AI augmentation: Three key trends driving the need for new tech in traditional commodities

AI augmentation: Three key trends driving the need for new tech in traditional commodities

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This has become particularly noticeable in when it comes to dealing with high-velocity, high-quantity food ingredient sectors such as cocoa, coffee or soybeans, and it is a concern not only for food ingredient suppliers but also large and small processed food brands as there is a direct impact on end-products.

“We commonly refer to these ingredients as ‘commodities’ today as the public idea of these food items is usually that of bulk quantity and less of differentiated grades or quality – but the fact is that these are the items that become the ingredients to be used in end-products, and it is important to ensure the quality is high from the procurement stage,”​ AI technology firm ProfilePrint Founder and CEO Alan Lai told FoodNavigator-Asia​.

“As opposed to sample testing, which is currently the most common way to ensure quality, the only feasible long-term method is to develop technology to do this, and here there is a very clear role for AI to play in the upstream grading of commodities to affect the downstream food products.

“This is becoming particularly clear is in the field of alternative protein, which we see as a high potential growth area because a lot of soybeans today are being used to make these alternative protein products, and selection of the wrong grade or type of soybeans in the beginning could mean ending up with a lousier product.

“There are many characteristics that need to be considered in choosing soybeans to make plant-based meat alternatives, for example – its freshness, its fibre content, its protein content, all of these need to be taken into consideration to make the end-product up-to-standard, and AI can help to do this quickly and efficiently.”

Rising consumer demand for products on both ends of the pricing scale – premium and lower-cost – also presents an interesting opportunity for AI technology to come into play.

“Prices again correlate back to grading, and this is where AI can come in and allow for faster grading to take place so that higher grade ingredients can be prioritised for use in premium ranges,”​ said Lai.

“This is important as the industry is now seeing premiumised demand, where consumers are looking for not just rice to fill their stomachs but rice that fits a desired sensory profile or has certain functional benefits – and they are willing to pay for this, which creates opportunities for the industry to be more premium and price products differently according to grades.

“On the other end of the spectrum, we are also seeing many consumers concerned about price hikes and also unwilling to compromise on product quality to maintain lower costs. This is a situation that could be overcome by using AI to help brands ascertain whether they are overproviding quality by unnecessarily using expensive ingredients, and if replacing a certain percentage of that ingredient with a cheaper source could still maintain end-product quality.”

Listen to the podcast above to find out more.

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