- Background
- What we're looking for
- What we can offer you
- Q&A
We are developing a new generation of high-moisture, shelf-stable ready-to-eat (RTE) meals that deliver safety, convenience, and high sensory quality. To ensure microbial stability, all inclusions must reach a final pH ≤ 4.5 prior to in-package pasteurization.
The current challenge lies in pre-acidifying vegetables and meat pieces using organic acid brines. Achieving the target pH often requires extensive trial and error due to variable diffusion rates, buffering effects, inclusion size/geometry, acid type and strength, and temperature. This slows development, increases cost, and risks sensory degradation.
Partners with proven expertise in product acidification, pre-built predictive modeling, and process understanding could help develop a validated predictive capability to reliably forecast pH evolution and equilibrium pH for acidified inclusions, translating these outputs into actionable guidance that supports robust, scalable acidification processes.
We are looking for a validated predictive modeling approach that characterizes acidification behavior in vegetable and meat pieces inclusions and reliably estimates pH trajectories over time (surface and core) and equilibrium pH for relevant matrices and sizes, translating predictions into actionable operating conditions (acid type/strength, bath composition, dwell time, temperature).
The ultimate goal is to use predictive insights to reduce trial-and-error and support the development of a formulation and brine processing strategy using organic acids, or acid blends that reliably achieve pH ≤ 4.5, demonstrate effective microbial inhibition, preserve taste, color, and texture, and comply with food safety requirements.
- Diffusion/buffering/mass transfer models parameterized for lactic, acetic, citric, and similar acids.
- Experimental datasets supporting model parameterization and validation.
- Models that incorporate matrix characteristics such as porosity, water activity, ionic strength, and buffering capacity, and temperature effects.
- Predictive tools estimating time to target pH and equilibrium pH for specified inclusion sizes (e.g., 5–15 mm dice) and other relevant geometries.
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Organic acids only: Solution developed using only the organic acids (ex, lactic, acetic, citric, or other similar acids)
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Accounts for key drivers such as diffusion, buffering capacity, acid strength, inclusion geometry, and temperature
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Demonstrates pH versus time profiles (surface and core) and equilibrium pH for both vegetables and meat pieces in brine process.
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Designed to support accurate predictions across at least 3 inclusion types, 3 organic acids, and 2 sizes per inclusion.
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Delivers a transparent model/tool (mechanistic, data driven, or hybrid) capable of outputting operating conditions (acid strength, bath composition, time, temperature) suitable for manufacturing use.
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Insights on how temperature, time, acid type, inclusion geometry and matrix properties impact acidification efficiency and sensory outcomes.
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Design space views (e.g., response surfaces, isopleths) and simple user interfaces (UI, spreadsheet, or API) to make predictions actionable for R&D testing environment.
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Scalability considerations including turnover, agitation/flow, brine to mass ratios, and continuous vs. batch options.
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Sensory integrity: predictive model that offers insights to understand potential undesirable taste, color, and texture with methods specified for measuring color (e.g., Lab* / ΔE*), firmness, and flavor vs. controls.
- Partners that without experience in food product acidification via brine process.
The Q&A is now closed.
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