We also need to know if the sponge cake’s structure, porosity, volume and shape will change over time. If it’s a sandwich cake, for instance, is the cream filling likely to leak out during shelf-life? There are also a raft of physical properties that we can analyse such as the first bite, bounce, crumb structure and moisture. Without forgetting the importance of a visually appealing golden cake crust, which we assess using a highly accurate iDigieye imaging system.
After scaling up our new range of dairy, vegan and low sugar ice cream products, we have identified several sensory and quality problems. Can you help us investigate?
The issues you’re experiencing will largely be the result of the scale up from the factory, compared to the process followed in the development kitchen. The full-scale processing equipment and freezing techniques may be affecting the product microstructure by, for example, changing the size and distribution of air bubbles, fat droplets, sugar crystals and ice crystals. And this in turn can affect the texture and performance of the ice cream during shelf-life. The actual over-run for each of the frozen products should also be assessed for any differences.
We can help by carrying out a Design of Experiment trial (DOE) to determine which ingredients and processing techniques will deliver the best results. For instance, evaluating the use of powdered ingredients with different particle sizes, or the impact of using different speeds and settings for mixers, pumps and aerators on the production line.
At the same time, we use a combination of specialist analytical tools and imaging techniques to monitor microstructural changes in the ice cream during shelf-life. These findings are then linked to sensory attributes so that we can better understand how to resolve any issues and improve product quality.
An excessive softness, for instance, could signify the ice cream has large areas of air bubbles that have coalesced. A grainy texture may be found to have much coarser ice crystals compared to softer products. While a dense texture may be due to poor emulsion stability or fat droplet aggregation prior to aeration or freezing. Whatever the sensory or quality issue, we can determine the cause and either reformulate the ice cream concept in question or suggest alternative production techniques for better results.
We have moved to a large-scale production site and want to understand if the new storage conditions will impact the processability of the raw materials used in our powdered beverage?
The best approach would be to use a combination of different analytical techniques to investigate how different environmental conditions, such as temperature and humidity, affect powder flowability and integrity during storage, production and transport.
For instance, we can identify the critical point at which moisture conditions will impact raw materials handling. Or how different storage temperatures will affect crucial powder properties such as bulk density, moisture content and water activity. We can then apply these learnings to your specific storage and production conditions.
What might this look like? We know that bridging of irregular shaped powder particles can happen when particulate materials interlock or bond together, potentially resulting in the formation of an arch above the outlet of a container. But if we scan the powder samples before and after storage under the appropriate conditions to determine the particle shape and size distribution, we can recommend how to improve flowability. Changing the type of milling could be a possible option.
Similarly, data from a shear cell test will flag up if powders are likely to consolidate or compact, causing issues during hopper/silo storage and transport. It’s also important to understand the so-called ‘wall friction’ of the powders because it tells us how much energy and force is needed to move the powder through the processing equipment. This can not only be used to improve the equipment design, but also prevent blockages or the build-up of stagnant, decaying material which has serious implications – both in terms of unwanted cleaning down time and food safety.
All these data outputs are combined and used to generate simple models which define the optimum storage and operational ranges for your powder materials. Once developed, we can also test these parameters by storing your powder samples in a controlled environment which mimics your chosen storage and processing conditions - and then refine the model if necessary.