“With traditional drying systems, you need to remove samples to monitor the process. But with smart drying, or precision drying, you can continuously monitor the process in real time, enhancing accuracy and efficiency,” said corresponding author Mohammed Kamruzzaman, assistant professor in the Department of Agricultural and Biological Engineering (ABE), part of the College of Agricultural, Consumer and Environmental Sciences and The Grainger College of Engineering at Illinois.
The researchers focus on three optical sensing systems - RGB imaging with computer vision, near-infrared (NIR) spectroscopy, and near-infrared hyperspectral imaging (NIR-HSI) – discussing the mechanisms, applications, advantages, and limitations of each.
They also provide an overview of standard industrial drying methods, such as freeze drying, spray, microwave, or hot-air oven drying, which can be combined with the precision monitoring techniques.
“You can use each of the three sensors separately or in combination. What you choose will depend on the particular drying system, your needs, and cost-effectiveness,” said lead author Marcus Vinicius da Silva Ferreira, a postdoctoral fellow in ABE.
The study found that NIR-HSI is the most comprehensive of the three techniques.
It scans the whole surface of the product, so it provides much more precise information about the drying rate and other features than NIR alone, since it extracts three-dimensional spatial and spectral information. The downside is that this method is more expensive.
All three methodologies must be combined with AI and machine learning to process the information, and the models must be trained for each specific application.
The researchers also developed their own drying system to test the various methods. They built a convective heat oven and tested the techniques on the drying of apple slices. They first combined the system with RGB and NIR; later they also tested the NIR-HSI system, the findings of which they plan to discuss in a forthcoming paper.
