Dynamic Imaging Particle Analysis for the Food and Beverage Industry
No matter what part you play in the food chain, knowing your ingredients is critical. In the food industry, a product’s success or failure is usually based upon subjective, qualitative attributes such as taste and texture. Unfortunately, since these attributes cannot be directly measured, the industry needs to rely on “tasting panels” using human subjects in order to gather information on prospective products. This is both time consuming and expensive.
The FlowCAM® can help decrease time to market by offering the ability to collect quantitative data in the laboratory that can be correlated to qualitative data such as taste and mouth-feel. It is well documented that particle size, and more importantly particle shape, correlate directly to the taste and mouth-feel of a food product.
Most particle analyzers show you a distribution of particle size only. The FlowCAM dynamic imaging particle analysis system gives you a picture and data for every particle measured. You can filter and sort particle images just like you would in a spreadsheet. Plus using powerful pattern recognition algorithms you can quickly and easily identify and quantitate individual particle types in a heterogeneous sample.
Ensure the contents of your product, detect process flaws early, and enhance R&D efforts using the FlowCAM. For use in both research and development and production settings, the FlowCAM can be used for analyzing: hydrocolloids, fibers, encapsulated products, carbohydrates, stabilizers, emulsions, gums, and flavorings. Keep reading for a few specific examples.
Some examples of how the FlowCAM can be used in the Food and Beverage Industry Include:
The manufacturer of a barbecue sauce needed to quantify particles in a pulp from one of its ingredients, to ensure that they were relatively uniform in size and shape after mixing and homogenization, as it greatly affects taste and mouth-feel.
In order to isolate these particles from all of the others in the sample, a library of the desired particle type was built and stored. Then VisualSpreadsheet® performed a statistical pattern recognition operation, to automatically find the particles that matched the desired particle type. The image to the right shows the ideal pulp particle as imaged by the FlowCAM.
The analysis concluded that 55 particles out of several thousand total were determined to be of the desired type. The FlowCAM provides the concentration and summary statistics for the desired particles only, omitting the other particles in the sample. This same statistical comparison can then be performed on other samples for comparison, eliminating the operator bias and error found in manual microscopy.
Since many more particles can be counted in much less time, the statistical confidence of the results will be much higher. By using an automated analysis with FlowCAM, the manufacturer was able to rapidly study the effects of several different variables in the manufacturing process, such as mixer speed, homogenization time, and temperature.
A beverage manufacturer was interested in characterizing size, shape, and concentration of large pulp particles in their juice. They had previously done this manually, but it was very time-consuming. Using the FlowCAM, they could characterize thousands of particles automatically in a matter of minutes. The below image shows results of FlowCAM run with fruit juice sample. The left-hand window shows summary statistics and graphs, and the right-hand window shows selected images of juice pulp.
FlowCAM provides a method to quickly check the quality of incoming raw materials, including sorbitol. Sorbitol has many uses, the most common being as a sugar substitute, and its application is often dependent upon particle size and shape. Below are the results of a FlowCAM run with Sorbitol sample. Left-hand window shows summary statistics and graphs, and the right-hand window shows selected images of sorbitol particles.
A chocolate manufacturer needed to determine the content of sugars in their final product. While the product being sampled contains many different types of particles, the sugars will appear to be crystalline in nature. This means that their shape will be quite distinct, and also that they will appear more transparent than the other particle images captured.
Using the FlowCAM, a direct correlation was found between the sweetness of the product and the concentration of crystalline particles. In this study, a premium milk chocolate sample had a 3.5% crystalline content, whereas a premium dark chocolate sample only had a 0.7% crystalline content. This directly correlates to the subjective observation found in a taste test that the milk chocolate is sweeter than the dark chocolate.
Below are the results of the automatic pattern recognition run with VisualSpreadsheet on a chocolate sample. Out of 20,000 particles contained in the original data (having a concentration of 7.5 million particles/ml), 360 crystalline particles were found having a concentration of 134,698 particles/ml.
Microencapsulation is a common technique for delivering particles in a wide range of applications, including foods. In the microencapsulation process a small amount of a substance (active ingredient) is packaged inside a second substance in order to shield the active ingredient from the surrounding environment. The FlowCAM yields tremendous insight into the process of coacervate formation, because you can dynamically monitor capsule formation over time while studying the effects of temperature, concentration, pH and other variables that affect the process. Below are FlowCAM images of encapsulated flavors.
Chromatography can be used in the food industry for quality control as it allows for the separation and analysis of additives, vitamins, preservatives, proteins, and amino acids. It can also separate and detect contaminants. The FlowCAM provides critical size and shape information on the column packing materials used in chromatography. This allows for tighter column density control, and in turn, better control of column performance. It also helps in the tracing of the damaged (non-spherical) particles that are often present in different lots of packing material.
The image below shows results of a FlowCAM analysis of column packing material. The image on the left shows round, acceptable particles, whereas the image of the right shows less round, unacceptable particles. The FlowCAM allows you to automatically characterize either acceptable or unacceptable particles, and delivers your statistical results immediately upon completion of the run.