EFFoST2024_Poster_Risk assessment and prediction models for fungal spoilage in strawberries post-harvest

Risk assessment and prediction models for fungal spoilage in strawberries post-harvest Acknowledgements: FRIETS Project funded under the European Union’s Horizon 2020 Framework, Grant Agreement No.101007783. Alessandra Marcon Gasperini1, 2 and Vasilis P. Valdramidis1, 3 1 Department of Food Sciences & Nutrition, Faculty of Health Sciences, University of Malta, Msida MSD 2080, Malta 2 MycoLab, Department of Clinical, Pharmaceutical and Biological Sciences, School of Life and Medical Sciences, University of Hertfordshire, Hatfield AL10 9AB, United Kingdom 3 Laboratory of Food Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Zografou, 157 71 Athens, Greece Fungal spoilage in strawberries represents a significant economic challenge for growers and distributors and poses serious food safety concerns due to the potential for mycotoxin contamination. The research in the project FRIETS focuses on developing processing techniques to improve the quality, nutritional value, and shelf-life of soft fruits like strawberries. The aim of this work was to identify the most common fungal contaminants able to produce mycotoxin in strawberries and to construct a comparative risk assessment system for evaluating and ranking fungal and mycotoxin hazards. Published datasets and peer-reviewed studies were used as representative references (n=71). Fungal contaminants in strawberries were clustered by genus, and mycotoxin concentrations were standardized to same unit and ranked for comparison to assess the most reported. Primary models were used to estimate the growth rates (µ) and lag phases (λ) for Alternaria, Penicillium and Aspergillus, with adjustment coefficients (k) for field conditions*. Alternaria, Aspergillus and Penicillium spp. were the most common spoilers in strawberries, with associated mycotoxins posing risks. Temperature control is crucial to mitigate spoilage, but species-specific growth factors must be integrated into risk assessments. Future research will assess mycotoxin risk to build model-based process optimization to produce safe and stable products. Risk assessment models predicted a low risk of spoilage when strawberries were stored and retailed at 4°C. However, increasing the retail storage temperature resulted in an increased risk of spoilage by Alternaria spp. Additionally, a scenario with storage at 4°C and retail at 24°C predicted a high risk of spoilage by Alternaria spp. and moderate spoilage risk by Aspergillus and Penicillium spp. d: Time elapsed after harvest (7 days = 5 in storage and 2 in retail ) Ts: Storage temperature Tr: Retail temperature µ: Growth rate (mm/day) To: Optimal temperature for growth : Lag phase at optimal temperature (days) Tavg,: Average temperature µadj.: Growth rate adjusted T avg = T s if d ≤ 5 T s + d − 5 2 × T r − T s if 5 < d ≤ 7 T s + T r 2 if d > 7 µ adj = µ × exp ቆ 0 . 1 × T avg − T o if T avg ≤ T o − 0 . 05 × T avg − T o if T avg > T o Fungal growth = ቊ 0 if d ≤  max ( µ adj × ( d −  ) , 0 ) if d >  Prediction of fungal growth post-harvest High - If spoilage is predicted and the proportion is greater than 20% Moderate - If spoilage is predicted but the proportion is 20% or less Low - no spoilage is predicted R isk level = " High" if ∑ P length P > 0 . 2 and any P . " Moderate" if ∑ P length P . ≤ 0 . 2 and any P "Low" if any P Risk level for fungal spoilage The probability of spoilage for each spoiler group was based on the colony sizes (growth) Probability of spoilage Visible spoilage to the naked eye Colony size threshold = 2 mm Upper level = 2.4 mm (+20%) lower level = 1.6 mm (-20%) METHODS RESULTS SUMMARY Strawberries had the highest number of reports on mycotoxin contamination within soft fruits searched in the literature. The presence of Alternaria associated mycotoxins (alternariol monomethyl ether and alternariol) were observed. Mycotoxins with regulatory limits, including fumonisins, aflatoxins, ochratoxin A, and patulin, were frequently reported. Alternaria ssp. Penicillium ssp. Aspergillus ssp. ----- Visible spoilage cut-off Days post-harvest (5 days storage, 2 days retail) Simulated fungal growth (mm) Figure 2. Simulated colony growth trajectories for three fungal groups (Aspergillus, Penicillium, and Alternaria) under different storage and retail temperature conditions post-harvest. Growth was modelled to assess time to visible spoilage, defined as reaching a threshold colony diameter of 2 mm. Shaded areas represent variability in growth rates based on upper and lower estimates. Horizontal lines indicate the visible spoilage threshold, beyond which spoilage is considered visually detectable. Figure 3. Fungal spoilage risk assessment matrix considering retail and storage temperatures over 7 days postharvest. 1) Acremonium spp.; 2) Aureobasidium spp.; 3) Epicoccum spp.; 4) Neosartorya spp.; 5) Rhizoctonia spp.; 6) Trichoderma spp.; 7) Clonostachys spp.; 8) Geosmithia spp.; 9) Geotrichum spp.; 10) Monilinia spp.; 11) Phytophthora spp.; 12) Sphaeropsidales spp.; 13) Ulocladium spp. Alternaria spp. Aspergillus spp. Penicillium spp. Fusarium spp. Botrytis spp. Cladosporium spp. Mucor spp. 1 2 3 4 5 6 7 8 9 10 11 12 13 Rhizopus spp. Figure 1. The assessment of contamination in strawberries has shown a high prevalence of Alternaria spp. followed by Aspergillus, Penicillium spp. *Hypothesis for the adjustment coefficient (k) for field conditions: growth ability is 90% reduced and the lag phase is prolonged by a 3.0 factor