Sweet potatoes are a popular food choice for consumers worldwide because of their delicious taste and nutritious quality. The red, tuberous root vegetable can be processed into chips and fries, and it has a range of industrial applications, including textiles, biodegradable polymers, and biofuels.
Sweet potato quality assessment is crucial for producers and processors because features influence texture and taste, consumer preferences, and viability for different purposes. A new study from the University of Illinois Urbana-Champaign explores the use of hyperspectral imaging and explainable artificial intelligence (AI) to assess sweet potato attributes.
“Traditionally, quality assessment is done using laboratory analytical methods. You need different instruments to measure different attributes in the lab, and you need to wait for the results. With hyperspectral imaging, you can measure several parameters simultaneously. You can assess every potato in a batch, not just a few samples. Spectral imaging is non-invasive, fast, accurate, and cost-effective,” said Mohammed Kamruzzaman, assistant professor in the Department of Agricultural and Biological Engineering (ABE), part of the College of Agricultural, Consumer and Environmental Sciences (ACES) and The Grainger College of Engineering at Illinois.
The study is part of a multi-state collaboration funded by the U.S. Department of Agriculture that includes researchers from Mississippi, North Carolina, Michigan, Louisiana, and Illinois. Each university addresses different aspects of the project; Kamruzzaman’s team focuses on the assessment of three chemical attributes — dry matter, firmness, and soluble sugar content (degree brix) — which affect the market price and whether a potato is suitable for the consumer or for processing.