WO2017205853A1 - An apparatus for creating chemotype symbols - Google Patents

Abstract

A label printing device that generates chemotype symbols is disclosed. Chemotyping provides a framework for classifying complex mixtures of natural products present in commodities such as Cannabis, Hops, spices, and essential oils. The label printing device receives input from a device for high resolution separation and analysis of complex mixtures of natural products, such as chromatography and spectrophotometry devices. It comprises 4 elements: a computer, an ink source for black & colored inks, and input and output trays or spools, for label stock. The computer, in turn, comprises 4 elements: data processing capability, multivariate analysis programs that perform Principal Component Analysis and Hierarchical Clustering Analysis, color mapping algorithms that assign color based on multivariate analysis output values, and a printer driver. The device produces colored symbols corresponding to the overall content pattern of natural products present in the individual commodity sample, thereby easily communicating complex information.

Description

AN APPARATUS FOR CREATING CHEMOTYPE SYMBOLS

AN APPARATUS FOR CREATING CHEMOTYPE SYMBOLS

BACKGROUND

This Patent Cooperation Treaty Patent Application claims the benefit of priority to earlier filed U.S. Provisional Patent Application entitled, "CANNABIS CHEMOVARS AND DERIVED PRODUCTS", to Abrams, filed May 26, 2016, having serial number 62/341,854 and is incorporated by reference in its entirety, to the extent not inconsistent with the present disclosure.

Related Art

In biology, the study of life, all living organisms are systematically organized into seven categorically ranked groups known as taxonomic classification. The taxonomic classification hierarchy, from the broadest, largest level to the smallest level, is as follows: kingdom, phylum, class, order, family, genus, and species. Human beings, for example, are taxonomically classified as the species homo sapiens. Similarly, marijuana is classified as the species Cannabis sativa L. ("cannabis"). Marijuana flowers are known for being abundant in cannabinoids, chemical compounds that interact with the human body due to the presence of cellular receptors belonging to the endocannabinoid system ("ECS"), present in all mammals.

The ECS can be thought of as the molecular gateway that cannabinoids specifically bind to, located throughout body, such as the brain as well as the peripheral and central nervous systems. Cannabinoids are produced by mammals as well as plants. There are two types of cannabinoids the ECS is capable of interacting with: (1) endogenous endocannabinoids; and (2) exogenous phytocannabinoids. Endogenous endocannabinoids refer to a specific group of biochemical compounds produced within a mammalian body. Exogenous phytocannabinoids refer to a specific group of biochemical compounds produced within a plant.

Beyond cannabinoids and their effect on the ECS, the human body also digests, absorbs, and interacts with many other substances that are biologically produced by the cannabis species. Through natural biological processes cannabis plants make hundreds, possibly thousands, of biochemical compounds called natural products. The term "natural products" inclusively refers to the entire profile of chemicals and proteins biologically made, or biosynthesized, during the lifecycle of a plant. Cannabinoids only represent one class of natural products in cannabis, and are comprised of hundreds of substances. Similarly, terpenoids are another common class of natural products, known for their aromatic structures.

In the 1970s, the United States passed the Controlled Substances Act and classified marijuana as a schedule I substance, which made it illegal to make, possess, or distribute the drug. Until the most recent wave of cannabis legalization occurred in the United States, the cannabis industry was largely an underground culture limited in medical or scientific research and development ("R&D"). Underground R&D was largely trade craft passed on by word of mouth by lineages of mentors as people were hesitant to publicly disclose their methods for fear of imprisonment. In contrast, much of the legally sanctioned cannabis R&D conducted by major universities was used to aid the federal government in tracking and shutting down illegal cannabis entities. Thus, a disparity was created between technical advancement and practical know-how.

Despite prohibition, cannabis "cultivators," people who specialize in growing and selectively breeding cannabis plants for desired characteristics, continued to breed new varieties

("varietals") within the cannabis species. As used in this patent, varietals are at the smallest end of subspecies classification. As a result of cannabis domestication, there are arguably at least five sub-species groupings before the point in which plants break off into varietal designation: three marijuana groups, including "indica," "sativa," "hybrid;" "ruderalis;" and hemp.

Marijuana varietals were bred to produce high levels of the psychoactive cannabinoid, delta-9- tetrahydrocannabinol ("THC"). Varietals that are within the indica grouping supposedly induce a sedative physiological effect, and are physically characterized by a short, bushy appearance, and a leaf shape of five chubby leaflets. In contrast, strains in the sativa grouping supposedly create an energizing physiological effect, and are physically characterized by taller, thinner branches and a leaf shape of seven longer, skinny leaflets. A hybrid strain is a strain that has been bred to induce both types of effects, a cross between an indica strain and a sativa stain. Ruderalis is the name given to a group of cannabis plants that appear in the wild. The hemp varietals have industrial uses such as in textiles and paper, as hemp cultivators prized a fibrous structural integrity over THC production. Fully understanding the relationship these sub-species groupings have to each other as well as the cannabis species require more R&D.

Varietals are colloquially referred as "strains," "cultivars," or "genetics." These colloquial terms were developed by underground cultivators in attempt to distinguish one cannabis plant from another at the subspecies level, beyond the hemp distinction. Note that the colloquial use of the word "genetics" has no relation to the scientific meaning of genetics. In science, genetics refers to the study of genes, the strings of molecules known as deoxyribonucleic acid ("DNA") that make up the genetic code that serves as a blueprint to life. Genome is the word used to refer to all the genes contained by an individual organism. Technically, each truly unique varietal has its own genome, also known as a genotype.

The colloquial use of the term "genetics" was propagated due to the mistaken assumption by cultivators that observable differences between individual plants were due to each plant having its own unique genome. In other words, an individual cannabis plant was often deemed a new varietal and given a new name because it looked different, smelled different, or created a different physiological effect when consumed. Nowadays, it is known that these differences are not necessarily the result of different genetics, but can be attributed to environmental and epigenetic factors, although genetic differences could also be the cause.

In nature, it is possible for any given genotype to have several possible physical manifestations of the same genes. The varying observable physical expression of the genotype is called a phenotype. Although the genome is the DNA blueprint, the phenotype is what is physically expressed by the organism in response to its environment. In other words, a phenotype represents one possible iteration of the representative genotype. Phenotypic expression is often affected by the aforementioned epigenetic and environmental factors. In modern genetics, organisms can be identified and classified at the subspecies level through analyzing genotypes or phenotypes and comparing them to known reference data sets called standards. Standards are a collection of selected data points that can serve as a baseline of comparison for an unknown sample. Standards can exist for any identifiable characteristic and are not limited to the subject of genetics.

Parameters are quantitative numbers that provide boundaries for analyzing data so that relevant conclusions can be drawn about the data. For example, cultivators unknowingly created standards for physical traits when comparing differences such as leaf size, leaflet number, or plant height between indica and sativa varietals. The parameters of seven leaflets per leaf and five leaflets per leaf were respectively used as a way of classifying cannabis at the subspecies level as sativa or indica.

Phenotypes and genotypes are independent of each other, meaning that organisms with the same genotype could have different phenotypes expressed; while those with the same phenotype could have different genotypes. In other words, the same genes can give rise to different cannabis morphologies, i.e. physical traits, due to differences in environmental factors. Two cannabis plants may be clones of each other, but each plant can be so affected by its growth conditions such as water, light, nutrients that different phenotypes are expressed. Phenotypes can be characterized by structural or biochemical differences.

Differences between phenotypes can be visible to the naked eye or exist at the microscopic or molecular level; whereas genotypes purely exist at molecular (without technical manipulation). Generally, microscopic and molecular traits can be visualized and analyzed with technology. Verifying whether the genes of each plant are the same or different often requires decoding ("sequencing") the DNA of both plants and comparing their genotypes, a very time consuming and expensive process that requires skilled scientists. Cannabis cultivators and distributors primarily analyze and report select natural product levels, which are due to differences in both phenotype and genotype.

To protect cannabis consumers and patients, many states in the United States are now requiring cannabis cultivators and distributors to test and report the contents of their products as well as track individual plants from the seed stage to the final product. Many samples now must pass analytical testing for herbicide, fungicide, pesticide and microbial levels. Moreover, several natural products that provide physiological effects have been selected to report as a parameters of quality control. These include cannabinoids such as THC, cannabigerol ("CBG"), cannabidiol ("CBD"), cannabinol ("CBN"); as well as terpenoids, a class of aromatic compounds, such as myrcene, limonene, and linalool.

More comprehensive standards and parameters are needed not only for the advancement of medicine and science, but also for consumer protection and quality control. Although reporting some cannabinoids, terpenoids, and contaminant levels is a positive step in regulation, laypeople often do not understand the meaning behind a series of reported parameters or chemical and biological numbers on a product label, let alone how those reported numbers are supposed to provide medical or recreational benefits. Further complicating matters is the entourage effect, the synergistic effect of cannabinoids with other phytochemicals, such as terpenoids, that either (1) act directly on the CB 1 or CB2 receptor; or (2) act indirectly by inhibiting enzymes responsible for the synthesis or degradation of endocannabinoids. In contrast to cannabinoids, terpenoids (and flavonoids) are very ubiquitous among land plants. Terpenoids, in a plant, contribute to its aroma.

It is generally known that cannabis provides many health benefits. It is suspected that the cannabinoid and natural product ratios produced by indica varietals provide medical benefits for anxiety or insomnia; and that sativa varietals provide medical benefits for depression. Because the cannabis plant is such a complex species, more comprehensive standards are needed so that these types of conclusion can be verifiably drawn. Medical and scientific R&D must be conducted to verify the perceived varietal correlations noted by patients and cannabis users. Before advanced R&D can take place, varietals and their resulting phenotypes must be taxonomically identified at the subspecies level.

As the legal cannabis industry develops, so does cannabis scientific research, discovery, and invention. The difficulty is merging the practically gained, and possibly scientifically inaccurate, underground knowledge with current high level scientific research and discovery in a way that the lay consumer understands. Doing so will enable consumers to make better, more well- informed decisions about the products they use. Those who wish to taxonomically identify and classify cannabis beyond the species level face many challenges. Quantifying sensory observations, creating reference standards, selecting meaningful parameters, and parsing phenotypes from varietals all must be overcome.

DETAILED DESCRIPTION

OVERVIEW

The present teachings pertain to methods and devices that create labels based on classifications of natural product mixtures. These complex mixtures of an assortment of natural products would typically be found in and comprise a part of commodities like spices, herbs, medicinal plants, and other biological organisms. The present disclosures create, assign, and print iconic representations of chemotypes. The disclosed embodiments quantify percipient observations by designating a chemotype based on a plurality of natural product standards and utilizing selected parameters. The parameters, standards, or data is mapped to wavelengths of light, such as visible color, ultraviolet, or infrared, which can be used as subspecies identifiers. The present teachings are both a method of designating chemotypes, as well as an apparatus that assigns wavelengths of light to chemotypes and prints the designation on a label. The present teachings can be used for comparison and verification of cannabis varietal chemotypes, as well as broader subspecies groupings. Beyond that, the invention also serves as a measure of natural product quality control as many factors can change the natural product makeup of a cannabis product before it reaches the end of the line, such as natural chemical degradations or inefficient storing methods. The present teachings de-convolute the entourage effect and thereby facilitate both accurate prescriptions by medical professionals and recommendations by point-of-sale dispensary employees.

For the purposes of the present disclosure, A Chemotype Symbol is a color, mark, sign, or word that indicates, signifies, or is understood as representing the content of one or more natural products in a commodity, as will now be described in detail.

PRESENT TEACHINGS

A chemotype is a grouping of natural products which together enable classification of organisms such as plants to levels below that of species. As previously discussed, terms like varietal, strain, and cultivar have also been used to assign plants to taxa below the species level. Chemotype (and chemovar) are just one more distinctive feature set to add to this list. Conceptually, chemotyping is a way to analyze the diverse patterns of a chemical fingerprint and reduce it to a simple icon with colored features. Assigning a color facilitates comparison and matching within a given commodity based on its chemical fingerprint in terms of chemotype groups

As previously discussed, cannabis plants may share the same genetic code but have different physical characteristics. This is due to cannabis plants interacting with the environment and epigenetic factors, which give rise to a plurality of possible phenotypes. Comparing phenotypes is one way to analyze the vast differences in cannabis varietals. A chemical phenotype, or chemotype, also known as a chemovar, is a unique chemical fingerprint of the natural products made by an individual plant. The chemotype is a more precise way to distinguish, classify and identify cannabis subspecies groupings and varietals due to the phenotypic differences that can exist between genotypes. Because cannabis contains and produces compounds that

physiologically interact with the human body, a detailed chemotype captures more relevant information than a genotype alone would. By comparing chemotypes, relationships and distinctions can be drawn regarding the complex natural product profiles made by cannabis. The disclosed embodiments operate as percipient observation translators wherein categories of detectable aromatic scent profiles based on percipient observations are determined, and the natural product mixtures are separated and analyzed in accordance with the disclosures above for chemotype designation and a chemotype is developed. In one embodiment, sensory observations can serve as parameters from which standards can be developed for a new chemotype, based on the aromatic profile detectable by the human nose.

By grouping cannabis based on percipiently observed aromas, such as Earth, Floral, Fruity or Fuel and representing each category's constituents with chemotypes, cannabis consumers and patients can empirically segregate cannabis into useful categories determined by the content of its principal terpenes. This principal terpene class includes those with putative activity within the endocannabinoid system. The clinical efficacy of mixtures of cannabinoids and terpenes (both naturally occurring and processed formulations) can be analyzed and correlations drawn.

A Plurality of Natural Products

• Terpenoids, Cannabinoids, Flavonoids, BCP, Ahum, CBD, THC, CBG, CBN

• Mixtures of many components in inexact proportions, usually natural, such as PLANT

EXTRACTS; VENOMS; and MANURE. These are distinguished from DRUG COMBINATIONS which have only a few components in definite proportions. These can be resolved or unresolved (UCM].

• Components of a specific mixture which can be useful in fingerprinting of complex mixtures

The embodiments of the present teachings rely on input about the quantities of natural products present as complex mixtures in a given commodity. This information is typically obtained using High Resolution Analysis Methods for separating and identifying Natural Product Mixtures. This is typically data produced from Chromatography devices and Spectrophotometry devices. In other words, a plurality of highly resolving analytical methods are used to partition the range of natural products within a given cannabis sample. Multiple samples are analyzed to create multiple data sets. Each sample generates an additional data set for each possible chemotype and parameters are selected for Multivariate Analysis (MVA).

A Plurality of Highly Resolving Analytical Product Separation Method

• The highly resolving analytical methods include, but are not limited to: FTIR and

chromatography (HPLC, GCMS].

• Gas Chromatography

o Gas chromatography (GC] is a common type of chromatography used in analytical chemistry for separating and analyzing compounds that can be vaporized without decomposition

o Typical uses of GC include testing the purity of a particular substance, or separating the different components of a mixture (the relative amounts of such components can also be determined]. In some situations, GC may help in identifying a compound. o n gas chromatography, the mobile phase (or "moving phase"] is a carrier gas,

usually an inert gas such as helium or an unreactive gas such as nitrogen. o The stationary phase is a microscopic layer of liquid or polymer on an inert solid support, inside a piece of glass or metal tubing called a column (an homage to the fractionating column used in distillation].

o The instrument used to perform gas chromatography is called a gas chromatograph

• Liquid Chromatography:

o Liquid chromatography (LC] is an analytical chromatographic technique that is useful for separating ions or molecules that are dissolved in a solvent. If the sample solution is in contact with a second solid or liquid phase, the different solutes will interact with the other phase to differing degrees due to differences in adsorption, ion-exchange, partitioning , or size. These differences allow the mixture components to be separated from each other

o Instrumentation : Simple liquid chromatography consists of a column with a fritted bottom that holds a stationary phase in equilibrium with a solvent. Typical stationary phases (and their interactions with the solutes] are: solids (adsorption], ionic groups on a resin (ion-exchange], liquids on an inert solid support (partitioning], and porous inert particles (size-exclusion].

o The mixture to be separated is loaded onto the top of the column followed by more solvent. The different components in the sample mixture pass through the column at different rates due to differences in their partioning behavior between the mobile liquid phase and the stationary phase..

• FTIR Spectroscopy

o Fourier transform infrared spectroscopy (FTIR] [1] is a technique which is used to obtain an infrared spectrum of absorption or emission of a solid, liquid or gas. An FTIR spectrometer simultaneously collects high spectral resolution data over a wide spectral range. This confers a significant advantage over a dispersive spectrometer which measures intensity over a narrow range of wavelengths at a time. o Fourier Transform-Infrared Spectroscopy (FTIR] is an analytical technique used to identify organic (and in some cases inorganic] materials. This technique measures the absorption of infrared radiation by the sample material versus wavelength. The infrared absorption bands identify molecular components and structures o The goal of any absorption spectroscopy (FTIR, ultraviolet-visible ("UV-Vis"]

spectroscopy, etc.] is to measure how well a sample absorbs light at each wavelength. The most straightforward way to do this, the "dispersive spectroscopy" technique, is to shine a monochromatic light beam at a sample, measure how much of the light is absorbed, and repeat for each different wavelength. (This is how some UV-Vis spectrometers work, for example.]

o The term Fourier transform infrared spectroscopy originates from the fact that a Fourier transform (a mathematical process] is required to convert the raw data into the actual spectrum

o Fourier transform spectroscopy is a less intuitive way to obtain the same

information. Rather than shining a monochromatic beam of light at the sample, this technique shines a beam containing many frequencies of light at once, and measures how much of that beam is absorbed by the sample. Next, the beam is modified to contain a different combination of frequencies, giving a second data point. This process is repeated many times. Afterward, a computer takes all this data and works backward to infer what the absorption is at each wavelength

Natural Product Standards and Parameters

• Cannabis species confirmation: BCP and AHum standards applied; BCP:AHum ratio

determined

• Terpenoids, Cannabinoids, Flavonoids

Cannabinoids, terpenoids, or even flavonoids are all natural products that can be resolved into chemotypes. For flavonoids, there is chemotaxonomic support that there are two-subspecies groupings possible based on an analysis of flavonoid variation that detected luteolin C- glycuronide in 30 of 31 plants assignable to sativa varietals, but not in 21 of 22 plants assignable to indica varietals.

When using terpenoids and cannabinoids to derive chemotype designations, more designations are possible. The present embodiments disclose a selection of 6 to 9 Cannabis Chemotypes (3 cannabinoid groups X 3 terpenoid groups) that are sufficient for grouping cannabis in a way that is meaningful for initial clinical efficacy studies. Terpenoid groups comprise the "Earth", "Floral", and "Fuel" categories. The terpenoids alpha-pinene, myrcene, and beta-caryophyllene are principal loading factors for PCA.

The Earth aroma category has a low level of alpha-pinene, a low level of myrcene, and a high level of beta-caryophyllene. The Flora aroma category has a high level of alpha-pinene, a high level of myrcene, and a low level of beta-caryophyllene. The Fuel aroma category has a low level of alpha-pinene, a high level of myrcene, and a high level of beta-caryophyllene.

Studies have shown that the terpenoids responsible for the olfactory classification of strains into "fuel", "floral", or "earth" are also responsible for modulating the endocannabinoid system to produce the varying strain-dependent effects. I ndeed, the principal Cannabis terpene β- caryophyllene has been shown to have direct activity on the CB₂ receptor in mouse models of neuropathic pain, and in doing so earns the alias "phytocannabinoid" along with THC and CBD. alpha-pinene has been implicated as an acetylcholine esterase inhibitor, thereby believed to promote memory and cognition- two hallmarks of the patient-reported "sativa effect".

Cannabis produces a large number of terpenoids, many of which are used as

aromatherapuetics to relieve stress and anxiety while others are used topically to treat skin conditions. I n order to accurately identify and quantify the various terpenoids in a cannabis product, analytical method validation is imperative. Due to the similarities in the physical properties of terpenoids, a major challenge in Cannabis analytics is the accurate identification of terpenoids in a complex matrix such as Cannabis flower.

An individual terpene synthase can produce multiple products from the same type of substrate. While surprising, that is the most likely reason underlying the tight correlation between β caryophyllene (BCP) and a-humulene (AHum) levels in Cannabis varietals. A 3:1 ratio

(BCP:AHum) when a valid methodology is used in gas chromatography. The closely related Cannabaceae family member, Humulus lupulus (Hops) expresses a homologous terpene synthase enzyme (H1STS1). It has been previously reported that Hops also produces both of these sesquiterpenes products, but at the reciprocal of the Cannabis ratio, (1:3 BCP:AHum). Available protein sequence data supports the hypothesis that single amino acid substitutions in the active site are responsible for these catalytic rate differences. The stability of this biochemical parameter permits us to recommend this ratio as a quality control parameter. We suggest that the 3:1 BCP:AHum ratio can be adapted as a quality control parameter for terpene analyses as well as an identification parameter for the Cannabis species.

Multivariate Analysis

• Multivariate Data Analysis refers to any statistical technique used to analyze data that arises from more than one variable.

• Multivariate analysis (MVA) is based on the statistical principle of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a time.

This essentially models reality where each situation, product, or decision involves more than a single variable.

• When available information is stored in database tables containing rows and columns, Multivariate Analysis can be used to process the information in a meaningful fashion.

• Principle Component Analysis utilized mapping natural product concentrations in 3-D space

• One embodiment resolves subspecies classification groups of floral, fuel, and earth

The multiple data sets generated from MVA are mapped in two dimensional ("2D") or three dimensional ("3-D") space based on the selected parameters and a device assigns a

representative wavelength of light, such as color, as a function of the selected natural product concentration to the mapped data. The present embodiments include a data processing element that creates a data matrix, spreadsheet, or relational database of the analytical data. Typically each sample would be represented by a data vector that includes concentrations of a panel of natural products whose values are interpolated from individual calibration standards for each natural product analyte. Additionally, spectra of each sample can be included whereby the absorbance at each wavelength represents a separate element of the data vector for a given sample.

Color Mapping Algorithm

MVA is an element of the disclosed embodiments that use a Color Mapping Algorithm ("CMA") to assign colors samples based on their MVA values. When available information is stored in database tables containing rows and columns, MVA can be used to process the information in a meaningful fashion. MVA includes statistical analysis platforms like Principal Component Analysis ("PCA") and Hierarchical Clustering Analysis ("HCA"). An additional key element of the present disclosures include a set of Color Mapping Algorithms ("CMA"), or wavelength generators. CMA are used to assign a wavelength of light, such as a color, based on a set of multivariate analysis values. For example, in one embodiment, a color can be assigned by mapping red, green blue ("RGB") values to each of three key and determinant natural products. Alternatively, a color may be assigned to a given sample based on values from a set of Principal Components mapped to colors using color hue, saturation, and Lumosity ("HSL") values.

Another embodiment assigns colors to samples based on their relative Euclidean Distances derived from HCA.

• There are a plurality of methods that may be used for the Color Mapping Algorithm (112].

The first embodiment describes a method based on mapping the concentrations of 3 key Cannabis terpenoids onto an RGB color space. In this embodiment, the 3 key Cannabis terpenoids described above, namely alpha-pinene, beta-caryophyllene, and myrcene are used. The algorithm is a software routine. It can also be writtenas a Macro in existing software such as Excel. In this example, the algorithm was programmed in an Excel worksheet. The details of this algorithm are described immediately below. In principle, though, the method entails first assigning each of the colors R, G, and B to one of the 3 key terpenoids described above. Second, the terpenoid concentrations are normalized. Third, assign a range that outpit color values may have within the 0 to 255 that is the full range for each of the 3 colors: R, G, and B. Fourth, apply the formula that converts the terpenoid concentration to a value between 0 and 255 of the RGB color code. Fifth, Tune or scale the output value by multiplying it by a coefficient selected to increase the vividness of the color and remove any unwanted washout due to too high a combined value of R, G, and B. too high a value leads to a bleached looking color rendition. Using a scaling coefficient less than 1 can mitigate this problem. Finally, the output values for the R, G, and B equivalents of each sample are passed to the driver which codes for the delivery of the appropriately colored printer ink to the label surface. Alternatively, RGB color mapping Macros are generally available and can be incorporated into an Excel worksheet to visualize the color result produced by the algorithm. The following is a detailed description how the color mapping of the 3 principal Cannabis terpenoids, alpha-pinene, beta-caryophyllene, and myrcene may be mapped onto an RGB color space.

1. Assign an individual terpenoid concentration to a particular element of the RGB color code: ie myrcene=Red, beta-caryophyllene =Green, and alpha-pinene =Blue.

2. Generate normalized terpenoid concentration for each sample: =terp cone / terp cone range

3. Determine range of each color component (R, G, or B] from setting: =color value (top] - color value (bottom]

4. Determine the unsealed color code equivalent of the terpenoid concentration by

multiplying the normalized terp cone X individual color value range and add it to bottom of color value range: =(norm terp cone X color value range] + color value (bottom]

5. Scale the color code equivalent by multiplying it by the input scale coefficient: =unscaled color code equivaltent X scaling coefficient

6. Paste the scaled color code array into the appropriate region and run the RGB color

assignment macro to assign the RGB color for each sample.

• A second embodiment of the Color Mapping Algorithm (112] includes a method based on determining the scores for each sample in terms of its position relative to the Principal Components of the entire dataset. The Principal Component values are derived from a Principal Components Analysis (PCA] applied to a dataset (matrix] of samples each having a measured concentration value for each analyzed terpenoid. The scores for each sample in terms of the first 2 Principal Components (Prnl and Prn2] are assigned values which correspond to color values for the Hue (H] parameter and the Saturation (S] parameter respectively, in HSL (L=Luminosity] color space. Principal component analysis (PCA] is one popular approach analyzing variance when dealing with multivariate data. In a multivariate dataset, random variables XI, X2,...Xn are all correlated (positively or negatively] to varying degrees. PCA analysis provides a way to understand patterns or groupings in the data.. Each sample has a score value in terms of each of the Principal Components of the dataset. There is one score value for each observation (row] in the data set, so there are N score values for the first component, another N for the second component, and so on. The score value for an observation, for say the first component, is the distance from the origin, along the direction (loading vector] of the first component, up to the point where that observation projects onto the direction vector. In practice, this is easily determined with available PCA software programs such as SAS / JMP, and the like.

In principle, the PCA - HSL Color Mapping Algorithm method has the following steps: Determine the Principal Component 1 (Prnl) score and the Principal Component 2 (Prn2) score for each sample using statistical analysis software such as SAS / JMP..

Because the Prnl and Prn2 scores may be positive or negative, they need to all be adjusted to positive values to carry out the color mapping algorithm. This is

accomplished by determining the positively adjusted Prn score for each value. Therefore the positively adjusted Prnl score for the sample = the sample Prnl score + (minimum Prnl score for the dataset], and repeating for the Prn2 score for each sample: positively adjusted Prn2 score = sample Prn2 score + (minimum Prn2 score for dataset] .Where | | means absolute value.

Determine the Prnl and Prn2 Score ranges respectively by determining the sum of:

(minimum Prnl Score for dataset] + (maximum Prnl Score for dataset], and (minimum Prn2 Score for dataset] + (maximum Prn2 Score for dataset] .

Determine the Normalized Prnl Score for each sample using the formula: Normalized Prnl Score = Positively Adjusted Prnl Score / Prnl Score Range. And determine the Normalized Prn2 Score for each sample using the formula: Normalized Prn2 Score = Positively Adjusted Prn2 Score / Prn2 Score Range.

Assign arbitrary minimum and maximum values to the values that the Hue Color Value (H) can have within the color mapping space. These values can range from 0 to255. A typical assignment is minimum (H) = 225 and maximum (H) = 25 .Repeat this for the Saturation Color Value (S). A typical set of values for these parameters are: minimum (S) = 75 and maximum (S) = 255 .

Determine the Range of (H) Color Values using the formula: (H) Color Value Range = (Maximum (H) assigned value - Minimum (H) assigned value | . Do the same for the Range of (S) Color values. The formula is: (S) Color Value Range = (Maximum (S) assigned value - Minimum (S) ssigned value] . Assign the Normalized Prnl score for each sample to an H color value using the formula: H Color value = minimum assigned (H) color value - (Sample Normalized Prnl Score X Range of (H) color Values). Do the same to determine the (S) Color value for each sample using the formula: S Color value = minimum assigned (S) color value + (Sample Normalized Prn2 Score X Range of (S) Color Values) .

Finally to actually map the assigned color values to a real color, paste the array of assigned H and S values into the appropriate region and run the HS color assignment macro to assign the HS colors for each sample. Because we are not using the Third Principal Component in this embodiment of the Color Mapping Algorithm, The (L) parameter is arbitrarily set = 128 for all samples. This value is the midpoint of the (L) range which can vary from 0 - 255 .

A third embodiment of the Color Mapping Algorithm (112] assigns colors to samples based on their relative Euclidean Distances as derived from Hierarchical Clustering Analysis (HCA] which is another type of MVA technique. In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA] is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types: [1]

Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. This "Agglomerative" approach to HCA is what has been used in the present embodiment.

Divisive: This is a "top down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.

In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram.

HCA clusters rows that group the points (rows] of a data table into clusters whose values are close to each other relative to those of other clusters. Hierarchical clustering is a process that starts with each point in its own cluster. At each step, the two clusters that are closest together are combined into a single cluster. This process continues until there is only one cluster containing all the points. This type of clustering is good for smaller data sets (a few hundred observations].

Hierarchical clustering enables you to sort clusters by their mean value by specifying an Ordering column. One way to use this feature is to complete a Principal Components analysis (using Multivariate] and save the first principal component to use as an Ordering column. The clusters are then sorted by these values.

Once the data is sorted into hierarchical clusters, the Euclidean Distance between each of any 2 data points can easily be determined. By including a reference sample in the analysis, each sample points Euclidean Distance to the reference standard can be determined. These distance values will be sorted in terms of position relative to the 1^st Principal Component of the overall dataset. (see above Embodiment]. A series of colors are then assigned to each range of the Euclidean Distance. This is commonly done by most Graphing Software programs that can assign a palette or theme of colors to a unique set of values. Using this method of HCA, ordered by the Prinl scores, and deriving Euclidean Distances for each sample point color is easily mapped to the single Euclidean distance value for each sample. In summary, in this 3^rd embodiment, the entire data vector of natural product concentration data (ie terpenoid concentrations] can be reduced to a single value, This value can easily be compared between samples to gauge similarity or difference. And this value can be mapped to a color to easily evaluate the similarity or difference between 2 or more samples at a glance. This powerful method can reduce sample data vectors with a large number of different elements to a single value for facile comparison.

• The following steps are carried out in order to derive the Euclidean Distance for each

sample compared to a reference value and map it to a color theme, scheme, or palette.

1. Start with a dataset that is in a format compatible with MVA. In this case, it is a database, spreadsheet, or table where the rows are samples and the columns are natural product concentrations. In particular, the natural products are terpenoids.

2. Perform a Ward Method Hierarchical Clustering Analysis based on standardized data'

3. Output the Euclidean Distance Matrix and select the column corresponding to the reference standard. It should therefore include a value = 0 for this Reference Sample. All other samples will have a value which is their respective Euclidean Distances from this Reference Standard.

4. Select a suitable color mapping scheme or theme using available functions present in most graphing programs to color a series of values in a graph.

5. Output the assigned color in the format compatible with the Driver (114].

Chemotype Designation Printer

• Chemotype designated by assigned light wavelengths in conjunction with icons and symbols

The present teachings also comprise a Label Printing Device. Input to the device is the color code derived from the color mapping algorithm. Output of the device is a label that is printed with the iconic representation of chemotype in the appropriately assigned color for a given sample.

Schematic Drawing with Explanations for The Label Printing Device

Schematic for Label Printing Device

102 Device for High Resolution Separation and Analysis of Complex Mixtures of Natural Products.

This device provides the input about the quantities of natural products present as complex mixtures in a given commodity to a Label Printing Device (104). . This is typically data produced from Chromatography devices and Spectrophotometry devices.

104 The Label Printing Device

This subject of the present invention. It includes the elements (106-120) which are described below.

106 Computing

This element provides the ability to carry out the requisite activities associated with the data analysis process. It contains RAM, a CPU, and a data storage device (such as a disk drive). It contains elements that are typically associated with a desktop , laptop, or tablet-type PC. It permits thedata processing and analysis functions associated with the elements 108 - 114 described below.

108 Data Processing

This element creates a data matrix, or spreadsheet, or relational database of the analytical data. Typically each sample would be represented by a data vector that includes concentrations of a panel of natural products whose values are interpolated from individual calibration standards for each natural product analyte. Additionally, spectra of each sample can be included whereby the absorbance at each wavelength represents a separate element of the data vector for a given sample.

110 Multivariate Analysis

The Multivariate Analysys (MVA) element can be used to process the information in a meaningful fashion when available information is stored in database tables containing rows and columns, MVA includes statistical analysis platforms like Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA). It is a typcal feature of statistical analysis platforms such as SAS (JMP) and the like.

112 Color Mapping Algorithm

This key element of the device includes a set of Color Mapping Algorithms. These algorithms are used to assign a color based on a set of multivariate analysis values. This can be done in several ways: For example a color can be assigned by mapping RGB values to each of 3 key and determinant natural products. Alternatively, a color may be assigned to a given sample based on values from a set of Principal Components mapped to colors using color HSL values. A third way assigns colors to samples based on their relative Euclidean Distances derived from HCA. In all cases, the invention uses a color mapping algorithm to assign colors samples based on their MVA values.

114 Driver

The final element of the Computing Device (106) is comprised of a Driver. A printer driver or a print processor is a piece of software that converts the data to be printed to the form specific to the printer which comprises part of the overall Label Printing Device (104). The purpose of the printer driver is to allow the device to do printing by providing the technical details (programming instructions) for the printing function.

116 Ink Source: Black & colored inks

An ink cartridge or inkjet cartridge is a component of an inkjet printer that contains the ink that is deposited onto paper during printing. Each ink cartridge contains one or more ink reservoirs containing liquid ink. Certain producers also add electronic contacts and a chip that

communicates with the printer. Color laser printers use colored toner which is dry ink. These are typically cyan, magenta, yellow, and black (CMYK). While monochrome printers only use one laser scanner assembly, color printers often have two or more.

118 Input Tray or Spool for: Label Stock

120 Output Tray or Spool for.Label Stock PCT Non-Prov Figures with Explanations

Figure 122 Gas Chromatography of Terpenoid Standards

Gas chromatography is a highly resolving method to separate and analyze complex mixtures of natural products. In this figure a mixture of terpenoid standards is resolved into individual component peaks. Each peak represents a separate terpenoid standard. Because of the complexity and the chance for misidentification of peaks in the actual samples containing the terpenoid to be measured, it is useful to have an internal adjunct validation method such as that provided by the beta-caryophyllene / alpha-humulene ratio described below in Figure XXX. In the actual process of measuring terpenoids in a natural product sample, the retention time (the time value on the x axis) of each peak in the sample would be compared to that of standard known terpenoids. Based on a similarity of retention time, the unknown in the sample can be matched to the standard and identified. Furthermore, the size of the peak corresponds to the amount of each component. So by comparing to a standard with a known amount of terpenoid, the amount of terpenoid in the sample can be determined. In addition to determining the retention time of each peak, a Mass Spectrometer is often coupled to the GC as a detection device. The sample can then be further identified based on its mass fragmentation pattern compared to the pattern of known standards.

Each sample will therefore produce a series of concentration values for each identified

(terpenoid) natural product. The output of this analysis can be compiled (using the data processing Element 108) into a data table where each sample is a row, and the row elements contains the values for the concentration of each of the natural products determined to be present in the sample. Figure 124

FT-IR Spectra of Cannabis Flower Samples

Fourier Transform - Infrared Spectroscopy is another embodiment of a highly resolving method of identifying the natural product components in a complex mixture. In this example, Cannabis Flower samples are analyzed in the FT-IR device. A plurality of peaks are observed. In this case, the peaks correspond to types of chemical bonds present in the natural product compounds present in the sample. In this embodiment, the x axis corresponds to the wavelength of light that is transmitted through the sample. The amount of transmission is measured for each wavelength and recorded on the Y axis. Together this result constitutes a spectrum of the sample. In this embodiment, there is a data value corresponding to transmission for each wavelength. Each sample will therefore produce a series of transmission values for each wavelength. The output of this analysis can be compiled (using the data processing Element 108) into a data table where each sample is a row, and the row elements contain the values for the transmission of the sample at each wavelength. Finally, it is noteworthy that while most of the Cannabis flower samples show pretty overlapping FTIR spectra, there is a region (expanded wavelength insert within the figure) that shows differences in the spectra among the samples. These differences can be analyzed using MVA (element 110) to define potential differences in the Chemotype of the Cannabis flower samples.

This figure represents the quantification of 3 key terpenoids: alpha-pinene, beta-caryophyllene, and myrcene in Cannabis flowers extracted with methanol. The samples were analyzed by GC and compared to standards. The samples originated from entries to a Cannabis Competition (called the Golden Tarp Awards (GTA)), which took place in Humboldt county, California in late summer 2016. The samples were submitted by cultivator-contestants in each of 4 aroma categories: "Floral", Fruity", "Earth", or "Fuel". A full panel of Cannabinoids and Terpenoids were analyzed by chromatography. The analytical results are now public domain and can be accessed here:

. In this way, we have a dataset that includes both terpenoid

concentrations and aroma categories.

Using this example, therefore, a statistical test called an Analysis of Variance (ANOVA) could be performed. The ANOVA can demonstrate whether there is a statistically significant difference between the "Earth", "Floral", or "Fuel" aroma categories in terms of their concentrations of 3 key terpenes, namely alpha-pinene, beta-caryophyllene, and myrcene. The results demonstrate that in fact there are very significant differences in the concentrations of these key terpenes depending on perceived aroma category.

Based on the results of the ANOVA performed above (Figure 126), it is possible to categorize the levels of the 3 key terpenoids into 2 binary classes: "high" and "low". This figure describes how the level of the 3 key terpenoids influences what aroma category Cannabis Flowers may be assigned to. Reducing this to a binary state for 3 key terpenes defines chemotypes for Cannabis based on perceived aroma. Alpha-pinene, beta-caryophyllene, and myrcene are the optimal 3 terpenoids to define the principal aroma categories.

The 3 Principal Aroma Categories of "Floral" , "Fuel", and "Earth" can be resolved as separate, clusters, or groupings in a 3D terpenoid space consisting of alpha-pinene, myrcene, and beta- caryophyllene.. It therefore follows to employ these 3 key terpenoids to color code Cannabis Flower samples. The color coding would correspond to and facilitate identification of key Cannabis chemotypes.

Figure 132

Figure 132 shows an overview of the Color Mapping Algorithm of the First Embodiment wherein the values for the concentrations of 3 key terpenes are mapped to colors using RGB values. Each of the 3 Key Terpenoids are assigned to one of the colors. In the present embodiment, Red ® is assigned to myrcene; green is assigned to beta-caryophyllene; and blue is assigned to alpha-pinene. Specific details about each step are in the accompanying text and in subsequent figures. In the present embodiment, the algorithm was coded within an Excel Spreadsheet. The final output was a colored cell corresponding to the derived color code using RGB values. The output values could just as easily be directed to the appropriate driver that would enable the printing of the color on a label.

Further details of the method by which values are input into the Color Mapping Algorithm of the first embodiment are presented in this figure. The arrows show the input for the boundary values for each of the RGB colors. The scaling coefficients are also input into the appropriate designated fields. The arrow pointing downward also points to the data matrix of key terpenoid concentrations for each of the samples. These are the input values to the algorithm as well.

An overview of the steps in the Color Mapping Algorithm of the First embodiment is shown in this figure. His is also described in the accompanying text.

In this figure, the excel code that converts a key terpene concentration value to an RGB color value is shown. The formula that converts the boxed cell is shown in the figure. Upon their generation, these RGB values may be output to a driver. In this case, the driver is depicted by the arrow which shows the flow of these output values to the driver that will generate the colors. The output colors are shown in the cells immediately to the left of the sample names themselves.

This Figure shows the top range of the colors assigned to each of the 3 aroma categories.

"Floral" is violet; "Earth" is green; and "Fuel" is a light brown with a slight greenish shade. Although the color choices for the aroma categories are arbitrary in this embodiment, the colors are selected to match perceptions about what colors may best match each aroma. This figure depicts what happens when the top range of each color value in the RGB series is slightly adjusted by reducing the value slightly. When the top range is set = 210, the colors each appear a bit lighter; when the top range is set = 192, the colors each appear a shade darker. The color output for each of the samples included in this dataset are presented in the figure above. The samples are grouped into columns depending upon their aroma category assigned by the

Cultivator-Contestant at the time of their submission. It can be seen that there is a correlation between sample color and aroma assignment. The "Floral" category exhibits more samples with colors in the greyish-blue-violet range, whereas the "Fuel" category shows samples largely in the greenish-brown range as would be expected. The "Earth" category contains samples with a brighter green color. The "Fruity" category is not a good discriminator of Cannabis aroma categories. It is apparently esily confounded with the other categories. This can be further observed by the fact that the samples classified as "Fruity" have color values from all categories. We have concluded that the "Floral" and "Fuel" categories are the most robust discriminators. "Earth" may be useful as signifying those samples that have undergone attenuation of their aromas due to volatilization of one or more their terpenoids due to inadequate storage or handling.

In order to adjust the ultimate brightness of the colors, a scaling factor is employed in the algorithm in this embodiment to fine tune the brightness or saturation of the color. This embodiment exhibits the utility of being able to fine tune the output colors by means of the scaling factor. In this set of examples: the scaling factor can either be set = 0.9 or =0.95, although other values are also appropriate. In this figure, the difference between setting the scaling factor = 0.9, or = 0.95 is depicted. Setting the scale factor to = 0.95 yields a slightly brighter range of colors compared to setting it = 0.9 . In conclusion, this embodiment of the Color Mapping Algorithm carries out the mapping of 3 key terpenoid concentrations present in the samples, which in the present embodiment are alpha- pinene, beta-caryophyllene, and myrcene. In other embodiments another set of 3 key terpenes may be selected. The concentration values are mapped to RGB color values and a color corresponding to the combined levels of the 3 key terpenes in the sample is assigned. This embodiment bases the color assignment on 3 parameters and can therefore provide a high level of resolution in distinguishing small differences between samples in terms of their 3 key terpenoid levels. Furthermore, this Color Mapping Algorithm is based on the use of terpenoid standards to assign the terpenoid concentrations in values to what is present in the samples. The method easily accommodates the addition of new samples into the database, as long as the terpenoid levels are quantified using the same standard set. Furthermore, if new terpenoid standards are adapted, they must be validated in order to demonstrate that they produce derived concentration values that are the same within experimental error of the previous lots of terpenoid standards. The present embodiment is a robust approach to permit the continuing addition of an ever increasing number of samples to create a collection of color-assigned samples based on natural product content.

Figure 158 documents an observation we have made during the course of numerous analyses of Cannabis samples within databases that comprise values for individual terpenoid concentrations. The beta-caryophyllene to alpha-humulene ratio is just around 3 in Cannabis. This means that for every 3 molecules of beta-caryophyllene that are produced from the substrate farnesyl pyrophosphate, one molecule of alpha humulene is produced from the same substrate. We therefore propose that this important ratio is a good Quality Assurance parameter as well as an identity marker for Cannabis material. In contrast, we show that in the closely related genus Humulus, both molecules are produced as well, but their relative production rate is the inverse of that seen in Cannabis. Both Cannabis and Hops belong to the Family Cannabiciae. The prediction that this ratio would be an excellent Quality Control parameter is borne out by the data in Figure 160 below. This Figure is a Control Chart that plots tthe beta-caryophyllene : alpha- humulene ratio in every Cannabis Flower sample submitted for Terpenoid analysis over an interval of time within a particular lab.. It can be seen that the Upper Confidence Limit (UCL), the Mean, and the Lower Confidence Limit (LCL) are 4.234, 3.124, and 2.014 respectively. This tight band allows the rejection of assays for samples that fall outside the ranges of the UCL and the LCL. These samples would be subjects for a repeat confirmatory assay. This is a powerful approach to controlling the quality of Cannabis Terpenoid quantification assays. Furthermore, in the context that beta-caryophyllene is a Key Terpenoid in the First embodiment of the Color Mapping Algorithm, the fact that the quantification of beta-caryophyllene can be quality controlled and validated through the use of the constant ratio of beta-caryophyllene : alpha-humulene provides further validity to a color Mapping Alsorithm that relies on beta caryophyllene as one of the key terpenoids included in the RGB Color Mapping Algorithm. The subject of the First embodiment.

Figure 144 describes a Second Embodiment of the Color Mapping Algorithm: In this example, The first 2 Principal Component Scores derived by PCA are mapped onto a 2 Dimensional HS(L) Color Space, using the Hue and Saturation color values only. The samples from the GTA Dataset described in the First Embodiment are plotted in Panel (A) of the Figure. Here the Principal Component 1 (Prnl) and Principal Component 2 (Prn2) scores are plotted in Score Plots. In this series of Score Plots, the Prnl scores are plotted on the X axis and the Prn2 sores are plotted on the Y axis. The Prnl and Prn2 scores are derived by MVA (Element 110). A distinctive pattern may be observed resembling an "L". This pattern is highlighted by the overlay of the 2 perpindicular lines, one undashed, the other with dashes. It can bew observed that the set of points from the GTA dataset of Embodiment 1 largely are associated with these 2 lines. In Panel (B), a completely independent dataset of samples (Presented by Jeffrey Raber, PhD at the 2016 MJ Business Conference and Expo, Science Symposium) with a collection of terpenoid values determined for each Cannabis Flower sample is plotted in the same way as (A). The Prnl Score and Prn2 Score axes are similarly scaled to that of (A). The overall pattern of the data is strikingly similar to that of Panel (A). Furthermore, the subject data of the present embodiment, namely a set of 65 Cannabis Flower samples had their terpenoid concentrations determined by GC. The GC was coupled to a Mass Spectrometer to unambiguously assign peaks to appropriate terpenoid compounds.. This high resolution analysis approach is known as Gas Chromatography with Mass Spectrometry Detection or GC-MSD. The resulting Prnl vs Prn2 Score Plot for the dataset of this 3^rd embodiment is shown in Panel (C). Again, the axes are scaled similarly to those in Panels (A) and (B). It is strikingly apparent that a similar pattern of the data can be observed. Finally, in Panel (D) a completely independent set of close to 2 dozen Cannabis Flower samples were analyzed for terpenoid content using GC. In this case the GC was coupled with a Flame Ionization Detector (FID) to permit identification of the eluting materials. A set of terpenoid standards was used to assign the peaks to each terpenoid and to quantify the amounts in each sample. It is apparent that most of the samples in this dataset had very similar Prnl and Prn2 scores since they clustered in a small group close to the bottom of the added solid line. A subset of samples can be seen associated with this line, similarly to what is observed with the other datasets depicted in Panels (A), (B), and (C). Each of the different datasets of Panels (A-D) were completely independently processed and analyzed at different times, in different places, and using different sets of standards and by different technical operators to carry out the analyses. Nonetheless, the patterns of the data when represented as Prnl vs Prn2 Score Plots are strikingly similar. This highlights a universal feature of Cannabis Flower Samples analyzed for Terpenoid concentration and presented in this format. This appears to be a universal pattern for a defined set of natural products analyzed in this particular commodity. We anticipate that other such characteristic patterns (albeit different in shape) would be evident for other commodities (such as herbs or spices). Such a pattern may be useful for identifying a given commodity or defining its overall profile with respect to a given set of natural products. In the present embodiments these natural products are terpenoids. In additional embodiments, it is likely that other sets of natural products such as flavonoids, ligninamides, alkaloids, and the like can be characteristically profiled in this way.

In order to perform the Color Mapping Algorithm of this embodiment, the characteristic profile for Cannabis Flower samples is overlaid on a 2 dimensional color array, where the horizontal dimension is the Hue (H) color value parameter, and the vertical dimension is the Saturation (S) color value parameter. In this particular embodiment, the 3^rd color value parameter, namely the Luminosity (L) color value parameter of the HSL color space is not used. For the 2 dimensional color array used in this embodiment, the Hue varies horizontally from a Violet / Purple on the left side through all the spectral colors ending with red on the right. The color varies from saturated and bright vibrant colors at the top of the array to a series of differently shaded greys at the bottom of the array. The position of the characteristic Cannabis Flower data shape in Prnl Score vs Prn2 Score space is overlaid on this color array. In this way, the colors corresponding to the various combinations of Prnl, Prn2 scores for each sample can be visualized in terms of what their output colors would be.

The PCA - HSL Color Mapping Algorithm method is described in the accompanying text. In this Figure 146, the output assigned color values for a subset of Cannabis Flower samples included in the 65 samples of this embodiment and depicted above in Figure 144 Pane (C) is shown. This subset of the data represents samples of a particular Cannabis strain called "Candyland". The colors assigned by this embodiment of the Color Mapping Algorithm vary slightly from a grey -green for Flower samples harvested from the bottom positions on branches to a slightly brighter shade of green for Flower samples harvested from the top positions on branches. This slight change in assigned output color would be a function of slightly differing terpenoid profiles in bottom Flowers vs top Flowers. This would be an entirely expected outcome of the application of this method.

Figure 148 is similar to Figure 146, except that another subset of Cannabis Flower data is presented. In this case, the Cannabis Flowers are from an "OG" strain. Furthermore, the samples originate from Flowers that were produced from plants that were either grown outdoors or in a greenhouse. Furthermore, a subset of these Flowers was either subjected to hand trimming or left untrimmed. For the set of outdoor grown samples, the output color varies from green for samples such as 2-i-O-C-T-u to a greenish-brown for samples such as 2-P-O-C-T-t and 2-P-O-C-T-u. There is a very slight difference between these samples with the untrimmed sample showing a bit more brown compared to the slightly greenish brown of the trimmed sample. This is consistent with a slightly different terpenoid profile resulting from the trimming of the sample. This observation is consistent with respect to what is known about the presence of sesquiterpenoids in the cytoplasm of leaves compared to monoterpenoids which are present in plastid and trichome structures predominantly. There is a perceived greater variation in the color range of the samples obtained from "OG" strain plants grown outdoors, compared to those grown in a greenhouse. The samples labeled 2-i-G-C-T-U, 2-P-G-C-T-t, and 2-P-G-C-T-u all show a more uniform shade of green. The 2-P-G-C-T-u sample is a slightly more yellow-green compared to its untrimmed counterpart. Again this observation is consistent with respect to what we know about Terpenoid production in leaves vs flowers, and is therefore an expected result. Finally, the more uniform colors among the samples from greenhouse grown plants is consistent with what we have observed about variability in this "OG" strain in terms of Cannabinoids like THCA. We have previously reported (Presentation at Emerald Scientific 2017 meeting in San Diego, CA on Feb 3, 2017) that the Cannabinoid levels vary less in greenhouse grown "OG" compared to outdoor grown "OG" strain Cannabis. In the description of this embodiment we extend this observation to include a lower variance in the terpenoids produced by greenhouse- grown compared to outdoor-grown Cannabis plants.

Figure 150 depicts the color output result for the "Tangie" Strain. In this subset of samples, the color output ranges are more in the blue-grey to blue-green range. The samples from Flowers harvested from bottom positions on the branch show a more grey -blue color compared to those harvested from positions on the top of the branches which show a more grey-green color. This data demonstrates that there are subtle but consistent differences in terpenoid profiles based on Flower position on the branch.

Finally in Figure 154, which is the last figure in the series for this embodiment, the output color values for a set of samples from the "Ogre" strain are shown. This subset of samples is pretty uniform. They exhibit a grey-blue-green color with Flowers harvested from bottom positons on the branches showing a slightly greater blue in their assigned output color compared to those harvested from top positions on the branches. Overall, this observation is consistent with what has been observed both for THCA concentration variance and Flower weight variance in samples of the "Ogre" strain. Among the 4 stains presented in this embodiment, "Ogre" showed the least amount of variance for each of these parameters, namely individual Terpenoid concentration, THCA concentration, and Flower weight.

In summary, this embodiment of the Color Mapping Algorithm uses Principal Component Analysis (PCA) to derive Prnl and Prn2 Scores respectively for each sample based on their individual terpenoid content, These scores are assiigned Hue (H) and Saturation (S) color value parameters of HSL color space. As shown in this embodiment, this is a 2 dimensional mapping approach. This contrasts with the method of the First embodiment that uses a 3 dimensional approach.

Furthermore, this embodiment would benefit from the use of Standards that are prepared whereby each would contain a defined mixture of Terpenoids at specific concentrations.

Moreover, the specific terpenoid concentrations are designed so that their derived Prnl and Prn2 Scores when used in conjunction with the set of Cannabis Flower samples intended for analysis, would place them towards the extremes of the Prnl and Prn2 scores observed for the type of commodity, such as Cannabis, which is being measured in this way. As described in Figure 144, since the overall shape or pattern of Cannabis Flower sample individual terpenoid data appears to be largely consistent from dataset to dataset, the use of such internal standards may help to provide both a Quality Assurance function as well as a way to normalize the Prnl and Prn2 Scores for samples analyzed at different times. The use of such quality control standards should permit some fine tuning of the data should that be needed to permit absolute comparison between datasets. In this embodiment, we recommend creating 4 different Principal Component Standard mixtures. One pair is created to bracket the extremes of the Prnl Scores; the second pair is created to bracket the extremes of the Prn2 Scores. As such, these Principal Component standards are included in every determination and facilitate the function of the MVA and the Color Mapping Algorithm elements.

Figure 154 describes the Third Embodiment of the Color Mapping Algorithm. In this example, the Euclidean Distances that are derived by HCA are assigned to a 1 dimensional Color Space. The same dataset of individual terpenoid concentrations determined for each Cannabis Flower sample that was analyzed using the PCA and HSL Color Mapping Algorithm of the 2^nd

Embodiment see Figures 144 Panel (c) and 146 - 152) is used to demonstrate the utility of this 3 embodiment. In this case, the MVA element makes use of Hierarchical Clustering. For this procedure, the same Prnl scores determined in the 2^nd embodiment are used as the parameter to order the agglomerative HCA procedure that is described in the accompanying text. In this way, the clusters are organized in such a way that they largely follow the axis of the greatest variance of the dataset, namely that of the 1^st Principal Component, also known as Prnl . A Euclidean Distance Matrix is derived and the column of values corresponding to any selected sample may be used as input to the Color Mapping Algorithm. In one such embodiment, a reference sample is used and all Euclidean Distances are determined between the reference sample and the series of test samples. Each test sample can then be assigned a color that corresponds to its Euclidean Distance relative to the reference sample. In the example shown above in Figure 154, the sample designated as #46 is assigned as a reference standard. All Euclidean Distances are evaluated relative to this reference standard. A color assignment or mapping algorithm can then easily assign a color to that singe distance value for each sample. There are many color assignment algorithms that may be used in this case. Such procedures are very often a part of graphing software programs that can assign a series of colors to a set of continuous variables. In the example of Figure 154, a Spectral theme of Color Mapping was applied. The reference sample stands out as blue, since its difference from itself is 0 and is the minimum value. The other samples show varying color depending upon the magnitude of their Euclidean Distances from this reference standard. In the example above there is a series of 4 samples on the right of the figure (and just to the left of the blue-colored reference standard) that contain different individual terpenoids than the bulk of the samples in the dataset. These are mapped to green and yellow and clearly contrast with the bulk of the samples in the dataset: these map mostly as various close shades of orange.

In summary, this 1 Dimensional Color Mapping of Euclidean Distance values derived from HCA may be considered as less resolving in terms of assigned color in comparison to the PCA - HS(L) or the 3 Key Terpenoid - RGB embodiments. However, this 1 Dimensional Euclidean Distance mapping embodiment is useful when there is a need to compare a set of samples within a single dataset. This embodiment would therefore find utility if it were desired to evaluate and indicate on a package label how similar or different the elements within a collection of a particular commodity, such as a set of spices, herbs, or Cannabis might be relative to each other. This information could be communicated to customers and the like by appropriately printing a colored symbol on the package label.

Figure 156 shows an example how such an appropriately colored symbol on a package might look. In this example the information about a products Cannabinoid content (as communicated via the set of black and white icons) is merged with the information about the terpenoid content of the product (in this case labeled as aroma category). The color that is printed is assigned by the Color Mapping Algorithm as described in the above embodiments. In this way, information about a Cannabis products Cannabinoid content and Terpenoid content is communicated in a single, easy to read and understand symbol or icon. The natural product content of the commodity can therefore be appreciated and understood at a single glance.

[001] While the above description has pointed out novel features of the present disclosure as applied to various embodiments, the skilled person will understand that various omissions, substitutions, permutations, and changes in the form and details of the present teachings illustrated may be made without departing from the scope of the present teachings.

[002] Each practical and novel combination of the elements and alternatives described hereinabove, and each practical combination of equivalents to such elements, is contemplated as an embodiment of the present teachings. Because many more element combinations are contemplated as embodiments of the present teachings than can reasonably be explicitly enumerated herein, the scope of the present teachings is properly defined by the appended claims rather than by the foregoing description. All variations coming within the meaning and range of equivalency of the various claim elements are embraced within the scope of the corresponding claim. Each claim set forth below is intended to encompass any apparatus or method that differs only insubstantially from the literal language of such claim, as long as such apparatus or method is not, in fact, an embodiment of the prior art. To this end, each described element in each claim should be construed as broadly as possible, and moreover should be understood to encompass any equivalent to such element insofar as possible without also encompassing the prior art. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising".

Claims

CLAIMS What is claimed is

1. A chemotype symbol generator comprising:

an unknown sample, further comprising mixtures of natural products in inexact proportions wherein said mixtures include but are not limited to plant material, plant extracts, or other biologicals such as manure or venom;

a plurality of highly resolving natural product separators;

a plurality of natural product concentration quantification assays, including but not limited to a terpenoid assay, a cannabinoid assay, and a flavonoid assay; a multivariate analyzer, comprising a plurality of quantification assays and quality control parameters and standards derived from natural product multivariate analysis;

a plurality of data sets generated from multivariate analysis, whereby said data sets are mapped in 2-dimensional or 3 -dimensional space;

a color mapping algorithm;

a plurality of chemotypes, comprising terpenoid concentrations wherein the terpenoid concentrations further comprise any terpenoid including but not limited to alpha-pinene, myrcene, and beta-caryophyllene; or cannabinoid concentrations further comprising THC, THCA, CBD, CBDA, and CBN; or flavonoid concentrations; or any combination of;

a known sample, comprising a fingerprint of known components that serves as a baseline fingerprint.

2. The mixtures of claim 1 resolve into terpenoids, cannabinoids, and flavonoids including but not limited to beta caryophyllene, alpha humulene, myrcene, alpha-pinene, or other terpenoids, flavonoids, and cannabinoids such as CBDA, , CBGA, CBG, and CBN.

3. Said chemotypes of claim 1 further comprise an Earth chemotype comprising a low level of alpha-pinene, a low level of myrcene, and a high level of beta-caryophyllene; a Floral chemotype comprising a high level of alpha-pinene, a high level of myrcene, and a low level of beta-caryophyllene; a Fuel chemotype comprising a low level of alpha-pinene, a high level of myrcene, and a high level of beta-caryophyllene.

4. Said terpenoid assay of claim 1 comprising a plurality of quality control parameters

including but not limited to a 3:1 ratio of the concentrations of beta-caryophyllene to alpha- humulene wherein said 3:1 ratio validates said quantification assay as accurate.

5. The color mapping algorithm of claim 1 assigns a color representative of said chemotype of claim 3.

6. Said color of claim 5 is designated and printed on a label.

7. The highly resolving natural product separators of claim 1 include, but are not limited to, chromatography or spectroscopy such as gas chromatography, liquid chromatography, or Fourier transform infrared spectroscopy.

8. The natural product quantification assays of claim 1 quantify the concentrations of said natural products in said unknown sample.

9. The multivariate analyzer of claim 1 plots natural product concentrations in 2- dimensional or 3-dimensional space utilizing a plurality of types of analysis including but not limited hierarchical clustering analysis or principle component analysis.

10. The known sample of claim 1 further comprising known parameters and standards

pertaining to cannabinoids such as THC, THCA, CBDA, CBD, CBN, CBG; flavonoids; or terpenoids such as alpha humulene, beta caryophyllene, myrcene, or alpha pinene. An apparatus for creating chemotype symbols, comprising: a commodity, containing at least one natural product, suitable for input into a high resolution analytical device; a data array, generated as an output of the high resolution analytical device, and; a multivariate analysis element, adapted to process the data array, wherein a color mapping algorithm is applied to a multivariate analysis output to generate a chemotype symbol.