Manufacturing
“Cost predictions and processing predictions have to be a central core to these decisions, so we are very focused on ramping up cost data in ROI models for our customers.”
Since implementing the Journey Foods platform,
Lynn’s customers, which include the likes of Ingredion and Unilever, have seen huge savings in both time and money, in large part because the platform helps them reduce the number of trial production runs required to switch to a new product or formulation.
“Sometimes smaller runs can take several days, weeks or months to get a trial out. But we’ve seen a reduction sometimes from 30 trials to four for changing over to a gluten-free cookie or an alternative like that,” Lynn says.
Getting to production faster also means savings in the hiring process. “That’s been our first goal; how do we decrease the time and money it takes to get a new formulation to the market and get products on the market for consumers faster,” Lynn says. “Those savings can then be handed down to anything from more expensive ingredients to reinvesting in better packaging.”
Becoming the best digital twin Journey Foods’ client base is currently spread across North America, Europe and Asia. Lynn’s aim now is to continue to grow in southern Europe and expand to West Africa and South America, while also improving the efficacy of the Journey Foods database. “We really want to focus on how to be the best digital twin to a lot of processes that exist today,” she explains. “A big goal of ours has been to extend out the integrations that we have and partnerships we have across the world so we can solve a lot of issues around collaboration.” For example, Lynn is looking into the other cost tools that are out there and whether there are nutritionists that could use the Journey Foods tool to help their clients. “Are there universities and food science programmes that can apply our data and tools?” she muses. “How can we continue to extend out the data in various industries and career types to continue to drive impact within food?” At the same time, Lynn and her team are continuing to hone their AI and machine learning models. “We’re looking at everything from what’s the best packaging for a food product to what’s the best ingredient to lower water use. Instead of going through multiple trials and calling multiple distributors, how can we get that into a platform that tells food companies that information instantly? These are the questions that we continue to ask as we continue to work on our Journey Foods platform,” she concludes. ●
Ingredients Insight /
www.ingredients-insight.com
Increase efficiency using AI and bioinformatics
The Journey Foods platform is just one example of how AI can be used to improve the effi ciency of the ingredient manufacturing sector.
Scientists with the USDA Agricultural Research
Service’s (ARS) Western Human Nutrition Research Center (WHNRC), at the University of California (UC), Davis, have joined forces with over 40 researchers from six organisations to form an institute with a similar goal to Lynn’s: meeting growing demands in the food supply chain by increasing effi ciencies using AI and bioinformatics. Their remit is slightly wider, covering the entire food system – from growing crops through to consumption.
The team, led by UC Davis, also includes UC
Berkeley, Cornell University and the University of Illinois at Urbana-Champaign. The project is funded by a $20m grant from USDA’s National Institute of Food and Agriculture. “The AI Institute for Next Generation Food Systems (AIFS) is dedicated to accelerating the use of artifi cial intelligence to optimally produce, process and distribute safe and nutritious food,” says Dr Danielle Lemay, a USDA research molecular biologist at WHNRC.
In the area of food processing and distribution, which is the most complex aspect in the food system chain according to AIFS, the organisation will focus on enabling better outcomes in food safety, nutritious value and reducing waste through advancements in AI and process innovation. To address food safety challenges, such as microbial and chemical contamination, AIFS is developing AI models to integrate food microbial ecology, chemometric and physical data sets, as well as creating digital twin models of food processing operations to include sanitation and food handling and transport to simulate pathogen transfer between humans and their environment.
Meanwhile, as demand grows to reduce chemical inputs and energy and water consumption during the production process, AIFS also plans to work on developing innovative food processing operations with a smaller production footprint.
This will include developing AI models to optimise food processing inputs (such as energy and water) and predict food processing outputs, integrating data sets from mechanical, thermal and chemical inputs. The organisation also hopes to predict and optimise product quality outputs such as texture, colour, fl avour and nutritional value by developing digital twin models of food processing operations.
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WAYHOME studio/
Shutterstock.com
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