CN116482054A - Method for rapidly detecting cannabinoid content in industrial cannabis sativa leaves based on near infrared spectrum analysis technology - Google Patents
Method for rapidly detecting cannabinoid content in industrial cannabis sativa leaves based on near infrared spectrum analysis technology Download PDFInfo
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2201/00—Features of devices classified in G01N21/00
- G01N2201/12—Circuits of general importance; Signal processing
- G01N2201/129—Using chemometrical methods
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
A method for rapidly detecting the cannabinoid content in industrial cannabis sativa leaves based on near infrared spectrum analysis technology belongs to the technical field of analysis of the cannabinoid content of industrial cannabis sativa plants. The method aims to solve the problems of complex detection steps and serious environmental pollution in the cannabinoid content determination by a high performance liquid chromatography determination method. The method comprises the following steps: 1. acquiring near infrared spectrum data of industrial cannabis sativa leaf powder and measured cannabinoid content data of industrial cannabis sativa leaf; 2. regression modeling is carried out to obtain a near infrared prediction model of the cannabinoid content in the industrial hemp flowers; 2. and measuring a near infrared spectrum curve of the industrial hemp flowers to be detected and substituting the near infrared spectrum curve into the model to obtain the cannabinoid content in the industrial hemp flowers to be detected. The method is quick and accurate, has low cost and avoids acetonitrile pollution; the contents of total THC and total CBD can be identified simultaneously, and the result is accurate and reliable. In the actual detection process, the operation personnel can operate through simple training without professional technicians, and the method is suitable for popularization and application.
Description
Technical Field
The invention belongs to the technical field of analysis of cannabinoid content in industrial cannabis plants, and particularly relates to a method for rapidly detecting the cannabinoid content in industrial cannabis leaves based on a near infrared spectrum analysis technology.
Background
Currently, about 500 compounds have been identified from industrial cannabis, of which the most interesting are cannabinoids with terpene phenolic compounds, more than 100 are known. In plant tissue, cannabinoids are biosynthesized in acidic (carboxylated) form. The Cannabidiol (CBD) has higher medical value and obvious curative effect on cancers, epilepsy, depression, anorexia, parkinsonism and other difficult and complicated diseases. To quantify the total THC content and total CBD content once present in fresh plant material, wherein the total THC content is the total content of both Tetrahydrocannabinol (THCA) and THC, the total CBD content is the total content of Cannabidiol (CBDA) and CBD. To cultivate low THC, or industrial hemp varieties with zero THC content and high CBD, it is necessary to detect the total THC and CBD content during industrial hemp growth. The traditional cannabinoid content detection method is based on high performance liquid chromatography technology, and the method is time-consuming, labor-consuming, expensive, and has many operation steps, needs professional operation, is difficult to grasp the operation method, has difficult error control, and cannot be used for large-scale and rapid detection and screening in the traditional industrial cannabis breeding and introduction processes.
The near infrared spectrum analysis technology utilizes 950-1650 nm light waves to absorb the frequency multiplication and the frequency combination of vibration of hydrogen-containing groups, and different groups and the same group have obvious difference on the absorption wavelength of near infrared light, so that the near infrared spectrum can be used as an effective carrier for acquiring macromolecular substance information of the hydrogen-containing groups. By analyzing the optical density of the transmitted or reflected light, the content of the component can be determined. The near infrared spectrum has the advantages of high measurement speed, high sensitivity, simple sample treatment, capability of measuring various components at the same time, and the like.
The traditional cannabinoid content determination adopts a high performance liquid chromatography determination method, the determination method has the disadvantages of expensive instrument and equipment, complex work, complex sample treatment and operation by professionals, and the acetonitrile solution utilized in the determination process can pollute the environment. Therefore, there is a need to develop a more convenient, environmentally friendly, rapid assay for cannabinoids. At present, various spectrum detection instruments are adopted to detect the cannabinoid content, for example, a Raman spectrometer is adopted to detect the cannabinoid content, and the problems of expensive equipment, high technical requirements and the like exist, so that the application range of the cannabinoid content is limited. The near infrared spectrometer has the advantages of low technical requirements, simple training, operation, no need of professional technicians, low detection cost and the like, but has low detection sensitivity and limited application due to weak absorption, so that development of a near infrared spectrum detection method with high sensitivity and accurate and reliable results for measuring the cannabinoid content is needed to be developed so as to further expand the method for measuring the cannabinoid content.
Disclosure of Invention
The invention aims to solve the problems of complex detection steps and serious environmental pollution in the measurement of the cannabinoid content of industrial cannabis sativa leaves by adopting a high performance liquid chromatography measurement method, and provides a method for rapidly detecting the cannabinoid content in industrial cannabis sativa leaves based on a near infrared spectrum analysis technology.
A method for rapidly detecting the content of cannabinoid in industrial cannabis sativa leaves based on near infrared spectrum analysis technology is realized by the following steps:
1. acquiring near infrared spectrum data of industrial cannabis sativa leaf powder for eliminating noise interference and cannabinoid content data of industrial cannabis sativa leaf;
2. regression modeling is carried out by using the two data, so as to obtain a near infrared prediction model of the cannabinoid content in the industrial hemp flowers;
2. and measuring a near infrared spectrum curve of the industrial hemp leaf powder to be detected, and substituting the near infrared spectrum curve into the near infrared prediction model of the cannabinoid content in the industrial hemp leaf to obtain the cannabinoid content in the industrial hemp leaf to be detected, thereby completing the detection method.
Further, in the step one, obtaining near infrared spectrum data excluding noise interference of the industrial hemp leaf powder: measuring the near infrared spectrum curve of the industrial hemp leaf powder of the calibration sample, removing abnormal sample data after pretreatment, and obtaining the near infrared spectrum data of the industrial hemp leaf powder for eliminating noise interference.
Further, the number of the calibration samples is 100; a plurality of near infrared spectrum curves are obtained by measuring the near infrared spectrum curve of each calibration sample.
Further, the acquisition conditions of the near infrared spectrum curve are as follows: using a near infrared spectrum analyzer, scanning range 950-1650 nm, 3 times per calibration sample acquisition, 3 replicates, data spectrum collection rate 100 times/sec.
Further, the near infrared spectrum analyzer is a Botong DA7200 type near infrared spectrum analysis analyzer of Perkin Elmer company.
Further, the pretreatment method is single or combined treatment of mathematical methods; the mathematical method is smoothing, standard normal variable transformation, trending correction and derivation.
Further, obtaining the cannabinoid content data of the industrial cannabis sativa leaves in the step one: the cannabinoid content data of the industrial cannabis sativa leaves of the calibration sample were measured by high performance liquid chromatography.
Further, the number of the calibration samples is 100.
Further, the regression modeling method in the second step is as follows:
a. the method comprises the steps of (1) establishing a plurality of near infrared correction models of the cannabinoid content by adopting a partial least square method according to near infrared spectrum data of industrial cannabis sativa leaf powder for eliminating noise interference and cannabinoid content data of industrial cannabis sativa leaf, and verifying correlation coefficients according to cross-verification standard deviation and cross-verification;
b. and selecting a model with the minimum cross-validation standard deviation and the maximum cross-validation correlation coefficient from the plurality of cannabinoid content near-infrared correction models, namely the cannabinoid content near-infrared prediction model in the industrial cannabis sativa leaves.
The invention has the beneficial effects that:
1. the method for rapidly detecting the cannabinoid content in the industrial cannabis sativa leaves based on the near infrared spectrum analysis technology has the advantages of rapid, accurate, simple and convenient detection method, low cost, accurate and reliable result of identifying the total THC and the total CBD content in the industrial cannabis sativa leaves, and effectively preventing the pollution of acetonitrile used in the high performance liquid chromatography to the environment.
2. According to the detection method, after the model is built, in the subsequent actual detection process, operators can operate through simple training, no professional technician is needed, the speed is high, the detection cost is low, and the method is suitable for popularization and application.
The invention is suitable for the rapid detection of the cannabinoid content in industrial cannabis sativa leaves.
Drawings
FIG. 1 is a near infrared spectrum scan of 100 parts of industrial hemp leaf powder in the example;
FIG. 2 is a graph showing the THC content of the industrial hemp leaf sample in the example;
FIG. 3 is a normalized THCA content distribution chart of an industrial hemp leaf sample in the example;
FIG. 4 is a graph showing the normalized CBD content distribution of samples of industrial hemp flowers in the examples;
FIG. 5 is a normalized CBDA content distribution of an industrial hemp leaf sample in the examples;
FIG. 6 is a graph showing the correlation between predicted and measured values of CBDA content of a sample to be tested, wherein +.is the predicted value, - - - - - - - - - - - - - - - - - - - - - -, the experimental value, - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - "indicates);
FIG. 7 is a graph showing the correlation between predicted and measured values of CBD content of a sample to be tested, wherein +.is the predicted value, - - - - - - - - - - - - - - - - - - - - - -, the experimental value, - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - "indicates);
FIG. 8 is a graph showing the correlation between predicted values and measured values of THCA content of samples to be tested, wherein +.is the predicted value, - - - - - - - - - - - - - - - - - - - - - -, the experimental value, - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -, the experimental value);
fig. 9 is a graph showing the correlation between predicted values and measured values of THC content of a sample to be tested, wherein +.is the predicted value, - - - - - - - - - - - - - - - - - - - - - -, the experimental value, - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -.
Detailed Description
The technical scheme of the invention is not limited to the specific embodiments listed below, and also includes any combination of the specific embodiments.
The first embodiment is as follows: the method for rapidly detecting the cannabinoid content in industrial cannabis sativa leaves based on the near infrared spectrum analysis technology in the embodiment is realized by the following steps:
1. acquiring near infrared spectrum data of industrial cannabis sativa leaf powder for eliminating noise interference and cannabinoid content data of industrial cannabis sativa leaf;
2. regression modeling is carried out by using the two data, so as to obtain a near infrared prediction model of the cannabinoid content in the industrial hemp flowers;
2. and measuring a near infrared spectrum curve of the industrial hemp leaf powder to be detected, and substituting the near infrared spectrum curve into the near infrared prediction model of the cannabinoid content in the industrial hemp leaf to obtain the cannabinoid content in the industrial hemp leaf to be detected, thereby completing the detection method.
The second embodiment is as follows: the first difference between this embodiment and the specific embodiment is that, in the step one, the near infrared spectrum data of the industrial hemp leaf powder excluding noise interference is obtained: measuring the near infrared spectrum curve of the industrial hemp leaf powder of the calibration sample, removing abnormal sample data after pretreatment, and obtaining the near infrared spectrum data of the industrial hemp leaf powder for eliminating noise interference. Other steps and parameters are the same as in the first embodiment.
And a third specific embodiment: this embodiment differs from the second embodiment in that the number of the calibration samples is 100; a plurality of near infrared spectrum curves are obtained by measuring the near infrared spectrum curve of each calibration sample. Other steps and parameters are the same as in the second embodiment.
The specific embodiment IV is as follows: the second difference between this embodiment and the specific embodiment is that the acquisition conditions of the near infrared spectrum curve are as follows: using a near infrared spectrum analyzer, scanning range 950-1650 nm, 3 times per calibration sample acquisition, 3 replicates, data spectrum collection rate 100 times/sec. Other steps and parameters are the same as in the second embodiment.
Fifth embodiment: the present embodiment differs from the second embodiment in that the near infrared spectrum analyzer is a boway DA7200 type near infrared spectrum analysis meter of perkin elmer. Other steps and parameters are the same as in the second embodiment.
Specific embodiment six: the second difference between the present embodiment and the specific embodiment is that the pretreatment method is a single or combined treatment of mathematical methods; the mathematical method is smoothing, standard normal variable transformation, trending correction and derivation. Other steps and parameters are the same as in the second embodiment.
Seventh embodiment: the first difference between this embodiment and the specific embodiment is that in the step one, the cannabinoid content data of the industrial hemp flowers are obtained: the cannabinoid content data of the industrial cannabis sativa leaves of the calibration sample were measured by high performance liquid chromatography. Other steps and parameters are the same as in the first embodiment.
Eighth embodiment: this embodiment differs from the seventh embodiment in that the number of the calibration samples is 100. Other steps and parameters are the same as in embodiment seven.
Detailed description nine: the first difference between this embodiment and the specific embodiment is that the regression modeling method in the second step is as follows:
a. the method comprises the steps of (1) establishing a plurality of near infrared correction models of the cannabinoid content by adopting a partial least square method according to near infrared spectrum data of industrial cannabis sativa leaf powder for eliminating noise interference and cannabinoid content data of industrial cannabis sativa leaf, and verifying correlation coefficients according to cross-verification standard deviation and cross-verification;
b. and selecting a model with the minimum cross-validation standard deviation and the maximum cross-validation correlation coefficient from the plurality of cannabinoid content near-infrared correction models, namely the cannabinoid content near-infrared prediction model in the industrial cannabis sativa leaves. Other steps and parameters are the same as in the first embodiment.
The technical solutions provided by the present invention are described in detail by the following examples, but they should not be construed as limiting the scope of the present invention.
Examples:
a method for rapidly detecting the cannabinoid content in industrial cannabis sativa leaves based on near infrared spectrum analysis technology comprises the following specific implementation processes:
acquiring near infrared spectrum data of industrial cannabis sativa leaf powder for eliminating noise interference and cannabinoid content data of industrial cannabis sativa leaf;
measuring a near infrared spectrum curve of the industrial hemp leaf powder of the calibration sample, removing abnormal sample data after pretreatment, and obtaining near infrared spectrum data of the industrial hemp leaf powder for eliminating noise interference:
collecting calibration sample industrial hemp flowers, naturally airing at a dark and ventilated place, thoroughly crushing by a crusher, sieving by a 40-mesh sieve, and carrying out near infrared spectrum scanning on 100 parts of industrial hemp flowers by using a Botong DA7200 type near infrared spectrum analyzer, repeating for three times to obtain a near infrared spectrum. The spectrum acquisition conditions are as follows: the scanning range is 950-1650 nm, and the data spectrum collection rate is 100 times/second. The results of the near infrared spectrum scan of 100 parts of industrial hemp leaf powder are shown in fig. 1.
The calibration sample refers to a sample adopted in modeling;
the calibration sample is from industrial hemp resource materials planted in a test base of the institute of economic crops of the academy of agricultural sciences of Heilongjiang in 2022, and 2/3 of industrial hemp leaves at maturity are taken as test materials.
Obtaining cannabinoid content data of industrial cannabis sativa leaves:
measuring the cannabinoid content in industrial hemp flowers by high performance liquid chromatography:
(1) Preparation of a Standard Curve
a. 1mL of THC, THCA, CBD and CBDA standard solution (1 mg/mL) were measured separately in 10mL brown volumetric flasks; diluting with chromatographic methanol solution, and keeping constant volume to obtain standard stock solution with concentration of 100mg/L, and keeping at-20deg.C in dark place for use;
b. respectively measuring THC, THCA, CBD of 100mg/L and 1mL of CBDA standard stock solution, diluting with chromatographic methanol in a 10mL brown volumetric flask to prepare THC, THCA, CBD and mixed standard working solution with CBDA concentration of 10mg/L, and storing in a dark place at-20 ℃ for later use;
c. THC, THCA, CBD and CBDA are prepared into mixed standard working solutions containing 0.5, 1.0, 5.0, 10.0 and 25.0 mg/L;
(2) Sample analysis
a. Weighing about 1.0g (accurate to 0.01 g) of crushed industrial hemp flowers into a 50mL centrifuge tube respectively;
b. adding 20mL (V: V=90:10) of methanol-n-hexane solution into the centrifuge tube, oscillating for 60min, centrifuging for 3min at 350 r/min;
c. transferring all the supernatant into a new 50mL centrifuge tube, adding 20mL (V: V=90:10) of methanol-n-hexane solution into the residue again, oscillating for 60min, centrifuging for 3min at 2500 r/min, mixing the extracts at 10000r/min, mixing for 1min by vortex, and filtering 2mL of the mixed solution with 0.22 μm organic filter membrane;
d. loading and analyzing in High Performance Liquid Chromatography (HPLC);
hplc condition settings: liquid chromatographic column: agilent ZORBAX Eclipse Plus C18 chromatographic column; flow rate: 1mL/min, mobile phase A is water, mobile phase B is acetonitrile, isocratic elution, V (acetonitrile): v (water) =65:35; column temperature: 30 ℃; sample volume 10. Mu.L; the detection wavelength was 250nm.
f. If the concentration of the sample treatment fluid exceeds the concentration range of the standard working curve, detecting after dilution;
(3) Sample content calculation formula
And carrying out regression analysis on the corresponding standard concentration x (mg/L) by using the chromatographic peak area y of the standard working solution target object to obtain a standard curve.
THC y＝50.863x-16.623R 2 ＝0.9994；THCAy＝39.140x-9.4792R 2 ＝0.9995；
CBD y＝48.952x-15.047R 2 ＝0.9997；CBDAy＝44.352x-15.664R 2 ＝0.9995；
The cannabinoid content of 100 samples was determined by HPLC as described above and the results are shown in Table 1. The THC, THCA, CBD and CBDA content of the cannabis flowers were made into normal distribution patterns, and as shown in fig. 2, 3, 4 and 5, it can be seen from fig. 2, 3, 4 and 5 that the THC, THCA, CBD and CBDA content distribution were in conformity with the normal distribution, and the data were representative.
TABLE 1 actual results of cannabinoid content for 100 samples
Near infrared spectrum data acquisition is carried out on all samples in the first step, the spectrum data are messy, the noise influence and the sample background influence are large, and the method cannot be directly used for modeling analysis. Therefore, the spectrum data needs to be preprocessed, the characteristic wavelength information is identified, and the accuracy and the reliability of the built model are improved. Spectral data preprocessing is the most important step in near infrared analysis, and the influence of different preprocessing methods on the establishment of a correction model is different. Before the model is built, spectrum pretreatment is carried out, and the influence caused by temperature fluctuation, wavelength drift, baseline drift and the like is eliminated through single or combined treatment of a smoothing, standard normal variable transformation (SNV), trending correction (DE) and derivative mathematical methods. And performing regression modeling by using an improved partial least squares (MPLS), and primarily evaluating the prediction capability of the model by an internal cross-validation mode.
Regression modeling is carried out by using the two data, so as to obtain a near infrared prediction model of the cannabinoid content in the industrial hemp flowers;
the software used to build the near infrared prediction model of cannabinoid content in industrial cannabis sativa leaves was WinISI.
The regression modeling method comprises the following steps:
a. the method comprises the steps of (1) establishing a plurality of near infrared correction models of the cannabinoid content by adopting a partial least square method according to near infrared spectrum data of industrial cannabis sativa leaf powder for eliminating noise interference and cannabinoid content data of industrial cannabis sativa leaf, and verifying correlation coefficients according to cross-verification standard deviation and cross-verification;
b. and selecting a model with the minimum cross-validation standard deviation and the maximum cross-validation correlation coefficient from the plurality of cannabinoid content near-infrared correction models, namely the cannabinoid content near-infrared prediction model in the industrial cannabis sativa leaves.
Comparing models established under different conditions, selecting a calibration model with small cross-validation deviation (SEC), small prediction Root Mean Square Error (RMSE) and cross-validation correlation coefficient R 2 Large models. The optimal modeling equation is shown in table 2.
Table 2 spectral data preprocessing method
Measuring near infrared spectrum curves of industrial cannabis sativa leaf powder of a calibration sample, removing abnormal sample data after pretreatment, and reserving verification set data to verify the accuracy of a model in the process of obtaining near infrared spectrum data of the industrial cannabis sativa leaf powder for eliminating noise interference; the data of the verification set is firstly predicted by using a near infrared prediction model of the cannabinoid content in the industrial cannabis sativa leaves, and then the cannabinoid content in the industrial cannabis sativa leaves is measured by using the high performance liquid chromatography which is the same as that of the correction group, and the results are shown in tables 3 and 4. The THC content prediction standard deviation is 0.169, the ratio performance deviation (SEC) is 3.0465, and the cross validation coefficient is 0.9974, which shows that the model can be successfully used; the THCA content prediction standard deviation is 2.341, the ratio performance deviation (SEC) is 3.0676, and the cross validation coefficient is 0.99965, which shows that the model can be successfully used; the standard deviation of the CBD content prediction is 0.915, the ratio performance deviation (SEC) is 3.0319, and the cross validation coefficient is 0.9985, which shows that the model can be successfully used; the CBDA content prediction standard deviation was 19.371, the ratio performance deviation (SEC) was 2.9676, and the cross validation coefficient was 0.9984, indicating that the model was successful. True value: detecting the obtained value by high performance liquid chromatography; predicted value: and obtaining a numerical value of a near infrared prediction model of the cannabinoid content in the industrial hemp flowers.
TABLE 3 comparison of actual and predicted values of THC and THCA validation sets in Industrial Cannabis sativa leaves
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TABLE 4 comparison of measured and predicted values for CBD and CBDA validation sets in leaves of Industrial hemp
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Model application
Detecting the sample to be detected, wherein the correlation diagram of the experimental value and the predicted value is shown in the diagrams of fig. 6, 7, 8 and 9, and R of the predicted value and the experimental value of the whole sample 2 All reach more than 0.9, and have higher prediction accuracy, so that the model can be used for rapid determination and preliminary screening of the content of general cannabinoid in industrial cannabis sativa leaves.
From the above examples, the present invention provides a method for detecting the cannabinoid content in industrial cannabis sativa leaves based on near infrared spectrum technology, and the detection method of the present invention can simultaneously identify the contents of tetrahydrocannabinol, tetrahydrocannabinolic acid, cannabidiol and cannabidiol acid in industrial cannabis sativa leaves, thereby obtaining the contents of total TCH and CBD, and solving the problems of complex steps and environmental pollution of the conventional cannabinoid content determination method in industrial cannabis sativa leaves in the prior art.
Claims (9)
1. A method for rapidly detecting the content of cannabinoid in industrial cannabis sativa leaves based on near infrared spectrum analysis technology is characterized by comprising the following steps:
1. acquiring near infrared spectrum data of industrial cannabis sativa leaf powder for eliminating noise interference and cannabinoid content data of industrial cannabis sativa leaf;
2. regression modeling is carried out by using the two data, so as to obtain a near infrared prediction model of the cannabinoid content in the industrial hemp flowers;
2. and measuring a near infrared spectrum curve of the industrial hemp leaf powder to be detected, and substituting the near infrared spectrum curve into the near infrared prediction model of the cannabinoid content in the industrial hemp leaf to obtain the cannabinoid content in the industrial hemp leaf to be detected, thereby completing the detection method.
2. The method for rapidly detecting cannabinoid content in industrial cannabis sativa leaves based on near infrared spectroscopy analysis technology as claimed in claim 1, wherein the acquisition of near infrared spectroscopy data of the industrial cannabis sativa leaf powder excluding noise interference in step one is characterized in that: measuring the near infrared spectrum curve of the industrial hemp leaf powder of the calibration sample, removing abnormal sample data after pretreatment, and obtaining the near infrared spectrum data of the industrial hemp leaf powder for eliminating noise interference.
3. A method for rapid detection of cannabinoid content in industrial cannabis leaves based on near infrared spectroscopy as claimed in claim 2, characterized in that the number of said scaled samples is 100; a plurality of near infrared spectrum curves are obtained by measuring the near infrared spectrum curve of each calibration sample.
4. The method for rapidly detecting the cannabinoid content in industrial cannabis sativa leaves based on the near infrared spectrum analysis technology as claimed in claim 2, wherein the collection condition of the near infrared spectrum curve is as follows: using a near infrared spectrum analyzer, scanning range 950-1650 nm, 3 times per calibration sample acquisition, 3 replicates, data spectrum collection rate 100 times/sec.
5. The method for rapidly detecting the cannabinoid content in industrial cannabis sativa leaves based on the near infrared spectrum analysis technology as claimed in claim 2, wherein the near infrared spectrum analyzer is a Botong DA7200 type near infrared spectrum analysis analyzer of Perkin Elmer company.
6. A method for rapid detection of cannabinoid content in industrial cannabis sativa leaves based on near infrared spectroscopy as claimed in claim 2, characterized in that the pre-treatment method is a single or combined treatment of mathematical methods; the mathematical method is smoothing, standard normal variable transformation, trending correction and derivation.
7. The method for rapidly detecting the cannabinoid content in industrial cannabis sativa leaves based on the near infrared spectrum analysis technique as claimed in claim 1, wherein the obtaining of the cannabinoid content data of industrial cannabis sativa leaves in the step one is characterized in that: the cannabinoid content data of the industrial cannabis sativa leaves of the calibration sample were measured by high performance liquid chromatography.
8. The method for rapidly detecting cannabinoid content in industrial cannabis leaves based on near infrared spectroscopy as claimed in claim 7, wherein the number of the calibration samples is 100.
9. The method for rapidly detecting the cannabinoid content in industrial cannabis sativa leaves based on the near infrared spectrum analysis technique as claimed in claim 1, wherein the regression modeling method in the step two is characterized in that:
a. the method comprises the steps of (1) establishing a plurality of near infrared correction models of the cannabinoid content by adopting a partial least square method according to near infrared spectrum data of industrial cannabis sativa leaf powder for eliminating noise interference and cannabinoid content data of industrial cannabis sativa leaf, and verifying correlation coefficients according to cross-verification standard deviation and cross-verification;
b. and selecting a model with the minimum cross-validation standard deviation and the maximum cross-validation correlation coefficient from the plurality of cannabinoid content near-infrared correction models, namely the cannabinoid content near-infrared prediction model in the industrial cannabis sativa leaves.
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