The story of KeyLeaf/POS’s venture into machine learning began with asking a very simple question: why not use all of the data collected over the years to predict future actions? While this question sounds like a radical way of approaching ingredient science, it is exactly the type of thinking KeyLeaf/POS decided to implement to help guide innovation in producing new ingredients.
In the past, in-depth research and development (R&D) took ingredient scientists and process engineers a lot of time and energy, and would often not yield successful results. Previously, scientist’s personal experience and unique knowledge on raw materials, functional products, and ingredients, combined with extensive research using current literature, were key to R&D. Scientists would use their collective knowledge and repeatedly test the production process, tweaking all the elements to finally come up with an optimal process to produce a suitable ingredient. This was a very research intensive and cost intensive process, but what if there was a more efficient program that could help streamline this process and guide scientists to a successful result just from knowing the raw materials involved or the desired health benefits?
This is where KeyLeaf/POS’s machine learning technology comes in. With over 40 years of data on R&D for innovative ingredients and processing, KeyLeaf/POS decided to use the information they currently have to help streamline their development of new ingredients. Dr. Nalantha Wanasundara, Computational Scientist, as well as several others at KeyLeaf/POS have worked hard to construct a program that records and compiles information from all the data that KeyLeaf/POS has gathered over their many years of R&D work. Harnessing this data, the program can predict what raw materials or processing methods are needed to achieve a certain health or functional benefit from an ingredient. In its simplest form, the database searches its information and predicts what raw materials and processing methods are needed to optimize the taste, texture, appearance, and cost of an ingredient. Using the database, scientists will be able to simply search a specific health benefit, and all of the information on the raw materials, as well as the processing methods needed to achieve an ingredient with that specific health benefit, will be displayed. They can also further refine their search by including greater detail. For example, they can search for a specific health benefit that comes from plants rather than meat.
By harnessing this big-data to facilitate innovation, this system has the potential to save ingredient scientists and processing engineers hundreds of hours in R&D and discovery work. For the first time ever, scientists and engineers can now answer the question - “where can we find the raw materials, and how can we process it?” - at the beginning of their search, with KeyLeaf/POS’s new software pointing them in the right direction early on. With innovations like these, KeyLeaf/POS can maintain its competitive advantage and stay on top of health trends in food.
With more information constantly being added, the program will continue to learn about the best methods of processing for each different raw material and resulting ingredient. Like a brain, the program learns from the data to get better at its own job. “The interesting thing about this database is that it is never complete,” says Nalantha. “We will keep adding data and information to make the predictions more accurate. More data equals better answers.”
KeyLeaf/POS continues to work on this innovation and others like it so that their partners and clients can master their research and development when it comes to innovative ingredients.
KeyLeaf/POS – lead through innovation.