Researchers develop ways to measure quality of fruits, vegetables
How do we know when a mango is perfectly ripe and ready to eat? How do we know when avocadoes are suitable for transport? Will this tomato variety thrive in drier climates?
These are questions that Rick van de Zedde and his team at Wageningen University in the Netherlands proposed before developing advanced technology for objective and automated quality control of fruits, vegetables and other crops. The technology is referred to as quality phenomics.
Concerned with the measurement of phenomes, phenomics considers physical and biochemical traits of organisms – in this case, fruit and vegetables – and analyzes how they change in their environment.
Van de Zedde said that the work was inspired by requests and problems from industry stakeholders, including breeders and trading companies.
“Their goal was to understand and improve quality developments in fruit and vegetables in the logistics chain and in the breeding process,” he said.
At Wageningen, quality phenomics researchers use a wide variety of equipment to assess products quickly, reliably and nondestructively. Their research incorporates 3D product reconstruction (size, shape and color), near infrared spectroscopy (which looks at water, nutrients and ripeness) and acoustic sensors (which test ripeness and texture).
In a talk at this year’s Fruit Logistica trade fair in Berlin, Germany, Van de Zedde, senior researcher at Food and Biobased Research, Wageningen, said that assessments are fast, reliable and non-destructive. He also discussed the specific technology developed to test specific products.
From a consumer perspective, he said, not every mango is delicious when you buy it in the supermarket. There is huge variability not only on the supermarket shelves, but also on the tree. This makes bringing ready-to-eat product to the consumer a challenge.
In order to better understand what’s going on inside the fruit, Van de Zedde and his team took acoustic measurements. The aim, he said, is to manage the ripening process more efficiently, reduce waste and improve the quality of the fruit on the shelves. During the course of their research, they discovered that initial firmness and fruit size play an important role in predicting the speed of maturation. Using acoustics allowed Van de Zedde to further measure firmness in a non-destructive manner.
“If you ‘tick’ on a mango that is very firm, you get like a high sound,” he said. “If it’s really soft, you get a low sound. Our goal is to really understand when does it reach, within a few days, the ready-to-eat level, when you can actually sell it to a consumer.”
Not only do the researchers take acoustic measurements, they also use compression tests to see how easy it is to bend the fruit’s skin. They then compare those measurements with the acoustic measurements. The same test is also used for bananas and avocados.
“Our goal is to find ways to non-destructively predict the quality of this particular mango,” Van de Zedde said.
Another piece of equipment, PTR-TOF-MS, takes airborne measurements to measure volatiles produced by the fruit.
In another example, Van de Zedde shared research on apples infected with two types of fungus. The goal was to try to figure out how the fungus was growing and which storage conditions contributed to that growth.
“We can use this information for optimizing storage,” he said.
While breeders, researchers and trading companies benefit most from quality phenomics, the technology is also an asset to the processing sector. For example, quality phenomics can be used to control entry of fresh produce, allowing processors to choose the most suitable produce for end products, said Van de Zedde.
“Currently, this is manually done by experts,” he said. “We focus on automation of these tasks.”
As part of a larger research and development program, Van de Zedde and his team at Wageningen are designing a toolbox for companies that will enable them to check the quality of fruit, vegetables, flowers and potatoes at the beginning of produce chains. Doing so will allow them to better predict the quality of products as they move down the chain and allow them to make logistical decisions on their journey from farm to fork.
— Melanie Epp, contributing writer
