New Study Suggest Misnamed Cannabis Varieties

As cannabis gets legalized in more and more states, research on the plant become imperative. New study suggests that machines can assist cannabis research by filling in the gaps. Identifies missing chemotype data and Cannabinoid concentration profiles.

Ever since the legalization of cannabis in many states, the cannabis industry has seen unprecedented growth. With greater legalization, now more than ever, there is a greater need to study the chemical components of the plant.

Due to regulations limiting cannabis research, intoxicating chemicals, tetrahydrocannabinol (THC), and therapeutic cannabidiol (CBD) are the only two known ingredients of the plant. Yet, the plant contains a whole array of lesser-known ingredients that interact with each other to create what is known as the ‘entourage effect’.

What is the entourage effect?

Entourage Effect

Is the theory that all the compounds in a cannabis plant work together, and when consumed together, they produce a better effect than when taken alone.

Cannabis plants contain more than 120 different phytocannabinoids. Phytocannabinoids act on your endocannabinoid system, which works to keep your body in homeostasis, or a balanced state.

Machine Based Learning

A recent study conducted at the University of Colorado suggests that machines can greatly help to fill the gaps in what we know about cannabis ingredients. They can additionally provide important insights into the entourage effect.

In this study, the researchers evaluated seven cannabinoids from 17,611 Cannabis samples, representing an unknown number of distinct varieties grown in four state-level markets within the United States.

Cannabis Industry Mislead

In their research, scientists used machine learning to study various algorithms and statistics. They found that chemical makeup and potency are not reliable indicators of whether a certain kind of cannabis is best for recreational, medicinal, industrial use, or anything else.

The study reaffirms the misnaming of Cannabis varieties by the industry [1819], since strain identity cannot be predicted according to the clustering groups, even though the clusters are reflective of the chemotype (Fig 3 and S3 Fig in S1 File). (Vergara et al. Modeling cannabinoids from a large-scale sample of Cannabis sativa chemotypes)

In the future, the researchers note that they will continue to fill in the data gaps with the help of machine learning. They encourage other researchers to do the same for greater collaboration in the cannabis industry to generate more inferences.

Mandatory Chemotype Testing

In order to improve the understanding of the Cannabis consumed for medical patients, chemotype testing must be made mandatory. However, testing facilities do not have standardized measurement protocols, cannabinoid analysis methods vary widely across laboratories [28], there are no institutional oversight to validate testing entities or their methodologies.(Vergara et al. Modeling cannabinoids from a large-scale sample of Cannabis sativa chemotypes)

One of the lead authors of the study, Brian Keegan, hopes that with the integration of machines in cannabis research, users could review the ingredients of cannabis, much like they do with nutritional components in food items.

“Machine learning has played a huge role in shaping other industries, from Facebook and Twitter to Target,” said author Daniela Vergara. “It can help fill in the blanks for the cannabis industry as well.”

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