CN104568813A - Multi-index fast detection method of dogwood medicinal materials - Google Patents

Abstract

The invention provides a multi-index fast detection method of dogwood medicinal materials. The multi-index fast detection method comprises the following steps of: acquiring the dogwood medicinal materials; determining the contents of morroniside and loganin in the dogwood by using a high-performance liquid chromatography, determining the content of water by using a drying method, and determining the content of extract by using a cold-extraction method; collecting near-infrared spectrum data of dogwood medicinal material powder; preprocessing spectrum at the modeling waveband of the near-infrared spectrum; establishing a fast detection model for the water, the extract and the morroniside and the loganin in the dogwood medicinal materials; and using the established model for fast determination of the water, the extract and the morroniside and the loganin in the dogwood medicinal materials. The multi-index fast detection method provided by the invention has the advantages that a near-infrared spectrum technology is introduced as the fast detection method of the contents of the water, the extract, the morroniside and the loganin in the dogwood medicinal materials; and compared with the traditional method, the established analysis method has the advantages that whether the quality of the medicinal materials is qualified can be fast judged, whether the medicinal materials can enter the following production-process links can be determined, the requirements of fastness and high efficiency in production can be met, and the application prospect for site medicinal-material screening and comprehensive quality evaluation is achieved.

Description

A multi-indicator rapid detection method for Cornus officinalis

technical field

The invention belongs to the field of near-infrared detection, and in particular relates to a multi-index rapid detection method of cornus officinalis.

Background technique

Cornus officinalis is the dried and mature pulp of Cornus offfficinalis Sieb. et Zucc. In the clinical practice of traditional Chinese medicine, its mature fruit is used as medicine after the pitting is removed. one. The 2010 edition of the Chinese Pharmacopoeia records that Cornus officinalis has the functions of nourishing the liver and kidney, astringent and solidifying, and is used for dizziness, tinnitus, waist and knee pain, impotence and nocturnal emission, enuresis, frequent urination, metrorrhagia, profuse sweating, prostration, internal heat and quenching thirst. The chemical composition of Cornus officinalis is very complex, mainly including organic acids and their esters and iridoid glycosides as the main active ingredients. In recent years, scholars at home and abroad have conducted a lot of research on the pharmacological effects and chemical components of Cornus officinalis. Cornus officinalis has shown good curative effects in reducing blood sugar and blood lipids, antibacterial and anti-inflammatory, anti-aging, anti-shock and regulating immunity.

Raw material inspection is the source of process quality analysis and control. Due to differences in geographical location, climatic conditions, growth environment and other factors, the content and type of active ingredients of the same type of medicinal materials from different origins are often quite different, so it is necessary to evaluate the quality of the original medicinal materials. At present, the traditional detection method is time-consuming and laborious, and it is difficult to be widely used in production practice. Therefore, it is necessary and promising to develop a method that can quickly detect the quality of Cornus officinalis for on-site screening of medicinal materials and comprehensive quality control.

Near-infrared spectroscopy (NIR) technology is an indirect analysis technology, which realizes qualitative or quantitative analysis of unknown samples through the establishment of calibration models, and has the characteristics of fast, non-destructive, in-situ and pollution-free. In recent years, near-infrared spectroscopy, as an indirect analysis technology, has been applied in the quality control and production application fields of traditional Chinese medicine, including the qualitative and quantitative analysis of medicinal materials, compound Chinese medicine and various dosage forms of traditional Chinese medicine, and the use of optical fiber probe technology to realize the production process of traditional Chinese medicine. online continuous monitoring, etc. The near-infrared spectroscopy technology is applied to the rapid quality detection of Cornus officinalis, and its quality is controlled from the source of the production of Cornus officinalis preparations, so as to ensure the safety, stability and effectiveness of the final product quality, and achieve the purpose of rapid and efficient quality control.

Contents of the invention

The purpose of the invention is to provide a multi-indicator rapid detection method for the medicinal material of Cornus officinalis. The near-infrared spectroscopy technology can be used to quickly determine the water content, extract content, loganin and morroniside in the medicinal materials of Cornus officinalis, and realize the rapid evaluation of the quality of the medicinal materials of Cornus officinalis.

The purpose of the present invention is to realize through the following technical solutions:

(1) Collect medicinal materials of Cornus officinalis from different origins and batches

The samples of Cornus officinalis from different origins were collected, and the medicinal materials were crushed and passed through a 60-mesh sieve to obtain the medicinal powder of Cornus officinalis with uniform particle size.

(2) Determination of various quality control indicators of Cornus officinalis

Water content, extract content, loganin and morroniside content were selected as the key quality control indicators of Cornus officinalis; the drying method was used to determine the water content, the cold soaking method was used to determine the extract content, and the HPLC method was used to determine the The contents of glucoside and morroniside were all referred to the relevant determination methods for Cornus officinalis in the 2010 edition of "Chinese Pharmacopoeia".

(3) Collect near-infrared spectral data of Cornus officinalis

Weigh the medicinal powder of Cornus officinalis, put it in a weighing bottle, keep the surface of the powder flat, collect the near-infrared spectrum by the diffuse reflectance method, take the air as the reference, scan the number of 32, the resolution is 8 cm ^-1 , and the scanning spectrum range is 4000-12000 cm ^-1 .

(4) Preprocessing of near-infrared raw spectra

Preprocess the near-infrared raw spectrum collected in step (3) to filter information and reduce noise, using 1 to 2 methods in the first derivative, second derivative, multivariate scattering correction, Norris smoothing, subtracting a straight line, and SNV The combination of preprocessing the spectrum with spectral outliers removed, as follows:

1) The water content model of Cornus officinalis is preprocessed by the first derivative + vectorized normalization (SNV);

2) The extract content model of Cornus officinalis was preprocessed by vector normalization (SNV) spectrum;

3) The morroniside content and loganin content models of Cornus officinalis were preprocessed by normal vectorization (SNV) and multivariate scatter correction, respectively.

(5) Select the modeling bands of each quality control index of Cornus officinalis, and establish a near-infrared quantitative model

After preprocessing the near-infrared spectrum of the dogwood sample, the modeling bands were selected as follows: the modeling band of the moisture model was 7502.1-4597.7 cm ^-1 ; the modeling band of the extract model was 6102-4246.7 cm ^-1 ; And loganin content model modeling bands are 7502.1-6098.1cm ^-1 and 5450.1-4246.7 cm ^-1 , using partial least squares (PLS) method to establish a quantitative calibration model between the near-infrared spectrum and the above quality control indicators.

The established correction model uses four parameters of correlation coefficient (R), relative analysis error (RPD), cross-validation root mean square (RMSECV) principal component fraction (Factor) to examine the performance of the model, and uses prediction mean square error (RMSEP), relative Deviation (RSEP) to evaluate the prediction ability of the model for unknown samples. When the R value is close to 1, when the RPD value is greater than 2.5 and the larger the evaluation model performance is better, the prediction accuracy is higher. When the RMSEP is smaller, the RSEP value is less than 10%. The time-evaluation model has good predictive ability and can meet the requirements of rapid detection of Cornus officinalis.

(6) Verification of calibration model

Use the near-infrared original spectrum of the verification set sample described in step (3) to verify the model described in step (5). After using the same preprocessing method as the calibration set sample spectrum, import the established calibration model and verify the model. Performance, the measured moisture content of Cornus officinalis ≤ 16.0%, extract content ≥ 50.0%, and loganin content ≥ 0.60%, then the dogwood medicinal material is judged to be a qualified sample, which meets the quality requirements and can be put into subsequent production processes such as extraction.

Another object of the present invention is to provide the application of the detection method in the multi-index rapid detection of the medicinal material of Cornus officinalis.

The present invention introduces near-infrared analysis technology into the detection of Cornus officinalis medicinal materials, realizes the rapid determination of various quality control indicators (moisture, extract, loganin and morroniside content), and controls the quality of raw materials from the source in the production of traditional Chinese medicine , shorten the detection time, save production costs, improve production efficiency and economic benefits, and fully ensure the stable and reliable quality of dogwood preparations.

Description of drawings

Accompanying drawing 1 is the near-infrared original absorption spectrum diagram of the medicinal material powder of Cornus officinalis.

Accompanying drawing 2 is the correlative diagram of the actual measured value and the near-infrared predicted value of the moisture content of the medicinal material powder of Cornus officinalis.

Accompanying drawing 3 is the correlative diagram of the actual measured value and the near-infrared predicted value of the extract content of the medicinal powder of Cornus officinalis.

Accompanying drawing 4 is the correlative diagram of the measured value and near-infrared predicted value of the morroniside content of the medicinal material powder of Cornus officinalis.

Accompanying drawing 5 is the correlative diagram of the actual measured value and near-infrared predicted value of loganin content in the medicinal material powder of Cornus officinalis.

Accompanying drawing 6 is the comparison chart of the actual measured value of the moisture content of the medicinal material powder of Cornus officinalis and the near-infrared predicted value.

Accompanying drawing 7 is the comparison chart of the actual measured value and the near-infrared predicted value of the medicinal powder extract of Cornus officinalis.

Accompanying drawing 8 is a comparison chart of the measured value of the morroniside content of the medicinal material powder of Cornus officinalis and the predicted value of near-infrared.

Accompanying drawing 9 is the comparison diagram of the actual measured value and the near-infrared predicted value of the content of loganin in the medicinal material powder of Cornus officinalis.

Detailed ways

The present invention will be further described in conjunction with drawings and embodiments.

Embodiment 1 :

(1) Data collection of near-infrared spectroscopy of Cornus officinalis

Cornus officinalis medicinal materials from different origins were crushed and passed through a 60-mesh sieve to obtain the medicinal powder of Cornus officinalis with a relatively uniform particle size; 2 g of the medicinal materials of Cornus officinalis was precisely weighed and placed in a weighing bottle to keep the surface of the powder flat, and collected by diffuse reflection method Near-infrared spectrum, using air as a reference, the number of scans is 32, the resolution is 8 cm ^-1 , the range of the scanning spectrum is 4000-10000 cm ^-1 , each batch of samples is scanned 5 times, and the average spectrum is taken. The near-infrared original absorption spectrum of Cornus officinalis powder is shown in Figure 1.

(2) Determination of moisture content in Cornus officinalis

Moisture determination method : The moisture content of Cornus officinalis is determined according to the drying and weighing method of the Pharmacopoeia. Take a flat bottle (X ₀ ) that has been dried to a constant weight (the difference between two consecutive weighings is less than 5mg), and 2g of Cornus officinalis is weighed precisely. (X ₁ ), bake in a vacuum oven at 105°C for 5 hours, take it out and cool in a desiccator for 30 minutes, weigh, then bake in a vacuum oven for 1 hour, weigh (X ₂ ), if the weight difference is more than 5mg, continue to bake in the oven , until the difference is less than 5mg. According to the lost weight, calculate the water content (%) in the test sample.

Moisture content (%) = (X ₁ -X ₂ +X ₀ )/X ₁ ×100.

(3) Preprocessing of near-infrared raw spectra

The near-infrared quantitative analysis model of the water content index in the sample was established by using the partial least square method (PLS). Before the model is established, the abnormal points of the calibration set spectrum are judged to improve the accuracy of the model. At the same time, the original spectrum is eliminated by the appropriate spectral preprocessing methods such as smoothing and differentiation to eliminate the influence of the instrument background or drift on the signal, and select the appropriate band to extract effective information. Reduce calculations and shorten modeling time. The original spectrum of Cornus officinalis adopts a large number of preprocessing methods provided by OPUS software, including first-order derivatives, second-order derivatives, multivariate scattering correction, Norris smoothing, subtracting a straight line, and a combination of 1 or 2 methods in SNV. to extract. According to the model parameters, the spectral preprocessing method of the final moisture content is the first derivative + vectorized normalization (SNV).

(4) Select the modeling band for the moisture content of Cornus officinalis, and establish a near-infrared quantitative model

After spectral pretreatment of Cornus officinalis samples, the modeling band of moisture model is 7502.1-4597.7 cm ^-1 . Chemometrics software was used to correlate the obtained near-infrared spectrum information with the standard values measured by the reference method, and the partial least squares (PLS) method was used to establish a quantitative calibration model between the near-infrared spectrum and the above-mentioned quality control indicators.

Embodiment 2 :

(1) Near-infrared spectrum collection: The near-infrared spectrum used is the spectrum collected by the moisture content modeling in Example 1

(2) Determination of the extract content of Cornus officinalis

Determination method of extract content: Take about 2g of Cornus officinalis medicinal material, put it in a 250mL Erlenmeyer flask, add 50mL of water, seal it tightly, soak in cold and weigh it. Shake it from time to time for the first 6 hours, then let it stand for 18 hours, weigh the mass, subtract the lost mass with insufficient water, and shake well. Centrifuge in a 15 mL centrifuge tube for 30 min at a speed of 3800 r/min, accurately measure 10 mL of the supernatant, put it in a flat bottle that has been dried to constant weight (X ₀ ), evaporate to dryness on a water bath, and place in Dry at 105°C for 3 h, cool in a desiccator for 30 min, and weigh quickly and accurately (X ₂ ). Calculate the extract content (%) of the test sample based on the dry product.

Extract content (%)=(X ₂ -X ₀ )×5/X ₁ ×100.

(3) Preprocessing of near-infrared raw spectra

The near-infrared quantitative analysis model of the extract content index in the sample was established by using the partial least square method (PLS). Before the model is established, the abnormal points of the calibration set spectrum are judged to improve the accuracy of the model. At the same time, the original spectrum is eliminated by the appropriate spectral preprocessing methods such as smoothing and differentiation to eliminate the influence of the instrument background or drift on the signal, and select the appropriate band to extract effective information. Reduce calculations and shorten modeling time. The original spectrum of Cornus officinalis adopts a large number of preprocessing methods provided by OPUS software, including first-order derivatives, second-order derivatives, multivariate scattering correction, Norris smoothing, subtracting a straight line, and a combination of 1 or 2 methods in SNV. to extract. According to the model parameters, the spectral preprocessing method of the final extract content is vectorized normalization (SNV).

(4) Select the modeling band for the extract content of Cornus officinalis, and establish a near-infrared quantitative model

After the cornel sample was pretreated by spectrum, the modeling band of the extract model was 6102-4246.7cm ^-1 . Chemometrics software was used to correlate the obtained near-infrared spectrum information with the standard values measured by the reference method, and the partial least squares (PLS) method was used to establish a quantitative calibration model between the near-infrared spectrum and the above-mentioned quality control indicators.

Embodiment 3 :

(1) Near-infrared spectrum collection: the near-infrared spectrum used is the spectrum collected by the moisture content modeling in Example 1

(2) Determination of the content of morroniside and loganin in Cornus officinalis

The content of morroniside and loganin was determined by high performance liquid chromatography: a. The pretreatment method was: take about 0.1 g of Cornus officinalis medicinal material powder (passed through a 60-mesh sieve), accurately weigh it, put it in a stoppered conical flask, and accurately add Heat 25 mL of 80% methanol to reflux for 1 h, weigh again, and use 80% methanol to make up for the weight loss. The extract was centrifuged in a 1.5mL centrifuge tube for 10 min at a speed of 13000 r·min ^-1 , and the supernatant was obtained. b. Liquid chromatography conditions: Chromatographic column: Agilent Eclipse XDB-C18 analytical column (4.6×250mm, 5μm); mobile phase: acetonitrile-water (15:85, v/v); detection wavelength 240 nm, flow rate The injection volume was 0.8 mL·min ^-1 , the injection volume was 10 μL, and the column temperature was 25°C.

(3) Preprocessing of near-infrared raw spectra

The near-infrared quantitative analysis model of loganin and morroniside content indicators in samples was established by partial least squares (PLS). Before the model is established, the abnormal points of the calibration set spectrum are judged to improve the accuracy of the model. At the same time, the original spectrum is eliminated by the appropriate spectral preprocessing methods such as smoothing and differentiation to eliminate the influence of the instrument background or drift on the signal, and select the appropriate band to extract effective information. Reduce calculations and shorten modeling time. The original spectrum of Cornus officinalis adopts a large number of preprocessing methods provided by OPUS software, including first-order derivatives, second-order derivatives, multivariate scattering correction, Norris smoothing, subtracting a straight line, and a combination of 1 or 2 methods in SNV. to extract. According to the model parameters, the final morroniside content and loganin content models of Cornus officinalis were preprocessed by vector normalization (SNV) and multivariate scattering correction respectively

(4) Select the modeling bands for the content of morroniside and loganin in Cornus officinalis, and establish a near-infrared quantitative model

After spectral pretreatment of Cornus officinalis samples, the modeling bands of morroniside and loganin models were 7502.1-6098.1 cm ^-1 and 5450.1-4246.7 cm ^-1 . Chemometrics software was used to correlate the obtained near-infrared spectrum information with the standard values measured by the reference method, and the partial least squares (PLS) method was used to establish a quantitative calibration model between the near-infrared spectrum and the above-mentioned quality control indicators.

Embodiment 4 :

(1) Establishment of near-infrared quantitative model

After removing abnormal samples, 63%~75% of the samples were randomly selected as the calibration set, and 25%~37% of the samples were used as the validation set for prediction. The model uses four parameters of correlation coefficient (R), relative analysis error (RPD), cross-validation root mean square (RMSECV) and principal component score (Factor) to investigate the performance of the model, and uses the relative prediction deviation (RSEP) to evaluate the model's ability to predict the unknown For the predictive ability of the sample, when the R value is close to 1, when the RPD value is greater than 2.5 and the larger the evaluation model performance is better, the prediction accuracy is high. When the RSEP value is less than 10%, the evaluation model has better predictive ability and can meet the requirements of Cornus officinalis. requirements for rapid testing.

Table 1 compares the modeling results of the near-infrared models of the four indicators. It can be seen from Table 1 that the near-infrared models of the four indicators have good linearity, the correlation coefficients are all above 0.90, and the RPD values are above 2.3, indicating that the established The near-infrared quantitative calibration model works better. The correlation diagram between the measured value and the predicted value of the moisture content is shown in attached drawing 2, the correlated diagram between the measured value and the predicted value of the extract content is shown in the attached drawing 3, the measured value and prediction of the content of morroniside and loganin Correlation diagrams between the values are shown in Figures 4 and 5, respectively.

(2) Validation of the quantitative calibration model

Four quantitative models were used to predict the content of moisture, extract, morroniside and loganin in the validation set samples respectively. The comparison of the actual measured value of the moisture content and the near-infrared predicted value is shown in Figure 6, the comparison of the actual measured value of the extract content and the near-infrared predicted value is shown in Figure 7, and the measured values of the content of morroniside and loganin and the near-infrared predicted value See Figure 8 and Figure 9 for comparison of the values. It can be seen that the measured values of the four indicators of Cornus officinalis are close to the near-infrared predicted values.

Table 2 is a summary of the parameters of the near-infrared model prediction results of four different indicators. It can be seen from Table 2 that the RMSEP of moisture and extracts are all below 1.0, and the RSEP of the moisture and extract models is within 10%, while morroniside and The loganin model RSEP control is acceptable within 20%, indicating that the established near-infrared analysis model of the four quality control indicators has good predictive ability and stability.

(3) If the water content of Cornus officinalis measured by the above method is ≤16.0%, the extract content is ≥50.0%, and the content of loganin is ≥0.60%, then the medicinal material of Cornus officinalis is judged to be a qualified sample, which meets the quality requirements and can be put into subsequent production such as extraction. links.

The invention proposes a multi-indicator express detection method for the medicinal material of Cornus officinalis. The results showed that the content of moisture, extract, loganin and morroniside in Cornus officinalis could be quickly analyzed by near-infrared spectroscopy. The method saves time and is non-destructive, improves production efficiency and economic benefits, provides a new method for quality control of medicinal materials, controls the quality level of the medicinal preparation of dogwood from the production source, and ensures the safety and reliability of the finished preparation.

Claims (3)

1. a Fructus Corni method for quick, is characterized in that, is realized by following steps:

(1) gather Fructus Corni: gather different batches and the fruit of medicinal cornel sample on date, medicinal material after crushed, is crossed 60 mesh sieves, is obtained even-grained Fructus Corni powder;

(2) each quality control index is measured: get moisture, extract content, morroniside and the Determination of Loganin crucial quality control index as Fructus Corni, adopt moisture analysis content, cold-maceration is adopted to measure extract content, Fructus Corni, after pre-service, adopts high effective liquid chromatography for measuring morroniside and Determination of Loganin;

(3) gather Fructus Corni near infrared spectrum data: take Fructus Corni powder, be placed in measuring cup, keep powder surface smooth, adopt diffuse reflection method to gather near infrared spectrum, take air as reference, scanning times is 32, and resolution is 8 cm ^-1, scanning optical spectrum scope is 4000-12000 cm ^-1;

(4) pre-service of near infrared original spectrum: adopt the method for mahalanobis distance, sample lever value and student's residual error to judge abnormal sample, then pre-service is carried out to the spectrum eliminating spectral singularity value, specific as follows:

A () fruit of medicinal cornel moisture model adopts first order derivative+vector quantization normalizing (SNV) pre-service;

B () fruit of medicinal cornel extract content model adopts vector quantization normalizing (SNV) Pretreated spectra;

C the morroniside content of () fruit of medicinal cornel and Determination of Loganin model adopt vector quantization normalizing (SNV) and multiplicative scatter correction pre-service respectively;

(5) select Fructus Corni each quality control index modeling wave band, set up near infrared quantitative model:

By the near infrared spectrum of fruit of medicinal cornel sample after Pretreated spectra, its modeling band selection is: water model modeling wave band is 7502.1-4597.7 cm ^-1; Extract model modeling wave band is 6102-4246.7cm ^-1; Morroniside and Determination of Loganin model modeling wave band are 7502.1-6098.1cm ^-1with 5450.1-4246.7 cm ^-1, employing offset minimum binary (PLS) method sets up the quantitative calibration models between near infrared spectrum and above-mentioned each quality control index;

(6) checking of calibration model:

With the checking collection sample described in step (3), the model described in step (5) is verified, after adopting the preprocess method identical with calibration set sample spectra, import built calibration model, the performance of verification model, when moisture≤16.0% of the unknown Fructus Corni recorded, extract content >=50.0%, Determination of Loganin >=0.60%, then judge that this Fructus Corni is qualified samples, conform to quality requirements.

2. a kind of Fructus Corni multiple index quick detecting method according to claim 1, it is characterized in that: the calibration model that step (5) is set up adopts related coefficient (R), relation analysis error (RPD), cross validation root mean square (RMSECV) number of principal components (Factor) four parameters investigate model performance, adopt prediction mean square deviation (RMSEP) simultaneously, relative deviation (RSEP) carrys out the predictive ability of evaluation model to unknown sample, when R value is close to 1, when RPD value is greater than 2.5 and larger evaluation model performance is better, prediction accuracy is high, when RMSEP is less, when RSEP value is less than 10%, evaluation model has good predictive ability, the requirement that the fruit of medicinal cornel detects fast can be met.

3. the application of a kind of Fructus Corni multiple index quick detecting method according to claim 1 in Fructus Corni multi objective detects fast.