methodologies were IMPACT 2002+, ReCiPe 2016 (Dekker et al., 2019),
CML–IA baseline (Guinée et al., 2002), EDIP 2003 (Hauschild and Wenzel, 1998), EF
(Huijbregts et al., 2008), EN 15804 +A2 (EN, 2012), Environmental Prices (De Bruyn et
al., 2018), EPD (PRe, 2019) and ILCD (Chomkhamsri et al., 2011). The IA methodologies
evaluated by this study are available on SimaPro 9.0.0.49—a widely used software for
LCA analysis (Herrmann et al., 2015; Hiloidhari et al., 2020).
One limitation of this research comes from the limited number (one industrial-scale company refining oil from olive kernel) of investigated olive kernel oil factories. Another limitation of this study is that, compared with studies on the LCA of extra virgin olive oils—the highest quality of olive oils—this olive kernel study does not consider the olive quality indices, as suggested by Salomone et al. (2015).

<figure><img src="image_19.png" /><figcaption>**Figure 4.4:** Measuring the circularity of olive kernel oil production using LCA methodology</figcaption></figure>

**4.3. Results and Discussion**

4.3.1. Interpretation of mid-point LCA results

The quantified amounts of the environmental impacts of olive kernel oil production, based on different impact categories, are presented in Table 2. The environmental impact of 1 kg of olive kernel oil production on GWP was calculated as 1.37 kg CO2eq. This value of the abovementioned impact category for 100 MJ energy generation was 4.32 kg CO2eq.

The GWP of one liter of olive oil production is equal to 1.6 kg CO2eq, according to
Espadas-Aldana et al. (2019). They also indicated that the agricultural phase is the most impactful phase in the olive oil supply chain, responsible for 0.46 kg CO₂eq/kg of GWP. In the case of refining oil from olive kernel, olives are not used in the system, and the by-product of the olive postharvest waste was used to produce oil. Then, the environmental impacts of the agricultural phase are not accounted for in this system. Therefore, the environmental impacts of oil production from olive kernel are low, making the conventional olive processing systems more circular through waste valorization (olive kernel) of the system. If it is assumed that the production of oil from kernels means less olive oil needs to be produced elsewhere for cosmetic and pharmacological purposes (Figure 4.1), then this process can save around 0.23 kg CO₂ per each kg of oil produced from olive kernel in the investigated system.

<table><caption>**Table 4.2:** Environmental consequence of the olive kernel oil production system based on different impact categories</caption><thead><tr><th rowspan="2">Impact Category</th><th rowspan="2">Unit</th><th colspan="2">Quantity</th></tr><tr><th>Per 1 kg Produced Oil</th><th>Per 100 MJ Produced Energy</th></tr></thead><tbody><tr><td>Global warming</td><td>kg CO<sub>2</sub>eq</td><td>1.37</td><td>4.32</td></tr><tr><td>Non-renewable energy</td><td>MJ primary</td><td>26.40</td><td>83.44</td></tr><tr><td>Mineral extraction</td><td>MJ surplus</td><td>0.005</td><td>0.02</td></tr><tr><td>Ozone layer depletion</td><td>kg CFC-11eq</td><td>1.57 × 10<sup>-7</sup></td><td>4.97 × 10<sup>-7</sup></td></tr><tr><td>Non-carcinogens</td><td>kg C<sub>2</sub>H<sub>3</sub>Cl eq</td><td>0.02</td><td>0.05</td></tr><tr><td>Carcinogens</td><td>kg C<sub>2</sub>H<sub>3</sub>Cl eq</td><td>0.08</td><td>0.24</td></tr></tbody></table>

Continued on next page
<table><caption>**Table 4.2** – continued from previous page</caption><thead><tr><th rowspan="2">Impact Category</th><th rowspan="2">Unit</th><th colspan="2">Quantity</th></tr><tr><th>Per 1 kg Produced Oil</th><th>Per 100 MJ Produced Energy</th></tr></thead><tbody><tr><td>Ionizing radiation</td><td>Bq C-14 eq</td><td>1.94</td><td>6.14</td></tr><tr><td>Respiratory organics</td><td>kg C<sub>2</sub>H<sub>4</sub> eq</td><td>3.84 × 10<sup>-4</sup></td><td>1.39 × 10<sup>-3</sup></td></tr><tr><td>Respiratory inorganics</td><td>kg PM2.5eq</td><td>4.40 × 10<sup>-4</sup></td><td>1.21 × 10<sup>-3</sup></td></tr><tr><td>Aquatic ecotoxicity</td><td>kg TEG water</td><td>68.07</td><td>215.16</td></tr><tr><td>Terrestrial ecotoxicity</td><td>kg TEG soil</td><td>12.71</td><td>40.16</td></tr><tr><td>Aquatic eutrophication</td><td>kg PO<sub>4</sub> P-lim</td><td>3.28 × 10<sup>-5</sup></td><td>1.04 × 10<sup>-4</sup></td></tr><tr><td>Terrestrial acid/nutri</td><td>kg SO<sub>2</sub>eq</td><td>0.007</td><td>0.02</td></tr><tr><td>Land occupation</td><td>m<sup>2</sup>org.arable</td><td>0.009</td><td>0.03</td></tr><tr><td>Aquatic acidification</td><td>kg SO<sub>2</sub>eq</td><td>0.002</td><td>0.01</td></tr></tbody></table>

Figure 4.5 demonstrates the proportion of inputs to the environmental effects of the olive kernel chain. Natural gas consumption was the highest contributor to the most investigated impact categories. The share of natural gas from off-farm emissions on GWP in olive kernel oil was 83%. On-site emissions contributed to the impact categories of GWP, respiratory organics, respiratory inorganics, and terrestrial ecotoxicity; contributions of on-site emissions to these impact categories were 73, 12, 16, and 2%, respectively.
<figure><img src="image_20.png" /><figcaption>**Figure 4.5:** Contribution of consumed inputs to the environmental impact of the olive kernel oil production system.</figcaption></figure>

### 4.3.2. Interpretation of end-point damage assessment

Figure 4.6 displays the damage assessment of the olive kernel oil chain. According to the results, the production of 1 kg of oil from olive kernel led to $5.29 \times 10^{-7}$ DALY damage to human health. The current study investigated the environmental impacts associated with the olive kernel oil production system, and the chemical and microbiological health risk of the final produced oil was not involved in the LCA analysis. Further research is required to examine the human health risk caused by the consumption of oil produced from olive kernel, as well as to gain a proper understanding of the sustainability of the olive kernel oil production system throughout its life cycle. The damages of 1 kg of oil production from olive kernel to ecosystem quality, GWP, and resource depletion were $1.21 \times 10^{-2}$ PDF·m$^2$·yr., 1.37 kg CO$_2$eq, and 24.40 MJ, respectively (Figure 4.6). The environmental indices of the investigated olive kernel oil production system based on different phases of the production system are shown in Table 4.3.
<figure><img src="image_21.png" /><figcaption>**Figure 4.6:** Damage assessment of olive kernel oil production (per 1 kg produced oil).</figcaption></figure>

<table><caption>**Table 4.3:** Environmental impacts of the olive kernel oil production system based on different damage categories</caption><thead><tr><th rowspan="3">Damage category</th><th colspan="9">Off-Site</th><th colspan="3">On-Site</th></tr><tr><th colspan="3">Electricity</th><th colspan="3">Natural Gas</th><th colspan="3">Transportation</th><th colspan="3">On-Factory</th></tr><tr><th>Damage assessment</th><th>Normalized</th><th>Weighted</th><th>Damage assessment</th><th>Normalized</th><th>Weighted</th><th>Damage assessment</th><th>Normalized</th><th>Weighted</th><th>Damage assessment</th><th>Normalized</th><th>Weighted</th></tr></thead><tbody><tr><td>Human health</td><td>3.78 × 10<sup>-8</sup> DALY</td><td>2.53 × 10<sup>-6</sup></td><td>5.23</td><td>3.20 × 10<sup>-7</sup> DALY</td><td>4.51 × 10<sup>-5</sup></td><td>45.07</td><td>1.28 × 10<sup>-7</sup> DALY</td><td>1.80 × 10<sup>-5</sup></td><td>17.98</td><td>4.46 × 10<sup>-8</sup> DALY</td><td>6.29 × 10<sup>-6</sup></td><td>6.29</td></tr><tr><td>Ecosystem quality</td><td>3.95 × 10<sup>-3</sup><br>PDF.m<sup>2</sup>.yr</td><td>2.8 × 10<sup>-7</sup></td><td>0.29</td><td>3.46 × 10<sup>-2</sup><br>PDF.m<sup>2</sup>.yr</td><td>2.53 × 10<sup>-6</sup></td><td>2.53</td><td>8.08 × 10<sup>-2</sup><br>PDF.m<sup>2</sup>.yr</td><td>5.89 × 10<sup>-6</sup></td><td>5.89</td><td>2.02 × 10<sup>-3</sup><br>PDF.m<sup>2</sup>.yr</td><td>1.47 × 10<sup>-7</sup></td><td>0.15</td></tr><tr><td>Climate change</td><td>0.06 kg CO<sub>2</sub>eq</td><td>6.28 × 10<sup>-6</sup></td><td>6.28</td><td>0.13 kg CO<sub>2</sub>eq</td><td>1.35 × 10<sup>-5</sup></td><td>13.48</td><td>0.17 kg CO<sub>2</sub>eq</td><td>1.75 × 10<sup>-5</sup></td><td>17.52</td><td>1.00 kg CO<sub>2</sub>eq</td><td>1.01 × 10<sup>-4</sup></td><td>100.91</td></tr><tr><td>Resource depletion</td><td>1.08 MJ</td><td>7.12 × 10<sup>-6</sup></td><td>7.12</td><td>22.57 MJ</td><td>1.49 × 10<sup>-4</sup></td><td>148.52</td><td>2.75 MJ</td><td>1.81 × 10<sup>-5</sup></td><td>18.09</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>
The single scores of the damage categories in olive kernel oil are shown in Figure 4.7.

The total weighted environmental damage from refining oil from olive kernel was calculated as 395 µPt/FU. Natural gas was the largest contributor to the total environmental impacts of the studied system, with a share of 80% (including its background and on-site emissions). As explained in the Materials and Methods section, natural gas is used in olive kernel processing to heat water in order to (a) facilitate the separation of water and oil from pulp in the decanter, (b) contribute to the flow of the remaining pulp in the decanter, and (c) separate water from olive kernel oil in the separator. A preliminary experiment shows that the optimum dough temperature to extract purer oil is around 35 °C, which is associated with the circulating water temperature at around 60–70 °C in the studied system. Moreover, a temperature of around 35 °C for the water added to the separator might be efficient. Nevertheless, further research is needed to determine the optimum water temperature to avoid extra natural gas/heat consumption.

Khounani et al. (2021) showed that the total environmental burdens of olive oil systems can be reduced by 12% by applying agro-biorefinery strategies based on olive cultivation and the extraction of fruit and pomace oil. There are also other ways to valorize olive processing wastes, such as energy generation. One LCA study highlighted significant greenhouse gas emission savings through olive husk (the solid portion remaining after pressing olives) application in a mobile pyrolysis process (El Hanandeh, 2013). In another LCA study, Intini et al. (2012) reported the environmental advantages of using de-oiled pomace and waste wood as feedstock for biofuel production, in terms of greenhouse gas emissions reduction. Multiple environmental measures could be applied in order to
improve the sustainability of olive processing. In this regard, Martinez-Hernandez et al. (2014) indicated how integrated process schemes can be used to develop a sustainable Jatropha-based biorefinery system.

<figure><img src="image_22.png" /><figcaption>**Figure 4.7:** Single scores of damage categories in the olive kernel oil production chain (unit = µPt)</figcaption></figure>

### 4.3.3. Uncertainty of GWP's Results

A unique IA methodology for LCA analysis in food systems does not exist. Different IA methodologies may apply for characterization, and the selection of IA methodology can therefore affect the final LCA results. The characterization index of GWP for refining oil from olive kernel, based on the application of various IA methodologies, is illustrated in Figure 4.8. These results may correspond with the findings of relevant LCA studies on
the same topic. The results revealed that the GWP of the production of 1 kg of olive kernel oil ranges from 1.37 to 1.47 kgCO2eq. The lowest estimation of GWP belonged to IMPACT 2002+, and the highest to the EDPI and EF methodologies. The results are in agreement with the reports by Fathollahi et al. (2018) and Paramesh et al. (2018), which indicated that selection of IA methodology can slightly affect LCA results in some impact categories in the food system. Therefore, it is recommended to consider the impact of IA selection as a source of uncertainty in future research concerning LCA in the food supply chain.

<figure><img src="image_23.png" /><figcaption>**Figure 4.8:** Effect of impact assessment methodology selection on the global warming potential (GWP) results.</figcaption></figure>
## 4.4. Conclusions

This study applied LCA to measure the circularity of a food waste system—in this case, refining oil from olive kernel. The damages of 1 kg of oil production from olive kernel to GWP, human health, ecosystem quality, and resource depletion were 1.37 kg CO2eq, 5.29 × 10⁻⁷ DALY, 1.21 × 10⁻² PDF·m²·yr., and 24.40 MJ, respectively. The results highlighted that the studied system is relatively circular, resulting in low environmental impacts. Olive processing systems could be made more circular through food waste valorization. By managing the consumption of energy sources, such as natural gas, olive kernel oil production systems can become environmentally efficient. In this regard, further research is needed to determine the optimum temperature of olive kernel dough in the decanter, as well as the optimum temperature for warm water when added to the separator in the process of oil extraction from olive kernel. Further research is also required to explore the human health and microbiological risks of the oil produced from olive kernels, which were not considered in this study.

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Chapter 5

LCA of innovative food technologies;

LCA of ohmic heating

Partially redrafted after:

Ghnimi S., Nikkhah, A., Dewulf, J., Van Haute, S. 2021. Life cycle assessment and energy comparison of aseptic ohmic heating and appertization of chopped tomatoes with juice. *Scientific Reports*, *11*, 13041.
## Summary

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 one 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.

## 5.1. Introduction

Tomato (Lycopersicon esculentum) is the second most important vegetable crop after potato, and the total worldwide tomato production was 182 million tons in 2017 (Anwar et al., 2019). There are different tomato-based products, such as purée, paste, juice, chopped tomatoes, and peeled tomatoes in tomato juice, resulting in various processing systems.
The environmental impacts of the agricultural phase of tomato production have been widely investigated in different parts of the world, such as Australia (Page et al., 2012), Albania (Canaj et al., 2020), Colombia (Bojacá et al., 2014), Iran (Khoshnevisan et al., 2014), Italy (Garofalo et al., 2017; Ronga et al., 2019), Spain (Muñoz et al., 2007; Antón et al., 2014; Neira et al., 2018), and Canada (Maham et al., 2020). More information regarding the LCA of the agricultural phase of tomato production systems can be found in Pineda et al. (2020). However, there are a few published documents related to the LCA of different tomato processing systems. Table 5-1 displays a summary of the relevant literature on the LCA of tomato processing systems.

Industrial food processing is the second most notable phase of the food supply chain, accounting for 28% of the total energy use. Moreover, industrial production, together with logistics and packaging (beyond the farm gate), are responsible for approximately half of the total energy consumption in the food chain (Monforti-Ferrario et al., 2015). The EU food industry is making significant contributions to improve energy efficiency while optimizing production processes through different approaches: (i) energy and heat recovery, (ii) selection of renewable energy sources to minimize the impact of energy consumed, and (iii) development and application of new sustainable “green and innovative” techniques in food processing. In comparison to conventional food processing technologies, green and innovative food processing technologies involve less processing time, reduced solvent and energy consumption, and a lower CO₂ footprint (Misra, Koubaa, et al., 2017). The most common innovative thermal and non-thermal technologies in the food industry are ohmic heating, microwave heating, radiofrequency, high-pressure processing, and pulsed electric fields. Ohmic heating provides a rapid and
uniform heating, consequentially reducing thermal damage compared to conventional heating and allows manufacturers to obtain high-quality products with minimum nutritional, sensorial, and structural changes (Ghnimi et al., 2014a). The conversion of electric energy into thermal energy during ohmic heating results in high energy efficiency (i.e., > 90%), which is considerably higher than those achieved by the traditional indirect heating technologies that rely on the burning of fuels, such as appertization, tubular heat exchangers, and plate heat exchangers (Pereira & Vicente, 2010; Ghnimi et al., 2014b; Ramaswamy, Marcotte, Sastry, & Abdelrahim, 2016; Yildiz & Guven, 2014). Aganovic et al. (2017) investigated the energy consumption and environmental life cycle of thermal, high-pressure processing (HPP), and pulsed electric field (PEF) technologies for tomato and watermelon preservation. The results indicated that the tomato juice farm-to-gate environmental impact was higher than that of watermelon juice, and the largest energy uptake was documented for HPP, followed by PEF and traditional thermal processing.

Italy leads tomato production in the EU with a share of 36%; the amount of total tomato production was 5.6 million tons (96% for processing) in 2015 (Costa and Heuvelink, 2018). Reviewing the related relevant literature, it has been highlighted that in-depth research has not yet been conducted on the environmental impacts of chopped tomatoes with juice (CTwJ) processing by conventional processing, including appertization and innovative electro-technology such as ohmic heating. Similarly, for energy efficiency, research studies focus only on specific unit operations and not on the entire processing line to determine energy consumption. Thus, this study aimed to perform an energy efficiency comparison (global line and thermal unit operations) and LCA of ohmic heating and appertization for CTwJ processing.
<table><caption>**Table 5.1: Summary of the literature on the LCA of tomato processing**</caption><thead><tr><th>Product</th><th>The studied region</th><th>Functional unit</th><th>Impact assessment methodology</th><th>Focus of the research</th><th>Environmental hotspots</th><th>Reference</th></tr></thead><tbody><tr><td>Paste and diced tomatoes</td><td>United States</td><td>One kilogram of canned, consumer-ready tomato paste</td><td>Not available</td><td>The environmental consequences of regional and national-scale food systems</td><td>Processing and retail packaging</td><td>Brodt et al. (2013)</td></tr><tr><td>Chopped tomatoes and peeled tomatoes in tomato juice and tomato purée</td><td>Italy</td><td>One kg of packaged product</td><td>CML 2001</td><td>Environmental impacts of various tomato based products</td><td>Agricultural phase and packaging</td><td>Del Borghi et al. (2014)</td></tr><tr><td>Packaged tomato puree</td><td>Northern Italy</td><td>700 g puree jar</td><td>CED, CML 2001 and ReCiPe</td><td>Environmental impacts of various phases of packaged tomato puree production process</td><td>Packaging and agricultural phases</td><td>Manfredi and Vignali (2014)</td></tr><tr><td>Tomato puree</td><td>Northern Italy</td><td>One kg of tomato purée</td><td>ILCD method</td><td>Anaerobic digestion of by-products</td><td>Packaging and agricultural phases</td><td>Bacenetti et al. (2015)</td></tr><tr><td>Tomato juice</td><td>German institute of food Technologies, Germany</td><td>One kg of packaged tomato juice</td><td>Not available</td><td>Thermal, high pressure processing, and pulsed electric fields technologies compression</td><td>Packaging</td><td>Aganovic et al. (2017)</td></tr><tr><td>Packaged peeled tomatoes</td><td>Italy</td><td>1 kg of processed tomato</td><td>ReCiPe.</td><td>Pulsed electric fields technology at an industrial scale</td><td>Canning</td><td>Arnal et al. (2018)</td></tr><tr><td>Fresh and dried organic tomato</td><td>Southern Sweden</td><td>One ton</td><td>ReCiPe midpoint</td><td>Environmental impacts of fresh and dried tomato supply chain</td><td>Agricultural phase, packaging and drying</td><td>Bosona, and Gebresenbet, (2018)</td></tr><tr><td>tomato puree</td><td>Iran</td><td>500 g packaged tomato puree in a steel can with a plastic cap</td><td>CML-IA baseline</td><td>Comparison of different tomato puree production phases</td><td>Packaging</td><td>Shahvarooghi Farahani et al. (2019)</td></tr><tr><td>Tomato-pasta sauce</td><td>The USA</td><td>1 kg product eaten at the consumer level</td><td>ReCiPe 2016</td><td>Cradle to grave environmental impact assessment</td><td>Processing and the agriculture phase</td><td>Parajuli et al. (2020)</td></tr><tr><td>Tomato ketchup</td><td>Austria</td><td>3.8 kg ketchup</td><td>Combination of a few methodologies</td><td>Packaging systems in the light of food waste</td><td>Packaging</td><td>Wohner et al. (2020)</td></tr><tr><td>Chopped tomatoes with juice</td><td>Italy</td><td>One kg of packaged CTwJ</td><td>CML-IA baseline, ILCD 2011 Midpoint, EDIP 2003, EDP 2013, ReCiPe midpoint, and IMPACT 2002+</td><td>Processing upon appertization and ohmic heating systems</td><td>-</td><td>Current Study</td></tr></tbody></table>
**5.2. Materials and Methods**

**5.2.1. Tomato processing**

Tomatoes (Solanum lycopersicum var. Hybrid) were grown in the Italian Puglia region.
They were transported to processing plants located 160 km away and then processed at
a local Italian factory. For the purpose of this study, the industrial appertization and aseptic
ohmic heating units were selected as described in Figures 5.1 and 5.2, respectively. The
preparation processes (grading, washing, peeling, and dicing) were the same for both
industrial lines. The tomatoes were peeled using a steam system (Cavalieri, Italy) under
pressurized steam at 110 °C for 10 s, followed by mechanical separation of the peels. A
linear dicer was used to cut tomatoes (Cavalieri, Italy). The tomatoes were first cut into 2
cm thick slices, and afterward to 1.7 cm-side cubes. Diced tomatoes and juice were mixed
in a tank for approximately 10 min for further processing by appertization or ohmic
heating. A volumetric pump was used to feed the ohmic heating and appertization units.
This pump minimizes product damage and enables moving fluid-solid mixtures containing
approximately 60% of diced tomatoes in 40% tomato juice. Figures 5.1 and 5.2 illustrate
the system boundaries in CTwJ processing and packaging upon appertization and ohmic
heating systems.

**5.2.2. Appertization line**

Diced tomatoes and juice were mixed in a tank for 10 min. They were pre-heated (ca. 60 °C) using direct steam injection before hot filling in tinplate cans. Cans sterilization was done using a tunnel sterilizer (Manzini, Italy) at 95 °C for 30 min with a production rate of 6 t/h of canned diced tomatoes with juice. The steam production system consists of firetube steam boilers (PB 100 model, Mingazzini, Parma, Italy) with a steam output of 10,000 kg/h and rated pressure of 12 bar.
<figure><img src="image_24.png" /><figcaption>**Figure 5.1:** Appertization line with 6 t/h capacity, including pre-processing, canning, sterilization and cooling.</figcaption></figure>
<figure><img src="image_25.png" /><figcaption>**Figure 5.2:** Aseptic Ohmic heating line with 4 t/h capacity, including pre-processing, preheating, sterilization, cooling and aseptic packaging</figcaption></figure>
### 5.2.3. Aseptic ohmic heating line

The process is based on the electrical resistance of the chopped tomato with the juice that is treated. Dissipation of the electrical energy when electric current flows through food leads to heat release (Joule effect) (Ghnimi et al., 2009). Ohmic heating was conducted in an industrial-scale unit (Emmepiemme, Italy), consisting of one stage for pre-heating the CTwJ up to 75 °C and a subsequent stage for heating them up to 102 °C. It then enters the insulated holding tubes before being cooled in tubular heat exchangers (Tetra Pak, Denmark) up to 20 °C. After cooling, the product is pumped into the aseptic storage vessel prior to aseptic packaging in multi-layer plastic pouches. The total electrical power of the ohmic system was 240 kW, and the average product flowrate was 4 t/h. The flowrate was recorded using an electromagnetic flowmeter (EMC, Auckland, New Zealand) with a precision of within 1% of the full range. The bulk temperatures were measured using platinum resistance probes (Pt 100 Ω to 0 °C with a ± 0.1 °C accuracy) placed at the inlet and outlet of each zone. Relative pressure was measured with manometers (JUMO, type 4AP30, Fulda, Germany) at the inlet and outlet of each zone with a precision of 0.1%. The power supply delivers bipolar potential pulses, and electrolysis is prevented by using a high-frequency alternating voltage (Ghnimi et al., 2008).

### 5.2.4. Energy auditing

Energy requirements were experimentally measured through energy auditing. Both processing lines were instrumented to determine thermal and electrical energy inputs and consumption.
The energy requirement for the appertization line encompasses the electricity used by conveyers, pumping systems, and line dicers; thermal energy is required for peeling, pre-heating prior to appertization, sterilization and cooling of the product.

The specific energy required for heating the cans ($E_c$) and their contents ($E_p$) was calculated using equations (5-1) and (5-2), where $n_c$ is the number of cans per cycle, $m_c$ is the weight of empty cans, $m_p$ is the weight of the product per can, $C_{pc}$ is the specific heat of cans, $C_{pp}$ is the specific heat of the product, $T_c$ is the temperature of the cans, $T_p$ is the temperature of the product, and $T_s$ is the steam temperature inside the sterilizer:

$$E_c = n_c \times m_c \times C_{pc} \times (T_s - T_c) \quad (5-1)$$

$$E_p = n_c \times m_p \times C_{pp} \times (T_s - T_p) \quad (5-2)$$

The energy input in the tunnel sterilizer ($E_s$) was calculated using equation (5-3), with $\dot{m}_{vs}$ as the steam mass flowrate, and $h_{vs}$ is the specific enthalpy of steam:

$$E_s = \dot{m}_{vs}h_{vs} \quad (5-3)$$

Ohmic heating occurs due to the electrical conductivity and ability of the electrical current to flow through it. The power clamp meter was connected to the electrical power supply to measure the given electrical power ($P_g$). The fraction of electrical power converted to thermal energy and dissipated in the product was calculated using equation (5-4), with $\dot{m}_{po}$ as the mass flowrate of the product in the ohmic heater, $C_{pp}$ is the specific heat of the product, $T_{pi}$ is the inlet temperature of the product, and $T_{po}$ is the outlet temperature of the product.

$$P_t = \dot{m}_{po} \times C_{pp} \times (T_{po} - T_{pi}) \quad (5-4)$$
The energy required for final cooling prior to aseptic packaging was calculated using equation (5-5), where $\dot{m}_{wc}$ is the mass flowrate of cooling water, $C_{pw}$ is the specific heat of water, $T_{iw}$ is the inlet temperature water, and $T_{ow}$ is the outlet temperature of water in the heat exchanger:

$$P_t = \dot{m}_{wc} \times C_{pw} \times (T_{ow} - T_{iw}) \quad (5-5)$$

### 5.2.5. Software

EDraw Max (ver. 9.1, 2018; Sheung Wan, Hong Kong) was used for the representation of the appertization and ohmic heating flowcharts. The LCA analysis was performed using the professional SimaPro software (ver. 8.1.0 Analyst) and adapted Ecoinvent 3.2 database.

### 5.2.6. LCA application

The objective of this study was to perform an attributional life cycle environmental assessment of CTwJ processing and packaging upon appertization and ohmic heating systems. The functional unit (FU) was considered as one kg of CTwJ, which is a single reference of the product, requiring 1.6 kg of fresh tomato. Mass-based FU is common in food processing LCA (see Table 5.1). Figures 5.1 and 5.2 illustrate the system boundaries in CTwJ processing and packaging for the appertization and ohmic heating systems.

The inputs and outputs of the investigated system were quantified in the second phase of the LCA (Rafiee et al., 2016). The cradle-to-grave emissions were classified into background (off-site) and foreground (on-site) emissions (Paramesh et al., 2018). The background emissions include the emitted pollutants from the production of material inputs, for instance, the emissions released within the generation of electricity and natural gas. However, foreground emissions comprise the direct emissions from the consumption
of inputs in the investigated factory, for instance, emissions released within the combustion of natural gas. The background's emissions coefficients, such as the emissions of electricity generation and distribution, were adapted from the Ecoinvent database.

In the third phase of an LCA study, impact category selection and characterization are mandatory; however, normalization and weighing are optional (Dong and Ng, 2015). The IMPACT 2002+ methodology was applied as the baseline impact assessment methodology, given its inclusion of various impact and damage categories. It is also a combination of hybrid IMPACT 2002, Eco-Indicator 99, CML, and IPCC.

### 5.2.7. Uncertainty analysis of the impact assessment

There are some sources of uncertainties affecting the LCA results, including data quality, scenarios, and mathematical models underlying the impact assessment methods (Rafiee et al., 2016). As shown in Table 6.1, studies on the LCA of tomato processing have employed different impact assessment methodologies. In this chapter of the PhD dissertation, an uncertainty analysis was performed to investigate the effect of impact assessment selection on the LCA results of the case study. For this purpose, six impact assessment methodologies, i.e., EDIP 2003, CML-IA baseline, EDP 2013, ILCD 2011 Midpoint, ReCiPe midpoint, and IMPACT 2002+ were considered to be evaluated by LCA. The analyses were conducted using SimaPro V8.0.3.14.
## 5.3. Results and discussion

### 5.3.1. Energy auditing

An overview of the energy and water requirements for the aseptic ohmic heating and appertization lines is presented in Table 5.2.

<table><caption>**Table 5.2:** Overview of energy and water consumption for producing 1 kg of CTwJ through industrial appertization and aseptic ohmic heating lines.</caption><thead><tr><th></th><th>Appertization line</th><th>Aseptic Ohmic heating line</th></tr></thead><tbody><tr><td><strong>Pre-processing</strong></td><td></td><td></td></tr><tr><td>Electricity, Wh/kg</td><td>0.23</td><td>0.19</td></tr><tr><td>Natural gas, m³/kg</td><td>7.92 10<sup>-4</sup></td><td>2.38 10<sup>-5</sup></td></tr><tr><td>Water, m³/kg</td><td>0.37</td><td>0.34</td></tr><tr><td><strong>Processing</strong></td><td></td><td></td></tr><tr><td>Electricity, Wh/kg</td><td>3.19</td><td>53.97</td></tr><tr><td>Natural gas, m³/kg</td><td>1.48 10<sup>-2</sup></td><td>-</td></tr><tr><td>Water, m³/kg</td><td>1.32</td><td>1.35</td></tr></tbody></table>

The global specific energies for appertization and ohmic heating industrial lines for pre-processing and processing of CTwJ were 156 and 54 kWh/t, respectively. The energy requirement for the appertization line encompasses the electricity consumed by conveyers, pumping systems, and line dicers; the thermal energy required for peeling, pre-heating prior to appertization, and the energy required for sterilization, cooling, and packing of the product. Energy uptake for the ohmic heating includes the electricity used by conveyers, pumping systems, and line dicers; thermal energy required for peeling, ohmic pre-heating and ohmic sterilization; and energy consumption for cooling and aseptic packaging of the final 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 systems are 25% and 77%, respectively. For ohmic heating, the electrical energy input is converted to thermal energy by the Joule effect, where the chopped tomato with juice behaves like a resistor in an electrical circuit. Energy losses in the ohmic heating system are mainly due to the pre-processing step and the cooling of the product during the holding phase between ohmic pre-heating and ohmic heating steps of the product; this temperature gradient was around 4.5 °C. Adding thermal insulation to this holding zone will improve the energy efficiency of the ohmic heating system.

Energy losses in the appertization line are mainly due to the lack of insulation, lack of reuse of steam condensate, and non-condensation of part of the injected steam. Steam condensate could be reused to heat feedwater for the steam boiler, pre-heat utilities, or clean equipment. Another major reason is the non-synchronization of the cans flow rates between the filling and retorting levels, which induces a continuous injection of steam in the retort even with a low load of cans. This situation leads to large energy losses due to the high steam consumption. This low efficiency can be improved by installing a steam regulation in the retort, which will provide the required steam flow depending on the load of the cans.

### 5.3.2. Interpretation of LCA results

Table 5.3 displays the characterization indices for CTwJ production. The global warming potential, ozone layer depletion, and non-renewable energy consumption for 1 kg production of packaged CTwJ for the appertization system were determined to be 4.38 kg CO2eq, 1.34 × 10⁻⁷ kg CFC-11eq, and 52.15 MJ, respectively. However, the aforementioned amounts for 1 kg of packaged CTwJ production upon the ohmic heating
system were 2.52 kg CO₂eq, 4.00 × 10⁻⁸ kg CFC-11eq, and 24.94 MJ, respectively. The results clearly showed that the environmental impacts of CTwJ processing and packaging on appertization were higher than those of ohmic heating systems. In other words, CTwJ production by the ohmic heating system was more environmentally efficient. The global warming potential of tomato sauce production was reported to be 1.5 kg CO₂eq (Parajuli et al., 2020).

<table><caption>**Table 5.3:** Characterization indices for tomato juice production</caption><thead><tr><th>Impact category</th><th>Unit</th><th>Appertization</th><th>Ohmic heating</th></tr></thead><tbody><tr><td>Global warming</td><td>kg CO<sub>2</sub> eq</td><td>4.38</td><td>2.52</td></tr><tr><td>Mineral extraction</td><td>MJ surplus</td><td>67.11</td><td>0.02</td></tr><tr><td>Non-renewable energy</td><td>MJ primary</td><td>52.15</td><td>24.94</td></tr><tr><td>Ozone layer depletion</td><td>kg CFC-11 eq</td><td>1.34×10<sup>-7</sup></td><td>4.00×10<sup>-8</sup></td></tr><tr><td>Ionizing radiation</td><td>Bq C-14 eq</td><td>55.49</td><td>10.96</td></tr><tr><td>Respiratory organics</td><td>kg C<sub>2</sub>H<sub>4</sub> eq</td><td>0.0019</td><td>0.001</td></tr><tr><td>Respiratory inorganics</td><td>kg PM<sub>2.5</sub> eq</td><td>0.01</td><td>0.002</td></tr><tr><td>Non-carcinogens</td><td>kg C<sub>2</sub>H<sub>3</sub>Cl eq</td><td>0.11</td><td>0.03</td></tr><tr><td>Carcinogens</td><td>kg C<sub>2</sub>H<sub>3</sub>Cl eq</td><td>0.08</td><td>0.03</td></tr><tr><td>Aquatic ecotoxicity</td><td>kg TEG water</td><td>1943.03</td><td>505.51</td></tr><tr><td>Terrestrial acid/nutri</td><td>kg SO<sub>2</sub> eq</td><td>0.15</td><td>0.029</td></tr><tr><td>Terrestrial ecotoxicity</td><td>kg TEG soil</td><td>526.11</td><td>161.90</td></tr><tr><td>Aquatic acidification</td><td>kg SO<sub>2</sub> eq</td><td>0.06</td><td>0.01</td></tr><tr><td>Aquatic eutrophication</td><td>kg PO<sub>4</sub> P-lim</td><td>0.0006</td><td>0.0003</td></tr><tr><td>Land occupation</td><td>m<sup>2</sup>org.arable</td><td>0.20</td><td>0.09</td></tr></tbody></table>

Figures 5.3 and 5.4 illustrate the share of inputs in environmental burdens of CTwJ production upon appertization and ohmic heating systems, respectively. The results indicated that the tin production phase was the hotspot in CTwJ production by the appertization system. The agricultural phase was the second main contributor to the most impacted categories. More specifically, the agricultural phase of tomato production and
tin packaging accounted for 54.33% and 45.00% of the total global warming potential of CTwJ production, respectively. The results are in line with the study of Manfredi and Vignali (2014), which indicated that packaging was the main contributor to most impact categories for tomato purée production in a glass jar. Arnal et al. (2018) also highlighted that canning was the largest contributor to the total industrial-scale environmental impacts of packaged peeled tomato production systems. Packaging was reported to be responsible for the environmental impacts within all selected impact categories of tomato purée production, except for acidification and eutrophication (Shahvarooghi Farahani et al., 2019). Del Borghi et al. (2014) showed that packaging and agricultural phases had the highest adverse impacts on the environment during the production of 13 different tomato processed products. Many studies have also reported that the packaging phase is an environmental hotspot during the production of some other processed food products, such as dairy products (Djekic et al., 2014) and canned sardine (Almeida et al., 2015).

The results highlight the important contributions of the agricultural phase in the ohmic heating system in the most impacted categories. The agricultural phase accounted for 94.44% of the total global warming potential of CTwJ production by the ohmic heating system. In other words, the consumption of inputs, such as diesel fuel and chemical fertilizer cause a huge negative environmental impact during the CTwJ production supply chain.
<figure><img src="image_26.png" /><figcaption>**Figure 5.3:** Inputs contribution to the environmental impact of tomato juice production upon appertization</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>**Figure 5.4.** Inputs contribution to the environmental impact of tomato juice production upon ohmic heating</figcaption></figure>
Figure 5.5 and 5.6 show the normalized damage assessment of CTwJ processing and packaging upon appertization and ohmic heating systems, respectively. The largest adverse environmental impact belonged to the human health damage category upon CTwJ production in both systems. The direct emissions of fossil fuels during the tomato production supply chain play a key role in the human health damage category. The resources damage category was placed as the second damage category with higher adverse environmental impacts in packed CTwJ production.

<figure><img src="image_28.png" /><figcaption>**Figure 5.5:** Normalized values of input damages in CTwJ processing and packaging for the appertization system</figcaption></figure>
<figure><img src="image_29.png" /></figure>

**Figure 5.6:** Normalized values of inputs damages in CTwJ processing and packaging for the ohmic heating system

Figure 5.7 illustrates the normalized damage assessment of CTwJ processing and packaging for appertization and ohmic heating systems. The normalized damage assessment of CTwJ processing and packaging for the appertization system was higher than the ohmic heating system in all impact categories. So, the packed CTwJ for the ohmic heating system was more environmentally friendly. An LCA study on tomato processing also indicated that the application of PEF technology could mitigate environmental impacts (Arnal et al., 2018).
<figure><img src="image_30.png" /><figcaption>**Figure 5.7:** Normalized damage assessment of tomato juice processing and packaging for appertization and ohmic heating systems</figcaption></figure>

### 5.3.3. Uncertainty analysis of the impact assessment

Table 5.4 illustrates the indicators of CTwJ production for different impact assessment methodologies. The obtained results help to compare the results of this study with those published on LCA tomato-based products. The results showed that the global warming potential of 1 kg of packaged CTwJ ranges from 4.13 to 4.44 kg CO2eq, within the appertization line. In addition, the global warming potential of the ohmic heating system ranges from 2.50 to 2.54 kg CO2eq. The results also indicated that the ozone layer depletion for the production of 1 kg of packaged CTwJ ranges from 1.33 × 10⁻⁷ to 1.35 ×
10⁻⁷ kg CFC-11eq; however, this value for ohmic heating systems ranges from 3.99 × 10⁻⁸ to 4.03 × 10⁻⁸.

<table><caption>**Table 5.4:** Total characterized indicators of CTwJ within the selected impact assessment methodologies</caption><thead><tr><th rowspan="2">Impact assessment</th><th colspan="2">Global warming<br>(kg CO<sub>2</sub>eq)</th><th colspan="2">Ozone layer depletion<br>(kg CFC-11 eq)</th></tr><tr><th>Appertization</th><th>Ohmic heating</th><th>Appertization</th><th>Ohmic heating</th></tr></thead><tbody><tr><td>CML-IA baseline</td><td>4.44</td><td>2.50</td><td>1.34×10<sup>-7</sup></td><td>4.00×10<sup>-8</sup></td></tr><tr><td>EDIP 2003</td><td>4.44</td><td>2.50</td><td>1.34×10<sup>-7</sup></td><td>4.00×10<sup>-8</sup></td></tr><tr><td>EDP (2013)</td><td>4.44</td><td>2.50</td><td>1.34×10<sup>-7</sup></td><td>4.00×10<sup>-8</sup></td></tr><tr><td>ILCD 2011 Midpoint</td><td>4.44</td><td>2.50</td><td>1.33×10<sup>-7</sup></td><td>3.99×10<sup>-8</sup></td></tr><tr><td>ReCiPe midpoint</td><td>4.13</td><td>2.54</td><td>1.35×10<sup>-7</sup></td><td>4.03×10<sup>-8</sup></td></tr><tr><td>IMPACT 2002+</td><td>4.38</td><td>2.52</td><td>1.34×10<sup>-7</sup></td><td>4.00×10<sup>-8</sup></td></tr></tbody></table>

### 5.3.4. Mitigation strategies

As previously mentioned, packaging and agricultural phases were the environmental hotspots in both systems. One solution to reduce the environmental burdens of tomato product processes is related to the packaging phase; it could be weight reduction and transitioning to packaging materials with less environmental impacts, such as bio-based packaging (Del Borghi et al., 2014). Therefore, the mitigation of the environmental impacts of tomato cultivation, replacing the packaging materials with bio-based materials, and using the by-products of cultivation and processing for supplying a part of the energy requirement can be considered as the main strategy for environmental impact mitigation of the CTwJ production supply chain. In the case of agricultural phase of CTwJ supply chain, Muñoz et al. (2007) highlighted that the environmental impacts of 1 kg of tomato production in a greenhouse is less than that of an open field system due to the efficient use of water, fertilizers, and pesticides. A study compared three tomato farming systems (open field, greenhouse, and hydroponic) in terms of energy use patterns and concluded
that the hydroponic system was the most environmentally friendly system (Platis et al., 2021). Bojacá et al. (2014) believed that the implementation of integrated pest management programs could mitigate the environmental impacts of Colombian greenhouse tomato production. Bacenetti et al. (2015) compared the two scenarios of the tomato purée production supply chain. In the first scenario, tomato by-products were sent back to the farms as bio-fertilizers; in the second scenario, the by-products were used in terms of biogas generation. The results showed that the second scenario was more environmentally efficient. A study focusing on the valorization of tomato by-products (tomato seeds and peels) highlighted the potential application of whole tomato by-products for valuable compound recovery and sequential low-cost biosorbent production (Azabou et al., 2020).

Moreover, there are some measures which can be taken into consideration in order to mitigate the environmental impacts of CTwJ processing. For instance, a study focusing on the valorization of tomato by-products (tomato seeds and peels) highlighted the potential application of whole tomato by-products for valuable compound recovery and sequential low-cost biosorbent production (Azabou et al., 2020). Winans et al. (2020) applied LCA to study the diced tomatoes and paste production systems in California over a 10-year timeframe. They showed that the introduction of renewables in the life cycle of the production systems, such as solar-powered irrigation pumps, and on-site solar energy generation for facilities can mitigate the GWP impacts by 9–10%.

Given this study was conducted on an industrial scale, the effect of influencing variables in the manufacture process of CTwJ production was not optimized. Therefore, further research is needed to optimize the processes in terms of energy and
environmental impacts. In addition, replacing the packaging materials with bio-based
materials, and using the by-products of cultivation and processing for supplying a part of
the energy requirement, on the final energy and environmental impacts of CTwJ
production should be further explored.

5.4. Conclusions

This chapter of this dissertation is the first study to compare the energy requirement and life cycle environmental impact of a novel food processing technology (ohmic heating) with a conventional method (appertization) in the tomato processing industry, considered one of the largest food processing industries worldwide. Moreover, uncertainty analysis was performed through the application of six different impact assessment methodologies.

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 was 102 kWh/t of the product (or 65% energy saving).
The energy efficiencies of the appertization and ohmic heating systems are 25% and
77%, respectively. There are opportunities for energy optimization of the investigated
processes while maintaining the potential quality benefit. In the appertization system, a
more energy-efficient process could be obtained by reducing steam non-condensation
and installing a steam regulation, which will give the necessary steam flow depending on
the load of the cans. In the case of ohmic heating, adding thermal insulation to the holding
zone will improve the energy efficiency of the system.

From the LCA perspective, the uncertainty analysis results suggested that the global
warming potential of the production 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 2.54 to 2.78 kg CO2eq. Overall, CTwJ production by the ohmic heating system was
more environmentally efficient than traditional retort canning.

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Chapter 6

LCA of future food; edible insects

Partially redrafted after:

Nikkhah, A., Van Haute, S., Jovanovic, V., Jung, H., Dewulf, J., Cirkovic Velickovic, T., Ghnimi, S. 2021. Life Cycle Assessment of Edible Insects (Protaetia brevitarsis seulensis larvae) as a future protein and fat source. *Scientific Reports-Nature*. (third round of revision).
**Summary**

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 uncertainty analysis. The protein, fat contents, and fatty acid profile of the investigated insect (Protaetia brevittarsis 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.

**6.1. Introduction**

Owing to the increasing demand for meat, there is a need for discovering alternative sources of protein (Van Huis, 2015). Moreover, many studies have shown that most common food production systems, such as beef (Biswas and Naude, 2016; Perez-Martinez et al., 2018), chicken (Kalhor et al., 2016; Skunca et al., 2018; López-Andrés et al., 2018), pork (Reckmann et al., 2013; Winkler et al., 2016), fish (Buchspies et al., 2011; Dekamin et al., 2015), and plant-based products (bean, corn, soybean, and wheat) (Ghasempour and Ahmadi, 2019; Heusala et al., 2020) are not environmentally efficient, and that the existing production systems of protein sources have enormous environmental disadvantages.
Hence, the development of new alternative sustainable food sources is very important. In recent years, insects have been recommended as a novel food source for human consumption and the amount of insect production units has been increasing worldwide. Insect production can be considered as a solution for two problems, namely, the increasing demand for food by the production of edible insects, and waste management by composting food waste (Salomone et al., 2017). The following sections discuss the current status of edible insect production, small scale insect production systems, and the literature review on sustainability assessment of edible insect production.

### 6.1.1. Current status of edible insect production

Approximately 2,000 insect species are consumed as food around the world, particularly in tropical countries (Van Huis, 2015). Based on the European Food Safety Scientific Committee report, nine different insect species are currently recorded as being farmed for feed and food production (Halloran et al., 2017). For instance, there are approximately 20,000 cricket farms in Thailand, which produce 7,500 metric tons of insects per year which are used for domestic consumption and the rest for market (Van Huis et al., 2015). Currently, two billion people across the world eat insects, and insects are being consumed as food in approximately 80 countries (Lombardi et al., 2019).

In South Korea (one of the main consumers), edible insects were previously more widespread in the populations' nutrition. Under the economic development plan implemented in the 1970s, the production and also the consumption of edible insects decreased. In recent years, the consumption is on the rise again, and the value of the edible insect market in south Korea has increased from 143 million in 2011 to 259 million in 2015 (Shin et al., 2018).
There are many small scale edible insect startups and production units around the world. For example, most insect producers in Thailand are small and medium size enterprises which require relatively low land usage and capital investment (Durst and Hanboonsong, 2015). Moreover, environmental issues are a major factor with regard to the sustainable development of food production systems. Thus, this study applied Life Cycle Assessment (LCA) to estimate the environmental impact of small scale edible insect production in South Korea, as an example of small scale edible insect production and the nutritional value was assessed with a focus on proteins and fats.

## 6.1.2. Literature review

Various studies have been conducted with regard to the LCA of the production of insects for use as feed (Van Zanten et al., 2014; Le Feon et al., 2019; Thévenot, et al., 2018; Roffeis et al., 2020). However, in recent years, researchers have focused on the environmental life cycle impacts of edible insect production systems. Table 6.1 shows the summary of the relevant literature on the LCA of edible insect production systems. Oonincx and De Boer (2012) compared the protein production from two species of mealworms (Tenebrio molitor and Zophobasmorio) with conventional sources of protein like beef, milk, chicken, and pork. The results revealed that lower GHG emissions and land use are required for mealworm production, while the required amount of energy is similar to that of conventional and mealworm protein production systems. Thus, they concluded that mealworms are a more sustainable source of edible protein. Halloran et al. (2017) compared the environmental impact of cricket farms to that of chicken farms in Thailand. The results revealed that protein from insects is more environmentally efficient compared with that of chicken. One study reported that the direct CO2eq emissions from
bio-waste conversion using insects (black soldier flies) were 47 times lower compared with the emissions produced by an open windrow composting facility (Mertenat et al., 2019). Smetana et al. (2019) reported that the insect biomass is twice more environmentally efficient compared with that of chicken meat.

As an example, this study investigated the environmental sustainability of small scale edible insect production in South Korea using LCA methodology. The current study is the first research on the environmental impacts of *Protaetia brevitaris seulensis* (PBS). In addition, fatty acid profile, protein and fat contents of PBS were determined to assess the nutritive value of PBS edible insect.

<table><caption>**Table 6.1:** The relevant literature on LCA of edible insect-based food</caption><thead><tr><th>Insect species</th><th>The studied region</th><th>Functional unit</th><th>Impact assessment methodology</th><th>Focus of the research</th><th>Environmental hotspots</th><th>Reference</th></tr></thead><tbody><tr><td>Not available</td><td>Germany</td><td>One kg of dried defatted insect powder and 1 kg of ready for consumption fresh product without packaging</td><td>ReCiPeV1.08 and IMPACT 2002+</td><td>Compare insect-based food product with other food products</td><td>Feed production</td><td>Smetana et al. (2016)</td></tr><tr><td>Hermetia illucens (Black Soldier Fly)</td><td>Italy</td><td>One ton of food waste, one kg of protein and lipid</td><td>CML 2 baseline 2000, IPCC 2007, Cumulative energy demand method, and CML 2001</td><td>Food waste bioconversion by insect</td><td>Electricity consumption and transportation</td><td>Salomone et al. (2017)</td></tr><tr><td>Mealworm</td><td>Finland</td><td>One kg of mealworms</td><td>Not available</td><td>GWP of the future potential industrial scale</td><td>Feed crop production and direct heating energy</td><td>Joensuu and Silvenius, (2017)</td></tr></tbody><tfoot><tr><td colspan="7">Continued on next page</td></tr></tfoot></table>
<table><caption>Table 6.1 – continued from previous page</caption><thead><tr><td>Insect species</td><td>The studied region</td><td>Functional unit</td><td>Impact assessment methodology</td><td>Focus of the research</td><td>Environmental hotspots</td><td>Reference</td></tr></thead><tbody><tr><td>Gryllusbimaculatus De Geer (field cricket) and Acheta domesticus (house cricket)</td><td>Thailand</td><td>One kg of edible mass and one kg of protein</td><td>ILCD</td><td>Comparing environmental impacts of insect with chicken</td><td>Feed production</td><td>Halloran et al. (2017)</td></tr><tr><td>Hermetia illucens (Black Soldier Fly)</td><td>Indonesia</td><td>One ton of bio-waste</td><td>IPCC 2013 100a and ReCiPe Midpoint Hierarchist (H)</td><td>Bio-waste conversion using insect</td><td>Electricity consumption</td><td>Mertenat et al. (2019)</td></tr><tr><td>Hermetia illucens (Black Soldier Fly)</td><td>Not available</td><td>One kg of dried and pelletized organic fertilizer, one kg of fresh insect used as pet food, one kg of protein, and one kg of insect fat used as feed</td><td>IMPACT2002+</td><td>Sustainably of insect production as feed and food</td><td>Feed production and energy use</td><td>Smetana et al. (2019)</td></tr><tr><td>Black soldier fly (Hermetia illucens) and mealworm (Tenebrio molitor)</td><td>Poland</td><td>One kg</td><td>IMPACT2002+</td><td>Insect margarine</td><td>Raw materials consumption</td><td>Smetana et al. (2020)</td></tr><tr><td>Protaetia brevitarsis seulensis</td><td>South Korea</td><td>One kg of dried insect, protein, and fat</td><td>IMPACT 2002+, CML-IA baseline, EDIP 2003, EDP 2013, ILCD 2011 Midpoint, ReCiPe midpoint, and IMPACT 2002+</td><td>Protaetia brevitarsis seulensis larvae as a future protein and fat source</td><td>-</td><td>Current Study</td></tr></tbody></table>
**6.2. Materials and methods**

Figure 6.1 shows the LCA of PBS production. Accordingly, the inputs and yield of edible insects in South Korea were determined. Then, the obtained data were used to conduct cradle-to-gate environmental impact evaluation for the production systems. As a strong and standardized methodology, LCA was used to conduct the environmental consequences of edible insect as a future protein and fat source.

<figure><img src="image_31.png" /><figcaption>**Figure 6.1:** Life cycle assessment of *Protaetia brevittarsis seulensis* production.</figcaption></figure>

**6.2.1. Insect production system**

This study was conducted at Gwangmyeong-si in South Korea, which is located in the Mid-West region of Gyeonggi-do, and at a metropolitan area in Korea (the central part of the Korean Peninsula). The investigated region consists of 38.8% of mountainous area and 28.9% of arable land, where in rice was predominantly cultivated but as the agricultural population decreased, the agricultural activities shifted to the production of
high-value vegetable and fruit crops. Gwangmyeong-siles in an average agro-climatic
zone in South Korea with four distinct seasons, and average rainfall of 1,556 mm per
annum; the temperature ranges from an average low of -1.1 °C to an average high of 25.9
°C.

The investigated insect species is PBS larvae. PBS larvae is one of the five species
which are consumed in South Korea (Ghosh et al., 2017). The investigated production
unit uses mushroom waste to feed insects, and banana waste to feed immature insects.
Temperature is kept around 25 °C throughout the year. However, the relative humidity of
the farm was not managed during the process. The volume of the breeding box was 36
liters (600mm × 450mm × 200mm). On average, the investigated insects lay eggs every
seven to ten days. PBS has four life stages: egg, larva, pupa, and adult. It takes 10 weeks
for an egg to become a larva and then it is ready to be collected. The investigated farm
was a small insect farm with the capacity of 960 kg larvae (dry basis) production per year.
The insects produced in the studied system are available legally for consumption on
Korean markets. The investigated insect species (PBS) is relatively expensive in Korea
because of its medicinal properties. Korean food law has limits in PBS larvae on the
presence of heavy metals (lead, cadmium and arsenic) and microbial indicators of
hygiene/food safety (coliforms, E. coli) (Ministry of Food and Drug Safety, 2019).

**6.2.2. Sample preparation**

Air dried PBS larvae were collected from the insect farm located in Gwangmyeong-si,
South Korea. The dried sample was homogenized using mortar and a pestle, and stored
in a plastic box at -20°C until further analysis.
**6.2.3. Determination of protein and fat content**

The fat and protein content of PBS larvae were determined using standard methods. The nitrogen (N) and proteins were investigated by the Kjeldahl method (ISO 1871, 2009). The protein content was determined through multiplying the N content by the coefficient of 6.25.

The fat content was determined after ethyl ether extraction in a Soxhletapparatus for six hours. Subsequently, the ethyl ether was removed through a rotary evaporator. After that, the extracted sample was weighed until a constant sample weight was reached.

**6.2.4. Fatty acid methyl esters preparation**

The fatty acid composition was calculated by the GC/EI-MS of the fatty acid methyl esters (FAME), which were prepared by transmethylation based on the following procedure. In short, 25 mg of dried PBS larvae powder were measured in a Pyrex® test tube with a Teflon lined screw cap. Next, 3.3 mL of methanol/hydrochloric acid (2M in methanol) mixture were added into the tube. After vigorous vortexing for 5 to 10 s, 0.3 mL of chloroform, which contained an internal standard (13:0) and antioxidant (BHT), were added and the tube was tightly sealed. After vigorous vortexing for 30 s, the tube was heated at 90°C for 2 h. When it gets cooled off to room temperature, the FAME were extracted by adding 0.9 mL of miliQ water into the tube. The mixture in the tube was vortexed for 5 to 10 s, and then 1.8 mL of n-hexane were added and vortexed again for 20 to 30 s. The n-hexane layer containing the FAME was separated by centrifugation for 5 min at 4000 rpm. The uppern-hexane phase was drawn off and transferred to a sample vial for GC/EI-MS analysis. The preparation of FAME was performed in two duplicates.
**6.2.5. Analysis and identification of fatty acids using GC/EI-MS**

The analysis of FAME was done according to Ristivojević et al. (2021). In short, an Agilent 6890 gas chromatograph equipped with a DB-23 capillary column (30 m×0.25 mm id;film thickness of 0.25 µm) was used (Agilent Technologies Inc., Santa Clara, CA, USA). The capillary column was directly joined to an Agilent 5973 mass spectrometer (Agilent Technologies Inc.). The sample (1 µL) was injected into the capillary column with a split ratio of 10:1. Helium (purity of 5.0) was applied as the carrier gas with a flow rate of 0.6 mL/min. The temperatures of the detector and the injector were set to 230°C and 250°C, respectively.

The FAME were determined through comparing their retention times with those of the FAME standards (Supelco-37 FAME mix) under the same conditions, and through comparing their mass spectra with those stored in the Mass Spectral Library of the National Institute of Standards and Technology (NIST).

**6.2.6. Objective**

The objective of this study was to conduct an attributional life cycle environmental impact analysis of the small scale edible insect production in South Korea and assess the nutritional value of the investigated insect. Different functional units (FUs) for insect production systems have been considered for LCA, which means that all inputs and impact categories (ICs) in the assessment are normalized. As presented in Table 6.1, the mass-based FU is commonly used in LCA studies on edible insects. Therefore, in this study, 1 kg of dried insects was selected as the FU. Additionally, 1 kg of protein in insects as well as 1 kg fat were considered as the secondary FUs for comparing the environmental consequences of protein production from insects with other conventional
protein and fat sources of human nutrition. The system boundary of this research was the
cradle to farm gate insect production system, including inputs (electricity, water,
mushroom production wastes, banana peels, and transportation) as well as on-farm
operations (see Figure 6.1).

**6.2.7. Inventory analysis**

The cradle-to-gate environmental impact for edible insect production was evaluated using LCA. Table 6.2 presents the main primary inventory data for small scale edible insect production. The emitted pollutants were classified as the cradle-to-gate (background) and gate-to-gate (foreground) emissions. The emitted pollutants in the background phase (production of input materials) were adapted from the Ecoinvent 3.0 database using the Simapro 8.2.3.0 software. The foreground (on-site) emissions were the pollutants emitted during the composting process, such CO2, N2O, and CH4. The amounts of emitted pollutants during composting process were calculated based on EPA, 2010. Feed is a major factor with regard to the total environmental impacts of insect production either as a burden or as avoided impacts in case of waste treatment (Smetana et al., 2021). In this study, the emissions within composting process were included as on-site emissions (Table 6.2), and the bio-waste treatment was considered as an avoided product.
<table><caption>**Table 6.2:** Main primary inventory data for small scale *Protaetia brevitarsis seulensis* production.</caption><thead><tr><th>Inputs-outputs</th><th>Unit</th><th>Quantity</th></tr></thead><tbody><tr><td><em>Inputs</em></td><td></td><td></td></tr><tr><td>-Bio-waste (mushroom waste)</td><td>kg</td><td>3600</td></tr><tr><td>-Bio-waste (banana waste)</td><td>kg</td><td>300</td></tr><tr><td>-Water</td><td>m<sup>3</sup></td><td>324</td></tr><tr><td>-Electricity</td><td>kWh</td><td>357</td></tr><tr><td>-Transportation of bio-waste to insect farm</td><td>kg×km</td><td>180000</td></tr><tr><td>-Transportation of final product</td><td>kg×km</td><td>12000</td></tr><tr><td><em>Outputs</em></td><td></td><td></td></tr><tr><td>-Dried insect</td><td>kg</td><td>120</td></tr><tr><td>-Compost</td><td></td><td></td></tr><tr><td>-CO<sub>2</sub></td><td>kg</td><td>475.2</td></tr><tr><td>-CH<sub>4</sub></td><td>kg</td><td>14.4</td></tr><tr><td>-N<sub>2</sub>O</td><td>kg</td><td>1.08</td></tr></tbody></table>

### 6.2.8. Impact assessment and uncertainty analysis

The impact assessment (IA) methodologies of previous studies on the LCA of insects are presented in Table 6.1. The selection of the IA methodology may significantly influence the obtained results of every LCA study on food production systems. In this study, IMPACT 2002+ was employed as the baseline IA methodology owing to its various impact (15 impact categories) and damage categories. IMPACT 2002+ divides the 15 impact categories into four damage categories (endpoints level), i.e., climate change, resource depletion, human health, and ecosystem quality (Jolliet et al., 2003). This IA methodology is the hybrid application of IMPACT 2002, Eco-Indicator 99, CML, and IPCC. Additionally,
five other IA methodologies, namely, the ReCiPe midpoint, CML-IA baseline, EDIP 2003, EDP 2013, and ILCD 2011 Midpoint, were also evaluated for comparison with the baseline IA methodology, that is, IMPACT 2002+. The above-mentioned impact categories were compared in terms of the characterization indices of global warming potential and ozone layer depletion since those are the mutual impact categories considered by the six studied impact assessment methodologies.

**6.3. Results and discussion**

**6.3.1. Protein and fat content of *Protaetia brevitarsis seulensis***

Because one of the objectives of this study was to compare the environmental effects of insect protein with those of protein produced from conventional human nutrition sources, the protein content of larvae was determined and the LCA results are expressed per kg of insect protein. The protein and fat contents of the dried larvae of PBS were determined as 50.5% and 13.5%, respectively. The results are in good agreement with the results of previous studies, and revealed that the dried larvae contained more than 50% protein and between 10% and 25% fat (Yeo et al., 2013; Kim et al., 2017). The insects' protein content is generally similar to that of beef, pork, and chicken, and contains more polyunsaturated fatty acid with higher contents of various minerals, such as zinc and iron (Van Huis et al., 2015). There might be an overestimation of protein content measurement of the studied edible insect since the conversion rate of nitrogen to protein was considered as 6.25. In this regard, Boulos et al. (2020) claimed that the application of this conversion rate may lead to an average 17% overestimation of protein contents. But it is not considered in this PhD dissertation since the above-mentioned study was done on mealworms, crickets, and locusts, and there is not such a data available for PBS.
PBS *larvae* is one of the seven types of dried edible insects that are currently available in the Korean market. The larvae stage of PBS is also currently being used in traditional Chinese medicine (Suh Kang, 2012) since it may produce therapeutic effects for the treatment and prevention of various types of diseases (inflammatory disease, liver cirrhosis, and hepatitis) and cancers (hepatic and breast cancer) (Yoo et al., 2007; Kang et al., 2012). Therefore, in the near future, it can be used as a potential source of protein and fat.

The GC-MS (Gas Chromatography Mass Spectrometry) analysis of the fatty acid profile revealed the presence of 18 fatty acids, whose spectra overlap with the spectra from the NIST base (Table 6.3) with a probability of more than 93%. Amongst the 18 identified FAs, six of them were saturated FAs (SFA) (two of them has an odd number of C atoms:C15:0 and C17:0); eight were unsaturated FAs (four monounsaturated FAs (MUFA) and four polyunsaturated FAs (PUFA)); four were methyl FAs. The most abundant FA was oleic acid (60.38%), which along with palmitic, palmitoleic, and linoleic acid contributed to 90% of the total FA content. These results are in agreement with the results reported by Yeo et al. (2013).

The contribution of MUFA to the total FA content was 71.70%, which resulted in a very high MUFA value in the calculated SFA:MUFA:PUFA ratio of 3.3:12:1. This exhibits a discrepancy with the recommended SFA:MUFA:PUFA ratio for a healthy nutrition(1.25:1.5:1). Therefore, it can be concluded that fats from PBS are a good source of MUFA. Moreover, according to the literature, it is known that MUFAs promote a healthy blood lipid profile and improve blood pressure, insulin sensitivity, and glycemic control (Garg, 1998; Kris-Etherton et al., 1999; Ros et al., 2013).
The effectiveness of PBS larvae in traditional medicine for the treatment and prevention of various diseases (inflammatory disease, liver cirrhosis, and hepatitis) and cancers (hepatic and breast cancer) can be explained by the presence of various FAs, such as palmitic (16.28%), palmitoleic (8.32%), and oleic acid (60.38%) at a very high concentration in the fats of these larvae. Additionally, palmitoleic acid has been associated with increased insulin sensitivity and decreased lipid accumulation in the liver (Cao et al., 2008). Yoo et al. (2007) demonstrated that the dichloromethane extract from PBS, which contains FAs (palmitic and oleic acid), has anti-cancerogenic effects. For the first time, our study revealed the presence of four methyl-FAs. Branched-chain FAs are common constituents of bacteria and animal lipids. Amongst them, 13-methyltetradecanoic acid is the most abundant (1.28%), and has been well known to induce the apoptosis or programmed cell death of certain human cancer cells (Klein et al., 2013; Cai et al., 2013). According to the total fat, protein content, and FA analysis results, it can be concluded that the PBS larvae fed with banana waste can be used as a potential source of protein and fat.

<table><caption>**Table 6.3:** Fatty acid profile of *Protaetia brevittarsis seulensis* larvae; values are expressed as a mean value ± SD.</caption><thead><tr><th></th><th>Retention time (min)</th><th>Fatty acid</th><th>Content (% of total fatty acids)</th></tr></thead><tbody><tr><td>1</td><td>9.720</td><td>Myristic acid</td><td>C14:0 0.58±0.003</td></tr><tr><td>2</td><td>10.200</td><td>13-Methyltetradecanoic acid</td><td>C14:0 13 methyl 1.28±0.001</td></tr><tr><td>3</td><td>10.664</td><td>Pentadecanoic acid</td><td>C15:0 0.11±0.005</td></tr></tbody><tfoot><tr><td colspan="4">Continued on next page</td></tr></tfoot></table>
<table><caption>**Table 6.3** – continued from previous page</caption><thead><tr><th></th><th>Retention time (min)</th><th>Fatty acid</th><th>Content (% of total fatty acids)</th></tr></thead><tbody><tr><td>4</td><td>11.200</td><td>14-Methylpentadecanoic acid</td><td>0.49±0.034</td></tr><tr><td>5</td><td>11.718</td><td>Palmitic acid</td><td>16.28 ± 0.143</td></tr><tr><td>6</td><td>12.148</td><td>Palmitoleic acid</td><td>8.32±0.008</td></tr><tr><td>7</td><td>12.250</td><td>11-cis-Hexadecenoic acid</td><td>1.17±0.052</td></tr><tr><td>8</td><td>12.352</td><td>15-Methyhexadecanoic acid</td><td>0.50±0.018</td></tr><tr><td>9</td><td>12.550</td><td>14-Methylhexadecanoic acid</td><td>0.27±0.018</td></tr><tr><td>10</td><td>12.900</td><td>Heptadecanoic acid</td><td>0.10±0.012</td></tr><tr><td>11</td><td>13.300</td><td>6-cis-9-cis-12-cis-Hexadecatrienoic acid</td><td>0.31±0.010</td></tr><tr><td>12</td><td>14.200</td><td>Stearic acid</td><td>1.69±0.004</td></tr><tr><td>13</td><td>14.600</td><td>Oleic acid</td><td>60.38±0.021</td></tr><tr><td>14</td><td>14.725</td><td>11-cis-Octadecenic acid</td><td>1.83±0.023</td></tr><tr><td>15</td><td>15.317</td><td>Linoleic acid</td><td>4.92±0.056</td></tr><tr><td>16</td><td>15.800</td><td>γ-Linolenic acid</td><td>0.19±0.004</td></tr><tr><td>17</td><td>17.000</td><td>Arachidic acid</td><td>0.50±0.003</td></tr><tr><td>18</td><td>19.050</td><td>Arachidonic acid</td><td>0.31±0.007</td></tr></tbody></table>

### 6.3.2. LCA results

Table 6.4 presents the characterization indicators of edible insect production in South Korea. The results revealed that the investigated edible insect production system has beneficial environmental effects on certain ICs, such as land occupation, mineral extraction, aquatic and terrestrial ecotoxicity (4 ICs out of 15). In other words, this food production system can mitigate the environmental impacts of the abovementioned ICs,
due to utilization of biowaste (mushroom production waste and banana peels) to feed insects by turning something harmful for environment into compost. Previous studies on the LCA of chicken (Kalhor et al., 2016; Skunca et al., 2018; López-Andrés et al., 2018), beef (Biswas and Naude, 2016; Perez-Martinez et al., 2018), milk (Soltanali et al., 2015; Depping et al., 2017; Üçtuğ, 2019), and crop production (Habibi et al., 2019) have shown that the production of these protein sources has negative environmental effects on all investigated ICs.

Moreover, negative environmental effects on some ICs, namely, ozone layer depletion, non-renewable energy, aquatic eutrophication, ionizing radiation, carcinogens, aquatic acidification, non-carcinogens, respiratory inorganics, respiratory organics, terrestrial acid/nutria, and global warming, have been observed. The environmental impacts of 1 kg of dried insect production on global warming, ozone layer depletion, and renewable energy were calculated as 8.05 kgCO2eq, 1.58 × 10-7 kg CFC-11 eq, and 32.46 MJ, respectively. Moreover, the values of the abovementioned ICs for 1 kg of protein produced from insects were 15.93 kgCO2eq, 3.12 × 10-7 kg CFC-11 eq, and 64.63 MJ, respectively. The same values for 1 kg of fat produced from insects were calculated 59.60 kgCO2eq, 1.17 × 10-6 kg CFC-11 eq, and 241.75 MJ, respectively (Table 6.4). The GWP of farming 1 kg of insects and producing 1 kg of protein from insects in Thailand has been reported as 4.0 and 3.9 kgCO2eq, respectively (Halloran et al., 2017). The GWP of 1 kg of protein and lipids produced from the *Hermetia illucens* insect has been reported as 2.1 and 2.9 kgCO2eq, respectively (Salomone et al., 2017). Some insect species (*Hermetia illucens* and *Tenebrio molitor*) have been shown a promising potential to be used as an
alternative for animal and plant-based lipids products, such as butter or margarine (Smetana et al., 2020).

<table><caption>**Table 6.4:** Characterization indices of *Protaetia brevitars seulensis* production.</caption><thead><tr><th rowspan="2">Impact category</th><th rowspan="2">Unit</th><th colspan="3">Quantity</th></tr><tr><th>Per kg biomass</th><th>Per kg protein</th><th>Per kg fat</th></tr></thead><tbody><tr><td>Global warming</td><td>kg CO<sub>2</sub> eq</td><td>8.05</td><td>15.93</td><td>59.60</td></tr><tr><td>Non-renewable energy</td><td>MJ primary</td><td>32.46</td><td>64.63</td><td>241.75</td></tr><tr><td rowspan="2">Ozone layer depletion</td><td></td><td>1.58 × 10<sup>-7</sup></td><td>3.12 × 10<sup>-7</sup></td><td>1.17 × 10<sup>-6</sup></td></tr><tr><td>kg CFC-11 eq</td><td>2.76 × 10<sup>-4</sup></td><td>5.46 × 10<sup>-4</sup></td><td>2.04 × 10<sup>-3</sup></td></tr><tr><td>Aquatic eutrophication</td><td>kg PO<sub>4</sub> P-lim</td><td>59.74</td><td>118.29</td><td>442.49</td></tr><tr><td>Ionizing radiation</td><td>Bq C-14 eq</td><td>0.05</td><td>0.09</td><td>0.35</td></tr><tr><td>Carcinogens</td><td>kg C<sub>2</sub>H<sub>3</sub>Cl eq</td><td>0.01</td><td>0.01</td><td>0.04</td></tr><tr><td>Aquatic acidification</td><td>kg SO<sub>2</sub> eq</td><td>0.001</td><td>0.002</td><td>0.007</td></tr><tr><td>Respiratory organics</td><td>kg C<sub>2</sub>H<sub>4</sub> eq</td><td>0.02</td><td>0.04</td><td>0.15</td></tr><tr><td>Non-carcinogens</td><td>kg C<sub>2</sub>H<sub>3</sub>Cl eq</td><td>0.03</td><td>0.05</td><td>0.20</td></tr><tr><td>Terrestrial acid/nutri</td><td>kg SO<sub>2</sub> eq</td><td>1.68 × 10<sup>-3</sup></td><td>3.33 × 10<sup>-3</sup></td><td>1.25 × 10<sup>-2</sup></td></tr><tr><td>Respiratory inorganics</td><td>kg PM<sub>2.5</sub>eq</td><td>-312.47</td><td>-618.75</td><td>-2314.60</td></tr><tr><td>Aquatic ecotoxicity</td><td>kg TEG water</td><td>-0.04</td><td>-0.07</td><td>-0.26</td></tr><tr><td>Mineral extraction</td><td>MJ surplus</td><td>-136.46</td><td>-270.21</td><td>-1010.78</td></tr><tr><td>Terrestrial ecotoxicity</td><td>kg TEG soil</td><td>-0.10</td><td>-0.20</td><td>-0.75</td></tr><tr><td>Land occupation</td><td>m<sup>2</sup>org.arable</td><td>-0.10</td><td>-0.20</td><td>-0.75</td></tr></tbody></table>

**Figure 6.2** shows the proportion of inputs in the environmental effects of PBS larvae. The results revealed that the electricity consumption was the environmental point of interest in the production system. The on-site emissions accounted for the largest proportion of the environmental impact pertaining to global warming and respiratory inorganic ICs. Bio-waste consumption, which is used to feed insects, exerted beneficial environmental effects on all investigated ICs. In cricket production, the environmental

<footnote>* Negative values refer to savings and positive values refer to impacts.</footnote>
point of interest is related to the feed production (Halloran et al., 2017). Food waste has a promising nutrient properties that can be valorized for feeding edible insects (Varelas, 2019).

<figure><img src="image_32.png" /><figcaption>**Figure 6.2:** Proportion of consumed inputs in environmental impact of *Protaetia brevitars seulensis*.</figcaption></figure>

Figure 6.3 shows the normalized damage assessment of the investigated production process in terms of various consumption inputs. The normalized values of damage assessment of the edible insect production are also shown in Figure 6.4. The edible insect production system has positive environmental impact within the same categories of human health and ecosystem quality; however, it has negative impact on climate change and resource usage.
<figure><img src="image_33.png" /><figcaption>**Figure 6.3:** Normalized damage assessment of production system based on different consumption inputs.</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>**Figure 6.4:** Normalized values of different edible insect damage categories.</figcaption></figure>
The single scores of the damage categories in edible insect production are presented in Table 6.5. Based on the beneficial environmental impacts of edible insect production in various ICs, edible insects can become an environment efficient food production system for human nutrition by managing certain consumption inputs. In fact, only 40-50% of the produced biomass of cattle, poultry, and pigs are used directly as food. In contrast, the entire body of edible insects can be used as food (Heckmannet al., 2018). Moreover, insects as mini-livestock have many environmental benefits and similar nutritional quality compared with conventional livestock production systems (Van Huis, 2015). The primarily studies show that edible insect cell culture also may provide a more cost-efficient platform of cell-based meat system, according to the unique properties of insect cells (Rubio et al., 2019a,b; Rubio et al., 2020). Edible insects have the potential to be the future food given to their positive nutritional properties and relatively low environmental impacts; however, there are still food safety concerns associated with the consumption of insects, namely, the microbiological and chemical health risk (Garofalo et al., 2019; Imathiu, S., 2020). The current study, investigated the environmental impacts (climate change, resource depletion, human health, and ecosystem quality) associated with the PBS production system, and the microbiological and chemical health risk of the final product was not included in the LCA. Further research are needed to look at the microbiological, chemical health risk, and potential allergy related issues of this species toward moving to a sustainable edible insect-based production system.
<table><caption>**Table 6.5:** Single score of damage categories in edible insect production (unit= µPt).</caption><thead><tr><th></th><th>Bio-waste<br>treatment<br>(avoided<br>activity)</th><th>electricity</th><th>On-farm</th><th>Transportation</th><th>Total</th></tr></thead><tbody><tr><td>Human health</td><td>-0.06</td><td>0.21</td><td>0.0002</td><td>0.04</td><td>0.19</td></tr><tr><td>Ecosystem<br>quality</td><td>-0.13</td><td>0.03</td><td>0</td><td>0.01</td><td>-0.09</td></tr><tr><td>Climate change</td><td>-0.03</td><td>0.19</td><td>0.63</td><td>0.03</td><td>0.81</td></tr><tr><td>Resources</td><td>-0.05</td><td>0.23</td><td>0</td><td>0.03</td><td>0.21</td></tr><tr><td>Total</td><td>-0.27</td><td>0.67</td><td>0.63</td><td>0.11</td><td>1.14</td></tr></tbody></table>

### 6.3.3. Uncertainty analysis

The total characterization indices of PBS edible insects are presented in Table 6.6, as determined using various IA methodologies. These results may help in gaining agreement with the findings of relevant LCA studies on edible insects. Moreover, the results revealed that the global warming potential of farming 1 kg of insects ranges from 8.05 kgCO₂eq to 12.52 kgCO₂eq. The amount of ozone layer depletion caused by production of 1 kg of insects ranges from 1.57 × 10⁻⁷ to 1.58 × 10⁻⁷ kgCFC-11eq. The results pertaining to global warming potential was obtained using the IMPACT 2002+ midpoint and is remarkably different to the results obtained using other IA methodologies.

<footnote>* Negative values refer to savings and positive values refer to impacts.</footnote>
<table><caption>**Table 6.6:** Characterization indices of edible insects determined using various IA methodologies.</caption><thead><tr><th>Impact assessment</th><th>Global warming<br>(kg CO<sub>2</sub>eq)</th><th>Ozone layer<br>depletion<br>(kg CFC-11 eq)</th></tr></thead><tbody><tr><td>CML-IA baseline</td><td>11.49</td><td>1.58 × 10<sup>-7</sup></td></tr><tr><td>EDIP 2003</td><td>11.42</td><td>1.58 × 10<sup>-7</sup></td></tr><tr><td>EDP 2013</td><td>11.49</td><td>1.58 × 10<sup>-7</sup></td></tr><tr><td>ILCD 2011 Midpoint</td><td>11.56</td><td>1.57 × 10<sup>-7</sup></td></tr><tr><td><strong>IMPACT 2002+</strong></td><td><strong>8.05</strong></td><td><strong>1.58 × 10<sup>-7</sup></strong></td></tr><tr><td>ReCiPe midpoint</td><td>12.52</td><td>-</td></tr></tbody></table>

**6.4. Conclusions**

The development of sustainable food production systems is highly important for achieving food security. Moreover, environmental efficiency is one of the main pillars of sustainability. However, many conventional food production systems are not sustainable.

Thus, this study investigated the life cycle environmental sustainability of small scale PBS production. The obtained results revealed that edible insect production systems can be considered as a sustainable food production system owing to their positive environmental effects on 8 out of the 15 investigated ICs. Moreover, according to the total protein and fat, content, and fatty acid analysis results, it can be concluded that the PBS larvae fed with banana waste can be used as a potential source of protein and fat source. However, various negative environmental impacts were observed in some categories. For example,

<footnote>* The base scenario is shown in bold numbers.</footnote>
the global warming potential in the production of 1 kg of insects ranged from 8.05 kgCO₂eq to 12.52 kgCO₂eq based on the application of different IA methodologies. Finally, the environmental efficiency of the insect production system can be increased by managing certain inputs, such as electricity.

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Chapter 7

Modeling and optimization (AI-LCA)

Partially redrafted after:

Optimization of resource allocation in agri-food sector using coupled life cycle
assessment, genetic algorithm and Delphi methodology. *Journal of Cleaner
Production*. (ready to submit).
## Summary

Optimization of resource allocation in agri-food sector, in order to find the most appropriate mix of inputs, could contribute to mitigation of environmental impacts and moving toward a more circular food supply chain LCA has been coupled with several artificial intelligent-based optimization techniques in order to optimize food systems in terms of environmental impacts. One important step in an optimization problem in food system is to determine the bounds for the optimization variable. This study developed an optimization bound determination scheme through application of Delphi methodology. A hybrid LCA + multilayer perception artificial neural network + Delphi methodology + genetic algorithm was applied to optimize a food production system. Five functional units were considered for this study: one ton of produced pomegranate, one hectare, daily energy intake, vitamin C, and dietary fibre for 1 person. The results indicated that a remarkable amount of environmental impacts could be mitigated using this approach in a case study of pomegranate production.

## 7.1. Introduction

Optimization of resource allocation in agri-food sector, in order to find the most appropriate mix of inputs, could contribute to mitigation of environmental impacts and moving toward a more circular food supply chain. The LCA methodology has been coupled with some optimization techniques in order to optimize food systems in terms of environmental impacts. More specifically, it has been coupled with data envelopment analysis, particle swarm optimization, imperialist competitive algorithm, and genetic algorithms. Some studies indicated that artificial intelligent based approaches are more powerful than mathematical ones for environmental optimization of food systems (Khoshnevisan et al., 2015; Elhami et al., 2016; Nabavi-Pelesaraei et al., 2017). Table 7.1 shows recently published papers on the use of LCA based artificial intelligent-based optimization in FSC.
One important step in an optimization problem is to determine the bounds for the optimization variables. As it can be seen in Table 7.1, previous papers used the minimum and maximum of the obtained data as the bound for optimization of food systems in terms of environmental impacts. Normally, the data for LCA analysis in food systems are collected through face to face interviews, questionnaire or limited measurement in the food industry. An error can affect the final results of the optimization process. In this regard, Kanali et al., (2021) also believed that the lack of experiments to calculate the amount of fertilizers required, was a limitation for their optimization algorithm and the accurate range of that variable could contribute to provide more accurate results. Although some studies (Khoshnevisan et al., 2015; Soltanali et al., 2017) reported that the values for bounds were verified by local experts, but there is still a need for a standardized approach to determine the bounds of variables in an optimization problem in food supply chain. In this regards, Delphi can be applied as a validated scientific method in order to solve complex problems through expert consensus (Zhu et al., 2020). This methodology has been extensively used in the literature as an effective group-based judgment and decision-making (Belton et al., 2019). This methodology selects a panel of experts to go through a series of questionnaire-based interviews in order to achieve a consensus on a specific topic (Mahdavi et al., 2020). Thus, in this study, a hybrid LCA, genetic algorithm was applied to optimize a food production system and the variables' bound for the optimization problem were determined using Delphi methodology.
<table><caption>**Table 7.1:** Recently published papers on the use of LCA based artificial intelligent-based optimization in FSC</caption><thead><tr><th>Food system</th><th>Modeling</th><th>Determination of the bound</th><th>Reduction in environmental impacts</th><th>Optimization algorithm</th><th>Authors</th></tr></thead><tbody><tr><td>Watermelon</td><td></td><td>Min and max of data, and experts' suggestion for some inputs</td><td>27% in respiratory inorganics and 35% in global warming and non-renewable energy</td><td>Genetic algorithm</td><td>Khoshnevisan et al., (2015)</td></tr><tr><td>Paddy rice</td><td></td><td>Min and max of data</td><td>72% in GWP</td><td>Genetic algorithm</td><td>Nabavi-Pelesaraei et al., (2017)</td></tr><tr><td>Chicken meat</td><td>Process simulation, Monte Carlo, and artificial neural networks</td><td>Not available</td><td>15%</td><td>Genetic algorithm</td><td>López-Andrés et al. (2018)</td></tr><tr><td>Tomato</td><td>Artificial neural networks</td><td>Min and max of data</td><td>43% reduction with GA and 39% with PSO</td><td>genetic algorithm and Particle swarm optimization</td><td>Pishgar-Komleh et al., (2020)</td></tr><tr><td>Walnut</td><td></td><td>Min and max of data</td><td>40%</td><td>Imperialist competitive algorithm</td><td>Khanali et al., (2021)</td></tr><tr><td>Canola</td><td>Adaptive neuro-fuzzy inference system</td><td>Min and max of data</td><td>20%</td><td>-</td><td>Mousavi-Avval et al., (2021)</td></tr><tr><td>This study</td><td></td><td>Delphi Methodology</td><td></td><td></td><td>-</td></tr></tbody></table>

## 7.2. Materials and Methods

### 7.2.1. Life cycle assessment

The aim of the study needs to be specified in the first step of an LCA analysis. The aim of this LCA analysis was to assess the environmental impacts of fresh pomegranate production system. Five functional units (FUs) were chosen for the studied system, including one kg of produced pomegranate, and one hectare of pomegranate orchard. The above-mentioned FUs were selected to provide comparable results with other published LCA works on pomegranate and horticultural products (Canals et al., 2006; Vinyes et al., 2017; Alishah et al., 2019) which employed the same FUs. Moreover, three
nutrient-based FUs are also investigated in this study, i.e., daily requirement of vitamin
C, intake of energy, and dietary fibre. A comprehensive review of the inclusion of
nutritional properties in LCA was provided in the second chapter of this PhD dissertation.
The average daily requirement of vitamin C is assumed to be 55 mg/day (Nordic Council
of Ministers, 2014). Likewise, 9.9 MJ/day (2365 kcal/day) was considered as the
reference values for daily energy intake, according to Nordic reference values for male
and females aged 31-60 (Nordic Council of Ministers, 2014, p. 34). 30 g/day was assumed
as the recommended intake of dietary fibre (Svanes and Johnsen, 2019). The system
boundary of this study was the cradle to orchard gate of the system, including the
environmental consequences of the extraction of raw materials as well as on-orchard
operations.

In the second step of LCA, the inventory data of cradle-to-farm gate pomegranate
production was provided. The raw data of inputs-output were adapted from the literature
(Esmailpour-Troujeni et al., 2018). The data were collected from 65 rain-fed orchards
using face to face interviews. Off/on-orchard emissions were calculated using the data
provided by each orchard. The emissions' coefficients in the off-site phase (production of
input materials) were adapted from the Ecoinvent 3.0 database using the SimaPro
9.0.0.49 software. The datasets applied for calculation of emissions from off-site
operations are shown in Appendix C7.1. The coefficients for fertilizers' on-orchard
emission were provided in Figure 7.1.

The calculation of on-orchard emissions from pesticides was done based on the Margni
et al. (2002) model. Accordingly, the amounts of pesticides emitted to the soil, air, water,
and crop are 76.5%, 10%, 8.5%, and 5%, respectively. The carbon sequestration of
pomegranates trees and soil were not included in LCA analysis as there is not a well-defined procedure for its inclusion in LCA (Goglio et al., 2015). The on-orchard emission from fertilizers were provided in the appendix C7. 2.

IMPACT 2002+ was applied as the impact assessment methodology within the third step of the LCA analysis. This methodology covers 15 impact mid-pint categories into four endpoints level categories, i.e., climate change, resource depletion, human health, and ecosystem quality (Jolliet et al., 2003). The LCA analysis was done using Simapro 9.0.0.49 software. In the fourth step of LCA, the results were interpreted and the discussion is provided in the results and discussion section. In this study, LCA was performed two times, first one for investigating the environmental impacts of the real condition and one more time for evaluating the environmental impacts under optimized condition.

The input-output inventory of pomegranate production
(Esmailpour-Troujeni et al., 2018)

<figure><img src="image_35.png" /><figcaption>**Figure 7.1:** The primary inputs consumption for pomegranate production and their on-orchard emissions calculation</figcaption></figure>
### 7.2.2. Modeling

The relationship between the dependent variable (pomegranate yield) and independent variables was modeled using a multilayer perception (MLP) artificial neural network with back propagation algorithm. An MLP artificial neural network consist of at least three layers, including input, hidden, and output. The first layer is the input layer which has the same size as the independent variables. More specifically, in this study the variables for the input layer were diesel fuel, nitrogen, phosphorus, potassium, manure, agricultural machinery, human labor, herbicides, insecticides, and fungicides. In this study, the effect of the selection of different numbers of neurons in the hidden layer on the performance of MLP model was also investigated. Moreover, Logarithmic sigmoid and Tangent sigmoid were used as transfer functions for the hidden layer. In this study the output layer had one neuron (pomegranate yield). Two different training algorithm (Trainlm and Trainbr) were applied in this study.

### 7.2.3. K-Fold cross validation

The performance indices of ANN models can differ based on the data set chosen in the training step (Bowden et al., 2002; Kalantar et al., 2018). In other words, the random selection of training data set may affect the ANN results (Mukti et al., 2014). To overcome this issue, this study applied k-fold cross-validation method in order to a better estimation of ANN generalizability, consistently and robustness. This technique divides the original sample randomly into K subsamples (Baykan and Yılmaz, 2010). After that, a single sub sample is considered as the validation data for testing phase, and the remaining K-1 sub-samples as training data (Anand et al., 2013). These processes are repeated K times and each of the K sub-samples in this technique used exactly one time as the validation data
(Rohani et al., 2018). Normally, the k value is chosen as 5 or 10, as these values have been shown a better estimations of the capacity of models (Baldo et al., 2019). In this study, 5-fold cross validation is applied, and 100 runs were done for each training-test configuration.

### 7.2.4. Model performance evaluation

The following criteria were used to assess the performance of the developed ANN models: root mean square error (RMSE), coefficient of determination of the linear regressions equation between the measured and predicted values (R²). The equations of the above-mentioned performance criteria are provided as follows (Taki et al., 2021):

$$RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (Y_{predicted} - Y_{measured})^2} \qquad (1)$$

$$R^2 = \frac{\sum_{i=1}^{n} (Y_{predicted} - Y_{measured})^2}{\sum_{i=1}^{n} (Y_{predicted} - Y_{mean})^2} \qquad (2)$$

$$MAPE = \frac{1}{n} \sum_{i=1}^{n} \left| \frac{Y_{measured} - Y_{predicted}}{Y_{measured}} \right| \times 100$$

$$EF = 1 - \frac{\sum_{i=1}^{n} (Y_{measured} - Y_{predicted})^2}{\sum_{i=1}^{n} (Y_{measured} - Y_{predicted})^2} \qquad (3)$$

where Y measured is measured amount of pomegranate yield, and Y predicted is the predicated value by the developed ANN model (Bolandnazar et al., 2020). A model with the largest EF and R² and the smallest MAPE is considered as the best (Soltanali et al., 2017).
7.2.5. Genetic Algorithm

Genetic algorithm was applied to optimize the pomegranate production system. More specifically, it is used to find the best mixture of the inputs to achieve a greater pomegranate yield. For this purpose, a binary GA procedure was applied (Figure 7.2). The best developed model was employed as the fitness functions for GA optimization. The promenade yield was the fitness, and the inputs were the optimization variables. The next step is the determining of optimization variables’ bounds. This study developed an optimization bound determination scheme through application of Delphi methodology.

The Delphi technique was used to develop the optimization variables’ bounds. Experts, questionnaire, iteration, controlled feedback, anonymity of technical panel, analysis of the results, consensus, time, and facilitating team are the main pieces of a Delphi-based study (Samizadeh et al., 2017). Delphi is done based on the sequence of questionnaires distributed to a panel of experts. The coordinator of the experiment delivers an anonymous feedback to the panel after the first round of questions. After that, the participants may reconsider their opinions based on the provided feedback (Hirschhorn, 2019). This process continues until a coherent result is obtained. The appropriate selection of panellists is a key stage in the application of Delphi (Belton et al., 2019). In this study, the data for Delphi analysis were gathered by surveying 13 experts.

The population size of initial people was assumed as 100 samples. Chromosome refers to each solution in the population. The fitness of each chromosome was computed using the developed model. The chromosomes were arranged in an ascending order in terms of their fitness and 50% of their initial chromosomes were selected to produce the next generation. The parents from the remaining population were selected by tournament
selection procedure. Uniform crossover procedure was used to generate a new offspring and the next generation. The mutation rate was 30%. 20% of the bits in the new generation were randomly changed from 0 to 1, or vice versa. The production of new generation continued until a better solution was found. The modelling and optimization analysis were done by MATLAB R2019b software.

The limitation of this research comes from its limited number of investigated orchards. Another limitation of this study was the lack of available experiments results for determining the reasonable range of some inputs consumption, such as chemical fertilizers to be used for optimization problems. The limited number of available experts on choosing the appropriate ranges of inputs consumption within pomegranate production can be also considered as a limitation for the current study.
<figure><img src="image_36.png" /><figcaption>**Figure 7.2:** A hybrid life cycle assessment, genetic algorithm and Delphi framework</figcaption></figure>
## 7.3. Results and discussions

The results are explained under four subsections of i) LCA of current condition, ii) modeling, iii) optimization, iv) LCA of optimized scenarios.

### 7.3.1. LCA of current condition

Table 7.2 shows the inventory of on-orchard emissions in pomegranate production. The quantified amounts of environmental impacts of pomegranate production under current condition based on the selected impact categories are displayed in Table 7.2. The environmental impact of 1 ton of pomegranate production in a rain-fed system on GWP was determined 68.97 kg CO₂eq. This value for 1 hectare pomegranate orchard was 427.24 kg CO₂eq. For comparison, the GWP of one ton of apple was 89 kg CO₂eq (Bartzas et al., 2017), kiwifruit was 152 kg CO₂eq, peach was 172 kg CO₂eq (Nikkhah et al., 2017) and sweet cherries was 201 kg CO₂eq (Tassielli et al., 2018).

The GWP of fulfilling reference daily requirement of vitamin C, energy intake, and dietary fibre for 1 person from pomegranate were calculated 0.04, 0.20, and 0.05 kg CO₂eq, respectively. The same values for apple were reported as 0.3, 2.3, and 0.6 kg CO₂eq, respectively (Svanes and Johnsen, 2019).

<table><caption>**Table 7.2:** On-orchard emissions in pomegranate production</caption><thead><tr><th>Emissions</th><th>Unit</th><th>Quantity (per a hectare of pomegranate's orchard)</th><th>Quantity (for ton produced pomegranate)</th></tr></thead><tbody><tr><td colspan="4"><em>Emissions form diesel fuel combustion to soil</em></td></tr><tr><td>Cadmium (Cd)</td><td>kg</td><td>1.04 × 10<sup>-5</sup></td><td>1.69 × 10<sup>-6</sup></td></tr><tr><td>Lead (Pb)</td><td>kg</td><td>4.59 × 10<sup>-5</sup></td><td>7.41 × 10<sup>-6</sup></td></tr><tr><td>Zinc (Zn)</td><td>kg</td><td>2.81 × 10<sup>-2</sup></td><td>4.53 × 10<sup>-3</sup></td></tr><tr><td colspan="4"><em>Emissions form diesel fuel combustion to air</em></td></tr><tr><td>NH3</td><td>kg</td><td>1.16 × 10<sup>-3</sup></td><td>1.88 × 10<sup>-4</sup></td></tr><tr><td>C<sub>6</sub>H<sub>6</sub></td><td>kg</td><td>4.25 × 10<sup>-4</sup></td><td>6.86 × 10<sup>-5</sup></td></tr></tbody><tfoot><tr><td colspan="4">Continued on next page</td></tr></tfoot></table>
<table><caption>**Table 7.2** – continued from previous page</caption><thead><tr><th>Emissions</th><th>Unit</th><th>Quantity (per a hectare of pomegranate's orchard)</th><th>Quantity (for ton produced pomegranate)</th></tr></thead><tbody><tr><td>Benzo (a) pyrene</td><td>kg</td><td>1.75 x 10<sup>-6</sup></td><td>2.83 x 10<sup>-7</sup></td></tr><tr><td>Cd</td><td>kg</td><td>5.82 x 10<sup>-7</sup></td><td>9.40 x 10<sup>-8</sup></td></tr><tr><td>CO<sub>2</sub>, fossil</td><td>kg</td><td>1.84</td><td>29.64</td></tr><tr><td>CO, fossil</td><td>kg</td><td>0.34</td><td>0.06</td></tr><tr><td>Cr</td><td>kg</td><td>2.91 x 10<sup>-6</sup></td><td>4.70 x 10<sup>-7</sup></td></tr><tr><td>Cu</td><td>kg</td><td>9.92 x 10<sup>-5</sup></td><td>1.60 x 10<sup>-5</sup></td></tr><tr><td>N<sub>2</sub>O</td><td>kg</td><td>7.00 x 10<sup>-3</sup></td><td>1.13 x 10<sup>-3</sup></td></tr><tr><td>Heat, waste (MJ)</td><td>MJ</td><td>2.65 x 10<sup>3</sup></td><td>427.69</td></tr><tr><td>CH<sub>4</sub>, fossil</td><td>kg</td><td>7.53 x 10<sup>-3</sup></td><td>1.22 x 10<sup>-3</sup></td></tr><tr><td>Ni</td><td>kg</td><td>4.07 x 10<sup>-6</sup></td><td>5.56 x 10<sup>-7</sup></td></tr><tr><td>NO<sub>x</sub></td><td>kg</td><td>2.27</td><td>0.37</td></tr><tr><td>NMVOC</td><td>kg</td><td>1.25 x 10<sup>-1</sup></td><td>0.02</td></tr><tr><td>PAH</td><td>kg</td><td>1.91 x 10<sup>-4</sup></td><td>3.09 x 10<sup>-5</sup></td></tr><tr><td>Particulates, &lt; 2.5 um</td><td>kg</td><td>2.86 x 10<sup>-1</sup></td><td>0.05</td></tr><tr><td>Se</td><td>kg</td><td>5.82 x 10<sup>-7</sup></td><td>9.40 x 10<sup>-8</sup></td></tr><tr><td>SO<sub>2</sub></td><td>kg</td><td>5.88 x 10<sup>-2</sup></td><td>9.49 x 10<sup>-3</sup></td></tr><tr><td>Zn</td><td>kg</td><td>5.28 x 10<sup>-5</sup></td><td>9.40 x 10<sup>-6</sup></td></tr><tr><td colspan="4"><em>Emissions form fertilizers application to air</em></td></tr><tr><td>CO<sub>2</sub></td><td>kg</td><td>16.00</td><td>2.5</td></tr><tr><td>N<sub>2</sub>O</td><td>kg</td><td>0.35</td><td>0.06</td></tr><tr><td>NH<sub>3</sub> derived from pure nitrogen in chemical fertilizers</td><td>kg</td><td>2.65</td><td>0.43</td></tr><tr><td>NH<sub>3</sub> derived from pure nitrogen in FYM</td><td>kg</td><td>0.17</td><td>0.03</td></tr><tr><td>N<sub>2</sub>O derived from pure nitrogen in chemical fertilizers</td><td>kg</td><td>0.3</td><td>0.01</td></tr><tr><td>N<sub>2</sub>O derived from pure nitrogen in FYM</td><td>kg</td><td>0.003</td><td>0.001</td></tr><tr><td>NO<sub>x</sub></td><td>kg</td><td>0.10</td><td>0.02</td></tr><tr><td colspan="4"><em>Emissions form fertilizers application to water</em></td></tr><tr><td>NO<sub>3</sub><sup>-</sup></td><td>kg</td><td>29.92</td><td>4.83</td></tr><tr><td>P<sub>2</sub>O<sub>5</sub></td><td>kg</td><td>0.49</td><td>0.08</td></tr><tr><td colspan="4"><em>Emissions form fertilizers application to water</em></td></tr><tr><td>Cadmium (Cd)</td><td>mg</td><td>61834</td><td>9982</td></tr><tr><td>Chromium (Cr)</td><td>mg</td><td>13917</td><td>2246</td></tr><tr><td>Copper (Cu)</td><td>mg</td><td>139556</td><td>22528</td></tr><tr><td>Lead (Pb)</td><td>mg</td><td>121872</td><td>19673</td></tr><tr><td>Mercury (Hg)</td><td>mg</td><td>20</td><td>3</td></tr><tr><td>Nickel (Ni)</td><td>mg</td><td>4242</td><td>685</td></tr><tr><td>Zinc (Zn)</td><td>mg</td><td>25072</td><td>4047</td></tr></tbody></table>
<table><caption>**Table 7.3: Environmental impacts of pomegranate production according to different impact categories**</caption><thead><tr><th rowspan="2">Impact category</th><th rowspan="2">Unit</th><th colspan="5">Quantity</th></tr><tr><th>Per 1 ton produced pomegranate</th><th>Per 1 hectare pomegranate orchard</th><th>Daily requirement of vitamin C</th><th>intake of energy</th><th>Dietary fibre</th></tr></thead><tbody><tr><td>Global warming</td><td>kg CO<sub>2</sub>eq</td><td>68.97</td><td>427.24</td><td>0.04</td><td>0.20</td><td>0.052</td></tr><tr><td>Non-renewable energy</td><td>MJ primary</td><td>717.61</td><td>4445.46</td><td>0.39</td><td>2.04</td><td>0.54</td></tr><tr><td>Mineral extraction</td><td>MJ surplus</td><td>1.40</td><td>8.70</td><td>0.001</td><td>4.00 × 10<sup>-3</sup></td><td>0.001</td></tr><tr><td>Ozone layer depletion</td><td>kg CFC-11eq</td><td>8.05 × 10<sup>-6</sup></td><td>4.99 × 10<sup>-5</sup></td><td>4.34 × 10<sup>-9</sup></td><td>2.29 × 10<sup>-8</sup></td><td>6.04 × 10<sup>-9</sup></td></tr><tr><td>Non-carcinogens</td><td>kg C<sub>2</sub>H<sub>3</sub>Cl eq</td><td>209.14</td><td>1295.59</td><td>0.11</td><td>0.60</td><td>0.16</td></tr><tr><td>Carcinogens</td><td>kg C<sub>2</sub>H<sub>3</sub>Cl eq</td><td>0.97</td><td>6.02</td><td>5.79 × 10<sup>-4</sup></td><td>2.77 × 10<sup>-3</sup></td><td>0.001</td></tr><tr><td>Ionizing radiation</td><td>Bq C-14 eq</td><td>352.97</td><td>2186.57</td><td>0.19</td><td>1.01</td><td>0.26</td></tr><tr><td>Respiratory organics</td><td>kg C<sub>2</sub>H<sub>4</sub> eq</td><td>0.09</td><td>0.55</td><td>4.79 × 10<sup>-5</sup></td><td>2.53 × 10<sup>-4</sup></td><td>6.66 × 10<sup>-5</sup></td></tr><tr><td>Respiratory inorganics</td><td>kg PM2.5eq</td><td>0.20</td><td>1.21</td><td>1.06 × 10<sup>-4</sup></td><td>5.59 × 10<sup>-4</sup></td><td>1.47 × 10<sup>-4</sup></td></tr><tr><td>Aquatic ecotoxicity</td><td>kg TEG water</td><td>547557.35</td><td>3391997.30</td><td>295.25</td><td>1560.21</td><td>410.67</td></tr><tr><td>Terrestrial ecotoxicity</td><td>kg TEG soil</td><td>284724.90</td><td>1763808.10</td><td>153.53</td><td>811.30</td><td>213.54</td></tr><tr><td>Aquatic eutrophication</td><td>kg PO<sub>4</sub> P-lim</td><td>0.26</td><td>1.63</td><td>1.42 × 10<sup>-4</sup></td><td>7.52 × 10<sup>-4</sup></td><td>1.98 × 10<sup>-4</sup></td></tr><tr><td>Terrestrial acid/nutri</td><td>kg SO<sub>2</sub>eq</td><td>10.00</td><td>61.97</td><td>0.01</td><td>0.03</td><td>0.008</td></tr><tr><td>Land occupation</td><td>m<sup>2</sup>org.arable</td><td>13285.20</td><td>82298.87</td><td>7.16</td><td>37.85</td><td>9.96</td></tr><tr><td>Aquatic acidification</td><td>kg SO<sub>2</sub>eq</td><td>1.38</td><td>8.56</td><td>7.46 × 10<sup>-4</sup></td><td>3.94 × 10<sup>-3</sup></td><td>1.04 × 10<sup>-3</sup></td></tr></tbody></table>
Figure 7.3 illustrates the share of inputs to the environmental impacts of the investigated agricultural system. Chemical fertilizer consumption was the major contributor to the most investigated impact categories. The contributions of on-farm emissions to the aquatic ecotoxicity, non-carcinogens, terrestrial ecotoxicity impact categories were over 99%. Moreover, the contributions of on-site emissions to the GWO impact category was 61%.

<figure><img src="image_37.png" /><figcaption>**Figure 7.3:** Percentage of environmental impacts of consumed inputs within pomegranate production</figcaption></figure>

The damage assessment of the pomegranate production is demonstrated in Figure 7.4. The production of 1 ton of pomegranate led to $7.26 \times 10^{-4}$ DALY damage to human health, $1.68 \times 10^{4}$ PDF·m$^2$·yr. to ecosystem quality, 69 kg CO$_2$eq to GWP, and 719 MJ to resource depletion.
<figure><img src="image_38.png" /><figcaption>**Figure 7.4.** Damage assessment of one ton pomegranate production</figcaption></figure>

The total weighted environmental damage of one kg pomegranate production was determined to be 2.68 PtFU⁻¹ (Figure 7.4). Chemical fertilizers were the highest contributor to the total environmental impacts of the studied system, with a share of 80% (including its background and on-site emissions). The results are in line with a study on apple production that indicated the environmental impacts of production of chemical fertilizers was the largest contributor to environmental impacts (Bartzas et al., 2017).
<table><caption>**Table 7.4.** Environmental impacts of the pomegranate production system under current condition</caption><thead><tr><th></th><th></th><th>Human health</th><th>Ecosystem quality</th><th>Climate change</th><th>Resource depletion</th></tr></thead><tbody><tr><td rowspan="2">Total</td><td>Damage assessment</td><td>7.26 × 10<sup>-7</sup><br>DALY</td><td>16.77 PDF·m<sup>2</sup>·yr</td><td>0.07 kg CO<sub>2</sub>eq</td><td>0.72 MJ</td></tr><tr><td>Normalized</td><td>0.10</td><td>1.22</td><td>0.01</td><td>4.73 × 10<sup>-3</sup></td></tr><tr><td rowspan="2">On-site</td><td>Damage assessment</td><td>6.87 × 10<sup>-7</sup><br>DALY</td><td>2.27 PDF·m<sup>2</sup>·yr</td><td>4.22 × 10<sup>-2</sup> kg<br>CO<sub>2</sub>eq</td><td>0</td></tr><tr><td>Normalized</td><td>0.10</td><td>0.17</td><td>4.26 × 10<sup>-3</sup></td><td>0</td></tr><tr><td rowspan="2">Diesel fuel</td><td>Damage assessment</td><td>3.59 × 10<sup>-9</sup><br>DALY</td><td>1.93 × 10<sup>-2</sup> PDF·m<sup>2</sup>·yr</td><td>2.97 × 10<sup>-3</sup> kg<br>CO<sub>2</sub>eq</td><td>0.35 MJ</td></tr><tr><td>Normalized</td><td>5.07 × 10<sup>-4</sup></td><td>1.41 × 10<sup>-4</sup></td><td>3.00 × 10<sup>-4</sup></td><td>2.28 × 10<sup>-3</sup></td></tr><tr><td rowspan="2">Nitrogen chemical fertilizer</td><td>Damage assessment</td><td>1.14 × 10<sup>-8</sup><br>DALY</td><td>3.29 × 10<sup>-3</sup> PDF·m<sup>2</sup>·yr</td><td>1.10 × 10<sup>-2</sup> kg<br>CO<sub>2</sub>eq</td><td>0.21 MJ</td></tr><tr><td>Normalized</td><td>1.61 × 10<sup>-3</sup></td><td>2.40 × 10<sup>-4</sup></td><td>1.11 × 10<sup>-3</sup></td><td>1.36 × 10<sup>-3</sup></td></tr><tr><td rowspan="2">P<sub>2</sub>O<sub>5</sub> chemical fertilizer</td><td>Damage assessment</td><td>1.55 × 10<sup>-8</sup><br>DALY</td><td>2.83 PDF·m<sup>2</sup>·yr</td><td>7.25 × 10<sup>-3</sup> kg<br>CO<sub>2</sub>eq</td><td>0.11 MJ</td></tr><tr><td>Normalized</td><td>2.18 × 10<sup>-3</sup></td><td>0.21</td><td>7.32 × 10<sup>-4</sup></td><td>7.27 × 10<sup>-4</sup></td></tr><tr><td rowspan="2">K<sub>2</sub>O chemical fertilizer</td><td>Damage assessment</td><td>4.78 × 10<sup>-9</sup><br>DALY</td><td>11.60 PDF·m<sup>2</sup>·yr</td><td>3.40 × 10<sup>-3</sup> kg<br>CO<sub>2</sub>eq</td><td>0.03 MJ</td></tr><tr><td>Normalized</td><td>6.74 × 10<sup>-4</sup></td><td>0.85</td><td>3.44 × 10<sup>-4</sup></td><td>1.99 × 10<sup>-4</sup></td></tr><tr><td rowspan="2">Farmyard manure</td><td>Damage assessment</td><td>2.42 × 10<sup>-9</sup><br>DALY</td><td>0.07 PDF·m<sup>2</sup>·yr</td><td>1.29 × 10<sup>-3</sup> kg<br>CO<sub>2</sub>eq</td><td>0.01 MJ</td></tr><tr><td>Normalized</td><td>3.42 × 10<sup>-4</sup></td><td>4.99 × 10<sup>-3</sup></td><td>1.30 × 10<sup>-4</sup></td><td>6.87 × 10<sup>-5</sup></td></tr><tr><td rowspan="2">Pesticide</td><td>Damage assessment</td><td>1.12 × 10<sup>-9</sup><br>DALY</td><td>4.04 × 10<sup>-4</sup> PDF·m<sup>2</sup>·yr</td><td>8.25 × 10<sup>-4</sup> kg<br>CO<sub>2</sub>eq</td><td>0.02 MJ</td></tr><tr><td>Normalized</td><td>1.58 × 10<sup>-4</sup></td><td>2.95 × 10<sup>-8</sup></td><td>8.33 × 10<sup>-5</sup></td><td>1.03 × 10<sup>-4</sup></td></tr></tbody></table>

### 7.3.2. Modeling

The performance index (RMSE) of the developed models based on various numbers of neuron using K-fold cross validation are shown in Table 7.5. To find the best developed models, the best developed models were selected from K-fold analysis which can be seen in Table 7.6. The best model among the extracted models was selected to serve as the fitness function for Genetic algorithm. The selected model has the RMSE of 528 kg and R² of 0.91, an indication that the pomegranate output can be accurately predicted using MLP artificial neural networks.
<table><caption>**Table 7.5:** Average and standard deviation of RMSE for the developed models based on various numbers of neuron</caption><thead><tr><th rowspan="2">Training algorithm</th><th rowspan="2">Activation function</th><th colspan="8">Neuron number</th></tr><tr><th>5</th><th>10</th><th>15</th><th>20</th><th>25</th><th>30</th><th>35</th><th>40</th></tr></thead><tbody><tr><td rowspan="2">Trainlm</td><td>Logarithmic sigmoid</td><td>1076.24±369.63</td><td>985.66±236.49</td><td>1012.82±258.65</td><td>1020.12±489.99</td><td>1030.64±428.11</td><td>988.15±304.61</td><td>1018.43±320.66</td><td>1034.45±282.12</td></tr><tr><td>Tangent sigmoid</td><td>1155.08±402.39</td><td>1097.25±472.17</td><td>1218.31±581.63</td><td>1236.84±632.88</td><td>1206.73±579.87</td><td>1330.79±712.88</td><td>1265.25±497.84</td><td>1366.00±628.78</td></tr><tr><td rowspan="2">Trainbr</td><td>Logarithmic sigmoid</td><td>1765.64±140.70</td><td>1796.56±387.43</td><td>1616.23±426.16</td><td>1288.49±300.39</td><td>1245.95±215.20</td><td>1207.86±183.08</td><td>1252.38±285.07</td><td>1251.61±199.40</td></tr><tr><td>Tangent sigmoid</td><td>1334.21±423.35</td><td>1243.66±377.25</td><td>1194.61±178.62</td><td>1185.42±157.01</td><td>1219.57±256.91</td><td>1238.64±393.92</td><td>1204.83±188.77</td><td>1193.14±174.221</td></tr></tbody></table>

<table><caption>**Table 7.6:** The model performance indices of the best developed models extracted from K-fold analysis</caption><thead><tr><th rowspan="2">Training algorithm</th><th rowspan="2">Activation function</th><th rowspan="2">Neuron number</th><th rowspan="2">JC</th><th rowspan="2">RMSE</th><th rowspan="2">MAPE</th><th rowspan="2">EF</th><th rowspan="2">R<sup>2</sup></th><th colspan="3">Train</th><th colspan="3">Test</th><th colspan="3">Total</th></tr><tr><th>RMSE</th><th>MAPE</th><th>EF</th><th>R<sup>2</sup></th><th>RMSE</th><th>MAPE</th><th>EF</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td>T-lm</td><td>tanSIG</td><td>5</td><td>5</td><td>624.569</td><td>8.838</td><td>0.872</td><td>0.901</td><td>623.747</td><td>10.140</td><td>0.801</td><td>0.803</td><td>624.404</td><td>9.098</td><td>0.870</td><td>0.889</td></tr><tr><td>T-lm</td><td>tanSIG</td><td>10</td><td>19</td><td>483.417</td><td>5.888</td><td>0.921</td><td>0.921</td><td>782.106</td><td>14.825</td><td>0.645</td><td>0.824</td><td>556.140</td><td>7.675</td><td>0.897</td><td>0.902</td></tr><tr><td>T-lm</td><td>tanSIG</td><td>10</td><td>48</td><td>702.002</td><td>11.073</td><td>0.850</td><td>0.869</td><td>723.991</td><td>8.350</td><td>0.694</td><td>0.857</td><td>706.454</td><td>10.528</td><td>0.833</td><td>0.850</td></tr><tr><td>T-lm</td><td>tanSIG</td><td>10</td><td>80</td><td>652.806</td><td>9.247</td><td>0.866</td><td>0.866</td><td>657.967</td><td>9.549</td><td>0.781</td><td>0.812</td><td>653.842</td><td>9.308</td><td>0.857</td><td>0.859</td></tr><tr><td>T-lm</td><td>tanSIG</td><td>10</td><td>82</td><td>584.147</td><td>8.505</td><td>0.893</td><td>0.896</td><td>758.012</td><td>10.182</td><td>0.742</td><td>0.825</td><td>622.815</td><td>8.841</td><td>0.870</td><td>0.871</td></tr><tr><td>T-lm</td><td>tanSIG</td><td>10</td><td>97</td><td>555.557</td><td>8.071</td><td>0.902</td><td>0.903</td><td>748.664</td><td>10.240</td><td>0.748</td><td>0.875</td><td>599.179</td><td>8.504</td><td>0.880</td><td>0.885</td></tr><tr><td>T-lm</td><td>tanSIG</td><td>15</td><td>42</td><td>403.135</td><td>5.394</td><td>0.943</td><td>0.943</td><td>888.475</td><td>13.697</td><td>0.769</td><td>0.806</td><td>536.556</td><td>7.054</td><td>0.904</td><td>0.906</td></tr><tr><td>T-lm</td><td>tanSIG</td><td>15</td><td>49</td><td>564.001</td><td>7.974</td><td>0.857</td><td>0.857</td><td>1091.518</td><td>15.828</td><td>0.801</td><td>0.806</td><td>701.969</td><td>9.545</td><td>0.835</td><td>0.836</td></tr><tr><td>T-lm</td><td>tanSIG</td><td>20</td><td>19</td><td>418.274</td><td>5.270</td><td>0.941</td><td>0.942</td><td>723.688</td><td>12.762</td><td>0.696</td><td>0.892</td><td>494.679</td><td>6.769</td><td>0.918</td><td>0.920</td></tr><tr><td>T-lm</td><td>tanSIG</td><td>20</td><td>45</td><td>557.260</td><td>7.016</td><td>0.891</td><td>0.891</td><td>784.501</td><td>13.524</td><td>0.816</td><td>0.842</td><td>609.524</td><td>8.317</td><td>0.876</td><td>0.878</td></tr><tr><td>T-lm</td><td>tanSIG</td><td>20</td><td>71</td><td>450.859</td><td>5.548</td><td>0.916</td><td>0.920</td><td>1035.340</td><td>18.676</td><td>0.799</td><td>0.859</td><td>614.007</td><td>8.174</td><td>0.874</td><td>0.878</td></tr><tr><td>T-lm</td><td>tanSIG</td><td>40</td><td>56</td><td>538.532</td><td>7.429</td><td>0.909</td><td>0.910</td><td>997.768</td><td>12.027</td><td>0.515</td><td>0.826</td><td>656.598</td><td>8.349</td><td>0.856</td><td>0.862</td></tr><tr><td>T-lm</td><td>logSIG</td><td>5</td><td>15</td><td>485.944</td><td>6.923</td><td>0.923</td><td>0.924</td><td>929.903</td><td>12.821</td><td>0.666</td><td>0.827</td><td>601.545</td><td>8.103</td><td>0.879</td><td>0.886</td></tr><tr><td>T-lm</td><td>logSIG</td><td>5</td><td>69</td><td>316.037</td><td>4.170</td><td>0.958</td><td>0.958</td><td>997.627</td><td>17.654</td><td>0.776</td><td>0.850</td><td>528.162</td><td>6.867</td><td>0.907</td><td>0.910</td></tr><tr><td>T-lm</td><td>logSIG</td><td>5</td><td>71</td><td>507.515</td><td>6.485</td><td>0.893</td><td>0.896</td><td>768.571</td><td>14.546</td><td>0.889</td><td>0.895</td><td>569.384</td><td>8.097</td><td>0.892</td><td>0.894</td></tr><tr><td>T-lm</td><td>logSIG</td><td>5</td><td>82</td><td>608.659</td><td>8.850</td><td>0.884</td><td>0.888</td><td>1039.245</td><td>12.093</td><td>0.516</td><td>0.817</td><td>715.806</td><td>9.498</td><td>0.829</td><td>0.837</td></tr><tr><td>T-lm</td><td>logSIG</td><td>10</td><td>19</td><td>446.682</td><td>5.553</td><td>0.933</td><td>0.933</td><td>862.166</td><td>15.871</td><td>0.568</td><td>0.840</td><td>555.235</td><td>7.617</td><td>0.897</td><td>0.901</td></tr></tbody><tfoot><tr><td colspan="14" style="text-align: right;">Continued on next page</td></tr></tfoot></table>

<footnote>*The bold values represent the selected model for optimization.</footnote>
<table><caption>Table 7.6 – continued from previous page</caption><thead><tr><th rowspan="2">Training algorithm</th><th rowspan="2">Activation function</th><th rowspan="2">Neuron number</th><th rowspan="2">JC</th><th colspan="4">Train</th><th colspan="4">Test</th><th colspan="4">Total</th></tr><tr><th>RMSE</th><th>MAPE</th><th>EF</th><th>R<sup>2</sup></th><th>RMSE</th><th>MAPE</th><th>EF</th><th>R<sup>2</sup></th><th>RMSE</th><th>MAPE</th><th>EF</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td>T-lm</td><td>logsig</td><td>15</td><td>38</td><td>619.415</td><td>8.919</td><td>0.873</td><td>0.875</td><td>644.016</td><td>7.203</td><td>0.829</td><td>0.843</td><td>624.413</td><td>8.576</td><td>0.870</td><td>0.870</td></tr><tr><td>T-lm</td><td>logsig</td><td>15</td><td>42</td><td>337.401</td><td>4.426</td><td>0.960</td><td>0.960</td><td>1193.301</td><td>17.393</td><td>0.583</td><td>0.852</td><td>613.078</td><td>7.019</td><td>0.874</td><td>0.892</td></tr><tr><td>T-lm</td><td>logsig</td><td>20</td><td>53</td><td>558.536</td><td>7.239</td><td>0.883</td><td>0.884</td><td>999.732</td><td>18.174</td><td>0.738</td><td>0.827</td><td>670.420</td><td>9.426</td><td>0.850</td><td>0.858</td></tr><tr><td>T-lm</td><td>logsig</td><td>20</td><td>66</td><td>628.310</td><td>8.971</td><td>0.886</td><td>0.887</td><td>917.391</td><td>10.449</td><td>0.129</td><td>0.827</td><td>695.801</td><td>9.266</td><td>0.838</td><td>0.847</td></tr><tr><td>T-lm</td><td>logsig</td><td>20</td><td>95</td><td>526.680</td><td>7.198</td><td>0.896</td><td>0.896</td><td>906.579</td><td>11.006</td><td>0.808</td><td>0.819</td><td>621.523</td><td>7.960</td><td>0.871</td><td>0.872</td></tr><tr><td>T-lm</td><td>logsig</td><td>25</td><td>71</td><td>582.694</td><td>7.683</td><td>0.859</td><td>0.863</td><td>947.188</td><td>17.948</td><td>0.832</td><td>0.847</td><td>671.609</td><td>9.736</td><td>0.849</td><td>0.856</td></tr><tr><td>T-lm</td><td>logsig</td><td>30</td><td>19</td><td>434.918</td><td>5.149</td><td>0.936</td><td>0.939</td><td>711.859</td><td>12.788</td><td>0.706</td><td>0.804</td><td>502.664</td><td>6.677</td><td>0.916</td><td>0.916</td></tr><tr><td>T-lm</td><td>logsig</td><td>30</td><td>46</td><td>562.452</td><td>7.690</td><td>0.892</td><td>0.893</td><td>1077.450</td><td>17.534</td><td>0.595</td><td>0.823</td><td>696.607</td><td>9.659</td><td>0.838</td><td>0.850</td></tr><tr><td>T-lm</td><td>logsig</td><td>30</td><td>49</td><td>482.934</td><td>6.480</td><td>0.895</td><td>0.895</td><td>1141.707</td><td>14.103</td><td>0.782</td><td>0.846</td><td>668.789</td><td>8.005</td><td>0.851</td><td>0.857</td></tr><tr><td>T-lm</td><td>logsig</td><td>30</td><td>53</td><td>625.927</td><td>8.759</td><td>0.853</td><td>0.860</td><td>932.316</td><td>16.808</td><td>0.772</td><td>0.827</td><td>698.048</td><td>10.369</td><td>0.837</td><td>0.840</td></tr><tr><td>T-lm</td><td>logsig</td><td>30</td><td>95</td><td>655.861</td><td>10.198</td><td>0.839</td><td>0.847</td><td>1024.077</td><td>13.421</td><td>0.755</td><td>0.817</td><td>744.224</td><td>10.842</td><td>0.815</td><td>0.815</td></tr><tr><td>T-lm</td><td>logsig</td><td>35</td><td>38</td><td>533.524</td><td>7.693</td><td>0.906</td><td>0.907</td><td>516.959</td><td>6.714</td><td>0.890</td><td>0.891</td><td>530.253</td><td>7.497</td><td>0.906</td><td>0.907</td></tr><tr><td>T-lm</td><td>logsig</td><td>40</td><td>53</td><td>631.543</td><td>8.997</td><td>0.851</td><td>0.872</td><td>920.361</td><td>17.951</td><td>0.778</td><td>0.870</td><td>698.920</td><td>10.788</td><td>0.837</td><td>0.869</td></tr><tr><td>T-lm</td><td>logsig</td><td>40</td><td>66</td><td>604.520</td><td>9.035</td><td>0.895</td><td>0.895</td><td>820.751</td><td>9.677</td><td>0.303</td><td>0.851</td><td>653.515</td><td>9.163</td><td>0.857</td><td>0.869</td></tr></tbody></table>
### 7.3.3. Optimization

The optimized amounts of variables calculated using ANN+GA are shown in Table 7.7. Accordingly, the optimized amounts of diesel fuel and human labor were 46.59 L, and 333.58 h, respectively. These values for insecticides, and fungicides were 0.11, and 0.20 kg. The optimized amount of value for nitrogen chemical fertilizer we estimated as 21.82 kg.

<table><caption>**Table 7.7:** The optimized amounts of variables calculated using ANN+GA (per hectare)</caption><thead><tr><td>Input</td><td>Unit</td><td>Optimized value</td></tr></thead><tbody><tr><td>Diesel fuel</td><td>Lit</td><td>46.59</td></tr><tr><td>Human labor</td><td>h</td><td>333.58</td></tr><tr><td>Herbicides</td><td>kg</td><td>0</td></tr><tr><td>Insecticides</td><td>kg</td><td>0.11</td></tr><tr><td>Fungicides</td><td>kg</td><td>0.20</td></tr><tr><td>Farmyard manure</td><td>kg</td><td>537.86</td></tr><tr><td>Nitrogen (N) chemical fertilizer</td><td>kg</td><td>21.82</td></tr><tr><td>Phosphate (P<sub>2</sub>O<sub>5</sub>) chemical fertilizer</td><td>kg</td><td>0</td></tr><tr><td>Potassium (K<sub>2</sub>O) chemical fertilizer</td><td>kg</td><td>0</td></tr><tr><td>Agricultural machinery</td><td>h</td><td>11.97</td></tr></tbody></table>

### 7.3.4. LCA of optimized scenarios

The coupled application of LCA + ANN + Delphi + GA indicated the reduction of environmental impacts within various impact categories. The measured and optimized values of environmental impacts of pomegranate production are shown in Table 7.8. Accordingly, the environmental impacts of GWP can be saved by 86%. This value for
non-renewable energy impact category was calculated as 80%. Figure 7.5 illustrates the share of environmental impacts of inputs within measured and optimized scenario. Overall, the total weighted environmental damage of one ton pomegranate production was decreased to 0.32 PtFU⁻¹.

<table><caption>**Table 7.8:** Measured and optimized values of environmental impacts of pomegranate production</caption><thead><tr><th rowspan="2">Impact category</th><th rowspan="2">Unit</th><th colspan="2">Quantity (Per 1 ton produced pomegranate)</th></tr><tr><th>Measured</th><th>Optimized scenario</th></tr></thead><tbody><tr><td>Global warming</td><td>kg CO<sub>2</sub>eq</td><td>68.97</td><td>37.05</td></tr><tr><td>Non-renewable energy</td><td>MJ primary</td><td>717.61</td><td>397.64</td></tr><tr><td>Mineral extraction</td><td>MJ surplus</td><td>1.40</td><td>0.35</td></tr><tr><td>Ozone layer depletion</td><td>kg CFC-11eq</td><td>8.05 × 10<sup>-6</sup></td><td>4.66 × 10<sup>-6</sup></td></tr><tr><td>Non-carcinogens</td><td>kg C<sub>2</sub>H<sub>3</sub>Cl eq</td><td>209.14</td><td>124.51</td></tr><tr><td>Carcinogens</td><td>kg C<sub>2</sub>H<sub>3</sub>Cl eq</td><td>0.97</td><td>0.43</td></tr><tr><td>Ionizing radiation</td><td>Bq C-14 eq</td><td>352.97</td><td>151.99</td></tr><tr><td>Respiratory organics</td><td>kg C<sub>2</sub>H<sub>4</sub> eq</td><td>0.09</td><td>0.01</td></tr><tr><td>Respiratory inorganics</td><td>kg PM2.5eq</td><td>0.20</td><td>0.11</td></tr><tr><td>Aquatic ecotoxicity</td><td>kg TEG water</td><td>547557.35</td><td>323717.15</td></tr><tr><td>Terrestrial ecotoxicity</td><td>kg TEG soil</td><td>284724.90</td><td>162695.05</td></tr><tr><td>Aquatic eutrophication</td><td>kg PO<sub>4</sub> P-lim</td><td>0.26</td><td>0.79</td></tr><tr><td>Terrestrial acid/nutri</td><td>kg SO<sub>2</sub>eq</td><td>10.00</td><td>5.99</td></tr><tr><td>Land occupation</td><td>m<sup>2</sup>org.arable</td><td>13285.20</td><td>36.68</td></tr><tr><td>Aquatic acidification</td><td>kg SO<sub>2</sub>eq</td><td>1.38</td><td>0.01</td></tr></tbody></table>
<figure><img src="image_39.png" /><figcaption>**Figure 7.5:** Share of environmental impacts of inputs within measured and optimized scenario</figcaption></figure>

## 7.4. Conclusions

LCA has been applied for decades to investigate the environmental impacts of products and services in various sectors including agri-food. This study focused on developing a framework for application of this powerful and standardized methodology for optimization of food supply chain, not only evaluation. Another goal of this study was to introduce a methodology for determining the bounds for the optimization variables. In this regard, a hybrid LCA + multilayer perception artificial neural network + Delphi methodology + genetic algorithm, was developed to optimize food production systems. The developed
framework was applied for a rain-fed pomegranate production system. The results highlighted that a significant amount of environmental impacts could be potentially mitigated using the application of the proposed framework. Many agri-food production systems can benefit using this framework for optimization purposes. Further research is required to evaluate the proposed framework in order to optimize other aspects of sustainability of food systems, including economic and social.

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# Chapter 8

# Conclusions
## 8.1. LCA, past, trend, and the future direction

Recent studies highlight that most food production systems are not sufficiently sustainable. In this regard, moving toward sustainable food production is essential to address the UN development goals. Several methodologies have been proposed and developed to contribute to moving towards sustainability. Among them, the LCA methodology has been widely applied to evaluate various production systems. LCA has been widely used for evaluation of environmental impact of various food systems. It is well known as one of the best, most practical, and most standardized approaches in food supply chain. This methodology covers a comprehensive ranges of impact categories including human-health related impact categories. An LCA study consists of four phases of goal and scope definition, inventory analysis, impact assessment and interpretation. This method is still evolving and researchers are trying to optimize each phase of LCA and also extend the number of impact categories covered by LCA. There is a need for methodology development in food systems, extending LCA to emerging and novel food technologies. Moreover, LCA is not only an evaluation tool anymore, it can be used as a powerful decision tool for process improvement and optimization. It can also be used by policymakers as a decision support tool to select an environment-friendly production system. LCA is a promising tool that contributes to designing an environment friendly FSC. Nevertheless, three issues need to be addressed in LCA applications in FSC: (i) methodology development, focusing on the neglected aspects of LCA of FSC, including food safety and nutritional value and application of LCA as a tool to measure circularity toward moving to a circular FSC; (ii) development of LCA in innovative food technologies
and future foods; and iii) LCA application not only as an evaluation tool, but also for optimization.

## 8.2. LCA methodology development

The first LCA application was done in 1969 in the packaging industry. Since then, this methodology has been used for decades for evaluation of environmental impact of products and services. This methodology kept evolving and even though it is now considered as a powerful tool for assessing environmental impacts, but still it needs to be improved in order to respond to growing concerns regarding the environmental issues. This PhD developed this methodology for the application in FSC. Given that the food safety and nutritional value (as the important aspects for selecting the appropriate food production system) have been neglected in life cycle assessment of FSC, a framework based on the application of nutritional values as the functional units and also adding food safety indices as the disability-adjusted life year (DALY) value of the produced product to human health damage category was developed. In fact, this study has developed LCA through adding a new and important impact category to human health damage category.

Many food supply chains could benefit from using this framework in their LCA application. Moreover, the circular economy concept is gaining growing attention as an alternative to the linear economy “take, make, waste,” which exists now. The measurement of circularity is the first step in moving to a circular system. There is not a unique approach for measuring circular economy in FSC since the understanding of circular economy is still being explored. In this PhD thesis, LCA was applied to measure circularity in food supply chain using LCA for the case of refining oil from olive kernel.
## 8.3. LCA as a powerful tool for emerging and novel food systems

Various new technologies have been proposed and developed in the agri-food sector to manage cost, energy and environmental impacts of the production systems. It seems that some of these new developed technologies are not as green as we think. In this regard, in sustainability assessment of agri-food systems, various methods and models may be implemented. Sustainable development of new alternatives technologies in agri-food systems requires a thorough consideration of resource management in the production systems and a greater understanding of multiple environmental impacts. On the other hand, owing to the increasing demand for meat, there is a need for discovering alternative sources of protein. Moreover, many studies have shown that most common protein production systems are not environmentally efficient, and that the existing production systems of protein sources have enormous environmental disadvantages. Hence, the development of new alternative sustainable food sources is very important. In recent years, insects have been recommended as a novel food source for human consumption and the amount of insect production units has been increasing worldwide. Insect production can be considered as a solution for two problems, namely, the increasing demand for food by the production of edible insects, and waste management by composting food waste.

In this regard, in this PhD dissertation, an emerging food processing (ohmic heating), and a future food system (edible insect) were investigated using LCA. More specifically, the energy balance and LCA of ohmic heating (as an emerging food technology) for processing of chopped tomatoes with juice (CTwJ) were evaluated and the LCA results compared with the appertization system. Adverse environmental impact of CTwJ
processing and packaging by appertization were higher than those of ohmic heating
systems.

Application of LCA highlighted the beneficial environmental effects of the investigated edible insect on various impact categories (ICs), i.e., land occupation, mineral extraction, and aquatic and terrestrial ecotoxicity, due to utilization of bio-waste to feed insects. In addition, the entire body of some edible insects can be used as food, however, less than 50 percent of biomass of cattle, poultry, and pigs are being directly used as food. 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. Multiple functional units (biomass, protein and fat) were applied to study the system. The environmental hotspot of the systems was identified and the recommendations in order to enhance the sustainability of the systems were provided. Edible insects have the potential to be the future food given to their positive nutritional properties and relatively low environmental impacts; however, there are still food safety concerns associated with the consumption of insects, namely, the microbiological and chemical health risk. Further research is needed to look at the microbiological and chemical health risk of this species toward moving to a sustainable edible insect-based production system.

8.4. AI-LCA

Recent studies show that the hybrid application of LCA is more powerful than single LCA
applications. LCA can be combined with other methodologies to model, and optimize
processes. In this regard, LCA has been coupled with mathematical approaches
(regression, Cobb-Douglas, and data envelopment analysis), artificial-based intelligence
(artificial neural networks, adaptive neuro-fuzzy based interface, particle swarm
optimization, and genetic algorithms), and multi-criteria decision making (analytic
hierarchy process, and fuzzy analytic hierarchy process).

Optimization of resource allocation in agri-food sector, in order to find the most
appropriate mix of inputs, could contribute to mitigation of environmental impacts and
moving toward a more circular food supply chain. In this regard, environmental LCA is a
well known standardized methodology toward sustainability assessment. LCA has been
coupled with several artificial intelligent based optimization techniques in order to optimize
food systems in terms of environmental impacts. One important step in an optimization
problem in food system is to determine the bounds for the optimization variable. This
study developed an optimization bound determination scheme through application of
Delphi methodology. The developed framework (LCA + multilayer perception artificial
neural network + Delphi methodology + genetic algorithm) in this PhD was applied for a
rain-fed pomegranate production system and the results indicated that a significant
amount of environmental impacts could be mitigated using the application of the proposed
framework.

8.5. Uncertainty in LCA results

As it was discussed throughout this PhD dissertation, there are various sources of
uncertainty in LCA studies. Therefore, the LCA results reported in this dissertation are not
the absolute values, and they are obtained according to some assumptions in the LCA
analysis. The LCA results can be affected by the following assumptions: accuracy in the
collected data, the allocation strategy, functional unit, database, software, on-site
emissions coefficients, and impact assessment methodology selection. Various
approaches can be applied in order to to examine the consistency of the obtained LCA