CN112485238B - Method for identifying turmeric essential oil producing area based on Raman spectrum technology - Google Patents
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- 239000000341 volatile oil Substances 0.000 title claims abstract description 60
- 235000003392 Curcuma domestica Nutrition 0.000 title claims abstract description 48
- 235000003373 curcuma longa Nutrition 0.000 title claims abstract description 48
- 235000013976 turmeric Nutrition 0.000 title claims abstract description 48
- 238000001237 Raman spectrum Methods 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000005516 engineering process Methods 0.000 title claims abstract description 13
- 244000008991 Curcuma longa Species 0.000 title claims abstract 12
- 238000012937 correction Methods 0.000 claims abstract description 20
- 238000012795 verification Methods 0.000 claims abstract description 5
- 238000012545 processing Methods 0.000 claims abstract description 4
- 238000001069 Raman spectroscopy Methods 0.000 claims description 33
- 238000001228 spectrum Methods 0.000 claims description 25
- 238000009795 derivation Methods 0.000 claims description 20
- 238000004458 analytical method Methods 0.000 claims description 18
- 239000000126 substance Substances 0.000 claims description 11
- 238000009499 grossing Methods 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 6
- 239000013598 vector Substances 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 3
- 239000007788 liquid Substances 0.000 claims description 3
- 238000004451 qualitative analysis Methods 0.000 claims description 3
- 238000004445 quantitative analysis Methods 0.000 claims description 3
- 230000003595 spectral effect Effects 0.000 claims description 2
- 244000163122 Curcuma domestica Species 0.000 description 36
- 238000000513 principal component analysis Methods 0.000 description 14
- 238000004519 manufacturing process Methods 0.000 description 7
- 238000001514 detection method Methods 0.000 description 4
- 241000407170 Curcuma Species 0.000 description 2
- 235000014375 Curcuma Nutrition 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 2
- VFLDPWHFBUODDF-FCXRPNKRSA-N curcumin Chemical compound C1=C(O)C(OC)=CC(\C=C\C(=O)CC(=O)\C=C\C=2C=C(OC)C(O)=CC=2)=C1 VFLDPWHFBUODDF-FCXRPNKRSA-N 0.000 description 2
- 238000004811 liquid chromatography Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000003921 oil Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 239000010681 turmeric oil Substances 0.000 description 1
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- G—PHYSICS
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- 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
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/65—Raman scattering
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Abstract
The invention discloses a method for identifying a turmeric essential oil producing area based on a Raman spectrum technology. The method uses qualitative and quantitative identification methods at the same time, and comprises the specific steps of (1) collecting a correction set verification set and processing Raman spectrum data; (2) Establishing a judgment model, (2.1) establishing a PCA qualitative judgment model, and (2.2) establishing a PLS-DA quantitative judgment model; and (3) measuring the sample to be measured. The invention has the characteristics of small identification error, high identification speed and no damage.
Description
Technical Field
The invention relates to a method for identifying a turmeric essential oil producing area, in particular to a method for identifying a turmeric essential oil producing area based on a Raman spectrum technology.
Background
The producing area of the turmeric essential oil is always a more concerned type of problem, and the turmeric essential oil in different producing areas has different contents of main components and has great difference in efficacy and price. Therefore, the method has very important significance for identifying and identifying the turmeric essential oil in different producing areas. Meanwhile, whether the essential oil is produced in a certain area or not can be identified, and the situation that the essential oil is insufficient can be avoided. Since the main color and taste of turmeric essential oil do not have a large difference between different production areas, in this case, sensory evaluation and the like of the conventional methods are not reliable. As a conventional method for identifying essential oils, analysis of each component is generally performed using liquid chromatography to roughly judge the origin of the essential oil. The time period for judging by using the chromatograph is too long, each sample needs about 1-2 hours of detection period, and simultaneously, the detection cost is high.
The raman spectroscopy technology is a fingerprint spectrum identification technology, when a laser beam irradiates a substance, the substance generates a raman scattering peak, and the position of the scattering peak represents the group of chemical components contained in the substance, so that the raman scattering peak can be used for substance component analysis. The scattering signal is collected and processed by a high-precision spectrometer, and the spectra of various substances are processed and analyzed by data to achieve the aim of quantitative and qualitative detection.
At present, the production place of a substance identified by a Raman spectrum technology is developed and applied, but the specific identification method is different from one identification object to another, and the identification method of different substances does not have much reference and reference significance. Therefore, the research and design of a Raman spectrum identification method aiming at the turmeric essential oil have very important significance for quickly identifying the origin of the turmeric essential oil.
Disclosure of Invention
The invention aims to provide two methods for identifying the producing area of turmeric essential oil based on Raman spectrum technology, one method is a Principal Component Analysis (PCA) method, belongs to qualitative identification, and the other method is a partial least square discriminant analysis method, belongs to quantitative identification. The invention has the characteristics of small identification error, high identification speed and no damage.
The technical scheme of the invention is as follows: a method for identifying the origin of turmeric essential oil based on Raman spectroscopy technology, and simultaneously using a qualitative and quantitative identification method, comprises the following steps:
(1) Collecting correction set verification set and processing Raman spectrum data
a. Selecting 10 parts of turmeric essential oil samples of different known producing areas as a correction set for primarily establishing a model, selecting 2 parts of samples of each producing area as a prediction set for determining whether the prediction model is reliable, storing the samples at the temperature of 0-4 ℃, and ensuring the stability of chemical substances and components of the samples before being measured;
b. introducing the Raman spectra of all collected samples into analysis and calculation software, smoothing the original Raman spectrum data to reduce noise interference of the original data, and performing first-order derivation on the original Raman spectrum data, wherein the derived Raman spectrum data is 600-850cm -1 、1140-1250cm -1 、1350-1520cm -1 And 1550-1720cm -1 The four wave bands are main intervals for displaying differences among the curcuma oil in different producing areas;
(2) Establishing a decision model
2.1 establishment of PCA qualitative determination model
a. The PCA automatic analysis module is started by importing data after Raman spectrum derivation, and software automatically forms a two-dimensional PCA classification quadrant of a Raman vector according to the difference of the data;
b. the characteristic vector values of the established classification judgment model are distributed in a PCA score quadrant graph, and three essential oils are qualitatively distinguished by observing the result of the score quadrant graph, wherein the step is qualitative distinguishing. If the classification effect is not good, adjusting the selected characteristic wave band until the result of qualitative analysis can meet the requirement of sample differentiation, and finally training ten different points into a correction model;
2.2 establishment of PLS-DA quantitative determination model
By importing data after Raman spectrum derivation, a PLS-DA automatic analysis module is started, the characteristic wave band is selected, a quantitative determination model is established by using a PLS-DA classification method, and 10 samples of three essential oil producing places are trained into a correction model which can be quantized by a score value;
(3) Determination of samples to be tested
After the calibration model is established, a sample can be tested, the turmeric essential oil sample to be tested is taken out of a refrigerator, the temperature is restored, a Raman spectrometer is used for testing Raman signals of the turmeric essential oil sample, and then qualitative and quantitative methods are respectively used for identification, wherein the identification method comprises the following steps:
introducing the Raman signal of the sample to be detected into analysis and calculation software, performing smoothing treatment and first derivation on the spectrum in the software, introducing the spectrum data subjected to first derivation into the established correction model, if the spectrum of the turmeric essential oil is distributed in the middle of the trained PCA model, and judging whether the sample to be detected is in a specified interval and the class of the sample to be detected according to the distribution condition of the spectrum in the set; or introducing the Raman signal of the sample to be detected into analysis and calculation software, performing smoothing treatment and first derivation on the spectrum in the software, introducing the spectrum data subjected to first derivation into the established PLS-DA correction model, and if the spectrum of the turmeric essential oil has the characteristic value in the range interval of the corresponding class, judging whether the sample to be detected is in the specified interval and the class of the sample to be detected.
In the method for identifying the producing area of the turmeric essential oil based on the raman spectroscopy technology, the sample selected in the step (1) is pure turmeric essential oil oily liquid of each producing area.
In the method for identifying the producing area of the turmeric essential oil based on the Raman spectrum technology, when the Raman spectrum data is processed, the wavelength of the exciting light of the Raman spectrometer is 1064nm, and the range of the measuring wave is 200-1800cm -1 The exposure time is 10-15s and the laser power is up to 500mw.
The invention has the advantages of
According to the method, a correction model is established according to the characteristics of the turmeric essential oil in different areas, the Raman spectrum data of a to-be-detected product is processed, and then the Raman spectrum data is compared and judged with the established model, so that the identification of the production area of the turmeric essential oil is realized. Due to the short time of Raman spectrum measurement, when the origin of the turmeric essential oil is identified, the testing speed is very high, the identification can be completed within several seconds, and the sample is not required to be processed. Compared with the traditional liquid chromatography, the method has consistent accuracy, but low Raman spectrum cost and nondestructive in-situ detection. In addition, the laser wavelength of 1064nm is selected, so that the background fluorescence of the sample is reduced as much as possible, and the identification accuracy is improved.
To verify that the method of the present invention can achieve determination of turmeric essential oil, the inventors conducted the following experiments:
(1) Respectively taking 10 parts of turmeric essential oil samples of Yunnan, sichuan and Guangxi, and numbering the turmeric essential oil samples, wherein the number of the Yunnan is s1-s10, the number of the Sichuan is s11-s20, and the number of the Guangxi is s21-s30;
(2) First, all samples were subjected to raman spectroscopy and PCA classification, the results are shown in fig. 3, and after labeling the production zone names, in fig. 4. As can be seen from fig. 3 and 4, the 10 samples were modeled for each group, and the points of each group formed a set, and these three sets were relatively independent, and each set did not intersect each other in the quadrant graph, indicating that the three groups of samples could be completely distinguished.
(3) The model was initially trained using the method of pls-Da, in this case using the commercial software unscramble for the samples described aboveAnd (5) classifying. Wherein based on the characteristics of Curcuma rhizome essential oil, raman peaks at 7 positions are selected as linear classification and judgment basis, which are 794cm respectively -1 、1137cm -1 、1200cm -1 、1370cm -1 、1432cm -1 、1576cm -1 And 1608cm -1 . And taking the Raman values after 7 position derivatives as a classification basis, and performing classification marking on the Raman spectrum of the sample of the same origin by using PLS-DA. According to the characteristic value rule of PLS-DA and for simplifying data calculation, the characteristic value of a specific south-Yunnan area production place is 1, the characteristic value of a Guangxi area is 2, the characteristic value of a Sichuan area is 3, and a model with the well-defined characteristic values is operated by using a PLS-DA program. The standard values of the model and the self-training results and error values of the model are shown in table 1:
TABLE 1 turmeric essential oil sample prediction value classification in different regions
The curve of the trained model is shown in fig. 5, and the variance is 97.9%, which is greater than 95%, indicating that the correlation of the model is high, and can be used as the standard of model training.
(4) After the model is established, the actual sample is predicted
In actual samples, 2 are Yunnan producing areas, 2 are Guangxi producing areas, and 2 are Sichuan producing areas, and the predicted values are shown in Table 2:
TABLE 2 actual prediction of turmeric essential oil samples in different regions
| Prediction value | Relative error | Reference value | |
| s31 | 1.094641 | 0.284626 | Yunnan province |
| s32 | 1.098947 | 0.272439 | Yunnan province |
| s33 | 1.997297 | 0.125216 | Guangxi province |
| s34 | 1.993584 | 0.126344 | Guangxi province |
| s35 | 3.09558 | 0.14013 | Sichuan |
| s36 | 3.084694 | 0.133292 | Sichuan |
The predicted value is obtained by introducing the raman spectrum data of the six samples to be tested into the established PLS-DA correction model of the turmeric essential oil. The results of tables 1 and 2 can be shown in fig. 6, where s1-s30 are 10 samples of turmeric essential oils from Yunnan, sichuan and Guangxi, respectively, representing model specimens of three major production areas of essential oils. s30-s36 are the samples to be predicted, two samples each in Yunnan Guangxi and Sichuan. From FIG. 6, it is easy to distinguish and identify their classifications, samples between 0.8 and 1.2 that can be considered Yunnan province, samples between 1.8 and 2.2 that can be considered Guangxi province, and samples between 2.8 and 3.2 that can be considered Sichuan province. If the numerical range is outside this range, it is considered that the sample does not belong to the provinces samples, or does not belong to the turmeric essential oil sample.
Drawings
FIG. 1 is an original Raman spectrum;
FIG. 2 is the first derivative of the original Raman spectrum of FIG. 1;
FIG. 3 shows the results of PCA classification of three source samples;
FIG. 4 is the view of FIG. 3 with the birth area labeled;
FIG. 5 is a training model curve;
fig. 6 is a prediction result image.
Detailed Description
The present invention is further illustrated by the following examples, which are not to be construed as limiting the invention.
Examples of the invention
A method for identifying the origin of turmeric essential oil based on Raman spectroscopy technology, and simultaneously using qualitative and quantitative identification method, comprises the following steps:
(1) Collecting correction set verification set and processing Raman spectrum data
a. Selecting 10 parts of turmeric essential oil liquid samples of different known producing areas as a calibration set for primarily establishing a model, selecting 2 parts of samples of each producing area as a prediction set for determining whether the prediction model is reliable, storing at the low temperature of 0-4 ℃, and ensuring the stability of chemical substances and components before being measured;
b. introducing the Raman spectra of all collected samples into analysis and calculation software, smoothing the original Raman spectrum data to reduce noise interference of the original data, and performing first-order derivation on the original Raman spectrum data, wherein the derived Raman spectrum data is 600-850cm -1 、1140-1250cm -1 、1350-1520cm -1 And 1550-1720cm -1 These four bands are the main intervals where turmeric oil shows distinction in different places of production.
(2) Establishing a decision model
2.1 establishment of PCA qualitative determination model
a. Starting a PCA automatic analysis module by importing data after Raman spectrum derivation, and forming a two-dimensional PCA classification quadrant of Raman vectors by software automatically according to the difference of the data;
b. the characteristic vector values of the established classification judgment model are distributed in a PCA score quadrant graph, three essential oils are qualitatively distinguished by observing the result of the score quadrant graph, the step is qualitative distinguishing, if the classification effect is not good, the selected characteristic wave band is adjusted until the result of qualitative analysis can meet the requirement of sample distinguishing, and finally, ten different points are trained into a correction model.
2.2 establishment of PLS-DA quantitative determination model
And (3) starting a PLS-DA automatic analysis module by importing data derived from the Raman spectrum, selecting the characteristic wave band, establishing a quantitative determination model by using a PLS-DA classification method, and training 10 samples of three essential oil producing places into a correction model which can be quantified by using a score value.
(3) Determination of a sample to be tested
After the correction model is established, a sample can be tested, the turmeric essential oil sample to be tested is taken out of a refrigerator, rewarming is carried out, a Raman spectrometer is used for testing a Raman signal of the turmeric essential oil sample, and then qualitative and quantitative methods are respectively used for identification, wherein the identification method comprises the following steps:
introducing a Raman signal of a sample to be detected into analysis and calculation software, performing smoothing treatment and first-order derivation on a spectrum in the software, introducing spectral data subjected to first-order derivation into an established correction model, if the spectrum is the spectrum of turmeric essential oil, distributing the spectrum in the middle of a trained PCA model, and judging whether the sample to be detected is in a specified interval and the class of the sample to be detected according to the distribution condition of the spectrum in a set; or leading the Raman signal of the sample to be detected into analysis and calculation software, carrying out smoothing treatment and first derivation on the spectrum in the software, leading the spectrum data subjected to the first derivation into the established PLS-DA correction model, and judging whether the sample to be detected is in a specified interval and the class of the sample to be detected if the spectrum of the turmeric essential oil has the characteristic value in the range interval of the corresponding class.
When the Raman spectrum data is processed, the wavelength of exciting light of the Raman spectrometer is 1064nm, and the range of measuring wave is 200-1800cm -1 The exposure time is 10-15s and the laser power is up to 500mw.
The above description is only for the purpose of creating a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive concept of the present invention within the technical scope of the present invention.
Claims (1)
1. A method for identifying the producing area of turmeric essential oil based on Raman spectrum technology is characterized in that: a method of simultaneous qualitative and quantitative identification comprising the steps of:
(1) Collecting correction set verification set and processing Raman spectrum data
a. Selecting 10 parts of turmeric essential oil samples of different known producing areas as a calibration set for primarily establishing a model, selecting 2 parts of samples of each producing area as a verification set for predicting whether the model is reliable, storing at the temperature of 0-4 ℃, and ensuring the stability of chemical substances and components before being detected; the turmeric essential oil sample is pure turmeric essential oil oily liquid of each producing area; when the Raman spectrum data is collected, the wavelength of exciting light of a Raman spectrometer is 1064nm, and the range of measuring wave numberIs 200-1800cm -1 The exposure time is 10-15s, and the highest laser power is 500mW;
b. introducing the Raman spectra of all collected samples into analysis and calculation software, smoothing the original Raman spectrum data to reduce noise interference of the original data, and performing first-order derivation on the original Raman spectrum data, wherein the derived Raman spectrum data is 600-850cm -1 、1140-1250cm -1 、1350-1520cm -1 And 1550-1720cm -1 The four wave number sections are main sections which show differences among turmeric essential oils in different producing areas;
(2) Establishing a decision model
2.1 Establishment of PCA qualitative judgment model
a. Starting a PCA automatic analysis module by importing data after Raman spectrum derivation, and forming a two-dimensional PCA classification quadrant of Raman vectors by software automatically according to the difference of the data;
b. the characteristic vector values of the established classification judgment model are distributed in a PCA score quadrant graph, three essential oils are qualitatively distinguished by observing the result of the score quadrant graph, the step is qualitative distinguishing, if the classification effect is not good, the selected characteristic wave band is adjusted until the result of qualitative analysis can meet the requirement of distinguishing samples, and finally, ten different points are trained into a correction model;
2.2 Establishment of PLS-DA quantitative determination model
The method comprises the steps of starting a PLS-DA automatic analysis module by importing data after Raman spectrum derivation, establishing a quantitative determination model by using a PLS-DA classification method, and training 10 samples of three essential oil producing areas into a correction model which can be quantized by using score values; selecting 7 Raman values obtained by differentiating Raman peaks as classification bases according to the characteristics of the turmeric essential oil, wherein the Raman values of the 7 Raman peaks are 794cm respectively -1 、1137 cm -1 、1200 cm -1 、1370 cm -1 、1432 cm -1 、1576 cm -1 And 1608cm -1 ；
(3) Determination of samples to be tested
After the calibration model is established, a sample can be tested, the turmeric essential oil sample to be tested is taken out of a refrigerator, the temperature is restored, a Raman spectrometer is used for testing Raman signals of the turmeric essential oil sample, and then qualitative and quantitative methods are respectively used for identification, wherein the identification method comprises the following steps:
introducing a Raman signal of a sample to be detected into analysis and calculation software, performing smoothing treatment and first-order derivation on a spectrum in the software, introducing spectral data subjected to first-order derivation into an established correction model, if the spectrum is the spectrum of turmeric essential oil, distributing the spectrum in the middle of a trained PCA model, and judging whether the sample to be detected is in a specified interval and the class of the sample to be detected according to the distribution condition of the spectrum in a set; or introducing the Raman signal of the sample to be detected into analysis and calculation software, performing smoothing treatment and first derivation on the spectrum in the software, introducing the spectrum data subjected to first derivation into the established PLS-DA correction model, and if the spectrum of the turmeric essential oil has a characteristic value in the range interval of the corresponding class, judging whether the sample to be detected is in the specified interval and the class of the sample to be detected; when the Raman signal is collected, the wavelength of the exciting light of the Raman spectrometer is 1064nm, and the range of the measuring wave number is 200-1800cm -1 The exposure time is 10-15s, and the highest laser power is 500mW;
the producing areas are Sichuan, yunnan and Guangxi.
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| 基于拉曼光谱的大米快速分类判别方法;孙娟 等;《食品与机械》;20160131;第32卷(第01期);第41-45页 * |
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