AU2018101606A4 - A method for identifying meconopsis quintuplinervia regel from different geographical origins - Google Patents
A method for identifying meconopsis quintuplinervia regel from different geographical origins Download PDFInfo
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- 241001047059 Meconopsis quintuplinervia Species 0.000 title claims abstract description 82
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- 239000003814 drug Substances 0.000 abstract description 20
- 238000011161 development Methods 0.000 abstract description 3
- IOLCXVTUBQKXJR-UHFFFAOYSA-M potassium bromide Chemical compound [K+].[Br-] IOLCXVTUBQKXJR-UHFFFAOYSA-M 0.000 description 30
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Abstract
Abstract The present invention provides a method for detecting a Tibetan medicine Meconopsis quintuplinervia Regel from different geographical origins. The medicine Meconopsis quintuplinervia Regel from various geographical origins can be simply, intuitively, quickly, effectively and accurately detected using the method, providing a scientific basis for the protection and rational development and utilization of resources in these geographical origins.
Description
A Method for Identifying Meconopsis quintuplinervia Regel from Different Geographical Origins
Technical Field
The present invention relates to the field of medicine detection technologies, and in particular, to a method for detecting Meconopsis quintuplinervia Regel from different geographical origins.
Background
Meconopsis quintuplinevria Regel, also known as fivevein Meconopsis herb, maoguoqi, yemaojin, etc., is a species endemic to the Qinghai-Tibet Plateau. It is a widely used, authentic and native Tibetan medicine in Qinghai-Tibet Plateau in a position of great importance, with a Tibetan name of Oubei wanbao. It is a Papaveraceae, Meconopsis Vig. perennial herb concentrated in the Qinghai-Tibet plateau region, and grows mainly in alpine meadows and thickets on shady slopes between 3200 and 3800 meters above sea level, with the main active ingredients of alkaloids, flavonoids and volatile oil compounds. Meconopsis quintuplinervia Regel is a traditional Tibetan medicine, which is often used as a single medicine or compound recipe, having the efficacies of clearing heat, relieving cough and asthma, diuresis, diminishing inflammation, relieving pain, etc., and can be used to treat pneumonia, hepatitis, headache, edema, skin diseases, hepatic and pulmonary heat symptoms complex, etc. In addition, it is contained in classic books of Tibetan medicine, Somaratsa, the Four Medical Tantras, Jing Zhu Materia Medica, etc., and has been described in detail in the aspects of ecology, morphology, efficacy, etc. It is still used as a valuable Tibetan medicine and is also a traditional medicine peculiar to Tibetan medicine. In over 200 prescriptions of Tibetan medicine contained in the Pharmaceutical Standards of the Ministry of Health of China (Tibetan medicine, 1995 edition), there are as many as 28 prescriptions with Meconopsis quintuplinervia Regel used as a component of medicine; almost all prescriptions for treating hepatobiliary diseases contain this medicine, such as Ershi Wuwei Meconopsis Pills, Shiwuwei Luodi Mingmu Pills, Ershi Wuwei Songshi Pills, etc. It can be seen that Meconopsis quintuplinervia Regel has great resource consumption, wide application and
2018101606 25 Oct 2018 excellent quality of medicinal materials, and it has a high medicinal status and medicinal value in Tibetan medicine.
At present, the price and demand trend of Meconopsis quintuplinervia Regel in Chinese medicine markets have been on a steady or slight upward trend in recent years, with gradually increasing market demands and good market application prospects. Although this medicine has good quality, large consumption and wide scope of application in the traditional Tibetan medicine, current research reports are limited to the studies on its biological characteristics, microscopic identification, active ingredient identification and determination, pharmacology, efficacy, certain genetic characteristics, etc. Systematic studies around the overall quality evaluation of its medicinal resources are not perfect, which is not conducive to the comprehensive development and utilization of Meconopsis quintuplinervia Regel resources. The quality of a medicine is closely related to its ecological environment, and appropriate geographical origins are an important factor for the production of high-quality medicines. Therefore, it is necessary to accurately identify the geographical origins of Meconopsis quintuplinervia Regel. At present, the literatures on the identification of Meconopsis quintuplinervia Regel are found only in the master thesis Analysis on Characteristics of Infrared Spectra of Meconopsis Quintuplinervia Regel in io Different Populations in Qinghai Province by Zhao Qingshuai, University of Chinese Academy of Sciences. However, only the infrared spectrum characteristics of samples were determined when different populations of Meconopsis quintuplinervia Regel were analyzed, and then different samples were distinguished based on the intensity differences between is absorption peaks. The method, which distinguishes Meconopsis quintuplinervia Regel only based on the intensity of absorption peak, is vulnerable to the influence of random factors, causing certain errors on the results and affecting the accuracy of identification results, and is to be further improved.
Summary of the Invention
In view of the above problems, the present invention aims to provide an accurate and effective method for detecting Meconopsis quintuplinervia Regel from different geographical origins.
The present invention first provides a method for collecting a mid-infrared
2018101606 25 Oct 2018 spectrum of Meconopsis quintuplinervia Regel, comprising the following steps: mix a Meconopsis quintuplinervia Regel sample with KBr, form tablets, and collect mid-infrared spectrum data; where the ratio of the sample to KBr is 1:100, the particle size is 200 meshes, the number of scans is 32, the resolution is 4 cm'1, and the pressure is 1.9 T.
Preferably, the collection range of the spectra is 4000-400 cm'1.
Preferably, the Meconopsis quintuplinervia Regel sample is Meconopsis quintuplinervia Regel whole plant, flower, stem or leaf.
The present invention also provides a modeling method for Meconopsis io from different geographical origins, comprising the following steps:
(1) Take Meconopsis quintuplinervia Regel samples from different geographical origins, and collect mid-infrared spectrum data using the above method;
(2) Import the collected mid-infrared spectrum data into TQ software, and is build a model using the Discriminant analysis method.
Preferably, the Meconopsis quintuplinervia Regel from different geographical origins is randomly collected from highland areas in Qinghai, specifically, from Qinghai Qilian Youhulugou, Qinghai Qilian Binggoudaban, Qinghai Qilian Biandukou, Qinghai Daban Mountain, Qinghai Menyuan
Xianmi Forest Farm, Qinghai Menyuan Xianmi Kazigou, Qinghai Huzhu
Baimuxia, Qinghai Laji Mountain, Qinghai Huangzhong County Qunjia Township, Qinghai Hualong Qingsha Mountain, Qinghai Xunhua Gangcha Township, Qinghai Xunhua Dalijia Mountain, Qinghai Shuangpengxi Forest Farm, Qinghai Tongren Maixiu Yakou, Qinghai Maqin Heitu Mountain,
Qinghai Guoluo Banma and Qinghai Yushu Zhongda Township.
Preferably, the Meconopsis quintuplinervia Regel sample is Meconopsis quintuplinervia Regel whole plant, flower, stem or leaf.
The present invention also provides a model for identifying geographical origins of Meconopsis quintuplinervia Regel that is built and obtained using 30 the above method.
The present invention also provides a method for identifying geographical origins of Meconopsis, comprising the following steps:
1) Take the Meconopsis quintuplinervia Regel samples to be detected, and collect mid-infrared spectrum data using the above method;
2) Import the mid-infrared spectrum data of the Meconopsis quintuplinervia
2018101606 25 Oct 2018
Regel to be detected into the above model, and make a judgment.
Preferably, the Meconopsis from different geographical origins is Meconopsis quintuplinervia Regel randomly collected from highland areas in Qinghai, specifically, from Qinghai Qilian Youhulugou, Qinghai Qilian 5 Binggoudaban, Qinghai Qilian Biandukou, Qinghai Daban Mountain,
Qinghai Menyuan Xianmi Forest Farm, Qinghai Menyuan Xianmi Kazigou,
Qinghai Huzhu Baimuxia, Qinghai Laji Mountain, Qinghai Huangzhong County Qunjia Township, Qinghai Hualong Qingsha Mountain, Qinghai Xunhua Gangcha Township, Qinghai Xunhua Dalijia Mountain, Qinghai io Shuangpengxi Forest Farm, Qinghai Tongren Maixiu Yakou, Qinghai Maqin
Heitu Mountain, Qinghai Guoluo Banma and Qinghai Yushu Zhongda Township.
Preferably, the Meconopsis quintuplinervia Regel sample is Meconopsis quintuplinervia Regel whole plant, flower, stem or leaf.
is When the medicinal material is detected by using spectroscopy, a lot of noises and background information exist besides the information of the medicinal material itself. How to extract weak chemical composition information from complex and overlapping spectra to improve the measurement accuracy is a difficult point of the technology. To solve this 20 difficult point, the sample pretreatment method and the spectrum analysis method are the key factors. The present invention provides a mid-infrared detection method for identifying geographical origins of Meconopsis quintuplinervia Regel. The method can be used for accurate detection.
According to the detection method, the overall chemical quality 25 characteristics of Meconopsis quintuplinervia Regel resources are integrated through specific pretreatment of preferred samples and spectral analysis method, so that Meconopsis quintuplinervia Regel from different geographical origins can be accurately detected. The method is simple, intuitive, quick and highly accurate, provides a scientific basis for quality 30 monitoring of Meconopsis quintuplinervia Regel, guarantees the protection and rational development and utilization of its resources, and also provides a beneficial supplement for the improvement of the quality standards for Tibetan medicinal materials.
Obviously, various modification, replacement or change can be made to 35 the above contents based on ordinary technical knowledge and customary
2018101606 25 Oct 2018 means in the field and the above basic technical thoughts of the present invention.
The following further describes the above contents of the present invention by describing embodiments. But it shall not be construed that the scope of 5 the above subject is limited to the following embodiments. Any technology realized based on the above contents falls in the scope of the present invention.
Brief Description of Drawings
FIG.1 shows the distribution of sampling points of Meconopsis io Quintuplinervia Regel;
FIG.2 shows the comparison of spectrograms of the theoretical optimal combination with the actual optimal combination;
FIG.3 shows whole plant discriminative models built by different discrimination methods, where sub-figure a shows the model result of is discriminant analysis processing; sub-figure b shows the model result of distance match processing;
FIG.4 shows whole plant discriminative models built by different spectrogram processing methods, where sub-figure a shows the model result of original spectrogram; sub-figure b shows the model result of 20 Log10 processing; sub-figure c shows the model result of first-derivative processing; sub-figure d shows the model result of second-derivative processing;
FIG.5 shows the mid-infrared discriminative model of geographical origins at two random sampling points in the whole plant; where a red dot 25 represents a training set of the first random sampling point, and a red plus sign (+) represents a validation set of the first random sampling point; a blue dot represents a training set of the second random sampling point, and a blue plus sign (+) represents a validation set of the second random sampling point;
FIG.6 illustrates the cluster analysis of the whole plant at two random sampling points, where b represents Bomuxia, and D represents Binggou Daban;
FIG.7 shows the mid-infrared discriminative model of geographical origins at two random sampling points in the flower, where a red dot represents a 35 training set of Bomuxia, and a red plus sign (+) represents a validation set
2018101606 25 Oct 2018 of Bomuxia; a blue dot represents a training set of Binggou Daban, and a blue plus sign (+) represents a validation set of Binggou Daban;
FIG.8 illustrates the cluster analysis of the flower at two random sampling points, where b represents Bomuxia, and D represents Binggou Daban;
FIG.9 shows the discriminative model of geographical origins at two random sampling points in the stem, where a red dot represents a training set of Bomuxia, and a red plus sign (+) represents a validation set of Bomuxia; a blue dot represents a training set of Binggou Daban, and a blue plus sign (+) represents a validation set of Binggou Daban;
io FIG. 10 illustrates the cluster analysis of the stem at two random sampling points, where b represents Bomuxia, and D represents Binggou Daban;
FIG. 11 shows the discriminative model of geographical origins at two random sampling points in the leaf, where a red dot represents a training set of Bomuxia, and a red plus sign (+) represents a validation set of is Bomuxia; a blue dot represents a training set of Binggou Daban, and a blue plus sign (+) represents a validation set of Binggou Daban;
FIG. 12 illustrates the cluster analysis of the leaf at two random sampling points, where b represents Bomuxia, and D represents Binggou Daban.
Description of Embodiments
The following describes embodiments for further explanation, but the present invention is not limited to these embodiments.
The experimental materials and instruments used in the present invention are as follows:
1. Instruments and reagent
Fourier transform infrared spectrometer (model: IS 50, Thermo Nicolet), DTGS detector, crusher (Tianjin Taisite Instrument Co., LTD.), oven (Shanghai YiHeng Scientific Instruments Co., Ltd.), electronic balance (ME104, 0.0001 g), agate mortar, tablet-pressing mould (diameter: 13mm, 30 PIKE, USA), and water purifier (Millipore-Q Integral 3, Millipore). Potassium bromide (spectroscopic pure).
2. Experimental materials
Dried and crushed plant samples of Meconopsis quintuplinervia Regel.
Embodiment 1 Infrared spectrum experiment for identifying
2018101606 25 Oct 2018
Meconopsis quintuplinervia Regel from different locations
Based on the scale of geographic distribution and gradient of ecological distribution, collect Meconopsis quintuplinervia Regel samples during the florescence period from May to June from 17 different places (Table 1) within Qinghai Province. The distribution of sampling points is shown in
FIG.1. During sample collection, the distance between plants in the same population is at least 10-15 m, and at least 40-50 samples are collected in each population. Then, mix the samples, bring them back to the laboratory, clean them with ultra-pure water, dry them in the shade, crush, and filter io them with a 200-mesh screen for subsequent use. At the same time, record the geographic locations of the sampling points using GPS. The original plant samples are identified by Lu Xuefeng, a researcher of the Northwest Institute of Plateau Biology, Chinese Academy of Sciences, as Meconopsis quintuplinervia Regel.
Table 1 Collection Information of Samples of Meconopsis Quintuplinervia Regel
| S.N. | Sampling Point | Longitude (°) | Latitude (°) | Altitude (m) |
| P1 | Qinghai Qilian | 99.79 | 38.19 | 3290.7 |
| P2 | Qinghai Qilian Binggou | 100.24 | 38.07 | 3493.5 |
| P3 | Qinghai Qilian | 100.90 | 38.06 | 3391.3 |
| P4 | Qinghai Daban | 101.41 | 37.36 | 3544.2 |
| P5 | Qinghai Menyuan | 101.95 | 37.45 | 3328.0 |
| P6 | Qinghai Menyuan | 101.80 | 37.28 | 2939.7 |
| P7 | Qinghai Huzhu Bomuxia | 102.21 | 37.01 | 3362.8 |
| P8 | Qinghai Laji Mountain | 101.46 | 36.37 | 3536.7 |
| P9 | Qinghai Huangzhong | 101.60 | 36.31 | 3553.7 |
| P10 | Qinghai Hualong | 101.97 | 36.28 | 3387.9 |
| P11 | Qinghai Xunhua | 102.25 | 35.68 | 3436.5 |
| P12 | Qinghai Xunhua Dalijia | 102.74 | 35.57 | 3617.3 |
| P13 | Qinghai Shuangpengxi | 102.33 | 35.47 | 3671.5 |
| P14 | Qinghai Tongren Maixiu | 101.85 | 35.23 | 3571.6 |
| P15 | Qinghai Maqin Heitu | 100.41 | 34.50 | 3452.0 |
| P16 | Qinghai Guoluo Banma | 100.66 | 32.89 | 3733.0 |
| P17 | Qinghai Yushu Zhongda | 97.12 | 32.34 | 4554.0 |
1. Experimental method
1.1 Mid-infrared one-dimensional spectrum collection
1.1.1 Optimization of mid-infrared spectrum collection conditions a Orthogonal test
2018101606 25 Oct 2018
Considering the factors that may affect the infrared spectra, an orthogonal experiment table (Table 2) with 5 factors and 4 levels was designed, with a total of 16 groups of experiments conducted. Take the ML sample (Meconopsis quintuplinervia Regel) for full-band spectral scanning, collect spectra in 16 groups of experiments according to the conditions, calculate the average spectrogram of each experiment (n=3), and select the actual optimal combination based on the maximum absorbance.
Table 2 Factor-level Table of Mid-infrared Orthogonal Test
| A | B | C | D | E | |
| Factor-level | Ratio of | Particle Size | Number of | Resolution | Pressure |
| Sample:KBr | (Meshes) | Scans (Times) | (cm’1) | (TONs) | |
| L1 | 1:50 | 80 | 8 | 2 | 1.0 |
| L2 | 1:100 | 100 | 16 | 4 | 1.3 |
| L3 | 1:150 | 160 | 32 | 6 | 1.6 |
| L4 | 1:200 | 200 | 64 | 8 | 1.9 |
The absorbance of the sample ranges from 0.8 to 1.2 and is optimal around 1.0. The results show that No. 1 (the ratio of sample to KBr is 1:50, particle size is 80 meshes, number of scans is 8 times, resolution is 2 cm-1, and pressure is 1 T) and No. 8 are comparatively suitable. As the variance of the correlation coefficient of No. 1 is 0.0236, and that of No. 8 is 0.0001; the correlation coefficient of No. 1 is smaller (1.0000, 0.8680, 0.6935) and that of No. 8 is greater (1.0000, 0.9870, 0.9787), the actual optimal experimental combination is No. 8, that is, A2B4C3D2E-1 (the ratio of sample to KBr is 1:100, particle size is 200 meshes, number of scans is 32 times, resolution is 4 cm’1, and pressure is 1 T).
Table 3 Mid-infrared Orthogonal Test Table
| Test No. | A (Sample:KBr | B (Particle | Factor C (Number of Scans) | D (Resolution) | E Pressure (TONs) | Maximum Absorbance |
| 1 | 1 | 1 | 1 | 1 | 1 | 1.075 |
| 2 | 1 | 2 | 2 | 2 | 2 | 1.434 |
| 3 | 1 | 3 | 3 | 3 | 3 | 1.240 |
| 4 | 1 | 4 | 4 | 4 | 4 | 1.361 |
| 5 | 2 | 1 | 2 | 3 | 4 | 0.583 |
| 6 | 2 | 2 | 1 | 4 | 3 | 0.852 |
| 7 | 2 | 3 | 4 | 1 | 2 | 0.775 |
| 8 | 2 | 4 | 3 | 2 | 1 | 0.949 |
2018101606 25 Oct 2018
| 9 | 3 | 1 | 3 | 4 | 2 | 0.628 |
| 10 | 3 | 2 | 4 | 3 | 1 | 0.695 |
| 11 | 3 | 3 | 1 | 2 | 4 | 0.669 |
| 12 | 3 | 4 | 2 | 1 | 3 | 0.654 |
| 13 | 4 | 1 | 4 | 2 | 3 | 0.499 |
| 14 | 4 | 2 | 3 | 1 | 4 | 0.474 |
| 15 | 4 | 3 | 2 | 4 | 1 | 0.545 |
| 16 | 4 | 4 | 1 | 3 | 2 | 0.470 |
b Range analysis
Conduct the range analysis (Table 4) on the above experimental results.
The results of factor A are good at level 2, factor B at level 2, factor C at level 4, factor D at level 2, and factor E at level 2. Therefore, the theoretical optimal combination is A2B2C4D2E2 (that is, the ratio of sample to KBr is
1:100, particle size is 100 meshes, number of scans is 64 times, resolution is 4 cm'1, and pressure is 1.3 T). The R values indicate that the sequence of various factors by the impact on the absorbance is: ratio > particle size > resolution > number of scans > pressure.
Table 4 Results of Range Analysis of Mid-infrared Orthogonal Test
| K Value | A (Ratio of Sample:KBr) | B (Particle Size) | C (Number of Scans) | D (Resolution) | E Pressure (TONs) |
| K1 | 5.110 | 2.785 | 3.066 | 2.978 | 3.264 |
| K2 | 3.159 | 3.455 | 3.216 | 3.551 | 3.307 |
| K3 | 2.646 | 3.229 | 3.291 | 2.988 | 3.245 |
| K4 | 1.988 | 3.434 | 3.33 | 3.386 | 3.087 |
| K1 average | 1.278 | 0.696 | 0.767 | 0.745 | 0.816 |
| K2 average | 0.790 | 0.864 | 0.804 | 0.888 | 0.827 |
| K3 average | 0.662 | 0.807 | 0.823 | 0.747 | 0.811 |
| K4 average | 0.497 | 0.859 | 0.833 | 0.847 | 0.772 |
| R | 0.781 | 0.168 | 0.066 | 0.143 | 0.055 |
c Comparison of the theoretical optimal combination and actual optimal combination
The correlation coefficients of the results in three tests of the theoretical optimal combination are 1.0000, 0.9970 and 0.9903, respectively, and RSD is is 0.501 %; the correlation coefficients of the results in three tests of the actual optimal combination are 1.000, 0.9955 and 0.9954, respectively, and the RSD is 0.264 %, indicating that the actual optimal combination has good repeatability. In addition, FIG.2 indicates that there are more burrs in the theoretical spectrogram and the absorbance is not as high as that of
2018101606 25 Oct 2018 the actual spectrogram, so the actual optimal condition is selected in the experiment.
Because pressure has the least influence on the absorbance, and the influence of the ratio of sample to KBr on the spectrogram is 14.2 times that of absorbance, the pressure of pressing tablet is set to 1.9 T for better tablet pressing effects and saving of tablet pressing time. That is to say, the conditions for collecting the infrared one-dimensional spectrogram in the test are: ratio of sample to KBr is 1:100, particle size is 200 meshes, number of scans is 32 times, resolution is 4 cm’1, and pressure is 1.9 T.
io d Variance analysis
If there is no blank term in the orthogonal test, the factor with the minimum range is generally taken as the error term. As the ranges of factors C and D are small and the difference between them is not great, C (number of scans) and E (pressure) are taken as the error terms herein. The variance is analysis (Table 5) indicates that the ratio of sample to KBr has an extremely significant influence on the result, the particle size and resolution have a significant influence on the result, while the number of scans and pressure have little influence on the result. F values indicate that the influence of each factor on the result is: ratio > particle size > resolution > 20 number of scans > pressure, which is consistent with the result of range analysis.
Table 5 Results of Variance Analysis of Mid-infrared Orthogonal Test
| Factor | Degree | Sum of | Me | F | Significa | F0. | F0. |
| Ratio | 3 | 1.356 | 0.4 | 150. | 4.7 | 9.7 | |
| Particle | 3 | 0.073 | 0.0 | 8 | * | ||
| Number | 3 | 0.010 | 0.0 | 1 | |||
| Resoluti | 3 | 0.062 | 0.0 | 7 | * | ||
| Pressure | 3 | 0.007 | 0.0 | 0.67 | |||
| Error | 6 | 0.017 | 0.0 |
Note: ** denotes that the influence of the factor on the result is extremely significant,* denotes that the influence of the factor on the result is significant.
1.1.2 Methodological validation
Take Meconopsis quintuplinervia Regel as the sample, conduct the validation under the spectrum collection conditions determined by the preliminary experiment, that is, the ratio of sample to KBr is 1:100, particle
2018101606 25 Oct 2018 size is 200 meshes, number of scans is 32 times, resolution is 4 cm'1, and pressure is 1.9 T.
a Repeated test
Press tablets of the same sample for six successive times. With one of the times as the standard, the correlation coefficients of the six times are 1.0000, 0.9953, 0.9901,0.9865, 0.9845 and 0.9824, respectively, and RSD is 0.682 %, indicating good repeatability of the test.
b Precision test
Measure the same tablet of the same sample for six successive times.
io With one of the times as the standard, the correlation coefficients of the six times are 1.0000, 0.9927, 0.9926, 0.9882, 0.9885 and 0.9867, and RSD is 0.490 %, indicating good precision of the test.
c Stability test
Press one tablet of a sample, store it in a dryer, and measure it every one is hour for six times in total. With one of the times as the standard, the correlation coefficients of the six times are 1.0000, 0.9969, 0.9924, 0.9903, 0.9901 and 0.9885, and RSD is 0.452%, indicating good stability of the test.
Experimental results show that the method for collecting mid-infrared spectra can effectively collect mid-infrared spectra of Meconopsis.
1.2 Collection of infrared spectra
According to the conditions for collection of mid-infrared one-dimensional spectra selected in the orthogonal test, collect the mid-infrared one-dimensional spectrogram after KBr tablet pressing and sample preparation from powder (0.0030 g) of various Meconopsis quintuplinervia Regel samples. Scan each sample for three times, immediately remove the background interference of water and CO2 during scanning, with the range of spectrum collection of 4000-400 cm'1.
1.3 Selection of different model processing methods
Take the modeling of whole plant samples as an example, model the average spectrograms of whole plants of Meconopsis quintuplinervia Regel in the Qinghai Huzhu Bomuxia and Binggou Daban regions, select the band of 4000-500cnT1, and build a discriminative model of the geographical origins of Meconopsis quintuplinervia Regel for the original mid-infrared spectra. There are totally 37 whole plant samples in Huzhu
2018101606 25 Oct 2018
Bomuxia, 27 for training sets and 10 for validation sets; there are totally 15 whole plant samples in Binggou Daban, 12 for training sets and 3 for validation sets. The discrimination methods used in modeling are the qualitative analysis methods that come with the software -- discriminant analysis and distance match.
As shown in FIG.3, the difference between the whole plant discrimination models established by the two processing methods is not great, and the contribution rates of the first three principal components to the constructed model are the same (Table 6). Moreover, there is no difference in the io overall prediction rate of the model, but the models built by different methods have varied predictive effects of the two places, in particular, the prediction rate of one place processed by discriminant analysis is 66.67%, while tthat processed by distance match is 33.33% (Table 7). As a result, discriminant analysis is recommended.
Table 6 Contribution Rates of the First Three Principal Components to the Built Whole Plant Discrimination
Model by Different Processing Methods
| Discriminant | Principal | Contribution Rate % | Cumulative Contribution |
| Analysis | Component | Rate % | |
| 1 | 57.871 | 57.871 | |
| 2 | 22.459 | 80.330 | |
| 3 | 7.818 | 88.148 | |
| Distance Match | Principal | Contribution Rate % | Cumulative Contribution |
| Component | Rate % | ||
| 1 | 57.871 | 57.871 | |
| 2 | 22.459 | 80.330 | |
| 3 | 7.818 | 88.148 |
Table 7 Effects of Built Whole Plant Discrimination Model by Different Processing Methods
| Discrimi nant Analysi s | Bomuxia % | Binggou Daban % | Total % | Distanc e Match | Bomuxia % | Binggou Daban % | Total % |
| Recogn ition | 100 | 100 | 100 | Recogni tion | 100 | 83.33 | 94.87 |
2018101606 25 Oct 2018
| Rate | Rate | ||||||
| Predicti on Rate | 90 | 66.67 | 84.62 | Predicti on Rate | 100 | 33.33 | 84.62 |
1.4 Selection of different spectrogram processing methods
Take the whole plant of Meconopsis quintuplinervia Regel as the sample, model the average spectrum of Meconopsis quintuplinervia Regel whole plants collected in the above two regions in Qinghai using TQ software, 5 select the band of 4000-500cmcnT1, process by using the discriminant analysis method, and check the differences between discrimination models built by different spectral processing methods. There are totally 52 samples in the two places, 39 for training sets and 13 for validation sets.
Spectrogram processing methods include original spectrogram, logarithm io (that is, Log10), first derivative and second derivative.
As shown in FIG.4, the results of the four spectrogram processing methods are different: The model classifications of a and b processing methods are good, and in c and d processing methods, classifications in both places overlap; the contribution rate of the first three principle components to the is model in first and second derivatives methods is low, especially in the second derivative method, the contribution rate is 38.213%, while the contribution rate of the first three principal components to the model in the original spectrogram method is the same as that in the Log10 processing method (Table 8); the prediction rates of the discrimination model of
Meconopsis quintuplinervia Regel in both places are the same, but the recognition rates are slightly different (Table 9).
Table 8 Contribution Rates of the First Three Principal Components to the Built Whole Plant Discrimination
Model by Different Processing Methods
| Original | Principal | Contribution Rate % | Cumulative Contribution |
| Spectrogram | Component | Rate % | |
| 1 | 57.871 | 57.871 | |
| 2 | 22.459 | 80.330 | |
| 3 | 7.818 | 88.148 | |
| Log 10 | Principal | Contribution Rate % | Cumulative Contribution |
| Component | Rate % | ||
| 1 | 57.871 | 57.871 |
2018101606 25 Oct 2018
| 2 3 | 22.459 7.818 | 80.330 88.148 | |
| First Derivative | Principal | Contribution Rate % | Cumulative Contribution |
| Component | Rate % | ||
| 1 | 22.108 | 22.108 | |
| 2 | 20.633 | 43.092 | |
| 3 | 8.57 | 51.662 | |
| Second Derivative | Principal | Contribution Rate % | Cumulative Contribution |
| Component | Rate % | ||
| 1 | 22.108 | 16.649 | |
| 2 | 13.615 | 30.264 | |
| 3 | 7.949 | 38.213 |
Table 9 Effects of Whole Plant Discrimination Model Built in Different Spectrogram Processing Methods
| Original | Total % | Log 10 | Bomuxia % | Binggou Daban % | Total % | ||
| Spectra | Bomuxia % | Binggou Daban % | |||||
| gram | |||||||
| Recogn | Recogni | 100 | 91.67 | 97.44 | |||
| ition | 100 | 100 | 100 | tion | |||
| Rate | Rate | ||||||
| Predicti | Predict! | 90 | 66.67 | 84.62 | |||
| 90 | 66.67 | 84.62 | |||||
| on Rate | on Rate | ||||||
| First | Bomuxia | Binggou | Total | Second | Bomuxia | Binggou | Total % |
| Derivati | % | Daban % | % | Derivati | % | Daban % | |
| ve | ve | ||||||
| Recogn | 96.30 | 100 | 97.44 | Recogni | 92.59 | 91.67 | 92.31 |
| ition | tion | ||||||
| Rate | Rate | ||||||
| Predicti | 100 | 66.67 | 92.31 | Predict! | 100 | 66.67 | 92.31 |
| on Rate | on Rate |
1.5 Model building with different parts
1.5.1 Whole plant discrimination model building for geographical origins
Take samples from Qinghai Huzhu Bomuxia and Qilian Binggou Daban regions as the examples, use the TQ Analyst software, import the collected
2018101606 25 Oct 2018 mid-infrared one-dimensional spectrogram data of Meconopsis quintuplinervia Regel whole plants from Huzhu Bomuxia and Binggou Daban regions into the software for modeling (FIG.5), and use the modeling band of 4000-500 cm'1. There are totally 37 whole plant samples 5 in Huzhu Bomuxia, 27 fortraining sets and 10 for validation sets; there are totally 15 whole plant samples in Binggou Daban, 11 for training sets and 4 for validation sets. The discrimination method used in modeling is a qualitative analysis method that comes with the software -- discriminant analysis. As known from the model, the classifications of samples from the io two places are obvious, the prediction rate of the model is calculated based on the validation results (Table 10). The prediction rate of Bomuxia is 90%, that of Binggou Daban is 75%, and the total model prediction rate is up to 85.71%.
Table 10 Validation Results of Mid-infrared Discrimination Model of Geographical Origins in Whole Plants from
Bomuxia and Binggou Daban
| Bomuxia | Binggou Daban | Total | |
| Recognition Rate (%) | 100 | 90.91 | 97.37 |
| Prediction Rate (%) | 90 | 75 | 85.71 |
Import the mid-infrared one-dimensional spectrum of Meconopsis quintuplinervia Regel whole plants into the PC-ORD software, use correlation for distance measurement, with Median as the inter-group contact method, and obtain the clustering result of the whole plants (FIG.6).
As shown in the figure, there are two discriminant errors of Binggou Daban and eight discriminant errors of Bomuxia, and the total accuracy rate of clustering is 80.77%.
Table 11 Accuracy Rate of Clustering of Whole Plants from Bomuxia and Binggou Daban
| Number of Samples | Number of Errors | Accuracy Rate % | |
| Bomuxia | 37 | 8 | 78.38 |
| Binggou Daban | 15 | 2 | 86.67 |
| Total | 52 | 10 | 80.77 |
The results show that discriminant analysis of geographical origins can also be conducted on Meconopsis quintuplinervia Regel using hierarchical
2018101606 25 Oct 2018 cluster analysis, but the discrimination effect is inferior to that of the built discrimination model of geographical origins, and therefore the method for discriminating the geographical origins of Meconopsis quintuplinervia Regel is finally determined as building a discrimination model of the geographical origins by using the discriminant analysis method.
1.5.2 Flower discrimination model building for geographical origins Still use the TQ software to model the average spectrogram of Meconopsis quintuplinervia Regel flowers from Qinghai Huzhu Bomuxia and Binggou Daban regions (FIG.7), select the band of 4000-500cm'1, and build the io flower discrimination model of geographical origins of Meconopsis quintuplinervia Regel for the original mid-infrared spectra. There are totally 34 flower samples in Huzhu Bomuxia, 24 for training sets and 10 for validation sets; there are totally 24 flower samples in Binggou Daban, 17 fortraining sets and 7 for validation sets. The discrimination method used is in modeling is a qualitative analysis method that comes with the software -discriminant analysis. As known from the model, the classifications of samples from the two places are obvious, the prediction rate of the model is calculated based on the validation results (Table 12). The prediction rate of Bomuxia is 80%, that of Binggou Daban is 100%, and the total model prediction rate is up to 88.24%.
Table 12 Validation Results of Mid-infrared Discrimination Model of Geographical Origins in Flowers from Bomuxia and Binggou Daban
| Bomuxia | Binggou Daban | Total | |
| Recognition Rate (%) | 95.83 | 100 | 97.56 |
| Prediction Rate (%) | 80 | 100 | 88.24 |
Import the mid-infrared one-dimensional spectrum of Meconopsis quintuplinervia Regel flowers into the PC-ORD software, use correlation for distance measurement, with Median as the inter-group contact method, and obtain the clustering result of the flowers (FIG.8). As shown in the figure, there are seven discriminant errors of Binggou Daban and six discriminant errors of Bomuxia, and the total accuracy rate of clustering is 77.57 %.
Table 13 Accuracy Rate of Clustering of Flowers from Bomuxia and Binggou Daban
| Number of Samples | Number of Errors | Accuracy Rate % | |
| Bomuxia | 34 | 6 | 82.35 |
Binggou Daban
Total
70.83
13 77.57
1.5.3 Steam discrimination model building for geographical origins Use the TQ software to model the average spectrum of Meconopsis quintuplinervia Regel stems from Huzhu Bomuxia and Binggou Daban regions (FIG.9), select the band of 4000-500cm'1, and build the discrimination model of geographical origins of Meconopsis quintuplinervia Regel for the original mid-infrared spectra. There are totally 33 stem samples in Huzhu Bomuxia, 23 fortraining sets and 10 for validation sets; there are totally 24 stem samples in Binggou Daban, 16 for training sets and 8 for validation sets. The discrimination method used in modeling is a qualitative analysis method that comes with the software -- discriminant analysis. As known from the model, the classifications of samples from the two places are obvious, the prediction rate of the model is calculated based on the validation results (Table 14). The prediction rate of Bomuxia is 80%, that of Binggou Daban is 87.5%, and the total model prediction rate is up to 83.33%.
Table 14 Validation Results of Mid-infrared Discrimination Model of Geographical Origins in Stems from Bomuxia and
Binggou Daban
| Binggou | |||
| Bomuxia % | Daban % | Total % | |
| Recognition Rate | 95.65 | 100 | 97.44 |
| Prediction Rate | 80 | 87.5 | 83.33 |
Import the mid-infrared one-dimensional spectrum of Meconopsis quintuplinervia Regel stems into the PC-ORD software, use correlation for distance measurement, with Median as the inter-group contact method, and obtain the clustering result of the stems (FIG. 10). As shown in the figure, there are 16 discriminant errors of Binggou Daban and 6 discriminant errors of Bomuxia, and the total accuracy rate of clustering is only 61.40%.
Table 15 Accuracy Rate of Clustering of Stems from Bomuxia and Binggou Daban
| Number of Samples | Number of Errors | Accuracy Rate % | |
| Rnmi i via | 33 | a | R1 R2 |
| Rinnnou Daban | 24 | 16 | 33 33 |
| Jalal | az | 22 | alia |
1.5.4 Leaf discrimination model building for geographical origins
Use the TQ software to model the average spectrogram of Meconopsis quintuplinervia Regel leaves from Huzhu Bomuxia and Binggou Daban regions (FIG.11), select the band of 4000-500cm'1, and build the discrimination model of geographical origins of Meconopsis quintuplinervia Regel for the original mid-infrared spectra. There are totally 34 leaf samples in Huzhu Bomuxia, 24 for training sets and 10 for validation sets; there are totally 25 leaf samples in Binggou Daban, 17 for training sets and 8 for validation sets. The discrimination method used in modeling is a qualitative analysis method that comes with the software - discriminant analysis. As known from the model, the classifications of samples from the two places are obvious, the prediction rate of the model is calculated based on the validation results (Table 16). The prediction rate of Bomuxia is 100%, that of Binggou Daban is 87.5%, and the total model prediction rate is up to 94.44%.
Table 16 Validation Results of Mid-infrared Discrimination Model of Geographical Origins in Leaves from Bomuxia and Binggou Daban
| Binggou | |||
| Bomuxia % | Daban % | Total % | |
| Recognition Rate | 95.83 | 100 | 97.56 |
| Prediction Rate | 100 | 87.5 | 94.44 |
Import the mid-infrared one-dimensional spectrum of Meconopsis quintuplinervia Regel leaves into the PC-ORD software, use correlation for distance measurement, with Median as the inter-group contact method, and obtain the clustering result of the leaves (FIG. 12). As shown in the figure, there are seven discriminant errors of Binggou Daban and one discriminant error of Bomuxia, and the total accuracy rate of clustering is 86.44 %.
Table 17 Accuracy Rate of Clustering of Leaves from Bomuxia and Binggou Daban
2018101606 25 Oct 2018
| Number of Samples | Number of Errors | Accuracy Rate % | |
| Bomuxia | 34 | 1 | 97.06 |
| Binggou Daban | 25 | 7 | 72.00 |
| Total | 59 | 8 | 86.44 |
1.5.5 Comparison of discrimination models of geographical origins with different Meconopsis quintuplinervia Regel parts
Prediction results (Table 18) of the models built with different Meconopsis quintuplinervia Regel parts indicate that when the respective effects of the two sampling points of Bomuxia and Binggou Daban are analyzed, both the recognition rate and prediction rate of the models built using different parts vary with the place, but generally speaking, the difference in prediction rate is greater than that in recognition rate; the difference io between recognition rates of both places in the models built with flowers, stems and leaves is not great, and the gap between the models built with whole plants is great with the other three models. It is found in analyzing the overall effect of the models that the overall recognition rate of the four models is basically the same, the prediction rate of the leaf model is the is best, and there is no much difference in the predictive effects of the remaining three models. Considering the effects of the models built with different parts, the leaf is selected to build the discrimination model of geographical origins.
Table 18 Comparison of Validation Results for Discrimination Models of Geographical Origins with Different
Meconopsis Quintuplinervia Regel Parts
| BMX | BGDB | Total | ||
| Whole | Recognition Rate | 100 | 90.91 | 97.37 |
| Plant | Prediction Rate | 90 | 75 | 85.71 |
| Flower | Recognition Rate | 95.83 | 100 | 97.56 |
| Prediction Rate | 80 | 100 | 88.24 | |
| Stem | Recognition Rate | 95.65 | 100 | 97.44 |
| Prediction Rate | 80 | 87.5 | 83.33 | |
| Leaf | Recognition Rate | 95.83 | 100 | 97.56 |
| Prediction Rate | 100 | 87.5 | 94.44 |
2018101606 25 Oct 2018
1.5.6 Comparison of cluster analysis with different Meconopsis quintuplinervia Regel parts
The mid-infrared spectra of different Meconopsis quintuplinervia Regel parts obtain different cluster results under the same data processing conditions (use correlation for distance measurement, with Median as the inter-group contact method), the accuracy rates of clustering are also different, in the sequene of leaf (86.44%) > whole plant (78.85%) > flower (77.57%) > stem (61.40%) by the accuracy rate in descending order, the accuracy rate of clustering with the leaf part is the highest, which is the io same as the analysis result of the discrimination model of geographical origins.
1.6 Conclusions
1.6.1 Optimization of spectra collection
Considering the factors affecting mid-infrared spectra collection and after is tests and range analysis, the collection conditions of mid-infrared one-dimensional spectrogram are finally determined: the ratio of sample to KBr is 1:100, particle size is 200 meshes, number of scans is 32 times, resolution is 4 cm'1, and pressure is 1.9 T.
1.6.2 Selection of the modeling method
Use the qualitative analysis methods - Discriminant analysis and distance match, which come with the TQ Analyst software, take whole plants as examples, and investigate the effects of models built in the two methods. As a result, the model built in the discriminant analysis method has better effects.
1.6.3 Selection of the spectra processing method
Take whole plants as samples and model in the discriminant analysis method to check the modeling effects of different spectrogram processing methods - original spectrogram, logarithm (i.e. Log10), first Derivative and second derivative. The results show that the model built in the original 30 spectrogram method has better effects.
1.6.4 Results of modeling with different parts
Discrimination models of geographical origins are built with the whole plant, flower, stem and leaf parts of Meconopsis quintuplinervia Regel, and the results show that the model built with the leaf part has the best effect; at the 35 same time, the full spectrum absorbance data with the four parts is used for
2018101606 25 Oct 2018 cluster analysis, and as a result, the leaf part has the best clustering effect, which is consistent with the result of the discrimination model of geographical origins. It is finally determined to build the discrimination model of geographical origins with the leaf part. It will not destroy the primitive ecological environment and subsequent resource breeding of the plant resources, and will help to protect its primitive ecological environment and germplasm resources. This matches the current concept of giving priority to ecological environmental protection and has a good value of promotion and application.
Claims (10)
1) Take a Meconopsis quintuplinervia Regel sample to be detected, detect it using the method described in any of claims 1—3, and collect mid-infrared spectrum data;
2018101606 25 Oct 2018
(1) Take Meconopsis quintuplinervia Regel samples from different geographical origins, detect them using the method described in any of claims 1—3, and collect mid-infrared spectrum data;
1. A mid-infrared method for detecting Meconopsis quintuplinervia Regel, comprising the following steps: take a Meconopsis quintuplinervia Regel sample, mix it with KBr, form tablets, and collect mid-infrared spectrum data; where the ratio of the sample to KBr is 1:100, the particle size is 200 meshes, the number of scans is 32, the resolution is 4 cm'1, and the pressure is 1.9 T.
2) Import the mid-infrared spectrum data of the Meconopsis quintuplinervia Regel to be detected into a model described in claim 7, and make a judgment.
(2) Import the collected mid-infrared spectrum data into TQ software, analyze the data using the Discriminant analysis method, and obtain a model.
2. The detection method according to claim 1, wherein the collection range of the spectra is 4000-400 cm'1
3. The detection method according to claim 1, wherein the Meconopsis quintuplinervia Regel sample is the whole plant, flower, stem or leaf of Meconopsis quintuplinervia Regel.
4. A modeling method for identification of geographical origins of Meconopsis quintuplinervia Regel, comprising the following steps:
5. The detection method according to claim 4, wherein the Meconopsis from different geographical origins is the Meconopsis quintuplinervia Regel from highland areas in Qinghai.
6. The detection method according to claim 4, wherein the Meconopsis quintuplinervia Regel sample is the whole plant, flower, stem or leaf of Meconopsis quintuplinervia Regel.
7. The identification models of the geographical origins of Meconopsis quintuplinervia Regel that are built and obtained using the method described in any of claims 4~6.
8. A method for identifying the geographical origins of Meconopsis quintuplinervia Regel, comprising the following steps:
9. The detection method according to claim 8, wherein the Meconopsis from different geographical origins is the Meconopsis quintuplinervia Regel from highland areas in Qinghai.
10. The detection method according to claim 8, wherein the Meconopsis quintuplinervia Regel sample is the whole plant, flower, stem or leaf of Meconopsis quintuplinervia Regel.
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