An increasing number of novel technologies are being introduced to and used in the food industry to effectively tackle challenges related to food safety, nutritional quality, and sustainability. For many years, ensuring the safety of food has been a fundamental concern, with both global and regional implications. The most pressing issue of the food industry regarding food safety is the presence of microbial hazards, which can cause serious illnesses and outbreaks leading to severe economic damages with tremendous consequences, not only for the food and other companies, but also the governments (Fung, Wang, & Menon, 2018). Currently, the conventional methods of ensuring microbiological safety of food involve thermal treatments like pasteurization and sterilization. However, the conventional methods can have several limitations as they can lead to the loss of nutrients and food attributes, negatively affecting the product's end quality (Chandrasekaran, Ramanathan, & Basak, 2013). Additionally, conduction heating can lead to non-uniform temperature distribution, such as the formation of hot and cold spots, and challenges in maintaining consistent temperatures, particularly in dense or bulk products (Ozturk, Kong, Singh, Kuzy, & Li, 2017). To address these challenges, the food industry has shifted its focus to innovative heating technologies, such as ohmic heating, microwave heating (MW), and radio frequency (RF) to ensure food safety without compromising the quality of products (Van Impe et al., 2018).

RF has a dielectric heating method similar to MW, as both technologies utilise electromagnetic radiation which penetrates the food products and generates heat. RF has lower frequencies and longer wavelengths compared to MW. The prevailing frequency in the food industry is 27.12 MHz (wavelength of 11.1 m). This leads to a larger penetration depth, and hence, a more uniform heating (Altemimi et al., 2019). Initially, RF has been used for drying, disinfestation and thawing applications, but, in the last decade, numerous studies have been conducted using RF processing for a variety of products aiming at pasteurization (Di Rosa et al., 2019).

Thus far, the effect of food microstructure on RF treatments has not been explored. Most studies that have investigated RF treatment of liquid products were limited to specific real food products (e.g., Awuah, Ramaswamy, Economides, & Mallikarjunan, 2005; Geveke & Bronkhurst, 2004; Ukuku, Geveke, Cooke, & Zhang, 2008). Furthermore, studies that have investigated RF treatments of solid food products (such as gels) focused on heating uniformity (Birla, Wang, & Tang, 2008; Wang et al., 2003; Wang, Wig, Tang, & Hallberg, 2003), rheological and gelation properties (Ahmed, Ramaswamy, Alli, & Raghavan, 2007; Kar et al., 2020), water retention and structural properties (Wang et al., 2021; Wang et al., 2021). However, food intrinsic complexity influences the type of microbial growth (planktonic cells, submerged colonies, surface colonies), and, in turn, possibly influences the microbial resistance to RF. Hence, it is crucial to analyse and decipher the effect of certain food intrinsic factors on the RF inactivation of foodborne pathogens. The use of relevant food model systems is key as it ensures reproducibility and the absence of interfering background microflora. Additionally, it presents the advantage of allowing altering intrinsic food factors independently from one another, facilitating the isolation of the effect each factor can have on microbial inactivation (Baka, Vercruyssen, Cornette, & Van Impe, 2017).

One of the targeted food categories for RF processing is dairy products, since conventional heating applications lead to fouling in heat exchangers due to the overheating of the product when it comes into contact with the wall of the heat exchangers (Awuah et al., 2005). These products can contain a variety of foodborne pathogens, among which Salmonella species and Listeria monocytogenes (EFSA and ECDC, 2019). There have been numerous incidents of recalls and outbreaks implicating these microorganisms in different dairy products such as milk, cheese and dairy powders (Dag, Singh, Chen, Mishra, & Kong, 2022; Lindsay, Robertson, Fraser, Engstrom, & Jordan, 2021). Thus far, the use of RF has been studied for treatment of various dairy products, such as milk (Awuah et al., 2005; Di Rosa et al., 2018; Srisuma, Santalunai, Thosdeekoraphat, & Thongsopa, 2017), yoghurt (Siefarth, Tran, Mittermaier, Pfeiffer, & Buettner, 2014), goat's milk (Zhu, Guo, & Jia, 2014), skimmed or whole milk powder (Chen et al., 2013; Dag et al., 2022; Michael et al., 2014; Zhong et al., 2017) and powdered infant formula milk (Lin, Subbiah, Chen, Verma, & Liu, 2020; Zhang et al., 2020; Zhang et al., 2023; Zhang, Xie, Chen, Pandiselvam, & Liu, 2022). Nevertheless, the role of specific intrinsic factors of dairy products during RF inactivation treatments has yet to be investigated, with the influence of microstructural aspects as a prominent example. Intrinsic factors in dairy products refer to the inherent physical and chemical properties including composition (fat and protein content), water activity (aw) and pH, while the microstructural aspects describe how these components are intricately arranged. These factors can influence the product's behaviour during processing (Auty, 2018). These effects have to some extent been established for conventional thermal treatments, but, to the best knowledge of the authors, they have not been studied for RF treatments. For instance, the fat content in dairy can affect microbial resistance to heat, with higher fat levels potentially offering a protective mechanism for pathogens (Bermudez-Aguirre & Barbosa-Cánovas, 2008). Similarly, the protein content plays a crucial role, as protein denaturation during heat treatments can impact the thermal resistance of microbes (Atamer, Dietrich, Neve, Heller, & Hinrichs, 2010). Aw in dairy products is another critical factor, as it represents the amount of water available for microbial activity, with lower aw often correlating with increased microbial resilience to thermal processes (Wei et al., 2020). The pH also affects microbial heat resistance, where variations in acidity or alkalinity can increase or decrease the effectiveness of thermal inactivation (Blackburn, Curtis, Humpheson, Billon, & McClure, 1997). Finally, the microstructural aspects can influence heat transfer dynamics within the product, affecting the uniformity and efficiency of pathogen inactivation (Verheyen et al., 2020).

The innovative aspect of this study lies in the systematic study of RF treatments by using food-relevant, dairy-based systems with various microstructures. The primary focus was to investigate the effect of two factors, (i) the effects of fat content and (ii) matrix structure on RF thermal inactivation of two pathogenic microorganisms (i.e., S. Typhimurium and L. monocytogenes). In order to assess the effect of these factors in an objective way (i.e., using inactivation parameters), it was necessary to select a mathematical model that adequately describes the inactivation of the two microorganisms. A log-linear inactivation model, including a secondary model to account for the temperature dependency of the inactivation rate was therefore fitted to experimental data for both microorganisms in the four different systems.