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==Introduction== | ==Introduction== | ||
The non-psychotropic species ''[[Cannabis sativa]]'' L., referred to as industrial [[hemp]] [1], is characterized by containing minimal concentrations of [[Tetrahydrocannabinol|Δ<sup>9</sup>-tetrahydrocannabinol]] (THC), the main [[Psychoactive drug|psychoactive chemical]] component, and [[cannabidiol]] (CBD), a non-psychoactive substance that is often present in amounts similar to those of THC. [1,2] Hemp is mainly used for food or textile purposes and, in addition, offers great [[Cannabis (drug)|medicinal]] potential. Although the regulations of different countries vary according to the definition of the maximum accepted THC limit, industrial-hemp-producing countries require that the varieties used contain THC concentrations lower than 1%. In most European countries, the current upper legal limit for cultivation is 0.2% of THC and the ratio of CBD to THC should be greater than one. Currently, the maximum concentration legally permitted for [[Cannabis cultivation|cultivation]] is under debate in the European Union. [3] | |||
It is important to note that the [[Inflorescence|flower]] is the part of hemp with the highest significant content of [[cannabinoid]]s. [4] These, when heated, spontaneously [[Decarboxylation|decarboxylate]] to the “neutral” cannabinoids THC and CBD. This heat-labile characteristic of acidic cannabinoids (e.g., [[tetrahydrocannabinolic acid]] [THCA] and [[cannabidiolic acid]] [CBDA]) highlights the importance of using a low-temperature, non-destructive method to achieve a precise quantification of these molecules. [5] Moreover, for all stakeholders in the [[cannabis]] supply chain, a precise and trustworthy identification of these cannabinoids would be of great economic importance. [4] | |||
Traditionally, cannabinoid content has been determined by [[high-performance liquid chromatography]] (HPLC) and [[gas chromatography]] (GC). HPLC provides a full cannabinoid profile, but it has several associated disadvantages, including [[Sample (material)|sample]] destruction, complex instrumentation, involvement of hazardous chemicals, and longer sample preparation times, which limit its application on-site, where a fast and non-destructive process is preferable. [4] Similarly, GC is another preferred method for the determination of these compounds. However, it is a slow and expensive technique, requiring a tedious sample preparation stage that involves the extraction of the active ingredients through the use of organic [[solvent]]s, whose subsequent [[Contamination|residues]] must be managed with a considerable increase in cost and time. [6] | |||
These limitations have led to a search for faster and easier-to-use alternatives to HPLC and GC. [4] Therefore, it is important to develop a simple, fast, and sustainable method for the quantification of cannabinoids. In recent years, [[Spectroscopy|spectroscopic]] methods have emerged as techniques that are used on a wide range of biological samples without the need for extraction. [7] One such technique is [[near-infrared spectroscopy]] (NIRS), which is a fast, cost-effective, versatile, robust, and sustainable technique. In addition, it allows both quantitative and qualitative determinations of the main parameters, such as proteins, fats, humidity, ashes, starch, or sugar, of the raw materials related to the quality of agricultural products. [6] In recent years, the interest in NIRS applied to hemp has gained importance due to the moisture, volatile substances, and chemical compounds in herbal products absorbed in the NIR region. In general, NIRS combined with [[chemometrics]] has great potential in the analysis of natural plant products. [8] | |||
It should be taken into account that the cannabis flower is heterogeneous in nature, which presents a series of problems and drawbacks. It is a complex matrix, made up of a great variety of types of plant tissues and more than 500 different naturally produced chemicals. Moreover, it is a material that can vary widely between plants of the same crop, in an individual plant, and even within the same sample [9]. Consequently, no two parts of the cannabis flower are alike, and their cannabinoid content is likely to vary widely. In this scenario, NIRS technology is an adequate alternative for the analysis of heterogeneous vegetal samples and may therefore overcome the inherent heterogeneity of the cannabis plant. [4] | |||
NIRS has been applied to discriminate between cannabis “drug type” ([[chemotype]] I) and “fiber type” (chemotype II) [10], for the discrimination of leaves of [[Cannabis sativa]] L. and other plant species [11], and for the prediction of the growth stage of cannabis plants in the early stages of cultivation. [12] | |||
Marcel ''et al.'' [13] developed a prediction model of the chemical composition of the fiber and the central fraction of hemp (chemotype III) using NIRS combined with a partial least squares (PLS) regression analysis. Similarly, a procedure was developed for the identification and quantitative determination of synthetic cannabinoids in illicit herbal samples. The methodology was based on the measurement by [[Fourier-transform infrared spectroscopy]] of [[attenuated total reflectance]] (ATR-FTIR). [14] | |||
Moreover, the total content of THC and CBD in the cannabis flower has been determined by [[Fourier-transform infrared spectroscopy#Near-infrared|Fourier-transform near-infrared spectroscopy]] (FT–NIR). [4] Similarly, Sánchez-Carnerero ''et al.'' [6] studied the prediction of cannabinoid content using NIRS. They used both FT-NIR and NIR [[Spectrophotometry|spectrophotometers]] for their analysis and compared the results obtained with the two techniques. Similar results were obtained using both instruments, thus confirming that there is enough information in the spectral region of the NIR for the prediction of cannabinoids. | |||
More recently, Duchateau ''et al.'' [15] created two classification methods according to the European laws about the discrimination of the legal limits of ''Cannabis'' spp. using NIR. Valinger ''et al.'' [7] described the development of [[wikipedia:artificial neural network|artificial neural network]] (ANN) models for the prediction of the physical and chemical properties of industrial hemp extracts, based on the combination of [[Ultraviolet–visible spectroscopy|ultraviolet-visible]] near-infrared (UV-VIS-NIR) spectra. For this, two different extraction methods were prepared (solid–liquid extraction and microwave-assisted extraction). The results showed that reliable ANN models can be developed to describe the physical and chemical characteristics, without the need for pre-processing of the spectra. In a recent study, Risoluti ''et al.'' [16], using a MicroNIR spectrometer, developed a test for cannabinoid determination in commercial hemp flours spiked with THC, CBD, and [[cannabigerol]] (CBG). | |||
Therefore, the aim of this study was to evaluate the functionality of NIRS for the quantification of the main cannabinoids present in hemp samples. In addition, a study of the NIR spectra was carried out to identify the peaks. | |||
==Materials and methods== | |||
===Vegetal material=== | |||
Revision as of 15:56, 10 May 2022
| Full article title | Potential of NIRS technology for the determination of cannabinoid content in industrial hemp (Cannabis sativa L.) |
|---|---|
| Journal | Agronomy |
| Author(s) | Jarén, Carmen; Zambrana, Paula C.; Pérez-Roncal, Claudia; López-Maestresalas, Ainara; Ábrego, Andrés; Arazuri, Silvia |
| Author affiliation(s) | Universidad Pública de Navarra, Genscore Navarra S.L. |
| Primary contact | Email: cjaren at unavarra dot es |
| Year published | 2022 |
| Volume and issue | 12(4) |
| Article # | 938 |
| DOI | 10.3390/agronomy12040938 |
| ISSN | 2073-4395 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.mdpi.com/2073-4395/12/4/938/htm |
| Download | https://www.mdpi.com/2073-4395/12/4/938/pdf (PDF) |
 |
This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
Industrial hemp (Cannabis sativa L.) is a plant native to Asia and is considered to be a primary source of food, textile fiber, and medicines. It is characterized by containing minimal concentrations of Δ9-tetrahydrocannabinol (THC), which is the main psychoactive chemical component, and cannabidiol (CBD), a non-psychoactive substance. In most European countries, the maximum concentration legally allowed for cultivation is 0.2% of THC, and it is currently under debate whether to increase this level to 0.3%. Moreover, in many countries its production is being regulated and legalized, increasing the need for a rapid analysis method.
The present work evaluated the cannabinoid content in hemp using near-infrared spectroscopy (NIRS) technology in combination with chemometric techniques. For this, several samples of the Kompolti variety were analyzed. Samples were dried and ground, and the content of total THC (%) and total CBD (%) was determined by high-performance liquid chromatography (HPLC) with a diode array detector for reference measurements, and then the spectra were collected by NIRS. Principal component analysis and partial least square regression models were developed. Good coefficients of determination of cross-validation of 0.77 for THC and CBD, and a ratio of prediction to deviation >2 for total THC and CBD, were achieved. The results obtained show that NIRS technology has potential for the quantitative determination of cannabinoids. Therefore, this analytical method would allow a simpler, more robust, precise, and sustainable estimation than the current HPLC approach.
Keywords: CBD, THC, near infrared spectroscopy, quantification, HPLC, chemometrics
Introduction
The non-psychotropic species Cannabis sativa L., referred to as industrial hemp [1], is characterized by containing minimal concentrations of Δ9-tetrahydrocannabinol (THC), the main psychoactive chemical component, and cannabidiol (CBD), a non-psychoactive substance that is often present in amounts similar to those of THC. [1,2] Hemp is mainly used for food or textile purposes and, in addition, offers great medicinal potential. Although the regulations of different countries vary according to the definition of the maximum accepted THC limit, industrial-hemp-producing countries require that the varieties used contain THC concentrations lower than 1%. In most European countries, the current upper legal limit for cultivation is 0.2% of THC and the ratio of CBD to THC should be greater than one. Currently, the maximum concentration legally permitted for cultivation is under debate in the European Union. [3]
It is important to note that the flower is the part of hemp with the highest significant content of cannabinoids. [4] These, when heated, spontaneously decarboxylate to the “neutral” cannabinoids THC and CBD. This heat-labile characteristic of acidic cannabinoids (e.g., tetrahydrocannabinolic acid [THCA] and cannabidiolic acid [CBDA]) highlights the importance of using a low-temperature, non-destructive method to achieve a precise quantification of these molecules. [5] Moreover, for all stakeholders in the cannabis supply chain, a precise and trustworthy identification of these cannabinoids would be of great economic importance. [4]
Traditionally, cannabinoid content has been determined by high-performance liquid chromatography (HPLC) and gas chromatography (GC). HPLC provides a full cannabinoid profile, but it has several associated disadvantages, including sample destruction, complex instrumentation, involvement of hazardous chemicals, and longer sample preparation times, which limit its application on-site, where a fast and non-destructive process is preferable. [4] Similarly, GC is another preferred method for the determination of these compounds. However, it is a slow and expensive technique, requiring a tedious sample preparation stage that involves the extraction of the active ingredients through the use of organic solvents, whose subsequent residues must be managed with a considerable increase in cost and time. [6]
These limitations have led to a search for faster and easier-to-use alternatives to HPLC and GC. [4] Therefore, it is important to develop a simple, fast, and sustainable method for the quantification of cannabinoids. In recent years, spectroscopic methods have emerged as techniques that are used on a wide range of biological samples without the need for extraction. [7] One such technique is near-infrared spectroscopy (NIRS), which is a fast, cost-effective, versatile, robust, and sustainable technique. In addition, it allows both quantitative and qualitative determinations of the main parameters, such as proteins, fats, humidity, ashes, starch, or sugar, of the raw materials related to the quality of agricultural products. [6] In recent years, the interest in NIRS applied to hemp has gained importance due to the moisture, volatile substances, and chemical compounds in herbal products absorbed in the NIR region. In general, NIRS combined with chemometrics has great potential in the analysis of natural plant products. [8]
It should be taken into account that the cannabis flower is heterogeneous in nature, which presents a series of problems and drawbacks. It is a complex matrix, made up of a great variety of types of plant tissues and more than 500 different naturally produced chemicals. Moreover, it is a material that can vary widely between plants of the same crop, in an individual plant, and even within the same sample [9]. Consequently, no two parts of the cannabis flower are alike, and their cannabinoid content is likely to vary widely. In this scenario, NIRS technology is an adequate alternative for the analysis of heterogeneous vegetal samples and may therefore overcome the inherent heterogeneity of the cannabis plant. [4]
NIRS has been applied to discriminate between cannabis “drug type” (chemotype I) and “fiber type” (chemotype II) [10], for the discrimination of leaves of Cannabis sativa L. and other plant species [11], and for the prediction of the growth stage of cannabis plants in the early stages of cultivation. [12]
Marcel et al. [13] developed a prediction model of the chemical composition of the fiber and the central fraction of hemp (chemotype III) using NIRS combined with a partial least squares (PLS) regression analysis. Similarly, a procedure was developed for the identification and quantitative determination of synthetic cannabinoids in illicit herbal samples. The methodology was based on the measurement by Fourier-transform infrared spectroscopy of attenuated total reflectance (ATR-FTIR). [14]
Moreover, the total content of THC and CBD in the cannabis flower has been determined by Fourier-transform near-infrared spectroscopy (FT–NIR). [4] Similarly, Sánchez-Carnerero et al. [6] studied the prediction of cannabinoid content using NIRS. They used both FT-NIR and NIR spectrophotometers for their analysis and compared the results obtained with the two techniques. Similar results were obtained using both instruments, thus confirming that there is enough information in the spectral region of the NIR for the prediction of cannabinoids.
More recently, Duchateau et al. [15] created two classification methods according to the European laws about the discrimination of the legal limits of Cannabis spp. using NIR. Valinger et al. [7] described the development of artificial neural network (ANN) models for the prediction of the physical and chemical properties of industrial hemp extracts, based on the combination of ultraviolet-visible near-infrared (UV-VIS-NIR) spectra. For this, two different extraction methods were prepared (solid–liquid extraction and microwave-assisted extraction). The results showed that reliable ANN models can be developed to describe the physical and chemical characteristics, without the need for pre-processing of the spectra. In a recent study, Risoluti et al. [16], using a MicroNIR spectrometer, developed a test for cannabinoid determination in commercial hemp flours spiked with THC, CBD, and cannabigerol (CBG).
Therefore, the aim of this study was to evaluate the functionality of NIRS for the quantification of the main cannabinoids present in hemp samples. In addition, a study of the NIR spectra was carried out to identify the peaks.
Materials and methods
Vegetal material
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. Some grammar and punctuation was cleaned up to improve readability. In some cases important information was missing from the references, and that information was added.
