CN113376119A - Near-infrared online quality detection method for bighead atractylodes rhizome - Google Patents
Near-infrared online quality detection method for bighead atractylodes rhizome Download PDFInfo
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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
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
Abstract
The invention discloses a near-infrared online quality detection method for bighead atractylodes rhizome, which comprises the following steps of (1) sample preparation: taking samples of the rhizoma atractylodis macrocephalae decoction pieces of different batches; (2) collecting near infrared spectrum data: collecting and recording a near-infrared spectrogram of a white atractylodes rhizome sample and a near-infrared spectrogram of the white atractylodes rhizome after being powdered; (3) preprocessing the spectral data: respectively adopting an original spectrum, a first order derivation, a second order derivation, a multivariate scattering correction, a vector normalization, a convolution smoothing filter, a multivariate scattering correction and a vector normalization, a convolution smoothing filter and a multivariate scattering correction, and a convolution smoothing filter and a vector normalization to preprocess the near infrared spectrum data of the bighead atractylodes rhizome sample before and after powdering; (4) and establishing a quantitative correction model of the bighead atractylodes rhizome by adopting a partial least squares regression method. The method is quick and simple to operate, and the established model is accurate and reliable and can be used for quantitative analysis of the extract and the water content in the rhizoma atractylodis macrocephalae decoction pieces.
Description
Technical Field
The invention belongs to the technical field of medicinal material detection; in particular to a near infrared quality detection method of largehead atractylodes rhizome based on a partial least squares regression method (PLSR).
Background
The Atractylodis rhizoma is Atractylodes macrocephala Koidz of CompositaeAtractylodes macrocephalaKoidz, dried rhizome, warm in nature, has diuretic, anti-inflammatory and anti-aging effects, and is mainly used for treating spleen deficiency, abdominal distension, edema, spontaneous perspiration and other symptoms. Mainly produced in Anhui, Zhejiang and Hebei provinces. Recent research proves that the components of the atractylodes macrocephala polysaccharide, the sesquiterpene and the volatile oil are main pharmacological active components. The white atractylodes rhizome in China is widely distributed, has disordered varieties and has mixed germplasm, so that the improvement of the quality detection of the white atractylodes rhizome is an urgent problem at present.
At present, the bighead atractylodes rhizome is recorded in the ' pharmacopoeia of the people's republic of China 2020 edition ', and the quality control of the bighead atractylodes rhizome is mainly qualitative identification, and no quantitative detection and fingerprint spectrum research of specific chemical components exist. The existing quality control of the white atractylodes rhizome is mainly focused on two aspects of thin-layer identification and high performance liquid chromatography fingerprint spectrum of the white atractylodes rhizome medicinal material. TLC is the most common method for identifying the medicinal material of bighead atractylodes rhizome and its patent drugs. In the experiment, the thin-layer chromatography is adopted to detect the bighead atractylodes rhizome, and most of the white atractylodes rhizome is extracted by using lower organic solvents such as diethyl ether, petroleum ether, normal hexane and the like, and petroleum ether-ethyl acetate, cyclohexane-chloroform-ethyl acetate and the like are used as development systems. HPLC is the most common quality control method for rhizoma atractylodis macrocephalae, the method is rapid and sensitive, but rhizoma atractylodis macrocephalae medicinal materials in different producing areas are difficult to distinguish, and the repeatability is poor.
The invention adopts a near infrared quality detection method based on a cheat least squares regression method (PLSR), establishes an extract production online detection system through links such as raw material quality detection, extraction process detection and the like, can effectively solve the defects of inconvenient sampling, low efficiency, environmental pollution and the like in production detection, and improves the product quality; meanwhile, the method provides guidance for reaction termination, reduces energy consumption and realizes green production of the extract.
Disclosure of Invention
In order to solve the technical problems, the invention provides a near-infrared quality detection method for bighead atractylodes rhizome based on a partial least squares regression method (PLSR). The method is rapid and simple to operate, and the established model is accurate and reliable and can be used for quantitative analysis of the extract and the water content of the rhizoma atractylodis macrocephalae decoction pieces.
In order to achieve the above purpose, the technical scheme provided by the invention is as follows:
a near-infrared online quality detection method for bighead atractylodes rhizome comprises the following steps:
(1) sample preparation: taking samples of the rhizoma atractylodis macrocephalae decoction pieces of different batches;
(2) collecting near infrared spectrum data: collecting and recording a near-infrared spectrogram of a white atractylodes rhizome sample and a near-infrared spectrogram of the white atractylodes rhizome after being powdered;
(3) preprocessing the spectral data: respectively adopting an original Spectrum (Spectrum), a First derivative (1 stD), a Second derivative (2 stD), a Multivariate Scattering Correction (MSC), a vector normalization (SNV), a convolution smoothing filter (S-G), a multivariate scattering correction + vector normalization, a convolution smoothing filter + multivariate scattering correction, a convolution smoothing filter + vector normalization, and a pretreatment on near infrared Spectrum data of the bighead atractylodes rhizome sample before and after powdering;
(4) and establishing a quantitative correction model of the bighead atractylodes rhizome by adopting a partial least squares regression method.
Preferably, in the above method for detecting the near-infrared online quality of the atractylodes macrocephala koidz, the method for collecting the near-infrared spectrum in the step (2) is as follows: taking 10g of the pulverized rhizoma atractylodis macrocephalae sample, adding the powder into a quartz sample tube, and filling and flattening the sample; selecting a flat sample from the unfired sample, and enabling the sample to be in full contact with a near-infrared diffuse reflection optical fiber probe, wherein the test environment temperature is 25 ℃, and the relative humidity is 45% -60%; taking the background built in the instrument as a referenceComparing, and deducting background; the collection mode is diffuse reflection of an integrating sphere, and the wave number range is 12000-4000 cm-1Resolution of 8cm-1The number of scanning times is 64, each sample is scanned for 2 times, and the average spectrum is taken as the near infrared spectrum of the sample.
PLSR model optimization:
the quantitative model design of the near infrared spectrum adopts Python programming language, the integrated development environment is Pycharm consistency, and the operating system is Windows 7.
The original spectrum is preprocessed before the quantitative correction model is established, so that the influence of a plurality of factors such as high-frequency noise, scattered light, stray light, a sample state, instrument response and the like in the measuring process is avoided. The spectrum preprocessing can remove unnecessary information and improve the prediction accuracy of the model. The spectrum preprocessing method adopted by the model comprises the following steps: raw spectra (Spectrum), First derivative (1 stD), Second derivative (2 stD), Multivariate Scattering Correction (MSC), vector normalization (SNV), convolution smoothing filter (Savitzky-Golay filter, S-G), multivariate scattering correction + vector normalization, convolution smoothing filter + multivariate scattering correction, convolution smoothing filter + vector normalization.
And a proper spectrum waveband is selected, so that redundant information in the spectrum can be reduced, and the prediction accuracy of the model is improved. Meanwhile, when the PLSR method is used for modeling, different principal component numbers have great influence on the model prediction result. If the number of principal components is too high, an "overfitting" phenomenon occurs, but if the number of principal components is too small, the spectrum information used is too small. Taking R value, Root Mean Square Error (RMSE) and corrected mean square error (RMSEC) as indexes, the optimal pretreatment method of the powder-extract is convolution smoothing filtering, and the optimal spectral band is 8135.524-7753.629 cm-1 The number of selected main components is 2; the optimal pretreatment method of the powder and the water is an original spectrum, and the optimal spectrum band is 9099.906-6210.618 cm-1 The selected number of the main components is 5; the optimal pretreatment method of the decoction piece-extract is an original spectrum, and the optimal spectrum band is 8521.277-8139.381 cm-1 The selected number of the main components is 4; optimal pretreatment method for decoction pieces and waterThe method comprises convolution smoothing filtering and multivariate scattering correction, and the optimal spectral band is 7942.647-7753.629 cm-1 The number of selected principal components is 5. Preferred calibration models and evaluation parameters are shown in table 1.
TABLE 1 PLSR model and evaluation parameters
| Model (model) | Pretreatment method | Spectral band cm-1 | Number of major components | Correcting for R | RSMEP％ | RSMEC％ |
| Powder-extract | S-G | 8135.524～7753.629 | 2 | 0.337 | 2.95 | 6.53 |
| Powder-moisture | Spectrum | 9099.906～6210.618 | 5 | 0.908 | 0.3 | 0.45 |
| Decoction piece-extract | Spectrum | 8521.277～8139.381 | 4 | 0.488 | 2.71 | 6.15 |
| Decoction pieces-water content | S-G+MSC | 7942.647～7753.629 | 5 | 0.505 | 0.31 | 0.92 |
Compared with the prior art, the invention has the technical advantages that:
1. compared with the traditional empirical identification and index component content measurement, the quality control of the bighead atractylodes rhizome provided by the invention provides a new quality detection method, and the quality of the bighead atractylodes rhizome can be evaluated in real time, quickly and nondestructively.
2. According to the method, a Fourier transform Near Infrared (NIR) analysis technology is adopted to collect near infrared spectrograms before and after the rhizoma atractylodis macrocephalae decoction pieces are pulverized, a quantitative analysis model of the rhizoma atractylodis macrocephalae is established by a Partial Least Squares Regression (PLSR) method, and extract and water can be accurately measured. Verification results show that the method provided by the invention is quick and simple to operate, does not need extraction, does not lose medicinal materials, is accurate and reliable in the established model, and can be used for quantitative analysis of the content of extracts and water in the rhizoma atractylodis macrocephalae decoction pieces.
Drawings
FIG. 1 is a near infrared spectrum of pulverized Atractylodis rhizoma decoction pieces;
FIG. 2 is a near infrared spectrum of Atractylodis rhizoma decoction pieces directly measured without powdering.
Detailed Description
The present invention will be described below with reference to specific examples to make the technical aspects of the present invention easier to understand and grasp, but the present invention is not limited thereto. The experimental methods described in the following examples are all conventional methods unless otherwise specified; the reagents and materials are commercially available, unless otherwise specified.
Example 1
1. Experimental Material
1.1 Experimental drugs
The total amount of rhizoma Atractylodis Macrocephalae decoction pieces is 100, and all decoction pieces are from Nanjing Haiyuan Chinese medicinal decoction piece Co.
1.2 laboratory instruments and reagents
Bruker-sensor 37 fourier transform mid-ir and near-ir spectrometer, including OPUS5.0 software, Pbs detector (Bruker, germany); waters e2695 high performance liquid chromatograph (Waters corporation, USA) Waters2998 ultraviolet detector; one in ten thousand balance BSA2245-CW (Beijing Saedodus scientific instruments, Inc.); a one-hundred-thousandth balance model AG-285 (METTLER TOLEDO, Switzerland); KY-500E ultrasonic cleaner (Kunshan ultrasonic Instrument Co., Ltd.); HH-6 digital display constant temperature water bath (Changzhou national electric appliance Co., Ltd.); Milli-Q ultra pure water instruments (Millipore, USA); GeneSpeed X1 microcentrifuge (International trade for genetic Biotechnology, Shanghai, Inc.).
The absolute ethanol is analytically pure (Susheng chemical Co., Ltd., Sn-free city).
2. Experimental methods and results
2.1 acquisition of the near Infrared Spectrum
All 100 batches of largehead atractylodes rhizome decoction pieces are pulverized and sieved by a No. 5 sieve, and the pulverized and non-pulverized near-infrared spectrograms of the 100 batches of largehead atractylodes rhizome decoction pieces are respectively measured. Adding about 10g of the pulverized sample into a quartz sample tube, and filling and flattening the sample; and selecting a flat sample from the samples which are not pulverized, so that the sample can be fully contacted with the near-infrared diffuse reflection optical fiber probe. The test environment temperature is 25 ℃, and the relative humidity is 45-60%. And taking the background in the instrument as a reference, and subtracting the background. The collection mode is diffuse reflection of an integrating sphere, and the wave number range is 12000-4000 cm-1Resolution of 8cm-1The number of scanning times is 64, each sample is scanned for 2 times, and the average spectrum is taken as the near infrared spectrum of the sample. The near infrared spectra of pulverized and non-pulverized Atractylodis rhizoma decoction pieces are shown in FIG. 1 and FIG. 2, respectively.
2.2 measurement of extract
Hot dipping method: taking about 4g of rhizoma atractylodis macrocephalae sample, precisely weighing, placing in a 250ml conical flask, precisely adding 100ml of 60% ethanol, sealing, weighing, standing for 1 hour, connecting with a reflux condenser tube, heating to boil, and keeping slightly boiling for 1 hour. After cooling, the flask was taken off, the stopper was closed, the weight was again weighed, the lost weight was made up with alcohol, shaken well, filtered through a drying filter, 25ml of the filtrate was measured precisely, placed in an evaporation dish dried to constant weight, dried on a water bath, dried at 105 ℃ for 3 hours, placed in a desiccator for 30 minutes, and the weight was quickly weighed precisely. The extract content (%) in the test article was calculated as a dry article unless otherwise specified.
2.3 moisture determination of Atractylodis rhizoma decoction pieces
The moisture content of 100 batches of largehead atractylodes rhizome decoction pieces is measured according to a four-part moisture measurement method (general rule 0832) of the 2015 edition of Chinese pharmacopoeia and a second drying method.
Establishment of near infrared spectrum quantitative model of bighead atractylodes rhizome
3.1 modeling result after adding SPXY Algorithm
Partial least squares regression, PLSR: and (3) determining the number of main components and an optimal preprocessing method (a correction set and a verification set are obtained by adopting a spxy algorithm, wherein the proportion of the correction set is 80 percent, and the proportion of the verification set is 20 percent). Solving the prediction set R under different expansion times and different numbers of principal components and different preprocessing methods2Value, correction set R2Value, Root Mean Square Error (RMSEP), and corrected mean square error (RMSEC). Prediction set R2The higher the value, the lower the RMSEP value the better the model.
3.1.1 modeling of pulverizing Atractylodis rhizoma and extract content
The parameter is the principal component =2, the preprocessing is convolution smoothing filtering, and the whole spectrum is taken. 80% training, 20% testing. The average absolute error of 20 samples in the test set was 0.026, and the average of the absolute value of the relative error was 0.071. Prediction set R2Value, correction set R2The values, Root Mean Square Error (RMSEP) and corrected mean square error (RMSEC) were 0.090, 0.093, 0.035 and 0.066, respectively.
3.1.2 powdering and moisture modeling of Atractylodes macrocephala
The parameters are taken as principal components =8, the original spectrum is preprocessed, and the whole spectrum is taken. 80% training, 20% testing. The average absolute error of 20 samples in the test set was 0.0025, and the average of the absolute value of the relative error was 0.022. Prediction set R2Value, correction set R2The values, Root Mean Square Error (RMSEP) and corrected mean square error (RMSEC) values were 0.736, 0.945, 0.0029 and 0.0026, respectively.
3.1.3 modeling of Unpowdering and extract content of Atractylodes macrocephala Koidz
The parameters were principal component =5, pre-processing was vector normalization (SNV), and the entire spectrum was taken. 80% training, 20% testing. The mean absolute error of 20 samples in the test set was 0.024, and the mean of the absolute values of the relative errors wasWas 0.074. Prediction set R2Value, correction set R2The values, Root Mean Square Error (RMSEP), and corrected mean square error (RMSEC) values were-0.059, 0.344, 0.057, and 0.034, respectively.
3.1.4 Dougu not powdering and moisture modeling
The parameters were principal component =5, pre-processing was vector normalization (SNV), and the entire spectrum was taken. 80% training, 20% testing. The average absolute error of 20 samples in the test set was 0.0033, and the average of the absolute values of the relative errors was 0.028. Prediction set R2Value, correction set R2The values, Root Mean Square Error (RMSEP), and corrected mean square error (RMSEC) were 0.31, 0.52, 0.0041, and 0.0074, respectively.
3.2 modeling results after adding SPXY segmentation Algorithm
PLSR: and (3) determining an optimal wave band, an optimal main component number and an optimal preprocessing method (a correction set and a verification set are obtained by adopting a spxy algorithm, the proportion of the correction set is 80%, and the proportion of the verification set is 20%). Comparing the prediction set R under different wave bands, different main component numbers and different preprocessing methods2The closer to 1, the better the result.
3.2.1 powdering-extract
When the length of the wave band is 100, the optimal wave band is 1000-2The value was 0.3637. The error of 20 samples in the test set was 0.0229 on average and 0.063 on average for absolute value of relative error. Prediction set R2Value, correction set R2The values, Root Mean Square Error (RMSEP) and corrected mean square error (RMSEC) values were 0.3637, 0.1139, 0.0295 and 0.0653, respectively.
3.2.2 powdering-moisture
When the wave band length is 750, the optimal wave band is 750-2The value is 0.7138. The error of 20 samples in the test set was 0.0025 as the mean absolute error and 0.0215 as the mean absolute value of the relative error. Prediction set R2Value, correction set R2The values, RMSEP and RMSEC were 0.7138, 0.8237, 0.0030 and 0.0045, respectively.
3.3.3 Unpowderized-extract
When the wave band length is 100, the optimal wave band is 900-999, the number of the main components is 4, the preprocessing method is the original spectrum, the result is optimal, and the prediction set R is2The value is 0.3385. The error of 20 samples of the test set was 0.0209 in average absolute error and 0.0627 in average absolute value of relative error. Prediction set R2Value, correction set R2The values, RMSEP and RMSEC values are 0.3385, 0.2380, 0.0271 and 0.0615, respectively.
3.3.4 Unpowdering-moisture
When the wave band length is 50, the optimal wave band is 1050-2The value is 0.6063. The error of 20 samples in the test set is 0.0022 in average absolute error and 0.0190 in average absolute value of relative error. Prediction set R2Value, correction set R2The values, RMSEP and RMSEC were 0.6063, 0.2553, 0.0031 and 0.0092, respectively.
PLSR model test
Test set samples not participating in the modeling were externally validated. And inputting the sample into a quantitative model to obtain a predicted value, and inspecting the prediction capability of the model through the relative deviation of the predicted value and a true value obtained by a conventional method. The test results are shown in table 2. The average value of the absolute value of the relative error between the predicted value and the true value of the water model established by the largehead atractylodes rhizome powder is 2.15 percent; the average value of the absolute value of the relative error between the predicted value and the true value of the water model established by the largehead atractylodes rhizome decoction pieces is 1.9 percent; the average value of the absolute value of the relative error between the predicted value and the true value of the extract content model established by the largehead atractylodes rhizome powder is 6.3 percent; the average value of the relative error absolute values of the predicted value and the true value of the extract model established by the largehead atractylodes rhizome decoction pieces is 6.27%. The results show that the relative error between the predicted value and the true value of the moisture is small, the prediction result is accurate, and the model is successfully established. The relative error between the predicted value and the true value of the extract content is small, and the prediction result is accurate.
TABLE 2 test set sample prediction results
The above detailed description is specific to one possible embodiment of the present invention, and the embodiment is not intended to limit the scope of the present invention, and all equivalent implementations or modifications without departing from the scope of the present invention should be included in the technical scope of the present invention.
Claims (3)
1. A near-infrared online quality detection method for bighead atractylodes rhizome is characterized by comprising the following steps:
(1) sample preparation: taking samples of the rhizoma atractylodis macrocephalae decoction pieces of different batches;
(2) collecting near infrared spectrum data: collecting and recording a near-infrared spectrogram of a white atractylodes rhizome sample and a near-infrared spectrogram of the white atractylodes rhizome after being powdered;
(3) preprocessing the spectral data: respectively adopting an original spectrum, a first order derivation, a second order derivation, a multivariate scattering correction, a vector normalization, a convolution smoothing filter, a multivariate scattering correction and a vector normalization, a convolution smoothing filter and a multivariate scattering correction, and a convolution smoothing filter and a vector normalization to preprocess the near infrared spectrum data of the bighead atractylodes rhizome sample before and after powdering;
(4) and establishing a quantitative correction model of the bighead atractylodes rhizome by adopting a partial least squares regression method.
2. The near-infrared online quality detection method for bighead atractylodes rhizome according to claim 1, characterized in that the method for collecting near-infrared spectra in step (2) is as follows: taking 10g of the pulverized rhizoma atractylodis macrocephalae sample, adding the powder into a quartz sample tube, and filling and flattening the sample; selecting a flat sample from the unfired sample, and enabling the sample to be in full contact with a near-infrared diffuse reflection optical fiber probe, wherein the test environment temperature is 25 ℃, and the relative humidity is 45-60%; taking the background built in the instrument as a reference, and deducting the background; the collection mode is diffuse reflection of an integrating sphere, and the wave number range is 12000-4000 cm-1Resolution of 8cm-1The number of scanning times is 64, each sample is scanned for 2 times, and the average spectrum is taken as the near infrared spectrum of the sample.
3. The near-infrared online quality detection method for rhizoma atractylodis macrocephalae according to claim 1, characterized in that the optimal parameters for establishing a quantitative correction model for rhizoma atractylodis macrocephalae by the partial least squares regression method are as follows:
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