A new scanner developed by a team in Texas A&M University’s Agrilife extension program can distinguish hemp from tetrahydrocannabinol (THC)-rich cannabis instantly, the university recently announced.
The “hemp scanner” can easily fit in a police cruiser and doesn’t damage any of the product, the university says. It uses a method of scanning that identifies a material’s structure, similar to that of taking a fingerprint.
The study was published in January in the scientific journal RSC Advances.
Dmitry Kurouski, Ph.D., assistant professor of biochemistry and biophysics at the Texas A&M University College of Agriculture and Life Sciences, led the study. He says his colleague, David Baltensperger, Ph.D., professor of soil and crop sciences at the Texas A&M University College of Agriculture and Life Sciences, initiated the scanner’s creation, as he had worked with both farmers and police and knew of the demand for a better field test to distinguish the plants.
Kurouski had experience using a technique called Raman spectroscopy to conduct quick and noninvasive tests for plant diseases and nutritional content in food. The technique uses a laser light to illuminate structures within materials. These structures assemble patterns called Raman spectra, and they are unique to materials like a fingerprint, the university says.
Previous lab members Lee Sanchez, a research assistant, and Charles Farber, a graduate student, had developed a portable Raman scanner for past research. Kurouski suspected that device could be used to distinguish hemp from THC-rich cannabis. He just needed to figure out how the two plants’ Raman spectra differed.
Sanchez was tasked with testing dozens of samples of THC-rich cannabis and hemp, which he had to do near Denver, where adult-use cannabis is legal.
"Lee Sanchez was the hero who was traveling to Colorado three times, staying there in hotels and driving from one location to another. Most of those locations are old fire stations. They are not fancy greenhouses but old, shaky buildings with plants inside," Kurouski says in a news release.
Sanchez and Kurouski analyzed the spectra in their lab in Texas. The analysis found seven regions in the spectra that differed slightly among hemp and THC-rich cannabis varieties. The university says these seven regions distinguish the plants with 100% accuracy.
"We know plants from A to Z in terms of their spectroscopic signature," Kurouski says in the news release. "But when we saw such a crystal-clear picture of THC that appeared in one second of spectral acquisition, that was mind-blowing."
The team at Texas A&M is now looking to collaborate with the industry to mass-produce the scanner, which could feasibly begin in two or three years, Kurouski says.
The scanner could be used not only by farmers to test their crops during the growing process, but also by law enforcement to distinguish hemp from THC-rich cannabis in areas where the latter is still illegal.
Stories of hemp being mistaken for illegal cannabis have been rampant since hemp’s legalization in 2018. In January of 2019, for example, Idaho law enforcement stopped 6,700 lbs. of hemp on the way from an Oregon grower to Big Sky Scientific in Colorado. In June of that year, a pilot refused to take off with a shipment of legal hemp from Mountain Strong Hemp Company headed for Shady Lane Farms in Tennessee.
Texas hasn’t been immune to the issues. A truck carrying thousands of pounds of hemp was recently detained by law enforcement near Amarillo, and the driver spent weeks in jail awaiting confirmation that the cargo was legal, Texas A&M says. Stories like that became motivation to develop the scanner.
But according to the university, the potential to distinguish hemp from THC-rich cannabis is just the beginning. The team is now aiming to create a similar test for cannabidiol (CBD) so hemp farmers can estimate their plants’ value. Their research also revealed the scanner’s ability to distinguish among different varieties of hemp and THC-rich cannabis, which could help determine the quality of tested varieties.
"Our colleagues, the farmers, were positively surprised that we could identify the variety with 98% accuracy," Kurouski said. "That blew them away."