# Internet of Nonthermal Food Processing Technologies (IoNTP): Food Industry 4.0 and Sustainability
## 4 Sustainability of NTPs in Industry 4.0  
Many authors confirm that Industry 4.0 technologies have the potential of growth, competitiveness and industrial sustainability improvements [87; 88]. The agri-food sector has also started using advantages of this technology in terms of utilizing modern equipment, emerging information and communication technologies, joint with sensing, smart and sustainable technologies [89]. One approach in understanding sustainability in Industry 4.0 is associating the SDGs of the UN with the food supply chain through the prism of Food Industry 4.0 as depicted in Figure 4. Out of 17 SDGs, eight can be recognized as directly or indirectly connected with food [90]. Tools outlined in Figure 4 comprise of various Industry 4.0 technologies [89; 91; 92] applicable in the food supply chain. Food industry 4.0 can provide more basic resources and services to the poor people and bring them out of poverty [91], promote sustainable agriculture, sustainable food consumption and production patterns [93], provide new technologies for clean drinking water and sanitation [94], and combat climate change [95]. The social dimension of sustainability requires additional attention [91], where evaluation could be performed through the prism of poverty, inequality, prosperity, and peace and justice concerns [96]. The fourth Industrial revolution has the potential of addressing environmental and social issues spreading the conventional technologies [97], and improving competitiveness justifying its economic dimension [91]. Food industry 4.0 has the possibility of reducing costs joint with production flexibility and higher productivity [98; 99], where known attributes of non-thermal technologies such as short treatment time and higher quality/food safety values [58], widen the economic perspective. Speaking about food industry 4.0, non-thermal technologies need to find their place in the cyber space between physical (conventional food manufacturing) and digital technologies [91]. Besides the economic and social dimensions, of sustainability, environmental dimension on non-thermal technologies was not fully explored by scientist due to several obstacles in terms of differences in scales of the non-thermal units (lab scale vs industrial facility) [100], type of food products in focus (animal origin vs plant origin, physical state of the product-powder, solid, liquid) and constraints in using LCA approach [58]. Partial LCA approach is typically used to evaluate the environmental impact of nonthermal food processing technologies mapping the lab-scale process, setting scope and boundaries an defining the functional unit (FU) as an output reference as 1 kg/1 L of treated food product or even 1 treatment [4]. Figure 4: SDGs and Food Industry 4.0. Challenges in performing a LCA of nonthermal food processing technologies is displayed in Table 3, deployed through its main elements: mapping the process, setting the scope and boundaries, collecting data, calculating, evaluating and interpreting the results [12]. The Table 3 has been developed in line with research from [100] and [4]. Since lab-scale units are usually at a technology readiness level between 3 and 4, it is necessary to clearly define boundaries of the system in which the partial LCA is used. Inconsistent functional unit raises a certain risk [4] since adequate choice should enable their comparison in-between food products and food technologies. Based on the data collection methods, it is important to be able to validate the data and relate them to nonthermal food processing technologies and chosen FUs [12]. Although inventory analysis of a partial LCA (for a lab-scale unit) may seem simple, main issues is the potential of precise measuring of input (material and energy) and output flows (waste/discharge streams) especially since in some cases, these values may be hard to measure. Some authors state that this stage is of significant importance in LCA since it provides values od unit process data [101].