Difference between revisions of "Journal:Potential of NIRS technology for the determination of cannabinoid content in industrial hemp (Cannabis sativa L.)"

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==Results and discussions==
==Results and discussions==
===HPLC results===
===HPLC results===
For each of the samples analyzed, an individualized report was received with the sample identification data, the characteristics of the analysis methods used, and the results obtained from the HPLC-DAD analysis. The results provided by the reference laboratory, expressed as a percentage, included data for humidity, THC total, CBD total, and 12 other cannabinoids that were not the subject of this study. Thirty-two samples of 35 were analyzed by HPLC-DAD because three hemp plants infected with fungi were identified. The mean, the maximum, the minimum, and the standard deviation of the reference results can be seen in Table 1.
{|
| style="vertical-align:top;" |
{| class="wikitable" border="1" cellpadding="5" cellspacing="0" width="80%"
|-
  | colspan="13" style="background-color:white; padding-left:10px; padding-right:10px;" |'''Table 1.''' HPLC-DAD analysis results
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  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" |Samples
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" colspan="4" |Humidity (%)
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" colspan="4" |THC total (%)
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" colspan="4" |CBD total (%)
|-
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" |Min
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" |Max
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" |Mean
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" |SD
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" |Min
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" |Max
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" |Mean
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" |SD
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" |Min
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" |Max
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" |Mean
  ! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" |SD
|-
  | style="background-color:white; padding-left:10px; padding-right:10px;" |32
  | style="background-color:white; padding-left:10px; padding-right:10px;" |9.02
  | style="background-color:white; padding-left:10px; padding-right:10px;" |12.34
  | style="background-color:white; padding-left:10px; padding-right:10px;" |10.65
  | style="background-color:white; padding-left:10px; padding-right:10px;" |3.16
  | style="background-color:white; padding-left:10px; padding-right:10px;" |0.057
  | style="background-color:white; padding-left:10px; padding-right:10px;" |0.161
  | style="background-color:white; padding-left:10px; padding-right:10px;" |0.103
  | style="background-color:white; padding-left:10px; padding-right:10px;" |0.028
  | style="background-color:white; padding-left:10px; padding-right:10px;" |2.178
  | style="background-color:white; padding-left:10px; padding-right:10px;" |5.342
  | style="background-color:white; padding-left:10px; padding-right:10px;" |3.367
  | style="background-color:white; padding-left:10px; padding-right:10px;" |0.892
|-
|}
|}





Revision as of 16:30, 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)

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

Thirty-five hemp samples were obtained in collaboration with Genscore Navarra S.L. The specimens obtained were of the Kompolti variety, which is among the varieties authorized for the cultivation of industrial hemp in Spain. [17]

The plant material was weighed on a AB104 Mettler-Toledo analytical balance and dried in an oven at 60 °C for 24 hours, until a humidity between 8% and 13% was achieved, as recommended by regulation (EU) 2017/1155. [18]

Stems and seeds of more than 2 mm were removed from the dry samples and, with the help of a mortar, they were crushed until obtaining a semi-fine powder, in such a way that it could pass through a 1 mm mesh sieve. The samples were stored, without crushing them, in a dark place at a temperature below 25 °C. [18,19]

Spectra acquisition

Spectra were collected using an Acousto-Optic Tunable Filter (AOTF) NIR and Indium Gallium Arsenide (InGaAs) detector, called a Luminar 5030 Miniature "Hand-held," in the reflectance mode and equipped with Snap32! software (Brimrose Corporation of America, Sparks, MD, USA). A spectral range of 1200–2200 nm was used to obtain the spectra, with a sampling interval of 2 nm, and scanning speed of 60 ms. Each spectrum recorded by the instrument was the average of 50 scans.

In this study, 3 g of each of the 35 hemp powder samples was weighed and placed on the rotating cell of the AOTF-NIR spectrophotometer. As the sample rotates, the spectrum is measured so that different parts of the sample are scanned from above and inhomogeneities averaged. For each individual sample, three reflectance spectra were acquired by contacting the probe with the sample.

Reference measurements: HPLC

After acquisition of the NIRS data, the same hemp samples were removed from the Petri dishes and sent to a certified laboratory, ANANDA ANALYTICS LAB S.L., where an HPLC with diode array detector (HPLC-DAD) method was used for the determination of total THC and total CBD cannabinoids. The extraction was performed by ultrasound with subsequent methanol-chloroform decarboxylation. The mobile phase was acetonitrile (water [8:2 v/v], isocratic, stop time 8 minutes) according to Recommended methods for the identification and analysis of cannabis and cannabis products by the United Nations Office on Drugs and Crime. [19] Regarding the result, for a qualitative identification, the retention time and the DAD spectrum of the cannabinoid must match. The calculation for the quantitative results was carried out at the wavelengths of 220 and 240 nm.

The results were received after 10 days and, then, these reference chromatographic data were correlated with the spectral information to generate the NIR models for total THC and CBD prediction. Thirty-two of 35 samples were analyzed by HPLC-DAD, with three fungus-infected hemp plants being identified and excluded.

Multivariate data analysis

Data analysis was performed using the specific software The Unscrambler X v.10.4 (Camo Software AS, Oslo, Norway). First, principal component analysis (PCA) was performed with the full set of samples. PCA was applied to explore the spectral variability of the population [20], which also allows elimination of outliers that can have a negative effect on modeling. [21]

During the pre-model building phase, data pre-processing was carried out to eliminate the non-informative effects of light scattering or system noise. For the development of the models, in addition to working with the raw data, different data pretreatments were applied: spectra normalization, standard normal variate (SNV), standard normal variate and detrend (SNV-DT), multiplicative scatter correction (MSC), and first derivative. These are described as such:

  • Raw data: The absolute reflectance was obtained from the radiation measurements of the 35 samples, with three repetitions each.
  • Spectra normalization: Raw NIR spectra are often mathematically processed prior to development of the calibration model; such treatments include normalization, which is performed to minimize unwanted sources of data variation prior to calibration and to improve spectral characteristics. [22] Mean normalization was performed in this study, which is the most classical approach. In this normalization, each sample of the dataset (each row of the data matrix) is divided by its average.
  • SNV: To eliminate interferences due to path length effects, SNV consists of subtracting the mean value of the spectrum from each reflectance value at each wavelength and dividing it by the standard deviation. [23]
  • SNV-DT: This method was developed by Barnes et al. [24] to eliminate multiplicative scattering interferences and particle size, and to take into account the variation in the baseline change and curvilinearity in diffuse reflectance spectra. Detrend consists of fitting a second-order polynomial to the spectrum corrected by SNV, which is subtracted to eliminate the dispersion effect that is dependent on each wavelength. [25]
  • MSC: This is another preprocessing technique that corrects the displacements between samples due to the particles of the samples. [26] MSC is undertaken by using a reference spectrum and correcting the different spectra to it so that the baseline and the amplification effects are at the same average level in all spectra. [27] The basic concept of MSC is to remove non-linearities in the data caused by scattering from particulates in the samples. [28]
  • First derivative: The first derivative of the spectra, based on the Savitzky–Golay algorithm, is used to increase the spectral resolution and interpret the spectra. [29] One smoothing point was applied to the right and another to the left, and a polynomial of order two was used to smooth and eliminate random noise from the NIR spectra. Vasques et al. [30] confirmed that the Savitzky–Golay derivatives were among the best methods for preprocessing the spectra. Similarly, Ertlen et al. [31] reported that by using derivatives, more convenient information can be taken from NIR spectra.

After data pretreatments, a PLS regression analysis was applied to the dataset to build a model capable of predicting the content of cannabinoids, both for total THC and total CBD, in the hemp samples, and to be able to assess the effectiveness of NIR spectroscopy. For the validation of the model, cross-validation (CV) was used in order to calculate the relationships between spectral and chemical properties. Williams et al. [32] recommend CV for the evaluation of any calibration model based on small sets of samples below 100 units.

The performance of the calibration models was evaluated using the root mean square error of the calibration (RMSEC), the root mean square error of cross-validation (RMSECV), the coefficient of determination of calibration (R2c), the coefficient of determination of cross-validation (R2cv), and the ratio of prediction to deviation (RPD). The number of latent variables (LV) was used to prevent overfitting.

Results and discussions

HPLC results

For each of the samples analyzed, an individualized report was received with the sample identification data, the characteristics of the analysis methods used, and the results obtained from the HPLC-DAD analysis. The results provided by the reference laboratory, expressed as a percentage, included data for humidity, THC total, CBD total, and 12 other cannabinoids that were not the subject of this study. Thirty-two samples of 35 were analyzed by HPLC-DAD because three hemp plants infected with fungi were identified. The mean, the maximum, the minimum, and the standard deviation of the reference results can be seen in Table 1.

Table 1. HPLC-DAD analysis results
Samples Humidity (%) THC total (%) CBD total (%)
Min Max Mean SD Min Max Mean SD Min Max Mean SD
32 9.02 12.34 10.65 3.16 0.057 0.161 0.103 0.028 2.178 5.342 3.367 0.892


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.