Food Engineering & Food Processing

Highly specialised requests are first checked for feasibility from this information. Please do not send confidential data through the public form; describe it first and agree secure transfer afterwards.

1. 01Enquiry and requirement assessment
2. 02Technical scoping and proposal
3. 03Execution
4. 04Quality review and delivery, with an agreed round of revisions

Read the full project workflow.

In depth: how the work is done

1. Designed experiments and response-surface methodology

   Processing variables such as temperature, time, moisture and pressure are explored by factorial and response-surface designs to optimise quality attributes like colour, texture, nutrient retention and yield. Raw-material variability is handled by blocking on batch. Optimisation across several responses uses desirability functions or Pareto methods, and confirmation experiments check predictions.

2. Heat- and mass-transfer modelling

   Thermal processing, cooling, drying and frying involve conduction, convection and diffusion in materials whose properties change with temperature and moisture. Models use effective diffusivities and heat-transfer coefficients that are measured or fitted and depend on product and conditions. Numerical solutions require temperature-dependent properties for accuracy, and are validated with embedded temperature or moisture measurements.

3. Kinetic modelling of quality changes

   Degradation of vitamins, colour change, lipid oxidation and microbial inactivation are described by first-order, Weibull or other kinetic models with temperature dependence via Arrhenius or z-value approaches. Parameters are estimated by non-linear regression, and extrapolation beyond tested conditions is uncertain. Model discrimination and confidence intervals indicate how well the data support the chosen model.

4. Sensory and instrumental data analysis

   Instrumental texture, colour and chemical measurements are complemented by sensory panels. Panel data require design considerations such as randomisation of presentation order and panellist training, and analysis by mixed models or multivariate methods. Correlating instrumental and sensory measures helps develop objective quality indicators, with validation on separate samples.

5. Preservation, packaging and shelf life

   Shelf-life depends on water activity, temperature, oxygen, packaging and microbiology. Accelerated tests use elevated temperatures to speed reactions but may change mechanisms. We design shelf-life studies with appropriate sampling times, define failure criteria and report shelf-life with uncertainty, acknowledging that microbiological safety assessment requires specialist validation.

6. Emerging processing technologies

   High-pressure, pulsed electric field, ultrasound and microwave processing are studied for their effects on quality and safety. Study design should include conventional controls, characterise the treatment dose actually delivered and examine non-uniformity. Claims of equivalence to conventional processing require evidence under the relevant regulatory framework.

7. Measurement practice in food processing research

   Core temperatures in foods are measured with calibrated thermocouples whose placement strongly affects readings, particularly in small or heterogeneous products. Moisture, water activity and texture measurements follow standard methods with replicates and sample preparation described. We recommend recording ambient conditions, equipment loading and product initial state, because these influence results and are often omitted. Complete method descriptions let other groups reproduce and extend the work.

8. Scale-up from bench to pilot in food processing

   Equipment at bench scale differs in heat-transfer area per volume, airflow patterns and residence times from industrial systems. A model that works for a small dryer may need re-identified coefficients for a larger one. Pilot trials, with measurements of non-uniformity across trays or belts, reveal these differences. Reports present laboratory conclusions as such and list the questions that scale-up would have to answer.

Common pitfalls this service helps avoid

• Extrapolating kinetic models beyond tested temperatures. Mechanisms or rate controlling steps can change.
• Using constant properties in heat-transfer models. Thermal properties vary with temperature and moisture.
• Ignoring raw-material batch variability. Batch differences can be mistaken for treatment effects.
• Equating laboratory inactivation with process validation. Industrial food-safety validation follows regulated procedures.

What good reporting looks like

A food engineering report describes the material, processing conditions, experimental design, measurement methods, kinetic and transport models with parameters, validation, sensory or instrumental analyses, shelf-life study design with failure criteria and limitations. It does not validate processes for food safety, label claims or regulatory compliance, which require accredited laboratories and food-safety professionals.

An illustrative scenario

Hypothetical example written for explanation. It is not a client case, a result or a testimonial.

Imagine a researcher optimising hot-air drying of a fruit. A central composite design explores temperature and air velocity with batches as blocks. The model predicts an optimum balancing drying time and colour retention, and confirmation runs match within the prediction interval. A diffusion model fitted to drying curves is used to compare slab thicknesses. The report notes that fruit maturity varied and that safety attributes such as microbial load were not evaluated.

Questions to settle before you begin

• Which quality attributes define success?
• How do I block raw-material variability?
• Which temperature range do my kinetic data cover?
• How uniform is my treatment?
• Which safety questions require specialist validation?

Key terms

Water activity

A measure of water availability for microbial growth and reactions.

z-value

The temperature change that changes the decimal reduction time tenfold.

Effective diffusivity

An apparent diffusion coefficient used in drying models.

Desirability function

A way to combine multiple responses into one score.

Weibull model

A flexible model for inactivation or degradation kinetics.

Working together on Specialised Applied Sciences projects

Specialised applied-science engagements start with the field-specific question, data and methods, and with an honest check of whether the necessary domain expertise is available for your topic. We ask for protocols, datasets, instrument descriptions and relevant literature. Where a request is highly specialised, feasibility is confirmed before a proposal is issued. Analyses are documented and reproducible, results are reported with uncertainty, and any interpretation beyond the data is labelled. The service does not provide clinical, regulatory or safety advice.

Few projects need a single service in isolation. These are the services most often combined with this one.

• Design of Experiments (DOE)

  Planning of efficient experimental programmes using factorial, fractional and optimal designs so that factor effects and interactions are estimated with the fewest runs the question allows.

• Response Surface Methodology (RSM)

  Sequential experimentation and modelling to relate responses to several factors and locate favourable operating conditions, using central composite or Box-Behnken style designs and fitted quadratic surfaces.

• Heat Transfer & Thermal Simulation

  Conduction, convection and radiation analysis of components and systems, including conjugate heat transfer, transient heating and cooling, and thermal management studies.

• Packaging Technology & Sustainable Packaging

  Research support for packaging materials, structures and systems: barrier and mechanical performance, shelf-life studies, transport protection and sustainability assessment.