Microbial risk and health burden associated with the domestic preparation of lentils in France and Hungary

Journal Pre-proof Microbial risk and health burden associated with the domestic preparation of lentils in France and Hungary Rodney J. Feliciano , Jeanne-Marie Membr´ e , Louis Delaunay PII: S2352-3522(25)00017-9 DOI: https://doi.org/10.1016/j.mran.2025.100357 Reference: MRAN 100357 To appear in: Microbial Risk Analysis Received date: 15 July 2025 Revised date: 27 October 2025 Accepted date: 27 October 2025 Please cite this article as: Rodney J. Feliciano , Jeanne-Marie Membr´ e , Louis Delaunay , Microbial risk and health burden associated with the domestic preparation of lentils in France and Hungary, Microbial Risk Analysis (2025), doi: https://doi.org/10.1016/j.mran.2025.100357 This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. 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This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Highlights:  Assessment of risk with home-cooked lentil consumption in France and Hungary  B. cereus and L. monocytogenes as microbial hazards in home-prepared lentils  A probabilistic model with uncertainty and variability was developed using 2D Monte Carlo methods  Low risk from proper lentil consumption practices, but may increase with new consumption patterns  Need for QMRA before pushing new diets, ingredients or plant-based alternatives Microbial risk and health burden associated with the domestic preparation of lentils in France and Hungary Rodney J. Felicianoa, Jeanne-Marie Membréa, Louis Delaunaya Oniris VetAgroBio, INRAE, Secalim, Site de la Chantrerie, CS 40706, 44307 Nantes Cedex 3, France Abstract: Lentils are promoted as an alternative protein source due to their agricultural and nutritional benefits. However, information on the microbial risks associated with lentil consumption in domestic settings is limited. The countries of France and Hungary were selected to represent the lentil consumption in two different supply chains. Bacillus cereus was identified as a pathogen of concern in both hot and cold dishes, whereas Listeria monocytogenes was only identified in cold dishes. A probabilistic model accounting for uncertainty and variability was constructed, estimating the microbial concentration at subsequent stages of domestic handling: cooking, cooling, and 24–96 h of chilled storage. The number of foodborne illness cases and daily adjusted life years (DALY) were estimated at the point of consumption. The results were analysed at each stage; for example, at 96 h, B. cereus showed mean values of 2.52 [1.75; 4.12] log CFU/g in France and 2.03 [1.24; 3.56] log CFU/g in Hungary. For Listeria monocytogenes, the mean estimates were lower at 0.48 [−0.20; 1.16] log CFU/g in France and 0.69 [0.07; 1.36] log CFU/g in Hungary. The overall number of foodborne illness cases from both pathogens was computed based on the consumption frequency. They were estimated to be 0 [0–44] cases per 100,000 in France and 0 [0–5.8] in Hungary. The uncertainty intervals are relatively large, reflecting uncertainty in the estimates, meaning that the risk is not an absolute zero. Moreover, it is likely that dietary shifts towards less meat consumption, as promoted by various institutions, will occur. Extended batch cooking practices can pose an additional risk of foodborne illnesses in the future. Keywords: Bacillus cereus, Listeria monocytogenes, DALY, leguminous, domestic practices, food safety, Monte Carlo 1. Introduction Lentils are seen as a key crop of the future due to their agricultural and nutritional benefits. In terms of agriculture, they are known for their nitrogen fixation activity and ability to tolerate dry conditions. Their nutritional role has been reported to contribute to consumers' protein, vitamins, and mineral requirements (Conti et al., 2021; Iqbal et al., 2006; Romano et al., 2021). Therefore, lentils are positioned as a sustainable protein alternative in several proposed agroecological models (e.g., Afterres2050, Transition 2050, and TYFA 2050). These models are characterised by an increased consumption of non-animal protein (i.e., legumes) and a reduction in red meat (i.e., bovine) and, to a lesser extent, monogastric (i.e., chicken and pork) livestock (Billen et al., 2021; Duru and Therond, 2023). This shift from animal production will enable a focus on farming crops that have lower greenhouse gas emissions and preserve wildlife biodiversity. As part of regional policies, several European projects (e.g., Pulse Increase, legume generation, and smart protein project) and campaigns have been launched to encourage farmers to adopt this crop and people to increase lentil consumption in their diet. These trends have been observed in the European region, including in France and Hungary (e.g., European Union (EU) green deal and Cap proteins) (European Commission, 2021, 2020). However, this wide-range push towards dietary shifts is not without risks. Several studies have called for an analysis of the associated chemical, nutritional, and biological risks with newer trends and dietary patterns (Eygue et al., 2020; Guillier et al., 2016; Poissant et al., 2023). Lentils are a major crop in Canada, India, and Australia, which are the top producers. In the EU, they are imported from Canada, Turkey, and the USA, while local production is led by Italy and France. The consumption pattern of lentils varies across the world, with the highest in Asian countries (e.g., India), where it is the key protein source. However, in the EU, lentil consumption remains low with an increasing trend, particularly in France and Hungary (Mombert et al., 2024; Nagy et al., 2021). Lentil production increased from 75,0000 (2017) tonnes to 116,000 (2021), with France and Spain as the leading producers. The import volume of lentils ranged from 246,000 to 230,000 tonnes between 2017 and 2021 (Centre for the Promotion of Imports, 2023). This can also be seen with lentil-dedicated farmlands in Europe, which peaked around 2018 but declined after 2020 (Terres Univia, 2022). In these two countries, lentils are mostly consumed in domestic settings and prepared from dried lentils, followed by canned ones, making these their top market forms. The domestic preparation of dried lentils is done by batch cooking, and the leftovers are stored in the refrigerator until finished. In France, lentils are mostly prepared as a hot dish or cold salad dish, which is then mixed with other types of food. In Hungary, they are mostly prepared as hot soup or cold sandwich spreads (Nagy et al., 2021; Yabré and Membré, 2022). Dietary shifts can provide benefits for meeting nutritional requirements but can also cause exposure to food safety hazards, which can have implications on the health burden of consumers (Pires et al., 2020). An example is plant-based milk alternatives, which can meet protein and energy requirements (Craig et al., 2023) but can increase exposure to heavy metals and mycotoxins (Santillana Farakos et al., 2021). Dietary studies exploring current and alternative legume-based diets paint the same picture (Ferreira et al., 2023; Mihalache et al., 2024). In a nutrition intervention study, Ferreira et al. (2023) demonstrated that legumes did not aggravate nutritional deficiency, and there was a slight decline in vitamin B12. However, Mihalache et al. (2024) found that a legume-based diet reduced the negative impacts of red and processed meats but also posed challenges in meeting micronutrient (e.g., Fe and Se) needs and increased exposure to heavy metals (e.g., arsenic, lead, and cadmium) and mycotoxins (AFB1). Therefore, several studies have called for a more systematic assessment of novel dietary patterns (Eygue et al., 2020; Santillana Farakos et al., 2021). Assessing the impacts of food systems is a crucial prerequisite for promoting food consumption patterns and diets. To add complexity is the recognition that other impacts of food systems extend beyond consumer health. Therefore, there is a potential for contradictions between different food policies that shift diets that must be reconciled (e.g., sustainability and food safety) (Guillier et al., 2016). This can occur in the context of climate mitigation, where food safety measures can be added to food production activity. However, this may contribute to environmental impact via greenhouse gas emissions. As such, win–win solutions are can be achieved through assessments (i.e., lifecycle assessments and quantitative microbial risk assessments) (Feliciano et al., 2022). This indicates the need for different analytical tools that allow compromise without sacrificing human health (Guillier et al., 2016). Therefore, a holistic risk assessment framework has been proposed to incorporate tools that consider several dimensions when assessing food systems (e.g., economic, environmental, health, and nutrition) (Országh et al., 2024). Food handling and cooking practices can be vulnerable to microbial contamination (Byrd-Bredbenner et al., 2013; Fischer et al., 2007). Pathogens in raw meat (e.g., Escherichia coli, Campylobacter jejuni) can be cross-contaminated in the kitchen area through contaminated hands, chopping boards, and knives during food preparation (Kennedy et al., 2011). Several foodborne outbreaks have been linked to homes in different countries due to these contamination events (Redmond and Griffith, 2003). This highlights the role of home and existing practices, cooking conditions, and refrigeration in food safety. Therefore, modelling and capturing the conditions at home must be included in the risk assessment of the consumption. This can aid in understanding microbial risk and the possible control points. Microbial risk assessment is a tool that can be used for risk management by identifying the vulnerable segments of the supply chain (FAO and WHO, 2021). This can be performed by determining which practices or segments enable pathogen growth. Ultimately, food safety managers can aid in the design of products to limit microbial growth, and regulators or policymakers can determine whether dietary trends or shifts to new food products can pose foodborne microbiological risks. Given the increasing trend of lentil consumption and existing domestic practices, an assessment of risk and its possible burden on the population is still missing. Therefore, the current study aimed to estimate the health burden associated with the consumption and domestic preparation of lentils in France and Hungary. These countries were chosen to study lentil consumption due to their similar cooking practices (hot and cold dishes) and representation of two different supply chains. France reflects a supply chain that both produces and imports, whereas Hungary is a mostly importing country. This study applied a probabilistic modelling approach that incorporated consumer practices and data available in the literature. In addition, the results were expressed in terms of the total burden of diseases from the projected annual cases. This research aims to demonstrate the added value of risk assessments in informing the associated risks that emerge from increased lentil consumption. 2. Materials and Methods: 2.1. Identification of microbial hazards in lentils and domestic preparation The current study assessed the microbial risks associated with home-cooked lentil dishes in France and Hungary. In these countries, lentils are prepared in domestic settings from dried and canned lentils, making these the top market forms (Terres Univia, 2024). Lentils are prepared differently between the countries, as lentil salad in France and soup in Hungary, with differences in cooking duration. Nevertheless, consumers follow a similar batch-cooking approach, and leftovers are stored for consumption. Leftovers are usually reheated and eaten hot, but some are eaten cold, as in salads. Hazard identification was performed following the methodology described in the literature (Codex Alimentarius Commission, 1999). It was assumed that food safety management systems and prerequisite programs were implemented during raw lentil production. The list of possible microorganisms of concern was made by drawing on those associated with lentil products as reported by food rapid alert systems (e.g., EURASSFF, USFDA, Rappelconso.fr, and Nebih) and food microbiology literature (Anses, 2022; ICMSF, 2005). These microorganisms were then contextualised based on their possible entry points along the lentil supply chain (e.g., Listeria monocytogenes recontamination during cold storage) (Supplementary Table 1). Subsequently, a series of filtering steps was performed by selecting which microorganisms can survive at the point of consumption. First, most of the vegetative pathogens identified at the initial part of the supply chain were eliminated during the canning of lentils or assumed to be reduced significantly during batch-cooking at home and, thus, do not pose microbiological risks. Similarly, foodborne viruses that cannot propagate along the supply chain were not considered in this study. In contrast, at this stage, the spores of Clostridium botulinum, C. perfringens, and Bacillus cereus were retained. These spore formers may survive the conditions experienced by dried lentils and during cooking and may germinate during the storage of cooked lentils. However, C. botulinum and C. perfringens were removed, given that they are obligate anaerobes and are not relevant at domestic stages. Consumers were generally considered to respect good hygiene practices, including cooling for a short period at ambient temperature. However, recontamination events can introduce L. monocytogenes and Staphylococcus aureus after batch cooking, cooling, and subsequent storage. Staphylococcus aureus requires a prolonged storage period at ambient temperatures to enable population growth and toxin production. In a previous survey and as reflected in this study, lentils are stored at room temperature after cooling for a limited period. The leftovers are kept in the refrigerator and reheated or consumed cold until the entire batch is finished. Listeria monocytogenes was selected over other pathogens, as it grows well at refrigeration temperatures. However, recontamination events after batch cooking, cooling, and subsequent storage may enable L. monocytogenes to pose a risk if the lentil dish is eaten cold, as in a salad or spread. This pathogen poses a threat by being able to propagate during cold storage. Therefore, two pathogenic microorganisms were retained for microbial risk assessment, namely B. cereus and L. monocytogenes. These two are considered to pose microbiological risks given the domestic practices before consumption. Furthermore, Bacillus cereus was further specified in this study as a microbial hazard by selecting two (groups III and IV) of the seven groups (Guinebretière et al., 2010). This is due to their ability to grow at room temperature, pathogenicity, and prevalence in being implicated in foodborne disease outbreaks (Anses, 2021; Glasset et al., 2016). 2.2. Lentil consumption data and domestic practices of the French and Hungary populations French consumption data were collected from the third French Individual and National Food Consumption Survey (INCA3) conducted by the French Food Safety Agency (Dubuisson et al., 2019). This survey was carried out in February 2014 and September 2015 with 5855 individual participants. Among these were 284 individuals who declared that they consumed canned and dried lentils (150 young and 134 adults). The Hungarian consumption data were collected from the Hungarian national food consumption survey of the EUMENU project (National Food Chain Safety Office Hungary et al., 2020). This survey was conducted from 2018 to 2020 with 1585 participants, of whom 87 consumed lentils. These consumption data were used to estimate portion sizes. A non-parametric bootstrap procedure was carried out to capture the uncertainty. Their variability was described through gamma and Weibull distributions in France and Hungary, respectively. The domestic practices were determined from an online survey conducted among 556 lentil consumers in France (Yabré and Membre, 2022). This survey contained information on the frequency of lentil consumption (1–5 times per week) and consumption practices (e.g., leftover frequency, cooling, and storage duration). The leftover frequency and storage duration in Hungary were estimated from Koppel et al. (2016). The domestic practices and lentil consumption data used in the current study are summarised in Table 1. 2.3. Exposure model frameworks for Listeria monocytogenes and Bacillus cereus 2.3.1. Model framework overview The microbial risk assessment models were constructed for L. monocytogenes and B. cereus for both hot and cold lentil dishes in France and Hungary. These models followed a modular modelling approach (Nauta, 2001). The modules included initial contamination of raw dry lentils, bacterial destruction during cooking, growth during cooling or refrigeration, consumption, and risk estimation modules. The main difference between the models is the contamination pathway. Bacillus cereus contamination was assumed to start from spores in raw lentils that resist inactivation during cooking and grow during storage. Through the toxin produced during storage, B. cereus poses a risk to both heated and cold-served lentil leftovers. However, L. monocytogenes occurs through recontamination during the cooling and storage phase, posing a risk to cold leftovers. It was not considered for hot-served dishes due to inactivation during the reheating step. The inputs used in the model and specific conditions applied between France and Hungary are listed in Table 1. 2.3.2. Bacillus cereus exposure assessment model 2.3.2.1. Module 1: Initial contamination and prevalence of B. cereus in lentils The initial counts of B. cereus were taken from the reported spore concentration levels and prevalence among legume samples (i.e., red lentils and yellow split beans) (Blakey and Priest, 1980). In their study, the detection threshold was set to 2 log CFU/g, which explains why this value appears as the threshold in our study, modelled as a Bernoulli. The maximum level of B. cereus reported in their paper was 45,000 CFU/g, which we used as our upper bound. This information was used to construct the concentration levels as greater than 2 log10 (N0XBc>2) or less than 2 log10 CFU(N0XBc<2). The uncertainty and variability associated with these concentration levels were captured using a Pert distribution. These values were expressed per portion size (Eq. 1a) and as the number of bacterial cells (Eq. 1b). 𝑙𝑜𝑔10𝑁𝐵𝑐 < 2=𝑃𝑒𝑟𝑡 (𝑀𝑖𝑛;𝑀𝑜𝑠𝑡 𝑙𝑖𝑘𝑒𝑙𝑦;𝑀𝑎𝑥){𝑚𝑖𝑛:0.3+log10portion size 𝑚𝑜𝑑𝑒:1+log10portion size 𝑚𝑎𝑥:1.95+log10portion size} 𝑙𝑜𝑔10𝑁𝐵𝑐 > 2=𝑃𝑒𝑟𝑡 (𝑀𝑖𝑛;𝑀𝑜𝑠𝑡 𝑙𝑖𝑘𝑒𝑙𝑦;𝑀𝑎𝑥){𝑚𝑖𝑛:2+log10portion size 𝑚𝑜𝑑𝑒:2.3+log10portion size 𝑚𝑎𝑥:3+log10portion size } (1a) 𝑁𝐵𝑐 < 2=10𝑙𝑜𝑔10𝑁0𝑋𝐵𝑐 < 2 and 𝑁𝐵𝑐 > 2=10𝑙𝑜𝑔10𝑁0𝑋𝐵𝑐> 2 (1b) The initial contamination and prevalence of contamination were translated into different packs used for cooking, representing different homes. The modelling strategy was initiated by assigning the log contamination levels in the total lentil packs per year and then by representing the probability of one pack being contaminated. The total prevalence of B. cereus was captured by reflecting the uncertainties surrounding the contamination levels using a beta distribution (Eq. 2a) with the number of samples containing greater than 2 log10 CFU (NBc>2) and less than 2 log10 CFU (NBc<2) (s) and the total number of samples (n) tested (Blakey and Priest, 1980). The variability of contamination levels was computed using the Bernoulli distribution (Eq. 2b). 𝑃𝑡0 𝐵𝑐= 𝐵𝑒𝑡𝑎 (𝑠+1,𝑛−𝑠+1) (2a) 𝑃𝐵𝑐 𝑐𝑜𝑛𝑡𝑎𝑚=𝐵𝑒𝑟𝑛𝑜𝑢𝑖𝑙𝑙𝑖 (𝑃𝑡0 𝐵𝑐) (2b) The percentage of contaminated packs with less than 2 log10 CFU was described using a beta distribution (Eq. 3a) with the number of samples containing less than 2 log10 CFU (NBc<2) (s) and number of samples containing greater than 2 log10 CFU (NBc>2) (n) described (Blakey and Priest, 1980). The variability of B. cereus contamination in the lentil packs was captured using the Bernoulli distribution (Eq. 3b). % 𝑐𝑜𝑛𝑡𝑎𝑚 𝑝𝑎𝑐𝑘𝑠<2=𝐵𝑒𝑡𝑎 (𝑠+1,𝑛−𝑠+1) (3a) 𝑃𝑐𝑜𝑛𝑡𝑎𝑚𝑝𝑎𝑐𝑘<2=𝐵𝑒𝑟𝑛𝑜𝑢𝑖𝑙𝑙𝑖 (% 𝑐𝑜𝑛𝑡𝑎𝑚 𝑝𝑎𝑐𝑘𝑠<2) (3b) The total prevalence of B. cereus in lentil packs and its associated levels were computed (Eq. 4) and expressed as the quantity of B. cereus (Eq. 5). 𝑁0𝑡𝑜𝑡𝑎𝑙𝐵𝑐 =𝑁𝐵𝑐<2𝑥𝑃𝑐𝑜𝑛𝑡𝑎𝑚𝑝𝑎𝑐𝑘<2+(1−𝑃𝑐𝑜𝑛𝑡𝑎𝑚𝑝𝑎𝑐𝑘<2)𝑥 𝑁𝐵𝑐>2 (4) 𝑄𝑡0 𝐵𝑐=𝑁0𝑡𝑜𝑡𝑎𝑙𝐵𝑐𝑥𝑃𝐵𝑐 𝑐𝑜𝑛𝑡𝑎𝑚 (5) Bacillus cereus was estimated per strain, namely groups III and IV. This study focused on groups III and IV, as they are known to be the main toxin producers in the B. cereus group (Guinebretière et al., 2008). Due to the lack of data on their proportions in lentil matrices, we assumed a variable distribution ranging from 20 to 80% between these groups (Eq. 6). The estimations were reflected in a uniform distribution given an absence of literature data on the exact strain prevalence in lentils (Eq. 7). 𝑃𝐺𝐼𝐼𝐼 𝐵𝑐 =𝑈𝑛𝑖𝑓𝑜𝑟𝑚 (0.20,0.80) and 𝑃𝐺𝐼𝑉 𝐵𝑐 =(1−𝑃𝐺𝐼𝐼𝐼 𝐵𝑐) (6) 𝑁0𝐵𝑐 𝐼𝐼𝐼 𝑜𝑟 𝐼𝑉= 𝑄𝑡0 𝐵𝑐𝑥 𝑃𝐺 𝐼𝐼𝐼 𝑜𝑟 𝐼𝑉 𝐵𝑐 (7) 2.3.2.2. Module 2: Microbial inactivation during batch cooking of lentils The current module was built by modelling batch cooking in domestic settings. The inactivation parameters were obtained from Sym’Previus for B. cereus group III at 90°C and group IV at 100°C (Table 1). The cooking temperature was set at 95°C due to consumers’ practice of constantly stirring and opening the lid of the pan. Heat inactivation was then computed per group following the heat inactivation model (Eq. 8): D95 BcIII= D90BcIII X 10(90−95 𝑍𝐵𝑐 𝐼𝐼𝐼) and D95 BcIV= D100BcIV X 10(100−95 𝑍𝐵𝑐 𝐼𝑉) (8) The probability of survival was calculated based on Nauta (2001) and was performed per B. cereus group following a similar formula: 𝑃𝑠𝑢𝑟𝑖𝑣𝑖𝑣𝑖𝑛𝑔 𝐵𝑐𝐼𝐼𝐼 =10(−𝑡𝐻𝑇 𝐷95 𝐵𝑐𝐼𝐼𝐼) and 𝑃𝑠𝑢𝑟𝑖𝑣𝑖𝑣𝑖𝑛𝑔 𝐵𝑐𝐼𝑉 =10(−𝑡𝐻𝑇 𝐷95 𝐵𝑐𝐼𝑉) (9) The cooking times (tHT) differed between France, ranging from 25 to 35 min, while in Hungary it was 45–70 min. This difference indicates the cooked lentil dish most consumed in these two countries: lentil salad in France and lentil soup in Hungary. The variability surrounding the cooking times was described as a uniform distribution. The number of surviving B. cereus spores was computed by multiplying the probability of survival per group by the initial contamination (Eq. 10). The variability surrounding the spore survivors was described using a Poisson distribution. 𝑄𝐻𝑇 𝐵𝑐𝐼𝐼𝐼~ 𝑃𝑜𝑖𝑠𝑠𝑜𝑛 (𝑃𝑠𝑢𝑟𝑖𝑣𝑖𝑣𝑖𝑛𝑔 𝐵𝑐𝐼𝑉 𝑥 𝑁0𝐵𝑐𝐼𝐼𝐼) and 𝑄𝐻𝑇 𝐵𝑐𝐼𝑉~ 𝑃𝑜𝑖𝑠𝑠𝑜𝑛 (𝑃𝑠𝑢𝑟𝑖𝑣𝑖𝑣𝑖𝑛𝑔 𝐵𝑐𝐼𝑉 𝑥 𝑁0𝐵𝑐𝐼𝑉) (10) 2.3.2.3. Module 3: Microbial germination of spores during the cooling of lentils and refrigerated storage of leftovers The surviving spores can germinate and grow during the cooling and storage of lentil leftovers. This module was built based on the growth kinetics of B. cereus groups derived from Sym’Previus (Table 1) and conditions after cooking (e.g., cooling temperatures and time). In terms of the growth kinetics, the uncertainty surrounding lag times was expressed as uniformly distributed (Daelman et al., 2013; Laurent et al., 1999). The cooling temperature was assumed to be 19°C, and the cooling time was taken from the consumer survey (Yabré and Membré, 2022). The variability in the length of cooling time practiced by the consumer was described through an exponential distribution. The microbial growth rate during cooling was computed following the gamma concept (Zwietering et al., 1996) (Eq. 12) with the Cardinal model and inflection for temperature from Rosso et al. (1993) (Eq. 11) and the optimal growth rate from Ellouze et al. (2021). Finally, the B. cereus concentration was computed following the exponential growth model (Buchanan et al., 1997) (Eq. 13) and subsequently converted to the number of microorganisms. 𝛾𝑇𝐵𝑐𝐼𝐼𝐼𝑜𝑟 𝛾𝑇𝐵𝑐𝐼𝑉= { 𝑇𝑐𝑜𝑜𝑙𝑖𝑛𝑔< 𝑇𝑚𝑖𝑛,0 𝑇𝑐𝑜𝑜𝑙𝑖𝑛𝑔>𝑇𝑚𝑎𝑥, 0 (𝑇𝑐𝑜𝑜𝑙𝑖𝑛𝑔−𝑇𝑚𝑖𝑛)2 (𝑇𝑐𝑜𝑜𝑙𝑖𝑛𝑔−𝑇𝑚𝑎𝑥) (𝑇𝑜𝑝𝑡−𝑇𝑚𝑖𝑛)[(𝑇𝑜𝑝𝑡−𝑇𝑚𝑖𝑛)(𝑇𝑐𝑜𝑜𝑙𝑖𝑛𝑔−𝑇𝑜𝑝𝑡)−(𝑇𝑜𝑝𝑡−𝑇𝑚𝑎𝑥)(𝑇𝑜𝑝𝑡+𝑇𝑚𝑖𝑛−2𝑇𝑐𝑜𝑜𝑙𝑖𝑛𝑔)] (Eq.11) µ𝑐𝑜𝑜𝑙𝑖𝑛𝑔 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐𝐼𝑉 =µ𝑜𝑝𝑡 𝐵𝑐𝐼 𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐 𝐼𝑉 𝑥 𝛾𝑇𝐵𝑐 𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐 𝐼𝑉 (Eq.12) 𝑄𝑐𝑜𝑜𝑙𝑖𝑛𝑔 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐 𝐼𝑉= { 𝑄𝐻𝑇 𝐵𝑐 𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐 𝐼𝑉 𝑖𝑓 𝑙𝑎𝑔𝑐𝑜𝑜𝑙𝑖𝑛𝑔≥𝑡𝑐𝑜𝑜𝑙𝑖𝑛𝑔 𝑄𝐻𝑇 𝐵𝑐 𝐼𝐼𝐼𝑜𝑟 𝐵𝑐 𝐼𝑉 𝑥exp[µ𝑐𝑜𝑜𝑙𝑖𝑛𝑔 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐 𝐼𝑉 𝑥 (𝑡𝑐𝑜𝑜𝑙𝑖𝑛𝑔−𝑙𝑎𝑔𝑐𝑜𝑜𝑙𝑖𝑛𝑔)] 𝑖𝑓 𝑙𝑎𝑔𝑐𝑜𝑜𝑙𝑖𝑛𝑔<𝑡𝑐𝑜𝑜𝑙𝑖𝑛𝑔 (Eq.13) 2.3.2.4. Module 4: Microbial growth and storage conditions during refrigeration Batch-cooked lentils are not consumed in one serving, and leftovers are kept in the refrigerator and consumed over several occasions. The differentiating factor between the two countries is the duration of storage, portion size, and number of portions per dish (Table 1). In terms of refrigeration temperature, the same values were applied for both countries (Roccato et al., 2017). The distinction between France and Hungary is the leftover storage time and frequency (Koppel et al., 2016; Yabré and Membré, 2022). Microbial growth during this period was determined following a similar modelling approach as cooling, namely determining the growth rate during refrigeration with temperature in the fridge as the cardinal parameter (Eq. 14) in the gamma model (Eq. 15). This enabled the computation of the B. cereus concentration over time (Eq. 16) and was expressed as total B. cereus per serving of leftovers (Eq. 17). 𝛾𝑓𝑟𝑖𝑑𝑔𝑒 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝛾𝑓𝑟𝑖𝑑𝑔𝑒 𝐵𝑐𝐼𝑉 = { 𝑇𝑓𝑟𝑖𝑑𝑔𝑒< 𝑇𝑚𝑖𝑛 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐𝐼𝑉, 0 𝑇𝑓𝑟𝑖𝑑𝑔𝑒 > 𝑇𝑚𝑎𝑥 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐𝐼𝑉, 0 (𝑇𝑓𝑟𝑖𝑑𝑔𝑒−𝑇𝑚𝑖𝑛 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐𝐼𝑉)2 (𝑇𝑓𝑟𝑖𝑑𝑔𝑒−𝑇𝑚𝑎𝑥 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐𝐼𝑉) (𝑇𝑜𝑝𝑡−𝑇𝑚𝑖𝑛)[(𝑇𝑜𝑝𝑡−𝑇𝑚𝑖𝑛 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐𝐼𝑉)(𝑇𝑓𝑟𝑖𝑑𝑔𝑒−𝑇𝑜𝑝𝑡)−(𝑇𝑜𝑝𝑡−𝑇𝑚𝑎𝑥)(𝑇𝑜𝑝𝑡+𝑇𝑚𝑖𝑛 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐𝐼𝑉−2𝑇𝑓𝑟𝑖𝑑𝑔𝑒)] (14) µ𝑓𝑟𝑖𝑑𝑔𝑒 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐𝐼𝑉 =µ𝑜𝑝𝑡 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐𝐼𝑉 𝑥 𝛾𝑓𝑟𝑖𝑑𝑔𝑒 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐𝐼𝑉 (15) 𝑄𝑓𝑟𝑖𝑑𝑔𝑒 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝑄𝑓𝑟𝑖𝑑𝑔𝑒 𝐵𝑐𝐼𝑉 = { 𝑄𝑐𝑜𝑜𝑙𝑖𝑛𝑔 𝐵𝑐 𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐 𝐼𝑉 𝑖𝑓 𝑙𝑎𝑔𝑠𝑡𝑜𝑟𝑎𝑔𝑒≥𝑡𝑠𝑡𝑜𝑟𝑎𝑔𝑒 𝑄𝑐𝑜𝑜𝑙𝑖𝑛𝑔 𝐵𝑐 𝐼𝐼𝐼𝑜𝑟 𝐵𝑐 𝐼𝑉 𝑥exp[µ𝑓𝑟𝑖𝑑𝑔𝑒 𝐵𝑐𝐼𝐼𝐼 𝑜𝑟 𝐵𝑐𝐼𝑉𝑥 (𝑡𝑠𝑡𝑜𝑟𝑎𝑔𝑒−𝑙𝑎𝑔𝑠𝑡𝑜𝑟𝑎𝑔𝑒)] 𝑖𝑓 𝑙𝑎𝑔𝑐𝑜𝑜𝑙𝑖𝑛𝑔<𝑡𝑐𝑜𝑜𝑙𝑖𝑛𝑔 (16) 𝑄𝑠𝑒𝑟𝑣𝑖𝑛𝑔 𝐵𝑐 𝑡𝑜𝑡𝑎𝑙/𝑋ℎ=𝑄𝑓𝑟𝑖𝑑𝑔𝑒 𝐵𝑐𝐼𝐼𝐼 +𝑄𝑓𝑟𝑖𝑑𝑔𝑒 𝐵𝑐𝐼𝑉 𝑁𝑜.𝑠𝑒𝑟𝑣𝑖𝑛𝑔𝑠 𝑑𝑖𝑠ℎ (17) 2.3.2.5. Module 5: Risk estimation The microbial risk from B. cereus was determined by first establishing the cut-off values and subsequently assessing the concentration reached per specific time (Xh) (Eq. 18). The cut-off values were taken from the literature, determining the concentration at which B. cereus causes foodborne illness (Anses, 2021). 𝑅𝑖𝑠𝑘𝑋ℎ 𝐵𝑐= 𝑄𝑠𝑒𝑟𝑣𝑖𝑛𝑔 𝐵𝑐 𝑡𝑜𝑡𝑎𝑙/𝑋ℎ>𝐵𝑐𝑐𝑢𝑡𝑜𝑓𝑓×𝑃𝑜𝑟𝑡𝑖𝑜𝑛 𝑠𝑖𝑧𝑒 (18) Leftover lentils are either eaten cold or hot. During this iterative consumption of leftovers, it may be reheated or eaten cold, which can pose a microbial risk. The uncertainty of leftover times was Figure 1. The cumulative density of Bacillus cereus (upper panel) and Listeria monocytogenes (lower panel) in batch-cooked lentils in France at 0 and 96 h of storage. The dark grey region of the cumulative density curve corresponds to the 25th and 75th percentiles of the uncertainty, and the light grey region corresponds to the 95% uncertainty intervals. The figures for B. cereus show the single cut-off values. The L. monocytogenes figures show three cut-off values for low (yellow line), moderate (orange line), and high (red line) virulence strains. Figure 2. The cumulative density of Bacillus cereus (upper panel) and Listeria monocytogenes (lower panel) in batch-cooked lentils in Hungary at 0 and 96 h of storage. The dark grey region of the cumulative density curve corresponds to the 25th and 75th percentiles of the uncertainty, and the light grey area corresponds to the 95% uncertainty intervals. The figures for B. cereus show the single cut-off values. The L. monocytogenes figures show three cut-off values for low (yellow line), moderate (orange line), and high (red line) virulence strains 3.2. Risk estimates per portion from home-cooked lentils and leftovers The mean risk estimates per portion were determined over the storage time (Table 2). For L. monocytogenes, these estimates were null across the chilled storage period. In contrast, a non-zero risk per portion was estimated for B. cereus after 96 h of refrigeration. This can be observed within the upper uncertainty interval (CI) of the mean estimates at 7.00×10−4. The risk per portion at the upper CI was slightly higher for France than for Hungary, which was at 9.02×10−5. The overall risk associated with the lentil portions and their respective serving conditions (e.g., hot or cold) was determined (Table 3). Bacillus cereus was accounted for in both cold and hot served portions, while L. monocytogenes was only considered in cold servings. Very low risks were observed under both serving conditions, but with non-zero estimates in the upper CI. The hot-served portions were estimated to have slightly higher risks, with France having higher upper CI estimates at 1.05×10−4 than Hungary at 2.53×10−5. The same trend between countries was observed for cold servings of lentil dishes. In terms of L. monocytogenes, zero risk was estimated in both countries for the mean and CI values. The combined risk for all cold servings of lentil dishes reflects the previous zero mean values, except the upper-bound CI estimates. Between-country comparisons of total risks reflect the same trend, with France having higher risk estimates. The next step is to contextualise the risk estimated with the additional total burden of disease at the population level. Table 3. Estimated risk per portion of lentil dishes in France and Hungary. Serving conditions Risk [Mean, 95% CI] France Hungary B. cereus Hot served 0.00 [0.00; 1.05×10−4] 0.00 [0.00; 2.53×10−5] Cold-served 0.00 [0.00; 1.70×10−5] 0.00 [0.00; 3.82×10−6] L. monocytogenes Cold-served 0.00 [0.00; 0.00] 0.00 [0.00; 0.00] Risk from all cold servings 0.00 [0.00; 1.70×10−5] 0.00 [0.00; 3.82×10−6] Total risk from cold and hot servings 0.00 [0.00; 1.16×10−4] 0.00 [0.00; 2.84×10−5] 3.3. Health burden from L. monocytogenes and B. cereus contamination The estimated risks were computed as the attributable foodborne illness cases in the entire population or the number of illnesses per year (Table 4). The results have demonstrated that the mean estimates were zero for the number of people getting sick due to cold or hot dishes. However, the nonzero case estimates were observed within the upper bounds of the CI estimates. The pathogen driving the cases was B. cereus, with L. monocytogenes as a zero-projected case. The hot lentil serving portions posed a potentially higher risk than cold dishes, with upper-bound CI mean estimates of 27,101 cases in France and 493 cases in Hungary. In terms of the overall number of illnesses, these were higher for France (29,925 cases) than in Hungary (554 cases). These cases were expressed in DALYs by utilising the data on the average intakes and annual lentil consumption of the whole population of these two countries. This computation determined the additional health burden associated with these projected cases. Zero mean estimates were also observed with the upper CIs resulting in non-zero DALYs. Higher additional health burden were attributed to the hot servings than to the cold portions. Table 4. Estimated burden of disease for the French and Hungarian population associated with lentil-based dishes. France Hungary Burden estimates Lentil consumers and population statistics Average portion size (g) 39 73 The annual number of lentil intakes 94 122 Number in the population 68,000,000 9,590,000 % Consumers of dry lentils 4% 5.50% Number of people eating dry lentils 2,720,000 527,840 Health impact per year Mean [2.5th; 97.5th] No of illnesses due to the cold serving (B. cereus) 0.00 [0.00; 4,406] 0.00 [0.00; 75] No of illnesses due to the cold serving (L. monocytogenes) - - No of illnesses due to the hot serving (B. cereus) 0.00 [0.00; 27,101] 0.00 [0.00; 493] Total number of illnesses 0.00 [0.00; 29,925] 0.00 [0.00; 554] Total number of illnesses per 100,000 0.00 [0.00; 44] 0.00 [0.00; 5.8] DALYs DALYs of illnesses due to the cold serving (B. cereus) 0.00 [0.00; 10.13] 0.00 [0.00; 0.17] DALYs of illness due to the cold serving (L. monocytogenes) - - DALYs of illnesses due to the hot serving (B. cereus) 0.00 [0.00; 62.33] 0.00 [0.00; 1.13] 4. Discussion The results of this study indicate that the risk associated with lentil consumption is low in comparison with other dietary sources, such as broiler and red meat, raw vegetables, and dairy products (EFSA, 2023). These were implicated in hospitalised foodborne cases of listeriosis, salmonellosis, and STEC infections. Nevertheless, the selection of lentils was due to its promotion among the non-meat protein alternatives, given its multiple positive impacts in terms of agriculture and nutritional benefits (Iqbal et al., 2006; Romano et al., 2021; Tidåker et al., 2021). The focus on domestic settings lies in their vulnerability to microbial risk compared to other product forms (e.g., direct consumption of canned food). The results indicate that there is a very small risk, but not zero, which, if considered at a population level, can still have an impact. The current study determined a very low microbial risk with lentil consumption in France and Hungary. The findings of this study point to an impact when translated at a regional or national level, particularly in France and Hungary, where legume consumption is encouraged as part of the “healthy plate” (i.e., French National Nutrition and Health Program PNNS, 2019 and Hungarian nutritional recommendation Okostányér, 2021). These may influence people to consume more, including the positive perception associated with it. Therefore, regulators, particularly those who promote dietary shifts, must employ QMRA and other preliminary assessments. These estimates may change in other legume products, which are outside the scope of this study and the model developed. The non-zero estimates highlight the need to incorporate variability and uncertainty surrounding the inputs. These estimates can still be further refined by incorporating better data on domestic practices. Overall, it can be said that good cooking and domestic practices are currently being observed. Nevertheless, if people are pushing the storage time (e.g., 6 days), the risk estimates will change. From this, we can calculate different scenarios focusing on domestic practice changes. Other bacteria (e.g., spoilage bacteria) might come into play. Despite this change, the current model and associated modelling strategy can still be used with modification of the input variables (e.g., growth rate). The initial contamination and duration of refrigeration influenced the microbial concentration of leftovers over time. The initial counts were based on the literature (Beumer et al., 1996; Blakey and Priest, 1980), but a more recent study for these countries was not available for both bacteria. This indicates the need for more updated data on dried lentils, as consumption is being promoted. The change in the prevalence and levels of B. cereus spores can increase or reduce the estimated risks to the population. The probability associated with the prevalence of B. cereus in these packages was described by a beta distribution. This is a continuous distribution commonly used to describe the presence of B. cereus in packaged food products (Kwon et al., 2020; Wang et al., 2023). The initial concentration was described using the Bernoulli distribution. These counts were reduced by heating and significantly reducing microbial risks. Bacillus cereus grew during refrigeration, which partially undoes the effect of cooking. This is similar to the study of Kobayashi et al. (2021), who demonstrated the ability of B. cereus strains to grow after 4–10 days at 6–10°C. This study reports on psychrotrophic and nonpsychrotrophic strains in nutrient broth, and growth was estimated using lab medium after 4–28 days of incubation at constant temperatures (4, 6, 8, and 10°C) from 5 log spores/mL to visible turbidity. The presence of L. monocytogenes in leftovers suggests recontamination events after batch cooking. The low microbial counts reflect the low prevalence of L. monocytogenes in domestic refrigerators. In addition, if present, these were found in low levels (0–1 log CFU) (Beumer et al., 1996). Nevertheless, the prevalence used in this study is similar in Portugal (3.9%) and Greece (4.5%) (Azevedo et al., 2005; Sergelidis et al., 1997). Recontamination events have been the main driving cause of L. monocytogenes in foods and are linked to foodborne outbreaks in home and food manufacturing facilities (Møretrø et al., 2024; Zhang et al., 2022). The results demonstrate that current lentil cooking and post-cooking practices in France and Hungary have not significantly introduced recontamination. A more complex post-cooking scenario (e.g., addition of fresh ingredients in batchcooked lentils) and cross-contamination can impact these estimates (Possas et al., 2017). In addition, future scenarios can influence the food safety of lentils. Several studies have demonstrated that room temperatures can influence the microbial risk. This was demonstrated by the microbial spoilage risk of fruit juice (Kakagianni et al., 2016), evaporated canned milk (Kakagianni and Koutsoumanis, 2018; Koutsoumanis et al., 2022), and plant-based alternatives (Misiou et al., 2023). These results highlight that existing and projected temperatures during storage can impact spoilage due to the consequent change in the microbial concentration. Therefore, caution should be applied in the future, as there is a need for a more systematic evaluation of climate change in microbial risk assessment. The refrigeration temperatures used in this study are based on actual refrigerator temperatures for countries located in Western Europe. Refrigeration temperatures vary depending on location (Roccato et al., 2017). These changes, whether due to location or climate change, may affect food safety risks that can come from lentil consumption. These values must change accordingly if the current model is to be adopted in other countries, which may affect food safety risks. This has been observed in a similar study where concentrated milk has a higher spoilage probability due to hotter room temperatures and higher refrigeration temperatures (Misiou et al., 2023). The microbial safety risk of lentil portions was determined using cut-off values, which, if reached, will result in foodborne illness. The cut-off values were derived from scientific literature detailing the microbial concentration levels linked to foodborne illness for B. cereus and L. monocytogenes. The B. cereus cut-off values derived for this study are based on 105 CFU/g, which assumes that at this level, it can cause foodborne illness (Anses, 2021). This was the best available data given that a dose– response study with LD50 was not available for B. cereus cells or cereulide toxins (Rouzeau-Szynalski et al., 2020). The current model measures the risk from cold and hot dishes, reflecting both foodborne intoxication of B. cereus due to cereulide toxin and infection from vegetative cells. By ingesting toxins, we only evaluate emetic symptoms, and these symptoms are declared at 105 CFU/g of food. However, sometimes, food contaminated with 103 is sufficient to cause illness, and other times, more than 108 is needed (Anses, 2021). The prevalence of B. cereus strains in different food samples was not available. Therefore, it was assumed that B. cereus belonged to group III or IV, which are virulent strains according to the literature (Guinebretière et al., 2010; Mombert et al., 2024). Listeria monocytogenes is well-studied with existing LD50, and the prevalence of virulent strains in food samples enhanced the estimates (Pouillot et al., 2024). The low-risk estimates of foodborne illness were estimated for all lentil dishes except for the upperbound CI estimates. These show the uncertainty in the estimates, with large variability in the results. These non-zero estimates of risk translate into a high number of occurrences of foodborne illnesses per year at the population level. This finding demonstrates the importance of probabilistic risk assessment in capturing the uncertainty across the model. Furthermore, if these estimates were translated at the population level, it would result in projected cases of foodborne illnesses. Such information can help in estimating the additional burden of lentil consumption on the population. This bridges the gap between domestic practices and food safety in the supply chain and the public health impact. Furthermore, the results can guide public health officials in evaluating any future lentil consumption promotion policies. When comparing serving conditions, a high risk associated with hot dishes can be linked to a higher frequency of consumption. This can be linked with the trend in France and Hungary to reheat batchcooked lentils. The current study points to domestic practices (i.e., cooking, cooling, reheating, and refrigeration) and hygiene at home as the main drivers of microbial risks. These highlight the role of heating in reducing the initial microbial load. The cooling at room temperature for 12 h in this situation (i.e., low contamination level, low room temperature, high heat treatment) was not shown to pose microbial risks. Nevertheless, leftovers must be kept at a temperature lower than 6°C and consumed within 72 h. In France, this decrease in temperature must be achieved within 2 h. It was also demonstrated that food safety practices play a role, such as maintaining the cold temperature for cooked meals and proper cooking of foods. The implications of domestic practices go beyond the current scenario to other legume-based foods prepared in mixed dishes (e.g., legume with sausages). The existing risk associated with domestic preparation and storage practices will drive the risk in full or partial plantbased diets. This is expected to rise as the intake of legumes increases. Therefore, current domestic practices must be considered as lentil consumption is promoted (Ministère des Solidarités et de la Santé, 2019; National Institute of Pharmacy and Nutrition, 2021; PNNS, 2019; Okostányér, 2021). These results can also guide institutional catering establishments that employ large-scale batchcooking practices and storage. Lentil consumption is also being pushed in these settings as part of an effort to consume alternative proteins in both France and Hungary (Magrini et al., 2021; Nagy et al., 2021). The use of microbial risk assessment can also be done in other contexts, such as mixed food settings and with other protein alternatives. Separate microbial risk assessments must be performed with respect to its hazards, processing, and associated domestic practices. Nevertheless, these future assessments may be informed by the current study through the inputs used and the modelling framework applied. Additionally, the current study can be combined with these future assessments of other protein alternatives to have a more complete picture of the risk of protein alternatives and their impact on the overall diet of the population. Therefore, updated data are needed to determine the prevalence of virulent B. cereus strains and microbial concentration levels in imported or locally produced lentils. An updated survey on L. monocytogenes in domestic settings and refrigerators from these two countries is needed. The consumer data used (INCA 3) is the most comprehensive data available for the French population in terms of mean consumption. Its new version is still being prepared by the Santé publique France and Anses, which is called the Albane survey (https://www.enquetealbane.fr/). The current modelling approach can enhance future estimates of risks and additional health burdens to the population. Food consumption trends, particularly those of a plant-based diets, are on the rise due to their nutritional benefits and low environmental impact. These can also be linked to their positive consumer perception (Spendrup and Hovmalm, 2022). However, despite these plant-based foods being linked to several chemical risks (e.g., heavy metals and mycotoxins) (Lin et al., 2023). In lentils, the heavy metal cadmium, which is introduced in the field due to the use of chemical fertilisers, has been found in France (Plateforme de Surveillance de la Chaine Alimentaire, 2023) and other countries (Six and Smolders, 2014; Zhang et al., 2020). Therefore, chemical risk assessment should be performed in future studies of lentil consumption and the overall lentil supply chain. Several studies have also employed risk–benefit analysis in determining the chemical risk and nutritional benefit of substituting red meat with pulses (Fabricius et al., 2021). In terms of microbial risk, the reduction of meat consumption can lead to the reduction of foodborne illnesses linked to meat consumption (e.g., Salmonella, Escherichia coli, and Campylobacter) (Warmate and Onarinde, 2023). This can also reduce the introduction of meat in domestic settings, as microbes are usually cross-contaminated from meat and spread across kitchen surfaces and utensils (van der Vossen-Wijmenga et al., 2025). Overall, these highlight the necessity of including the assessment of microbiological risk before recommending new diets. The current study contributes to this juncture but from a microbial food safety perspective. This research highlights the added value of QMRA in determining the health burden that may arise from any unintended and implicit microbial risks. First, it estimates the microbial risk due to the consumption of one food product, and then, it promotes good consumer practices that can help minimise risks (if possible). In addition to these possible tools, a more nuanced and holistic view of food policy on dietary shifts must be considered. Steering consumer consumption is not easy, as it must be based on individual choices. Ultimately, policies that aim for “dietary shifts” must be an inclusive and multistakeholder approach that respects consumer rights in all of its aspects. Indeed, microbial risk, chemical risk, and nutritional benefit are not the only risks that may arise from the food supply chain. As mentioned in the introduction, environmental impact must also be considered, and a win–win solution must be attained. A recent publication highlights the different tools and frameworks that can be used for holistic risk assessments (Országh et al., 2024). This paper is a part of a bigger study incorporating other dimensions. 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