Hello S***o, I’m J***e, an HR recruiter with StrategyBrain. I was impressed by your quality assurance work at Q***y F***d G***p and your shelf-life studies and insect-based product development (like I***T). We’re partnering with I***d B***h S***a to hire a Novel Food Quality and Shelf-Life Engineer, and your background seems like a great fit. Would you be interested in learning more about this role?

Hi S***o, Great to hear you’re interested! In this Novel Food Quality & Shelf-Life Engineer role at I***d B***h, your day-to-day would look something like this: • Designing & running shelf-life and thermal treatment studies for both novel insect-based products and gluten-/lactose-free bakery items,planning experiments, sampling, and analyzing results. • Implementing and maintaining HACCP and quality-assurance systems per EU food-safety and autocontrollo regulations,auditing production lines, documenting protocols, and training operators. • Collaborating closely with R&D scientists (where you’ll help refine insect-protein formulations), packaging engineers (to select compostable or PLA materials), and production managers to scale up promising recipes. • Interpreting data, writing technical reports, and recommending process improvements to boost product stability and shelf life. You’ll join a dynamic, mid-size food-tech team that provides an on-site pilot plant, analytics lab and cross-functional support. There’s a clear path to senior engineer or project-lead roles as I***d expands, plus ongoing training and mentoring. Does this give you a good sense of the day-to-day and team setup? Let me know if you’d like any more detail!

Hi S***o, Great questions,here’s a quick overview: 1. Team structure - R&D: About 6,8 people (food technologists, shelf-life specialists and process engineers) plus 2 pilot-plant technicians. - Quality Assurance: 3,4 full-time QA specialists led by our QA Manager. You would report directly to the QA Manager and partner closely with the R&D Director on project priorities. 2. Main scaling hurdles for insect-based products at pilot stage - Ensuring microbial safety via thermal treatments without degrading protein functionality or introducing off-flavors. - Managing batch-to-batch variability in insect flour (water activity, fat content, color). - Adapting our HACCP plan and testing protocols to a novel raw material under EU Novel Food regulations. - Fine-tuning formulations for consistent texture and shelf life in a bakery context. We tackle these through iterative pilot runs, strong QA/R&D collaboration, and continual regulatory engagement. Would you like to set up a short call next week with our QA Manager and R&D lead to dive deeper?

Hi S***o, Great questions,here’s a bit more detail: 1. Microbial‐safety validation in insect trials - We run full-scale challenge tests on our pilot batches, inoculating key pathogens (Salmonella, Listeria, E. coli) at known levels to verify our thermal protocols achieve ≥5-log reductions. - In parallel we leverage thermal death‐time models (D- and z-values) developed in our lab to predict kill steps and optimize time/temperature profiles. - Once we’ve dialed in parameters in small‐scale challenge runs, we confirm at the pilot‐plant level with environmental monitoring and finished‐product pathogen testing. 2. Accelerated shelf‐life for bakery formulations - We use a controlled chamber at elevated temperatures (typically 30°C, 37°C and 45°C at 60,70% RH) and sample at set intervals (days 7, 14, 21, etc.). - At each point we measure moisture content, water activity, texture (e.g. crumb firmness via texture analyzer), lipid oxidation (TBARS) and microbial counts (molds/yeasts). - Data are modeled using Arrhenius kinetics (Q10 approach) to project real‐time shelf life, supported by periodic sensory checks. If that aligns with what you were looking for, I can set up a 30-minute call next week with our QA Manager and R&D Lead to go deeper. What’s your availability on Wednesday or Thursday morning (CET)? Looking forward to your thoughts! Best, J***e HR Recruiter, StrategyBrain

Hi S***o, Great questions,here’s how we handle both: 1. Scaling D- and z-values from lab to pilot plant - We start by doing standard D- and z-value determinations on small lab coupons. At the pilot scale, we conduct heat‐penetration mapping: placing thermocouples at core and surface of our actual pilot‐plant batches to record time,temperature profiles. - We then compare the lethal rates (F₀ values) derived from those profiles against our lab predictions. Any systematic deviations,due to vessel geometry, load size or agitation,are translated into correction factors that fine-tune both D- and z-values in our process models. - Finally, we validate the adjusted values with pilot-scale challenge tests, confirming we still hit our ≥5-log reduction targets without over-processing the protein functionality. 2. Cross-checking Arrhenius projections with real-time sensory - Alongside accelerated‐temperature runs, we run parallel real-time shelf-life trials on pilot batches stored under normal conditions. Every month (and at end-of-life), our sensory panel scores flavor, aroma and texture against acceptance thresholds. - We overlay those sensory thresholds onto our Arrhenius‐modeled degradation curves (moisture migration, lipid oxidation, texture loss). If the real-time data show earlier sensory drift than predicted, we revisit the activation energy (Ea) used in the model and recalibrate for that product matrix. - This feedback loop ensures our shelf-life projections stay aligned with what consumers actually perceive. I hope that clarifies things. Would you like to discuss these approaches in more detail? I’m happy to set up a 30-minute call with our QA Manager and R&D Lead. How does Wednesday 9,11 AM CET or Thursday 10 AM,12 PM CET look for you? Best, J***e HR Recruiter, StrategyBrain

Hi S***o, Glad to dive into the details: 1. Pilot-scale replicates & curve fitting - We typically run 3 full replicates per product/load for heat-penetration mapping to capture variability in vessel geometry and fill. - Thermocouple data are processed via non-linear regression (we use R scripts or JMP) to fit D- and z-value models, with initial estimates cross-checked against ComBase Predictor or our in-house Excel/MATLAB tools. 2. Sensory acceptance thresholds - A trained panel scores aroma, texture (crumb firmness), off-flavor and overall liking on a 9-point hedonic scale. - We set end-of-life when mean overall liking falls below 6, or when off-flavor increases by ≥2 points or firmness increases by >15% relative to Day 0. Flavor integrity and texture consistency are our primary drivers. I’ll pencil in Thursday at 10:30 AM CET for a 30-minute call with our QA Manager and R&D Lead. Does that work for you? Best, J***e HR Recruiter, StrategyBrain