WO2017205853A1 - Appareil pour créer des symboles de type chémotypes - Google Patents

Appareil pour créer des symboles de type chémotypes Download PDF

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WO2017205853A1
WO2017205853A1 PCT/US2017/034871 US2017034871W WO2017205853A1 WO 2017205853 A1 WO2017205853 A1 WO 2017205853A1 US 2017034871 W US2017034871 W US 2017034871W WO 2017205853 A1 WO2017205853 A1 WO 2017205853A1
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color
cannabis
sample
chemotype
analysis
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John Abrams
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—Two-dimensional [2D] image generation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
    • G01N2030/8813—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis

Definitions

  • taxonomic classification 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 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.
  • ECS endocannabinoid system
  • 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.
  • cannabinoids 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.
  • cannabis plants make hundreds, possibly thousands, of biochemical compounds called natural products.
  • 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.
  • terpenoids are another common class of natural products, known for their aromatic structures.
  • 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.
  • 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.
  • DNA deoxyribonucleic acid
  • a phenotype 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.
  • 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.
  • 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.
  • 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.
  • phenotypes 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.
  • cannabinoids such as THC, cannabigerol (“CBG”), cannabidiol (“CBD”), cannabinol (“CBN”
  • CBG cannabigerol
  • CBD cannabidiol
  • CBN cannabinol
  • terpenoids a class of aromatic compounds, such as myrcene, limonene, and linalool.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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
  • chemotype captures more relevant information than a genotype alone would.
  • 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.
  • 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.
  • 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 can be analyzed and correlations drawn.
  • EXTRACTS 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].
  • 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).
  • MVA Multivariate Analysis
  • the highly resolving analytical methods include, but are not limited to: FTIR and
  • GC Gas chromatography
  • 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,
  • 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].
  • LC Liquid chromatography
  • Simple liquid chromatography consists of a column with a fritted bottom that holds a stationary phase in equilibrium with a solvent.
  • Typical stationary phases are: solids (adsorption], ionic groups on a resin (ion-exchange], liquids on an inert solid support (partitioning], and porous inert particles (size-exclusion].
  • 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 o Fourier transform infrared spectroscopy
  • 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.
  • FTIR 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 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.]
  • 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
  • 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
  • flavonoids are all natural products that can be resolved into chemotypes.
  • 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.
  • terpenoids and cannabinoids 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.
  • BCP:AHum when a valid methodology is used in gas chromatography.
  • Hops 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).
  • H1STS1 Humulus lupulus
  • 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.
  • Multivariate Data Analysis refers to any statistical technique used to analyze data that arises from more than one variable.
  • Multivariate analysis is based on the statistical principle of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a time.
  • Multivariate Analysis can be used to process the information in a meaningful fashion.
  • 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
  • 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.
  • MVA is an element of the disclosed embodiments that use a Color Mapping Algorithm ("CMA") to assign colors samples based on their MVA values.
  • CMA Color Mapping Algorithm
  • 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.
  • RGB red, green blue
  • HSL Lumosity
  • Another embodiment assigns colors to samples based on their relative Euclidean Distances derived from HCA.
  • the first embodiment describes a method based on mapping the concentrations of 3 key Cannabis terpenoids onto an RGB color space.
  • 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.
  • 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.
  • 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.
  • Principal component analysis (PCA] is one popular approach analyzing variance when dealing with multivariate data.
  • 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..
  • 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
  • Normalized Prnl Score Positively Adjusted Prnl Score / Prnl Score Range.
  • Normalized Prn2 Score Positively Adjusted Prn2 Score / Prn2 Score Range.
  • 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.
  • HCA Hierarchical Clustering Analysis
  • 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.
  • 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.
  • the Euclidean Distance between each of any 2 data points can easily be determined.
  • 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.
  • 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.
  • a dataset that is in a format compatible with MVA.
  • it is a database, spreadsheet, or table where the rows are samples and the columns are natural product concentrations.
  • the natural products are terpenoids.
  • 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.
  • 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.
  • This subject of the present invention includes the elements (106-120) which are described below.
  • 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.
  • This element creates a data matrix, or spreadsheet, or relational database of the analytical data.
  • 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.
  • MVA Multivariate Analysys
  • PCA Principal Component Analysis
  • HCA Hierarchical Clustering Analysis
  • JMP JMP
  • 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.
  • 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.
  • 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
  • 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.
  • Gas chromatography is a highly resolving method to separate and analyze complex mixtures of natural products.
  • a mixture of terpenoid standards is resolved into individual component peaks. Each peak represents a separate terpenoid standard.
  • an internal adjunct validation method such as that provided by the beta-caryophyllene / alpha-humulene ratio described below in Figure XXX.
  • the retention time the time value on the x axis
  • 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.
  • 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.
  • Fourier Transform - Infrared Spectroscopy is another embodiment of a highly resolving method of identifying the natural product components in a complex mixture.
  • Cannabis Flower samples are analyzed in the FT-IR device. A plurality of peaks are observed.
  • the peaks correspond to types of chemical bonds present in the natural product compounds present in the sample.
  • 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.
  • 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.
  • GTA Golden Tarp Awards
  • 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:
  • ANOVA Analysis of Variance
  • 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.
  • 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.
  • 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.
  • 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.
  • these RGB values may be output to a driver.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • UCL Upper Confidence Limit
  • LCL Lower Confidence Limit
  • 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.
  • Figure 144 describes a Second Embodiment of the Color Mapping Algorithm:
  • 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.
  • the Principal Component 1 (Prnl) and Principal Component 2 (Prn2) scores are plotted in 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.
  • 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).
  • 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.
  • Panel (D) a completely independent set of close to 2 dozen Cannabis Flower samples were analyzed for terpenoid content using GC.
  • the GC was coupled with a Flame Ionization Detector (FID) to permit identification of the eluting materials.
  • FID Flame Ionization Detector
  • 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).
  • 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.
  • the 3 rd color value parameter namely the Luminosity (L) color value parameter of the HSL color space is not used.
  • 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 PCA - HSL Color Mapping Algorithm method is described in the accompanying text.
  • 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.
  • the Cannabis Flowers are from an "OG" strain.
  • the samples originate from Flowers that were produced from plants that were either grown outdoors or in a greenhouse.
  • a subset of these Flowers was either subjected to hand trimming or left untrimmed.
  • 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.
  • Figure 150 depicts the color output result for the "Tangie" Strain.
  • 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.
  • 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.
  • PCA Principal Component Analysis
  • 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.
  • 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.
  • Figure 154 describes the Third Embodiment of the Color Mapping Algorithm.
  • the Euclidean Distances that are derived by HCA are assigned to a 1 dimensional Color Space.
  • Embodiment see Figures 144 Panel (c) and 146 - 152) is used to demonstrate the utility of this 3 embodiment.
  • the MVA element makes use of Hierarchical Clustering.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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”.

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Abstract

Cette invention concerne un dispositif d'impression d'étiquettes qui génère des symboles de type chémotypes. Le chémotypage offre un cadre pour la classification des mélanges complexes de produits d'origine naturelle présents dans des produits tels que le cannabis, le houblon, les épices et les huiles essentielles. Le dispositif d'impression d'étiquettes reçoit des données d'entrée provenant d'un dispositif de séparation à haute résolution et d'analyse des mélanges complexes de produits d'origine naturelle, tels que des dispositifs de chromatographie et de spectrophotométrie. Il comprend 4 éléments : un ordinateur, une source d'encre pour des encres de couleur noire et colorées, et des plateaux ou bobines d'entrée et de sortie, pour le stock d'étiquettes. L'ordinateur comprend, lui aussi, 4 éléments : une fonctionnalité de traitement de données, des programmes d'analyses multivariées qui effectuent une analyse des composants principaux et une analyse de classification automatique de données par la méthode hiérarchique ascendante, des algorithmes de mappage couleurs qui attribuent une couleur en fonction des valeurs de sortie des analyses multivariées, et un pilote d'imprimante. Le dispositif génère des symboles colorés correspondant au profil global des teneurs des produits d'origine naturelle présents dans l'échantillon de produit individuel, ce qui permet de communiquer facilement des informations complexes.
PCT/US2017/034871 2016-05-26 2017-05-26 Appareil pour créer des symboles de type chémotypes Ceased WO2017205853A1 (fr)

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