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Method and system for rapidly detecting essential oil content of cinnamomum burmannii leaves

A detection method and blade technology, applied in the direction of measuring devices, preparation of test samples, material analysis through optical means, etc., can solve the problems of cumbersome operation, environmental pollution, complicated operation process, etc., and achieve the goal of saving manpower, material resources and financial resources Effect

Pending Publication Date: 2022-03-01
GUANGDONG ACAD OF FORESTRY +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] 1. Large amount of sampling: general materials need about 400g or more leaves. For species with low oil content, in order to obtain a certain amount of essential oil, it is necessary to increase the amount of input
[0008] 2. High energy consumption and high water consumption: the heat source of distillation is a temperature-controllable electric heating mantle, the capacity of the electric heating mantle is 50-20000mL, and its power is generally between 200W-3000W
[0009] 3. Large water consumption: Tap water is needed to condense steam during the distillation process. It is measured that more than 30 mL of water is required per second, and 108-216 kg of water is required for distillation for 1-2 hours.
[0010] 4. The use of organic solvents has certain pollution to the environment: after distillation, in order to reduce detection errors, organic solvents such as alcohol, acetone or ether must be used to repeatedly clean the reaction kettle, condenser tube, separating funnel, connecting pipe and other utensils 2- 3 times, the use of organic solvents has certain pollution to the environment
[0011] 5. The operation is cumbersome: the operation process is relatively complicated, from sampling, sampling, adding water, connecting conduits, heating, releasing the condensed water at the bottom, collecting essential oils, and cleaning the entire distillation system equipment to avoid experimental errors, etc., the whole process Need special supervision, time-consuming and labor-intensive

Method used

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  • Method and system for rapidly detecting essential oil content of cinnamomum burmannii leaves
  • Method and system for rapidly detecting essential oil content of cinnamomum burmannii leaves
  • Method and system for rapidly detecting essential oil content of cinnamomum burmannii leaves

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Experimental program
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Effect test

Embodiment 1

[0134] In March 2019, the modeling was carried out and the model was verified, and 6 individuals with abnormal data were eliminated from the 88 individual plant data. Using the measured and near-infrared spectral data of 82 individual plants, a prediction model for the essential oil content of the leaves of Cinnamomum japonica was established by using the near-infrared spectral technology combined with the partial least squares method. By selecting different spectral intervals, different spectral data preprocessing methods and different principal component numbers to compare and analyze the built prediction models, the results show that the selection of full-band spectrum, first derivative processing (FD) ) plus standard normal transformation (SNV) combined spectral preprocessing method and the prediction model of the essential oil content of Yinxiang essential oil built when the principal component number is 8 has the best effect, the correlation coefficient of the correction ...

Embodiment 2

[0138] In October 2019, the actual application verification of the model was carried out. 16 clones were collected in Meixian District, Meizhou City, and each clone had one individual plant. The traditional distillation measurement and the built model were used to predict the essential oil of 16 Cinnamomum sinensis leaves. The results showed that there was a good correlation between the predicted value and the measured value, the correlation coefficient R was 0.9247, and the predicted root mean square error RMSE was 0.2683. Using SAS software for a given significance level of 0.05, the predicted results of the model and the measured values ​​were paired by T test, and the results showed that there was no significant difference between the two (t=0.53, P=0.5978>0.05), indicating that the prediction of the model was accurate higher degree. The results are shown in Table 6.

[0139] Table 6 The actual application results of the near-infrared pre-model of the essential oil conten...

Embodiment 3

[0142] In November 2019, the actual application of the model was verified, and 12 samples of the leaves of a single plant of Yinxiang were collected in Pingyuan County, Meizhou City. The traditional distillation measurement and the built model were also used to predict the essential oil content. The results showed the predicted value and the measured value. There is a good correlation between them, the correlation coefficient R is 0.8936, and the prediction root mean square error RMSE is 0.1656. Using SAS software for a given significance level of 0.05, the predicted results of the model and the measured values ​​were paired by T test, and the results showed that there was no significant difference between the two (t=0.33, P=0.7421>0.05), indicating that the prediction of the model was accurate higher degree. The results are shown in Table 7.

[0143]Table 7 The actual application results of the near-infrared pre-model of essential oil content in the leaves of Cinnamomum sine...

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Abstract

The invention relates to a rapid detection method and system for the essential oil content of cinnamomum burmanni leaves. The method comprises the following steps: collecting essential oil content data of leaves of a set number of cinnamomum burmannii individuals; individual cinnamomum burmannii leaves are ground into powder, and a near infrared spectrum curve of cinnamomum burmannii leaf powder is obtained; preprocessing the near infrared spectrum curve; dividing the near infrared spectrum data without noise interference into correction set data and verification set data; according to the essential oil content data and the correction set data, establishing a near-infrared cinnamomum burmanni leaf essential oil content prediction model; evaluating the accuracy of the near-infrared cinnamomum burmanni leaf essential oil content prediction model according to the verification set data; grinding the to-be-predicted cinnamomum burmannii leaves into powder to obtain to-be-predicted cinnamomum burmannii leaf powder; and putting the cinnamomum burmanni leaf powder to be predicted into the near-infrared spectrometer into which the near-infrared cinnamomum burmanni leaf essential oil content prediction model is introduced, and scanning to obtain a cinnamomum burmanni leaf essential oil content result to be detected. According to the method, the essential oil content data of the cinnamomum burmannii leaves can be obtained quickly, efficiently and environmentally.

Description

technical field [0001] The invention relates to the field of detection of the essential oil content of leaves of Cinnamomum burmanii, in particular to a method and system for quickly detecting the content of essential oils of leaves of Cinnamomum burmanii. Background technique [0002] Natural d-borneol (also known as borneol and plum slices) has been widely used in medicine, spices, cosmetics and food industries as a precious medicinal material and precious spice since ancient times. Its main medicinal function is to rejuvenate the mind, clear away heat and relieve pain. It is an important component of more than 60 kinds of Chinese patent medicines such as Compound Danshen Dripping Pills and Shuangliao Houfeng San. Historically, my country did not produce natural D-borneol, and relied on imports from Indonesia in Southeast Asia. Cinnamomum burmannii is a species of the genus Cinnamomum lauraceae, widely distributed in southern my country. In the 1970s, Chinese researchers...

Claims

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Application Information

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IPC IPC(8): G01N21/359G01N21/3563G01N1/28
CPCG01N21/359G01N21/3563G01N1/28
Inventor 连辉明伍观娣何波祥蔡燕灵汪迎利梁东成叶龙华张春花莫云豹李兵侯晨
Owner GUANGDONG ACAD OF FORESTRY
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