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Welcome to the Cannabis Data Science Meetup Group.

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So we're always here just to crunch numbers, have fun, have a good time.

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So we've been looking, we started in the West Coast, started in the Northwest looking at

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Washington.

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So we looked at all the Washington State data.

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Then have been gradually moving our way East, looked at some Colorado data, and then spent

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a bit of time in Oklahoma, crunching numbers in Oklahoma since they've just come online

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with medical.

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Pretty recently.

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And so things are shaking out there.

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Then continuing through the Northwest, looked at Illinois and Michigan, which both have

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interesting markets.

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So Michigan has had medical for a long time now.

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So people in Michigan, cannabis is nothing new to them.

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And there's some established businesses there.

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There's thought leaders there.

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Great scene.

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Illinois is interesting as well.

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In Illinois, my main takeaway is there's a bit of a license cap that's really defined

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the industry there.

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So there's only a couple dozen licensees.

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So there's not much competition there.

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You see some fairly high prices in Illinois.

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They are moving in the direction of, okay, we're going to allow some micro businesses,

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we're going to allow some craft growers.

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So we'll see the direction Illinois goes.

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So it's much different than the other states.

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And so now we're just continuing moving East.

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And so here I'll go ahead and share my screen and share what we've done and are doing.

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Awesome.

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Great.

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So cannabis data science group, it's put on essentially by, you know, Candlelitics.

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And so here we're just going to be hosting some of the data.

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And so as we noted, the map's getting quite filled in here.

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So we've got recreational states in the darker green and medical and the lighter green and

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then not permitted in Tampa.

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So there's only a dozen or so states that have no cannabis permitted at all, including

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my home state, North Carolina.

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And in fact, at the cannabis science conference, I've been speaking to someone who works at

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a hemp laboratory in North Carolina.

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And they were also lamenting on the fact that North Carolina is battling to be last in the

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game.

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So it will be interesting to see if and if so, then when some of these other states may

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come online.

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And so to get an understanding of, okay, what may they look like when they come online,

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let's start looking at some of these Northeastern states.

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So keep in mind that the map may be a little misleading.

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So Virginia is shaded green.

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However, they won't technically have recreational sales until I believe 2022 or 2023.

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Oh, yeah, Paul, who's a regular joining here.

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Welcome, Paul.

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So sort of small crowd today, but that's okay.

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So crunching numbers nonetheless, and we're essentially looking at the East Coast today.

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Very good.

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I'll just listen in for now, Keegan.

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Okay, we can have a bit more of a back and forth later because there's a lot to talk

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about.

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Yeah, sorry for being late.

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Oh, no, no.

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Glad to have you.

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So essentially, we were saying, okay, we're looking at the East Coast, but some of these

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states aren't coming online yet.

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Virginia is not online yet.

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I was looking and New Jersey may have some additional, I mean, some existing medicinal

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laws on the books, but their recreational program is not up and running yet.

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It does not appear.

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And then New York is not quite up and running yet.

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So that leads us to these other Northeastern states.

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And so just to show you some of the data that we found here.

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So Massachusetts has some good public data that can be accessed through an API.

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So I have poked at this data before.

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So this is worthwhile looking at.

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Maine is, you know, they're making a laudable attempt to make their laudable, applaudable

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attempt to make their data accessible.

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It's a little hard to get though.

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So from what I can tell, they may only have the past three months in sales data and they

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do have a list of licensees.

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However, a little hard to extract this data.

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However, where there is a will, there is a way.

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So we may have to revisit Maine.

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But what I stumbled upon, which I think we'll find quite interesting, is Connecticut has

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done an outstanding job at making their data available.

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And so here's a data point that I think will have fruitful analysis moving forward.

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So we essentially have, okay, we have the number of registered patients and we have

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the registered patients per county.

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And so I think this is an interesting data point because remember before we were looking

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at, okay, what's the total number of licensees per capita?

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Well, it would make a bit more sense to look at licensees per patient.

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So you know, so we can understand, okay, how many licensees are required to serve a set

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number of patients.

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So interesting data point there.

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And then the data that we'll be diving into today, which I promised a while back that

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I would try to find some and somehow coincidentally stumbled upon some.

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So essentially a long time ago I said, oh, wouldn't it be interesting if we could find

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terpene data?

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Because there's so much more emphasis on terpenes these days.

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So it would be nice to have a measure of various terpenes that are out there.

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And lo and behold, Connecticut appears to publish what they call their brand registry.

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And so I am not certain if this is every single product that is sold or just a running list

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of various products that get sold.

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And so that's what we will be trying to discover today is essentially we've got a data set

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here of just shy of 11,000 products.

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And what's really cool about this is we have a lot of data here.

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So you know, a picture is worth a thousand words.

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So for example, we have, well, this looks like just the stock photo, but you know, real

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cannabis flower.

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And corresponding with this, they also have the certificate of analysis.

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So you know, this is verifiable data here.

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So we've got, you know, we've got our data set and we even have the certificates of analysis

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to go along with the data.

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So quite a defensible data set here.

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So without further ado, let's dive into it.

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Unless Paul or Hastings, you don't have any thoughts or comments or anything yet?

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Not for me, Keegan, it's just nice that they were able to put all this together around

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the actual product.

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Exactly because it would be incredibly interesting to tie these to sales.

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And so I'm sure the state could probably do that.

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And so we can at least start looking at, okay, what's the distribution of cannabinoids for

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various products in Connecticut, what's the distribution of herpes?

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So those are good measures.

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What products are being sold?

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I always think that's an interesting thing to look at is a lot of the cannabis industry

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is kind of oddly comes down to cool sounding names.

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Not always, but you know, what's in a name?

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So I always think that's something that's worth exploring.

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But anywho, let's just get this data is the first step.

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So there's a couple ways you can get this data.

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One you can just let me see here.

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Yes, so you can just download this data for whichever format you need.

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And then it's also available through two API's.

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There's the open data API, and then the Socrata open data API.

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And so have essentially just read in this data here using the Socrata API.

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So that's easy enough to do.

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Hold on.

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And let's just ensure that we have the data here.

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Awesome.

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So let's see.

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It looks like the API may have slightly newer data than is listed here in this table.

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So here there's 10,791 observations.

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And when we query the API, we have 10801.

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So I find that curious.

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So perhaps new products have been added.

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Perhaps there's a discrepancy.

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So we'll maybe uncover that as we go.

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And so first things first, you can look at the data.

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So I realized, oh, you can actually open a little data frame here in Spyder, which is

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interesting.

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So this is just the same data we've been looking at.

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And so we'll want to look at the terpenes and the cannabinoids.

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So without further ado, I'll just essentially be coding this up as we go.

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So I would have liked to prepare a bit more.

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We'll be looking at this data set for the first time together.

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So hopefully, at the very least, I would just like if it unless you have some data points

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you want to look at, essentially, the main data points I want to look to find are, one,

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what's the prevalence of the cannabinoids and terpenes for the samples?

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So this is basically going to be, OK, so what percent of the samples have a given terpene?

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Because that was the first thing I noticed was, OK, so we're looking here at terpenes.

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If you start looking at, say, just picking one of these out of your hat, you just start

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looking at limonene here.

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Well, you'll see a lot of samples don't contain any limonene.

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And then the ones that do, they may contain half a percent, 1% there.

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I just saw one like 3.7%.

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So essentially, to me, those are the interesting data points that jump out to me is, one, what

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percent of this sample even has limonene?

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And then, two, of the sample that does have limonene, what's the distribution?

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So if you're growing a strain and you expect it to have limonene, what is a high amount

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of limonene and what's a low amount?

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So we can try to answer those questions right now.

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And then the third question was, and then that will essentially help us find, answer

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the third question was, what's the most prevalent terpene?

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And actually, they were mentioning here at this Cannabis Science Conference, an interesting

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thing about terpenes here is it may not necessarily be the amount, but more the presence.

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So you say, oh, beta-karyophylline here, it's got a quite low concentration.

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You've got less than 1% here.

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But what they're finding is it's more just the presence of it that matters.

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So just having the presence will give you that effect.

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And I think they were saying that beta-karyophylline may even be quite one of the, it may essentially

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be one of the more intoxicating terpenes.

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So this one may be one of the terpenes that actually gives you one of the intoxicating

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effects.

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So let's just crunch these numbers.

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OK.

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So the first thing I noticed was, so let's say we're looking at beta-karyophylline.

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And also, I may have bad typing today, but that's OK.

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So let's just say, OK, so let's say, OK, what is, what are the values here that we may expect?

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So let's say, for beta-karyophylline.

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So a lot of times, I'll caution you if you're going to do this with a numerical series,

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you may end up with just a giant list.

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What I notice here is, OK, well, first off, they're all in strings.

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So we're going to have to convert these strings to floats.

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And then I noticed there is this occurrence where they basically say, oh, there's less

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than 0.1.

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Well, when you're measuring analytes at a laboratory, you really can't quantify something

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as 0.

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You can only quantify it as not detected.

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So the laboratories have a detection limit.

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So they can only detect analytes above a certain threshold.

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So the laboratory can only, in this case, I bet, could probably only detect beta-karyophylline

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of amounts greater than 0.1.

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So they probably code non-detects as less than 0.1.

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And so I would like to just go ahead and code these as zeros, because just for handling

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this in our analysis, it will be just fine to just handle those as zeros.

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So essentially, that is where we are at right now.

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And so first things first, there's probably more elegant ways to do this.

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But essentially, I just listed the terpenes here and was just going to iterate over the

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terpenes and fix those values.

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So basically, you know.

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So can you also notice that in some of those values, they have a percentage sign on there

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as well?

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Oh, yeah.

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So 1.31%.

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Exactly.

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So we'll have to replace these percentages.

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So hopefully, once we do that, we can convert these to floats and get these.

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So let's see here.

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So let's see, this starts with zero.

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Okay.

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I may not be doing this iteration correctly.

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The way I think I would like to do this is basically for a terpene and terpene.

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Way I'm just going to do this is, you know.

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So let's just replace.

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I would like to just replace all of these, but we may just have to do them.

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Let's see if this will just clean up these terpene data.

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Okay, so in fact, that was maybe not entirely what I wanted to do.

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I actually probably wanted to replace this with zeros.

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However, that essentially worked to just have to get the data from the API one more time.

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And so we've done that one to do, and we've also done a second to do.

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Okay, so next step, just to show you the whole data wrangling process from start to finish

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and hopefully finish with some cool charts.

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That's not good.

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We may have...

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See, ideally, I would like to just do this with what's called a regular expression where

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we just patch everything that begins with a less than.

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Just for the sake of speed, I'm just going to try to hard code in all of the ones we

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see.

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Okay, so now I think we should be able to maybe plot some of these.

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So let's see if we can't.

238
00:24:34,000 --> 00:24:46,560
For example, okay, so let's find all the data with liminine, which is what we'd set it first.

239
00:24:46,560 --> 00:24:52,680
So liminine, so let's just say data dot liminine.

240
00:24:52,680 --> 00:24:58,160
We want the data where...

241
00:24:58,160 --> 00:25:14,840
Actually, liminine as type float greater than zero.

242
00:25:14,840 --> 00:25:16,000
Let's see.

243
00:25:16,000 --> 00:25:25,680
I don't think this will necessarily work, but perhaps.

244
00:25:25,680 --> 00:25:30,600
Great.

245
00:25:30,600 --> 00:25:32,160
It looks like that actually worked.

246
00:25:32,160 --> 00:25:37,280
So now we just have a data set here.

247
00:25:37,280 --> 00:25:43,280
And we'll do this for all the terpenes, but just to do it for one first, just to see what...

248
00:25:43,280 --> 00:25:46,640
Just to kind of figure out what we're doing here.

249
00:25:46,640 --> 00:25:47,640
Interesting.

250
00:25:47,640 --> 00:25:55,040
So now we can see, okay, what percent of the sample actually has liminine.

251
00:25:55,040 --> 00:25:59,400
And the higher proportion than I thought.

252
00:25:59,400 --> 00:26:09,600
So about 30, about 35% of the samples have liminine, which I think is just such an interesting

253
00:26:09,600 --> 00:26:19,760
observation because it's a large proportion, but that still leaves 66% of samples that

254
00:26:19,760 --> 00:26:21,640
don't have liminine.

255
00:26:21,640 --> 00:26:26,400
So is this a popular terpene?

256
00:26:26,400 --> 00:26:29,880
Is it not a popular terpene?

257
00:26:29,880 --> 00:26:33,480
I think it's too early to tell.

258
00:26:33,480 --> 00:26:39,200
So Keegan, without the benefit of any lab knowledge or experience, so for liminine,

259
00:26:39,200 --> 00:26:40,880
were you saying...

260
00:26:40,880 --> 00:26:47,760
No, I think you're talking about another terpene that might have more of an effect on the user.

261
00:26:47,760 --> 00:26:51,160
So why the interest in liminine?

262
00:26:51,160 --> 00:26:55,640
Well, so I guess I should have filled you in here.

263
00:26:55,640 --> 00:27:03,380
So at the Cannabis Science Conference yesterday, they were mentioning...

264
00:27:03,380 --> 00:27:09,720
There was a really interesting speaker from Israel who's done some research at the Technion

265
00:27:09,720 --> 00:27:10,720
Institute.

266
00:27:10,720 --> 00:27:17,440
And so they're really leading the industry with data science there in Israel.

267
00:27:17,440 --> 00:27:23,880
And so what they've basically done is, okay, they've got all the medical patients in Israel

268
00:27:23,880 --> 00:27:32,960
essentially on a database, and then they can report their purported effects into the database.

269
00:27:32,960 --> 00:27:39,320
So that way, okay, they say, okay, the patient, they bought this strain and they purported

270
00:27:39,320 --> 00:27:42,080
these effects.

271
00:27:42,080 --> 00:27:59,760
And so they're essentially trying to, I guess, match prevalent terpenes with common effects.

272
00:27:59,760 --> 00:28:05,440
And essentially, these are, I think, some of the top...

273
00:28:05,440 --> 00:28:11,240
These are, I think, essentially some of the most prevalent terpenes you see are mercine,

274
00:28:11,240 --> 00:28:16,400
liminine, linalool, and beta-karyophyll.

275
00:28:16,400 --> 00:28:25,080
So I want to say that those in their studies were just the most prevalent terpenes.

276
00:28:25,080 --> 00:28:27,680
So that's why I picked them out.

277
00:28:27,680 --> 00:28:29,960
I'm going to add to...

278
00:28:29,960 --> 00:28:31,120
I have it recorded.

279
00:28:31,120 --> 00:28:36,600
So I'm going to have to re-listen to the recording because there was a specific reason I jotted

280
00:28:36,600 --> 00:28:38,640
those terpenes down.

281
00:28:38,640 --> 00:28:41,440
I want to say, was there prevalence?

282
00:28:41,440 --> 00:28:47,880
But that's a good question, Paul.

283
00:28:47,880 --> 00:28:51,040
So I'll have to maybe follow up, send you a message afterwards.

284
00:28:51,040 --> 00:28:52,040
Yeah, no worries.

285
00:28:52,040 --> 00:28:53,040
I just was curious.

286
00:28:53,040 --> 00:28:54,040
Yeah, thank you.

287
00:28:54,040 --> 00:28:56,160
I want to say they're the most prevalent.

288
00:28:56,160 --> 00:29:08,240
So what we can actually do is see, okay, which ones are the most prevalent in this study?

289
00:29:08,240 --> 00:29:16,440
So actually, we can probably do that here in five minutes.

290
00:29:16,440 --> 00:29:21,040
So we'll just say, okay, what's their prevalence?

291
00:29:21,040 --> 00:29:22,040
Okay.

292
00:29:22,040 --> 00:29:31,920
So for terpene and terpenes, we'll basically want to do what we just did with liminine.

293
00:29:31,920 --> 00:29:44,560
I just do it for a specific terpene.

294
00:29:44,560 --> 00:29:48,440
And let's see if...

295
00:29:48,440 --> 00:29:58,360
So basically, my prior is the top four will be mercine, liminine, linolule, and beta-karyophylline.

296
00:29:58,360 --> 00:30:03,400
That we'll find out here shortly.

297
00:30:03,400 --> 00:30:05,120
And so we'll just say...

298
00:30:05,120 --> 00:30:09,960
I'm not sure if prevalence is the right word here.

299
00:30:09,960 --> 00:30:17,080
But for now, we'll just use that.

300
00:30:17,080 --> 00:30:22,800
And so we'll basically say, oh, well, that was just going to be the amount that this

301
00:30:22,800 --> 00:30:28,600
terpene was actually served.

302
00:30:28,600 --> 00:30:34,560
Okay.

303
00:30:34,560 --> 00:30:42,400
So it looks like we may still have an odd character or two.

304
00:30:42,400 --> 00:30:43,400
Wonder...

305
00:30:43,400 --> 00:30:56,920
There's a reason I don't think that all...

306
00:30:56,920 --> 00:30:58,920
Okay.

307
00:30:58,920 --> 00:31:15,440
Just a few more odd characters in the mix.

308
00:31:15,440 --> 00:31:16,440
Okay.

309
00:31:16,440 --> 00:31:20,800
Let's get the data one more time and try this again.

310
00:31:20,800 --> 00:31:29,600
Okay, see, this is why I kind of wanted to...

311
00:31:29,600 --> 00:31:35,600
You bear with me.

312
00:31:35,600 --> 00:31:51,880
Let's see if we can't...

313
00:31:51,880 --> 00:32:17,600
Let's see... let's see here if we can't just replace everything that's zero point anything.

314
00:32:17,600 --> 00:32:26,240
Let's see if that may just...

315
00:32:26,240 --> 00:32:27,240
Okay.

316
00:32:27,240 --> 00:32:31,680
I'm going to do this one through twice.

317
00:32:31,680 --> 00:32:48,280
If there's many, we may have to...

318
00:32:48,280 --> 00:32:49,280
Okay.

319
00:32:49,280 --> 00:32:55,560
God love the data cleansing process.

320
00:32:55,560 --> 00:32:58,280
Exactly.

321
00:32:58,280 --> 00:33:02,280
Okay.

322
00:33:02,280 --> 00:33:07,320
Okay.

323
00:33:07,320 --> 00:33:08,800
We'll call them non-detects.

324
00:33:08,800 --> 00:33:09,880
Okay.

325
00:33:09,880 --> 00:33:19,760
So let's just try to exclude these real quick.

326
00:33:19,760 --> 00:33:24,640
Let's just identify them first.

327
00:33:24,640 --> 00:33:42,360
Okay, so let's just find everything that begins with a less than.

328
00:33:42,360 --> 00:33:46,040
Okay.

329
00:33:46,040 --> 00:33:57,400
Actually, let's just get everything that does not start with this.

330
00:33:57,400 --> 00:34:00,400
I wonder...

331
00:34:00,400 --> 00:34:03,400
Okay.

332
00:34:03,400 --> 00:34:22,400
Sorry that I'm having to...

333
00:34:22,400 --> 00:34:35,760
Oh, it looks like we can just use a negation symbol here.

334
00:34:35,760 --> 00:34:38,520
Okay.

335
00:34:38,520 --> 00:34:43,720
So we can just say, okay, hopefully we can just say, okay, the detects everything where

336
00:34:43,720 --> 00:34:48,320
it does not start with less than.

337
00:34:48,320 --> 00:34:57,800
This is a little bit ad hoc.

338
00:34:57,800 --> 00:34:58,800
That's sort of what we do here.

339
00:34:58,800 --> 00:35:01,840
We're just trying to crunch them quick.

340
00:35:01,840 --> 00:35:02,840
So...

341
00:35:02,840 --> 00:35:03,840
Oh, man.

342
00:35:03,840 --> 00:35:04,840
Okay.

343
00:35:04,840 --> 00:35:05,840
Okay.

344
00:35:05,840 --> 00:35:06,840
Okay.

345
00:35:06,840 --> 00:35:21,400
So...

346
00:35:21,400 --> 00:35:22,400
Okay.

347
00:35:22,400 --> 00:35:25,400
Hold on.

348
00:35:25,400 --> 00:35:44,400
Maybe just introduce the paper.

349
00:35:44,400 --> 00:35:48,880
Okay.

350
00:35:48,880 --> 00:35:57,380
kimchi-bap.

351
00:35:57,380 --> 00:36:03,300
Hello.

352
00:36:03,300 --> 00:36:09,600
Okay.

353
00:36:09,600 --> 00:36:19,600
Okay.

354
00:36:19,600 --> 00:36:23,600
Sorry that I'm having to sort of do this on the fly here.

355
00:36:23,600 --> 00:36:28,600
It's all good.

356
00:36:28,600 --> 00:36:32,600
It's good to see how you go about doing this.

357
00:36:32,600 --> 00:36:33,600
Yeah.

358
00:36:33,600 --> 00:36:36,600
Well, like I said, I would have done it here with the rig.

359
00:36:36,600 --> 00:36:38,600
Okay, so this is how I would normally do it.

360
00:36:38,600 --> 00:36:50,600
I would remove all the streams.

361
00:36:50,600 --> 00:36:53,600
I do exactly the same thing in R.

362
00:36:53,600 --> 00:36:56,600
I just Google the code.

363
00:36:56,600 --> 00:37:11,600
So, okay.

364
00:37:11,600 --> 00:37:21,600
Okay, this looks promising here.

365
00:37:21,600 --> 00:37:23,600
Okay.

366
00:37:23,600 --> 00:37:24,600
That's okay.

367
00:37:24,600 --> 00:37:26,600
New data set.

368
00:37:26,600 --> 00:37:29,600
We need some need to get it clean.

369
00:37:29,600 --> 00:37:34,600
Okay, so.

370
00:37:34,600 --> 00:37:43,600
The we say okay the non the text is where everything.

371
00:37:43,600 --> 00:37:53,600
Begins with a less fan and now.

372
00:37:53,600 --> 00:38:13,600
We can just do not the text.

373
00:38:13,600 --> 00:38:23,600
And then isolate that specific.

374
00:38:23,600 --> 00:38:33,600
It just needed to slow down a bit.

375
00:38:33,600 --> 00:38:45,600
Interesting.

376
00:38:45,600 --> 00:39:08,600
Okay, this is.

377
00:39:08,600 --> 00:39:12,600
Okay, so the non the text.

378
00:39:12,600 --> 00:39:33,600
It's weird.

379
00:39:33,600 --> 00:39:50,600
So I see what's happening. This one just doesn't begin with a couple extra extra decimal point there. Yeah, I wonder.

380
00:39:50,600 --> 00:39:59,600
I wonder if we could have done this.

381
00:39:59,600 --> 00:40:09,600
I think we could have done something.

382
00:40:09,600 --> 00:40:17,600
Okay, I think this may work a little better.

383
00:40:17,600 --> 00:40:29,600
So we'll just say, okay, this is we're going to say this is to numeric.

384
00:40:29,600 --> 00:40:35,600
Okay, so now we're just going to force everything to numeric.

385
00:40:35,600 --> 00:40:50,600
And at that point.

386
00:40:50,600 --> 00:41:05,600
That may work and I think this dot look line may mess up.

387
00:41:05,600 --> 00:41:23,600
Not super quickly.

388
00:41:23,600 --> 00:41:49,600
Or this is a key.

389
00:41:49,600 --> 00:41:56,600
So this looks like a magic line that we could use to sort it.

390
00:41:56,600 --> 00:42:01,600
First things first.

391
00:42:01,600 --> 00:42:05,600
These are the terpenes and their.

392
00:42:05,600 --> 00:42:25,600
The percentage of samples that they're found in.

393
00:42:25,600 --> 00:42:39,600
Okay, so it's in reverse order.

394
00:42:39,600 --> 00:43:00,600
This would be top terpenes.

395
00:43:00,600 --> 00:43:10,600
Trying to think of the best way.

396
00:43:10,600 --> 00:43:33,600
That may be the best way.

397
00:43:33,600 --> 00:43:40,600
So a little bit of an ad hoc day just to do a little exploratory analysis.

398
00:43:40,600 --> 00:43:43,600
So for next week we can get a bit.

399
00:43:43,600 --> 00:43:52,600
Now that we sort of have our questions defined a bit, we can, you know, hone in our analysis.

400
00:43:52,600 --> 00:44:13,600
We can still make some charts for today just to begin to visualize the data.

401
00:44:13,600 --> 00:44:38,600
And I think we just need to do.

402
00:44:38,600 --> 00:44:44,600
Hopefully we can just.

403
00:44:44,600 --> 00:44:54,600
Okay, so.

404
00:44:54,600 --> 00:45:03,600
Okay.

405
00:45:03,600 --> 00:45:16,600
So.

406
00:45:16,600 --> 00:45:24,600
Because I really just want to get this sorted here.

407
00:45:24,600 --> 00:45:34,600
I'm sorry, just sort of thinking on the spot.

408
00:45:34,600 --> 00:45:41,600
You may not want to go there Keegan, but I mean you could always export to CSV and just pivot around in Excel or something.

409
00:45:41,600 --> 00:45:55,600
You know what, I may not be a bad idea real quick.

410
00:45:55,600 --> 00:46:07,600
So.

411
00:46:07,600 --> 00:46:35,600
So I'm just going to make sure.

412
00:46:35,600 --> 00:47:00,600
Okay.

413
00:47:00,600 --> 00:47:06,600
Okay.

414
00:47:06,600 --> 00:47:15,600
Okay, so now we can get this to Excel.

415
00:47:15,600 --> 00:47:18,600
Not pretty today, but.

416
00:47:18,600 --> 00:47:25,600
At least begun, we can at least answer a question. So that's.

417
00:47:25,600 --> 00:47:28,600
Yeah, it does have to be pretty.

418
00:47:28,600 --> 00:47:34,600
We get, I mean, we're about to figure out what are the most prevalent terpenes in Connecticut. So.

419
00:47:34,600 --> 00:47:36,600
Yes, pretty cool.

420
00:47:36,600 --> 00:47:47,600
So yes, so simple enough now. So now we can just sort.

421
00:47:47,600 --> 00:47:58,600
Okay, so this is going to be the terpene and then the, you know, the percent of samples.

422
00:47:58,600 --> 00:48:05,600
There is present.

423
00:48:05,600 --> 00:48:14,600
Okay, and we actually want to sort this by the percent of samples where it was present from largest smallest.

424
00:48:14,600 --> 00:48:18,600
Okay.

425
00:48:18,600 --> 00:48:21,600
So.

426
00:48:21,600 --> 00:48:24,600
I have not actually heard of this terpene.

427
00:48:24,600 --> 00:48:32,600
It looks like beta U decimal. So I'm going to need to do some homework.

428
00:48:32,600 --> 00:48:44,600
I have heard of fence shown. So I do believe that fence shown is what gives cannabis. It's this like, like it's distinct like cannabis smell.

429
00:48:44,600 --> 00:48:53,600
So, so that would make sense why it was detected in, you know, 96%.

430
00:48:53,600 --> 00:49:02,600
I would even question what's going on with those last 4% of samples.

431
00:49:02,600 --> 00:49:09,600
And cam for two. So I think these two terpenes cam for and fence shown.

432
00:49:09,600 --> 00:49:19,600
I think that's just integral to the cannabis plant. And that's what gives it its unique smell.

433
00:49:19,600 --> 00:49:23,600
And then moving on down the list.

434
00:49:23,600 --> 00:49:28,600
So let's say, okay, I'm going to need to do some homework on this one.

435
00:49:28,600 --> 00:49:33,600
But it looks like these three may just be present in cannabis in general.

436
00:49:33,600 --> 00:49:42,600
So maybe that's why in Israel, they weren't really looking at cam for fence shown because they may just be taking that as a given.

437
00:49:42,600 --> 00:49:48,600
So then what I find interesting is, okay, so let's say those are just the given.

438
00:49:48,600 --> 00:50:02,600
Then yes, if you look at these, then our top, you know, for after those would be the beta-karyophylline, which I believe is what you would.

439
00:50:02,600 --> 00:50:14,600
So I think they were saying that this is essentially would be the best indicator if something's an indica or a sativa is does it have beta-karyophylline?

440
00:50:14,600 --> 00:50:19,600
And so this is what's going to give you the sedative sleepiness.

441
00:50:19,600 --> 00:50:23,600
I do believe is beta-karyophylline.

442
00:50:23,600 --> 00:50:35,600
And so it looks just from this, it looks like, you know, about half of the samples are what you call indica.

443
00:50:35,600 --> 00:50:44,600
And then moving down, there's the limonene, which is also on their list.

444
00:50:44,600 --> 00:50:50,600
And, you know, a lot of these terpenes you can find in other things.

445
00:50:50,600 --> 00:50:58,600
So of course, lemons, pomelene.

446
00:50:58,600 --> 00:51:07,600
Don't quote me on this, but I've got this sneaking hunch that that may be a similar terpene that's found in hops.

447
00:51:07,600 --> 00:51:10,600
So don't quote me on that one, though.

448
00:51:10,600 --> 00:51:22,600
But I think this is an interesting find because this wasn't one that I was expecting to be here in the top four.

449
00:51:22,600 --> 00:51:33,600
And then, of course, you've got beta-mercine, another one that I believe has, you know, a purported sedative effect.

450
00:51:33,600 --> 00:51:38,600
And then linalool, which is another one.

451
00:51:38,600 --> 00:51:41,600
And then this one comes in at the fifth.

452
00:51:41,600 --> 00:51:46,600
So that was one we were kind of looking for.

453
00:51:46,600 --> 00:51:55,600
And so if you just look at the breakdown of the numbers here, it looks like this is almost like another pack of its own.

454
00:51:55,600 --> 00:52:02,600
So, you know, about 30 to per beta-curiophiline 15.

455
00:52:02,600 --> 00:52:14,600
But these ones, about 30, 35 percent of all samples contain these terpenes.

456
00:52:14,600 --> 00:52:18,600
And did you give a question, Paul?

457
00:52:18,600 --> 00:52:27,600
Yeah, Keegan. So the conference that you're at, the science conference that you're at with the Israeli group and some of these items, these terpenes that they had mentioned,

458
00:52:27,600 --> 00:52:41,600
you said they're testing with their medicinal group to try and, like, capture some subjective feedback from the users and then kind of match it with the, correlate it with the terpenes?

459
00:52:41,600 --> 00:52:48,600
Exactly. So they're really they're really going after the medical benefits of the various compounds.

460
00:52:48,600 --> 00:52:52,600
So from terpenes all the way down to the cannabinoids.

461
00:52:52,600 --> 00:52:55,600
And so they're just doing a real data heavy approach.

462
00:52:55,600 --> 00:53:09,600
So they're saying, oh, you know, this patient, maybe they they're suffering from, you know, it could be cancer or maybe they have I'm not certain, but maybe they have anxiety.

463
00:53:09,600 --> 00:53:17,600
I think it's typically more severe conditions like epilepsy, cancer.

464
00:53:17,600 --> 00:53:22,600
I don't know what the big ones are, but they're typically pretty severe.

465
00:53:22,600 --> 00:53:27,600
And so then I'll have to tell you more about the data collection process in general.

466
00:53:27,600 --> 00:53:40,600
But from my understanding, you know, they've got like just a medical patient registry so that they see, OK, this is how much this patient is consuming.

467
00:53:40,600 --> 00:53:45,600
And then maybe they they fill in surveys where they.

468
00:53:45,600 --> 00:53:51,600
I don't need to get you more information about the surveys, but that's my my understanding.

469
00:53:51,600 --> 00:53:55,600
OK, yeah, it's interesting. Thanks for sharing that.

470
00:53:55,600 --> 00:54:07,600
And I think it's a it's quite different and interesting research than anyone else is doing because they're just going so heavily after the medical effects.

471
00:54:07,600 --> 00:54:12,600
That's just what they're they're heavily focused on.

472
00:54:12,600 --> 00:54:23,600
And they've got data like nobody else does because they essentially, I believe, like have the whole population of medical consumers in their database.

473
00:54:23,600 --> 00:54:30,600
Oh, yeah. And then and then they also have all the data about all the crops being grown.

474
00:54:30,600 --> 00:54:42,600
It's just, you know, here in the United States, I mean, like, you know, we don't have any good measure of like the medical program in California.

475
00:54:42,600 --> 00:54:47,600
You know, yeah, they've got a centralized system as opposed to ours.

476
00:54:47,600 --> 00:55:02,600
Exactly. So they're able to do a bit more sort of like the almost like controlled experiment type things there, which are needed for, you know, for.

477
00:55:02,600 --> 00:55:10,600
To make any determination about, you know, medical effects of these compounds.

478
00:55:10,600 --> 00:55:24,600
But so so I'll have to fill you in a lot more. So because because there's been a few things that have caught my interest, some things have come to light.

479
00:55:24,600 --> 00:55:41,600
So perhaps for next week, we could continue this this conversation real quick. Why don't we just plot just to end the end the day with a.

480
00:55:41,600 --> 00:55:50,600
We'll go all the way down to pure gall. So that's interesting. I remember that trip.

481
00:55:50,600 --> 00:55:57,600
It was just there we go. And so.

482
00:55:57,600 --> 00:56:08,600
So this is just the prevalence of terpenes. And so next week, we can start to look at, OK, what's the actual distribution of the concentration?

483
00:56:08,600 --> 00:56:19,600
And maybe we can even do some correlations next week and see, OK, are these terpenes correlated with any cannabinoids or what have you?

484
00:56:19,600 --> 00:56:24,600
So. But.

485
00:56:24,600 --> 00:56:41,600
And so. My last remark is, OK, so these may give you an idea of these are the most prevalent ones, but don't necessarily grow these terpenes out, because what I was learning is, OK.

486
00:56:41,600 --> 00:56:59,600
Even if the terpenes present in a real low concentration, well, these are just these are just rare. So if you find if you find a strain that, you know, reliably produces one of these not so common terpenes, that could give you.

487
00:56:59,600 --> 00:57:19,600
A competitive advantage. And then the other thing is, say your your plant maybe only produces beta curiophiline in a very small amount. Well, that may be sufficient, because basically what they're saying is a lot of these times it's like when you're like, it's like your titration.

488
00:57:19,600 --> 00:57:34,600
Like when you're when you're taking like a medical. Product, right. It's easy to take too much. So like you get like the idea is you may only need a little bit of beta curiophiline to get the effect.

489
00:57:34,600 --> 00:57:42,600
And then if you're you overdo it, it may become unpleasant.

490
00:57:42,600 --> 00:57:58,600
So so that was something that came up was. It may not necessarily be all about the concentration, but more just the presence of various.

491
00:57:58,600 --> 00:58:17,600
Yeah, and I wonder if there could be almost. Kind of opposite correlative effects of different terpenes. I don't know if that's even feasible, because I don't think about them, but, you know, as you increase one, maybe it negates the effect of the other or maybe having a combination of them together has a certain effect.

492
00:58:17,600 --> 00:58:32,600
Well, you raise a real interesting remark here. So, you know, a chemist once told me this is, I mean, basically your your plant can only produce like so much stuff, right? There's only like so much room.

493
00:58:32,600 --> 00:58:36,600
There's only like a hundred. It can only be like a hundred percent of the particles, right?

494
00:58:36,600 --> 00:58:52,600
And so it even actually comes down to the point where you may be right. Your plant may be producing a lot of terpenes. And if you're spending a bunch of energy on terpenes, maybe you spend a little less on cannabinoids.

495
00:58:52,600 --> 00:59:15,600
So you may have something that's real high in linalool. And because of that, you know, your THC or CBD may be slightly lower. And, and if you're breeding just for cannabinoids alone, then you may like ignore these high linalool strains or what have you.

496
00:59:15,600 --> 00:59:32,600
But, but those may actually be what consumers are looking for because those the high terpene concentrations, those are going to be the varieties that have real strong smells and may even have strong effects.

497
00:59:32,600 --> 00:59:33,600
Yeah.

498
00:59:33,600 --> 00:59:47,600
Yeah. So this may be this may be already known. I'm jumping in headfirst and a lot of things that I don't know about yet, which is exciting.

499
00:59:47,600 --> 01:00:10,600
But what in terms of determining the how a customer or consumer is responding to, I mean, in this case, the different, maybe a ratio of terpenes against each other. So you may have like, I don't even know how to pronounce the beta karyophylline.

500
01:00:10,600 --> 01:00:30,600
And so you've got 60% of your terpene count is like in that and then the rest of it is split up amongst five others. However, is there some type of performance metric that's like gauging a customer response to different strains that have these different levels or is that all very new?

501
01:00:30,600 --> 01:00:45,600
And like I said, basically, that's essentially what the people in Israel are after is because what they notice is cannabis is so there's so many different varieties.

502
01:00:45,600 --> 01:01:08,600
So you look at one variety to the next, like the terpene profile is just going to be completely different. And so that's why I guess they're really going after big data, because like if you're going to try to disentangle the effects, you may just need just a giant data set where you just have, you know,

503
01:01:08,600 --> 01:01:21,600
thousands and thousands of cancer patients that have tried thousands and thousands of different varieties, and they all have different terpene profiles, and then you can maybe try to disentangle it.

504
01:01:21,600 --> 01:01:24,600
But

505
01:01:24,600 --> 01:01:32,600
it's, I think it's basically a puzzle that people are running into it's basically

506
01:01:32,600 --> 01:01:41,600
cannabis is drawing a lot of attention from people, we're trying to figure out why there's a lot of compounds in there.

507
01:01:41,600 --> 01:02:01,600
And from my understanding, there's not been that many great patterns discovered, right, like I think people are starting to discover things like oh maybe like beta-caroophylline kind of intoxicating and maybe that explains its prevalence because people are actually breeding for beta-caroophylline

508
01:02:01,600 --> 01:02:06,600
and they don't even realize it.

509
01:02:06,600 --> 01:02:09,600
So I think, I think it's all new.

510
01:02:09,600 --> 01:02:11,600
Were you going to say something?

511
01:02:11,600 --> 01:02:27,600
No, I was just saying, I think that's really interesting. It's, I mean, at least here in Texas, it's all very, very new. I think they just last year allowed the sale of Delta 8.

512
01:02:27,600 --> 01:02:33,600
So you're seeing that pop up everywhere, but yeah, this is all very interesting information to me.

513
01:02:33,600 --> 01:02:41,600
Yes. And we've looked at Delta 8 in previous weeks and we noticed that Delta 8 is actually not that prevalent.

514
01:02:41,600 --> 01:02:51,600
So I think, I believe, and so I think if you basically found a strain that was producing high amounts of Delta 8, then that could be a winner.

515
01:02:51,600 --> 01:02:54,600
So

516
01:02:54,600 --> 01:03:04,600
then it's like what makes it produce a lot of Delta 8. It's like, oh, and this is where Paul mentioned, is that maybe correlated with a terpene.

517
01:03:04,600 --> 01:03:05,600
Sure.

518
01:03:05,600 --> 01:03:17,600
And so this is where we kind of get into the chemistry of things. And then this is not my expertise, but I think it has a lot of value to be added is basically

519
01:03:17,600 --> 01:03:35,600
right. So a lot of these chemical compounds are structurally related. So if you have, you know, a high prevalence of one terpene, it may kind of break down into various other

520
01:03:35,600 --> 01:03:40,600
compounds further down the line. So it's sort of like

521
01:03:40,600 --> 01:03:50,600
how the terpenes degrade, how the cannabinoids degrade varies as well. So there's a lot of variation going on.

522
01:03:50,600 --> 01:03:52,600
Interesting.

523
01:03:52,600 --> 01:04:06,600
So, so, so I think go ahead and conclude there since we've gone a bit over, but just want to thank you both for

524
01:04:06,600 --> 01:04:21,600
coming today. So by next week, I'll probably have digested the cannabis science conference a little bit more, and I'll have reviewed my recordings so that way I can share my notes

525
01:04:21,600 --> 01:04:35,600
with a bit more confidence with you. So the next week should have a bit more to share, and I'll have spent a bit of time to actually do some of these terpene charts and some of the terpene analysis.

526
01:04:35,600 --> 01:04:42,600
That way next week we can go a bit more in depth and not spend the whole time cleaning the data.

527
01:04:42,600 --> 01:05:02,600
That was useful and thanks again. I'm actually out of the country next week. So I'll miss our next conference but thanks for sharing the Connecticut data. I think I want to take a look at that just from more of a, you know, marketing perspective, some of the retail sales stuff.

528
01:05:02,600 --> 01:05:13,600
Exactly, because they have the product information so you can see what's selling in Connecticut. Yeah, yeah, yeah, I want to take a look at that so thanks for sharing it.

529
01:05:13,600 --> 01:05:20,600
Definitely, and I'll send you the links and whatnot afterwards. Okay, great. And nice to meet you Hastings.

530
01:05:20,600 --> 01:05:22,600
Nice to meet you Paul.

531
01:05:22,600 --> 01:05:29,600
Thank you both for coming, and until, until next week or the week after, stay productive as always.

532
01:05:29,600 --> 01:05:33,600
All right. Thanks guys. Bye.