Method for rapidly detecting antioxidant activity of dendrobium officinale medicinal material and application of method

Abstract

The invention belongs to the technical field of medicine detection, and particularly relates to a rapid detection method for antioxidant activity of dendrobium officinale medicinal materials and application of the method, and the method comprises the following steps: (1) collecting a plurality of batches of dendrobium officinale medicinal materials and crushing; (2) obtaining ABTS free radical scavenging rate, iron ion reducing capacity and DPPH free radical scavenging rate in the dendrobium officinale sample through a chemometrics method; (3) collecting the near-infrared original spectrum of the dendrobium officinale medicinal material; (4) after abnormal spectrums are removed, processing original near infrared spectrums by a standard normal variable transformation spectrum preprocessing method, and dividing a correction set and a verification set by adopting a KS algorithm to establish a partial least square regression model; the near infrared spectrum technology is adopted to detect the antioxidant activity of the dendrobium officinale medicinal material, the method has the advantages of being easy to operate, rapid, free of damage and high in precision, production cost can be saved, economic benefits can be improved, and the purpose of efficient quality control can be achieved on the traditional Chinese medicine production source.

Description

Technical Field

[0001] The invention belongs to the technical field of medical detection, and particularly relates to a method for quickly detecting the antioxidant activity of a Dendrobium officinale medicinal material and a use of the method. Background Art

[0002] Dendrobium candidum Dendrobium officinale ) belongs to the genus Dendrobium of the Orchidaceae family and has long been used as a healthy tea, beverage or cooking spice. Dendrobium officinale is known as the "first of the nine immortals in China" and is rich in polysaccharides, polyphenols, total flavonoids and alkaloids. It has pharmacological effects such as antioxidant, anti-tumor and hypoglycemic. Due to differences in origin, variety, harvesting season and processing methods, there are large differences in the quality of Dendrobium officinale raw medicinal materials and their Chinese patent medicines. Therefore, in order to ensure the quality stability and effectiveness of the final product, the quality inspection process of the raw medicinal materials is more important. Traditional medicinal material quality inspection methods are time-consuming and labor-intensive, and it is difficult to meet the requirements of the modernization of traditional Chinese medicine. In addition, a single indicator component is often used to control the quality of medicinal materials, ignoring the complexity of Chinese medicine components. Therefore, there is an urgent need for a new method that can comprehensively and efficiently evaluate the quality of Chinese medicinal materials, ensure their quality from the source of production, and achieve the purpose of rapid and efficient quality control.

[0003] Near infrared spectroscopy (NIRS) is a high-tech analytical technology that solves the above problems due to its fast analysis speed, good measurement repeatability, high analysis efficiency, low analysis cost, no sample destruction, and no sample pretreatment. Due to the problems of spectral bandwidth, severe peak overlap, weak absorption intensity, and low signal-to-noise ratio in near infrared spectroscopy, it is impossible to use NIRS to directly perform qualitative or quantitative analysis on samples. It is necessary to use chemometric methods such as partial least squares (partial least squares regression) to establish a quantitative or qualitative calibration model that correlates rich spectral information and target variables before predicting the composition and properties of the sample. In addition to useful spectral data, the near infrared full spectrum may contain many irrelevant variables, which reduces the robustness and prediction accuracy of the calibration model. Therefore, it is necessary to use chemometrics to separate and extract key spectral data information and eliminate the spectral baseline drift caused by sample particle size. The CARS algorithm is a feature variable selection method that combines the Monte Carlo sampling method with the regression coefficient of the partial least squares regression model. The algorithm gradually evaluates, analyzes, filters and eliminates each wavelength point in the spectrum, is suitable for wavelength selection of high-dimensional spectra, can simplify modeling speed and improve model accuracy. Therefore, the present invention proposes a rapid detection method for the antioxidant activity of Dendrobium officinale medicinal materials based on competitive adaptive weighted sampling combined with partial least squares regression algorithm. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to provide a method for quickly detecting the antioxidant activity of Dendrobium officinale medicinal materials and uses of the method.

[0005] The technical scheme adopted by the present invention is as follows: a method for rapid detection of antioxidant activity of Dendrobium officinale medicinal material, comprising at least one of the steps of ABTS free radical scavenging rate determination, iron ion reducing ability determination and DPPH free radical scavenging rate determination; A. The ABTS free radical scavenging rate determination includes the following steps: (1) collecting multiple batches of Dendrobium officinale medicinal materials, crushing and sieving, and obtaining a number of Dendrobium officinale powder samples; (2) Determination of ABTS free radical scavenging rate in Dendrobium officinale samples; (3) Collecting the original near-infrared spectrum of each Dendrobium officinale sample; (4) After removing abnormal spectra, the original near-infrared spectra were processed using the standard normal variable transformation spectrum preprocessing method, and the KS algorithm was used to divide the calibration set and the validation set to establish a partial least squares regression model; (5) A competitive adaptive weighted sampling combined with partial least squares regression quantitative model for ABTS free radical scavenging rate was constructed using the calibration set, and the model was validated using the validation set; (6) Obtaining a near infrared spectrum of the unknown Dendrobium officinale sample according to the method of step (3), and importing the established quantitative calibration model to obtain the ABTS free radical scavenging rate value of the unknown Dendrobium officinale medicinal material sample; B. The determination of iron ion reducing ability includes the following steps: (1) collecting multiple batches of Dendrobium officinale medicinal materials, crushing and sieving, and obtaining a number of Dendrobium officinale powder samples; (2) Determination of the iron ion reducing capacity in Dendrobium officinale samples; (3) Collecting the original near-infrared spectrum of each Dendrobium officinale sample; (4) After removing abnormal spectra, the original near-infrared spectra were processed using the standard normal variable transformation spectrum preprocessing method, and the KS algorithm was used to divide the calibration set and the validation set to establish a partial least squares regression model; (5) A competitive adaptive weighted sampling combined with partial least squares regression quantitative model of iron ion reduction capacity was constructed using the calibration set, and the model was validated using the validation set; (6) Obtaining a near-infrared spectrum of an unknown Dendrobium officinale sample according to the method of step (3), and importing the established quantitative calibration model to obtain the iron ion reducing capacity value of the unknown Dendrobium officinale medicinal material sample; C. The DPPH free radical scavenging rate determination includes the following steps: (1) collecting multiple batches of Dendrobium officinale medicinal materials, crushing and sieving, and obtaining a number of Dendrobium officinale powder samples; (2) Determination of DPPH free radical scavenging rate in Dendrobium officinale samples; (3) Collecting the original near-infrared spectrum of each Dendrobium officinale sample; (4) After removing abnormal spectra, the original near-infrared spectra were processed using the standard normal variable transformation spectrum preprocessing method, and the KS algorithm was used to divide the calibration set and the validation set to establish a partial least squares regression model; (5) A competitive adaptive weighted sampling combined with partial least squares regression quantitative model for DPPH free radical scavenging rate was constructed using the calibration set, and the model was validated using the validation set; (6) Obtaining a near-infrared spectrum of the unknown Dendrobium officinale sample according to the method of step (3), and importing the established quantitative calibration model to obtain the DPPH free radical scavenging rate value of the unknown Dendrobium officinale medicinal material sample.

[0006] As a preferred technical solution of the present invention, in the step (2) of determining the ABTS free radical scavenging rate, a chemometric method is used to determine the ABTS free radical scavenging rate in the Dendrobium officinale medicinal material; in the step (2) of determining the iron ion reducing ability, a chemometric method is used to determine the iron ion reducing ability in the Dendrobium officinale medicinal material; in the step (2) of determining the DPPH free radical scavenging rate, a chemometric method is used to determine the DPPH free radical scavenging rate in the Dendrobium officinale medicinal material.

[0007] As a preferred technical solution of the present invention, in the step (3) of the ABTS free radical scavenging rate determination, the iron ion reducing ability and the DPPH free radical scavenging rate determination, the specific process of collecting the near-infrared original spectrum of the Dendrobium officinale sample is as follows: accurately weigh the Dendrobium officinale medicinal material powder and place it in a weighing bottle, and use the diffuse reflectance method to collect the near-infrared original spectrum, with air as a reference; the spectrum collection conditions: the scanning range is 4000-10000 cm -1 , the number of scans is 64, and the resolution is 8 cm -1 , three spectra were collected for each sample and the average spectrum was calculated.

[0008] As a preferred technical solution of the present invention, in the step (4) of the ABTS free radical scavenging rate determination, the iron ion reducing ability and the DPPH free radical scavenging rate determination, the ratio of the number of samples in the calibration set and the number of samples in the validation set is 2:1.

[0009] As a preferred technical solution of the present invention, in the step (4) of the ABTS free radical scavenging rate determination, the iron ion reducing ability and the DPPH free radical scavenging rate determination, a quantitative calibration model is established using calibration set samples, and the prediction accuracy of the model is evaluated using a validation set.

[0010] As a preferred technical solution of the present invention, in the step (5) of the ABTS free radical scavenging rate determination, competitive adaptive weighted sampling combined with partial least squares regression algorithm is used to obtain the optimal wavelength subset, and the RMSECV value is 0.44%; in the step (5) of the iron ion reducing ability determination, competitive adaptive weighted sampling combined with partial least squares regression algorithm is used to obtain the optimal wavelength subset, and the RMSECV value is 2.64 μmol / L; in the step (5) of the DPPH free radical scavenging rate determination, competitive adaptive weighted sampling combined with partial least squares regression algorithm is used to obtain the optimal wavelength subset, and the RMSECV value is 2.06%.

[0011] As a preferred technical solution of the present invention, in the step (5) of the ABTS free radical scavenging rate determination, the iron ion reducing ability and the DPPH free radical scavenging rate determination, when using the CARS-partial least squares regression method to find the optimal wavelength subset, 80% of the random samples are used as the calibration set of the partial least squares regression model, and the RMSECV value is used as the criterion for parameter optimization. The wavelength subset with the smallest value is considered to be the optimal wavelength subset.

[0012] As a preferred technical solution of the present invention, the step (5) of determining the ABTS free radical scavenging rate, the iron ion reducing ability and the DPPH free radical scavenging rate also includes a step of evaluating the prediction performance of the established calibration model, and the evaluation index includes the calibration set correlation coefficient (R C ), prediction set correlation coefficient (R P ), root mean square error of the calibration set (RMSEC), root mean square error of the validation set (RMSEP), and relative deviation of prediction (RSEP).

[0013] The present invention provides a use of the above-mentioned method for detecting Dendrobium officinale in the field of quality detection and control of Dendrobium officinale medicinal materials.

[0014] The beneficial effects of the present invention are as follows: The invention discloses a rapid detection method for the antioxidant activity of Dendrobium officinale medicinal materials based on competitive adaptive weighted sampling combined with partial least squares regression algorithm, which introduces near-infrared spectroscopy technology and competitive adaptive weighted sampling combined with partial least squares regression algorithm into the quality control of Dendrobium officinale medicinal materials, and can quickly and non-destructively determine the contents of various quality control indicators (ABTS free radical scavenging rate, iron ion reducing ability and DPPH free radical scavenging rate), thereby effectively and quantitatively determining the antioxidant activity of Dendrobium officinale, shortening the detection time, and saving production costs; the competitive adaptive weighted sampling combined with partial least squares regression algorithm adopted can obtain the optimal wavelength subset and then construct a high-performance correction model, which can significantly improve the accuracy of the detection results. The invention method is simple to operate, has high prediction accuracy, can quickly determine whether the quality of Dendrobium officinale medicinal materials is qualified, and determine whether it can enter the subsequent production link, so that the quality of traditional Chinese medicine can be controlled at the source of production. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0016] Figure 1 This is the original near infrared spectrum of the Dendrobium officinale medicinal material powder of the present invention; Figure 2 The invention discloses a graph of optimal wavelength subsets optimized by combining competitive adaptive weighted sampling of ABTS free radical scavenging rate of Dendrobium officinale powder with a partial least squares regression quantitative model; Figure 3 It is a comparison chart of the measured value of ABTS free radical scavenging rate of the Dendrobium officinale medicinal material powder of the present invention and the near infrared predicted value; Figure 4 The invention discloses a graph of optimal wavelength subsets optimized by competitive adaptive weighted sampling combined with a partial least squares regression quantitative model for the iron ion reducing ability of the Dendrobium officinale powder; Figure 5 It is a correlation diagram of the measured value of the iron ion reducing capacity of the Dendrobium officinale medicinal material powder of the present invention and the near infrared predicted value; Figure 6 The invention discloses a graph of optimal wavelength subsets optimized by combining competitive adaptive weighted sampling of DPPH free radical scavenging rate of Dendrobium officinale powder with a partial least squares regression quantitative model; Figure 7 It is a correlation diagram of the measured value of the DPPH free radical scavenging rate of the Dendrobium officinale medicinal material powder of the present invention and the near infrared predicted value; Figure 8 The present invention is a flow chart of a method for rapid detection of antioxidant activity of Dendrobium officinale medicinal material. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings.

[0018] The following example of the present invention provides a method for rapid detection of antioxidant activity of Dendrobium officinale medicinal material and the use of the method.

[0019] The rapid detection method of the antioxidant activity of Dendrobium officinale medicinal material includes at least one of the steps of ABTS free radical scavenging rate determination, iron ion reducing ability determination and DPPH free radical scavenging rate determination, such as Figure 8 As shown, the antioxidant activity indicators include ABTS free radical scavenging rate, iron ion reducing ability and DPPH free radical scavenging rate. Example

[0020] A. The ABTS free radical scavenging rate determination includes the following steps: (1) 111 batches of Dendrobium officinale medicinal materials were collected, crushed, and passed through an 80-mesh sieve to obtain Dendrobium officinale medicinal material powder with uniform particle size. The powder was packed in self-sealing bags to obtain a total of 111 samples.

[0021] (2) The ABTS free radical scavenging rate in 111 samples of Dendrobium officinale was determined by chemometric method. The specific steps are as follows: ABTS solution and K2S2O8 solution were prepared with acetate buffer at pH 4.5 and mixed in proportion to prepare ABTS reaction solution for determination. In addition, potassium dichromate standard solution with a concentration range of 20-100 mg kg-1 was used to calibrate the UV-visible spectrophotometer. 100 mL ABTS solution (7 mM) was mixed with 1.76 mL K2S2O8 solution (140 mM), left in dark environment at room temperature for 12 h, and diluted with deionized water until the absorbance at 734 nm was 0.70±0.02, which was the ABTS working solution. Finally, 4 ml of ABTS working solution was mixed with 200 μL sample solution and left in dark environment at 25°C for 30 min. The absorbance of the sample at 734 nm was measured by UV-visible spectrophotometer (UV-1810, Puxi, China) with deionized water as the control. Each sample was measured three times, and the clearance rate (%) was calculated as follows: Clearance rate (%) = [A0-(A1-A2)] / A0×100 Where A0 is the absorbance of the control group, A1 is the absorbance of the sample solution mixed with ABTS working solution, and A2 is the absorbance of the sample solution without adding ABTS working solution.

[0022] (3) Accurately weigh 1 g of Dendrobium officinale powder and place it in a weighing bottle. Keep the powder surface flat and collect near-infrared spectra using diffuse reflectance method. Use air as reference, scan number 64, and resolution 8 cm-1 , the scanning spectrum range is 4000-10000 cm -1 , collected spectra of 111 samples, scanned each sample 3 times, and took the average spectrum. The original near-infrared spectra of 111 batches of Dendrobium officinale samples are shown in Figure 1 shown.

[0023] (4) After removing abnormal spectra, the original near-infrared spectra were processed using the standard normal variable transformation spectral preprocessing method. The KS algorithm was used to divide the calibration set and validation set to establish a partial least squares regression model. The final calibration set contained 75 D. officinale samples and the validation set contained 36 samples.

[0024] (5) Competitive adaptive weighted sampling combined with partial least squares regression algorithm is used to optimize the optimal wavelength subset. Finally, the RMSECV value is 0.44%. At this time, the optimal wavelength subset is as follows: Figure 2 shown.

[0025] (6) The optimal wavelength subset was introduced into the competitive adaptive weighted sampling combined with partial least squares regression quantitative model to establish a quantitative correction model between all spectra in the correction set and the ABTS free radical scavenging rate; the competitive adaptive weighted sampling combined with partial least squares regression quantitative model was used to predict the ABTS free radical scavenging rate of the validation set Dendrobium officinale samples to verify the prediction accuracy of the correction model.

[0026] (7) A step of evaluating the prediction performance of the calibration model, wherein the evaluation index includes the calibration set correlation coefficient (R C ), prediction set correlation coefficient (R P ), root mean square error of calibration set (RMSEC), and root mean square error of validation set (RMSEP). The closer the R value is to 1, the better the prediction performance of the model; the size of RMSEC and RMSEP is related to the chemical value of the sample. The smaller and closer these two parameters are, the better the model performance and the higher the prediction accuracy. The following Table 1 describes the parameters of the quantitative calibration model for the ABTS free radical scavenging rate of Dendrobium officinale medicinal materials. It can be seen from Table 1 that the R value of the calibration set is C The value was 0.930, and the RMSEC value was 0.34, indicating that the established ABTS free radical scavenging rate correction model has a good effect and can meet the requirements of quantitative detection of ABTS free radical scavenging rate of Dendrobium officinale medicinal materials. The competitive adaptive weighted sampling combined with partial least squares regression quantitative model was used to predict the ABTS free radical scavenging rate of Dendrobium officinale samples in the validation set. P The value was 0.821, and the RMSEP value was 0.51, which were close to the RMSEC value and both were smaller, indicating that the established competitive adaptive weighted sampling combined with partial least squares regression quantitative model for ABTS free radical scavenging rate of Dendrobium officinale had good stability and predictive ability. Figure 3This is a comparison chart between the measured values ​​of ABTS free radical scavenging rate of Dendrobium officinale in the correction set and the validation set and the near-infrared predicted value.

[0027] Table 1 Parameters of the calibration model for ABTS free radical scavenging rate of Dendrobium officinale Model <![CDATA[R C ]]> RMSEC <![CDATA[R P ]]> RMSEP ABTS free radical scavenging rate model 0.930 0.34 0.821 0.51 The unknown Dendrobium officinale medicinal material sample is obtained by the method of step (3) and the spectrum is introduced into the established competitive adaptive weighted sampling combined with partial least squares regression quantitative model to obtain the ABTS free radical scavenging rate of the unknown Dendrobium officinale sample. Example

[0028] B. The determination of iron ion reducing ability includes the following steps: (1) 111 batches of Dendrobium officinale medicinal materials were collected, crushed, and passed through an 80-mesh sieve to obtain Dendrobium officinale medicinal material powder with uniform particle size. The powder was packed in self-sealing bags to obtain a total of 111 samples.

[0029] (2) The chemometric method was used to determine the iron ion reducing capacity of 111 D. officinale samples. The specific steps are as follows: The FRAP working solution was prepared by mixing acetate buffer (0.3 mol / L), TPTZ (10 mM in 40 mM HCl) and FeCl3 (20 mM) in a ratio of 10:1:1. 3.6 mL of the FRAP working solution was mixed with 400 μL of the sample solution and allowed to stand at 37°C for 30 min. The absorbance of the sample at 593 nm was measured by a UV-visible spectrophotometer to determine the iron ion scavenging capacity of the sample. All samples were measured three times in parallel to obtain the average value. The scavenging capacity was expressed as FeSO4 concentration (μM). The linear regression formula of FRAP was y = 0.0019x + 0.0044, r2 = 0.9995, ranging from 50 to 500 μM.

[0030] (3) Accurately weigh 1 g of Dendrobium officinale powder and place it in a weighing bottle. Keep the powder surface flat and collect near-infrared spectra using diffuse reflectance method. Use air as reference, scan number 64, and resolution 8 cm -1 , the scanning spectrum range is 4000-10000 cm -1 , collected spectra of 111 samples, scanned each sample 3 times, and took the average spectrum. The original near-infrared spectra of 111 batches of Dendrobium officinale samples are shown in Figure 1 shown.

[0031] (4) After removing abnormal spectra, the original near-infrared spectra were processed using the standard normal variable transformation spectral preprocessing method. The KS algorithm was used to divide the calibration set and validation set to establish a partial least squares regression model. The final calibration set contained 75 D. officinale samples and the validation set contained 36 samples.

[0032] (5) Competitive adaptive weighted sampling combined with partial least squares regression algorithm was used to optimize the optimal wavelength subset. Finally, the RMSECV value was 2.64 μmol / L. At this time, the optimal wavelength subset was as follows: Figure 4 shown.

[0033] (6) The optimal wavelength subset was introduced into the competitive adaptive weighted sampling combined with partial least squares regression quantitative model to establish a quantitative correction model between all spectra in the correction set and the iron ion reduction capacity; the competitive adaptive weighted sampling combined with partial least squares regression quantitative model was used to predict the iron ion reduction capacity of the validation set of Dendrobium officinale samples to verify the prediction accuracy of the correction model.

[0034] (7) A step of evaluating the prediction performance of the calibration model, wherein the evaluation index includes the calibration set correlation coefficient (R C ), prediction set correlation coefficient (R P ), root mean square error of calibration set (RMSEC), and root mean square error of validation set (RMSEP). The closer the R value is to 1, the better the prediction performance of the model; the size of RMSEC and RMSEP is related to the chemical value of the sample. The smaller and closer these two parameters are, the better the model performance and the higher the prediction accuracy. The following Table 2 describes the parameters of the quantitative calibration model for the iron ion reducing ability of Dendrobium officinale medicinal materials. It can be seen from Table 2 that the R value of the calibration set is C The value was 0.958, and the RMSEC value was 1.93, indicating that the established iron ion reducing ability calibration model has a good effect and can meet the requirements of quantitative detection of iron ion reducing ability of Dendrobium officinale medicinal materials. The competitive adaptive weighted sampling combined with partial least squares regression quantitative model was used to predict the iron ion reducing ability of Dendrobium officinale samples in the validation set. P The value was 0.926, and the RMSEP value was 2.05, which were close to the RMSEC value and both were smaller, indicating that the established competitive adaptive weighted sampling combined with partial least squares regression quantitative model for the iron ion reduction ability of Dendrobium officinale has good stability and predictive ability. Figure 5 This is a comparison chart of the measured values ​​of the iron ion reducing capacity of Dendrobium officinale in the correction set and the validation set and the near-infrared predicted values.

[0035] Table 2 Parameters of the calibration model for the iron ion reducing capacity of Dendrobium officinale Model <![CDATA[R C ]]> RMSEC <![CDATA[R P ]]> RMSEP Ferrous ion reducing capacity model 0.958 1.93 0.926 2.05 (8) Obtaining a sample spectrum of the unknown Dendrobium officinale medicinal material sample according to the method of step (3), and importing the established competitive adaptive weighted sampling combined with partial least squares regression quantitative model to obtain the iron ion reducing ability of the unknown Dendrobium officinale sample. Example

[0036] C. The DPPH free radical scavenging rate determination includes the following steps: (1) 111 batches of Dendrobium officinale medicinal materials were collected, crushed, and passed through an 80-mesh sieve to obtain Dendrobium officinale medicinal material powder with uniform particle size. The powder was packed in self-sealing bags to obtain a total of 111 samples.

[0037] (2) The DPPH free radical scavenging rate in 111 samples of Dendrobium officinale was determined by chemometric method. The specific steps are as follows: Take 2 ml of sample solution and 2 ml of DPPH ethanol solution (0.1 mM), mix, shake well, and react for 30 minutes at room temperature in the dark. Use UV-visible spectrophotometer to measure the absorbance of the sample at 517 nm, with deionized water as the control. Each sample is measured three times, and the DPPH removal rate (%) is calculated as follows: Clearance rate (%) = [A0-(A1-A2)] / A0×100 Wherein, A0 is the absorbance of the DPPH ethanol solution without any sample solution, A1 is the absorbance of the sample solution containing the DPPH ethanol solution, and A2 is the absorbance of the sample solution containing the ethanol solution.

[0038] (3) Accurately weigh 1 g of Dendrobium officinale powder and place it in a weighing bottle. Keep the powder surface flat and collect near-infrared spectra using diffuse reflectance method. Use air as reference, scan number 64, and resolution 8 cm -1 , the scanning spectrum range is 4000-10000 cm -1 , collected spectra of 111 samples, scanned each sample 3 times, and took the average spectrum. The original near-infrared spectra of 111 batches of Dendrobium officinale samples are shown in Figure 1 shown.

[0039] (4) After removing abnormal samples, the original near-infrared spectra were processed using the standard normal variable transformation spectral preprocessing method. The KS algorithm was used to divide the calibration set and validation set to establish a partial least squares regression model. The final calibration set contained 75 D. officinale samples and the validation set contained 36 samples.

[0040] (5) Competitive adaptive weighted sampling combined with partial least squares regression algorithm is used to optimize the optimal wavelength subset. Finally, the RMSECV value is 2.06%. At this time, the optimal wavelength subset is as follows: Figure 6 shown.

[0041] (6) The optimal wavelength subset was introduced into the competitive adaptive weighted sampling combined with partial least squares regression quantitative model to establish a quantitative correction model between all spectra in the correction set and the DPPH radical scavenging rate; the competitive adaptive weighted sampling combined with partial least squares regression quantitative model was used to predict the DPPH radical scavenging rate of the validation set Dendrobium officinale samples to verify the prediction accuracy of the correction model.

[0042] (7) A step of evaluating the prediction performance of the calibration model, wherein the evaluation index includes the calibration set correlation coefficient (R C ), prediction set correlation coefficient (R P ), root mean square error of calibration set (RMSEC), and root mean square error of validation set (RMSEP). The closer the R value is to 1, the better the prediction performance of the model; the size of RMSEC and RMSEP is related to the chemical value of the sample. The smaller and closer these two parameters are, the better the model performance and the higher the prediction accuracy. The following Table 3 describes the parameters of the quantitative calibration model for the DPPH free radical scavenging rate of Dendrobium officinale medicinal materials. It can be seen from Table 3 that the R value of the calibration set is C The value was 0.931, and the RMSEC value was 2.06, indicating that the established DPPH free radical scavenging rate quantitative correction model has a good effect and is ideal for quantitative analysis. The competitive adaptive weighted sampling combined with partial least squares regression quantitative model was used to predict the DPPH free radical scavenging rate of Dendrobium officinale samples in the validation set. P The value was 0.812, and the RMSEP value was 1.76, which were close to the RMSEC value and both were smaller, indicating that the established competitive adaptive weighted sampling combined with partial least squares regression quantitative model for DPPH radical scavenging rate of Dendrobium officinale had good stability and predictive ability. Figure 7 It is a comparison chart of the measured values ​​of DPPH free radical scavenging rate of Dendrobium officinale medicinal materials in the correction set and the validation set and the near-infrared predicted value.

[0043] Table 3 Parameters of the calibration model for DPPH radical scavenging rate of Dendrobium officinale Model <![CDATA[R C ]]> RMSEC <![CDATA[R P ]]> RMSEP DPPH radical scavenging rate model 0.931 2.06 0.812 1.76 (8) Obtaining a sample spectrum of an unknown Dendrobium officinale medicinal material sample according to the method in step (3), and importing the established competitive adaptive weighted sampling combined with partial least squares regression quantitative model to obtain the DPPH free radical scavenging rate of the unknown Dendrobium officinale sample.

[0044] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for rapid detection of antioxidant activity of Dendrobium officinale medicinal material, characterized in that: The method comprises at least one of the steps of ABTS free radical scavenging rate determination, iron ion reducing ability determination and DPPH free radical scavenging rate determination; A. The ABTS free radical scavenging rate determination includes the following steps: (1) collecting multiple batches of Dendrobium officinale medicinal materials, crushing and sieving, and obtaining a number of Dendrobium officinale powder samples; (2) Determination of ABTS free radical scavenging rate in Dendrobium officinale samples; (3) Collecting the original near-infrared spectrum of each Dendrobium officinale sample; (4) After removing abnormal spectra, the original near-infrared spectra were processed using the standard normal variable transformation spectrum preprocessing method, and the KS algorithm was used to divide the calibration set and the validation set to establish a partial least squares regression model; (5) A competitive adaptive weighted sampling combined with partial least squares regression quantitative model for ABTS free radical scavenging rate was constructed using the calibration set, and the model was validated using the validation set; (6) Obtaining a near infrared spectrum of the unknown Dendrobium officinale sample according to the method of step (3), and importing the established quantitative calibration model to obtain the ABTS free radical scavenging rate value of the unknown Dendrobium officinale medicinal material sample; B. The determination of iron ion reducing ability includes the following steps: (1) collecting multiple batches of Dendrobium officinale medicinal materials, crushing and sieving, and obtaining a number of Dendrobium officinale powder samples; (2) Determination of the iron ion reducing capacity in Dendrobium officinale samples; (3) Collecting the original near-infrared spectrum of each Dendrobium officinale sample; (4) After removing abnormal spectra, the original near-infrared spectra were processed using the standard normal variable transformation spectrum preprocessing method, and the KS algorithm was used to divide the calibration set and the validation set to establish a partial least squares regression model; (5) A competitive adaptive weighted sampling combined with partial least squares regression quantitative model of iron ion reduction capacity was constructed using the calibration set, and the model was validated using the validation set; (6) Obtaining a near-infrared spectrum of an unknown Dendrobium officinale sample according to the method of step (3), and importing the established quantitative calibration model to obtain the iron ion reducing capacity value of the unknown Dendrobium officinale medicinal material sample; C. The DPPH free radical scavenging rate determination includes the following steps: (1) collecting multiple batches of Dendrobium officinale medicinal materials, crushing and sieving, and obtaining a number of Dendrobium officinale powder samples; (2) Determination of DPPH free radical scavenging rate in Dendrobium officinale samples; (3) Collecting the original near-infrared spectrum of each Dendrobium officinale sample; (4) After removing abnormal spectra, the original near-infrared spectra were processed using the standard normal variable transformation spectrum preprocessing method, and the KS algorithm was used to divide the calibration set and the validation set to establish a partial least squares regression model; (5) A competitive adaptive weighted sampling combined with partial least squares regression quantitative model for DPPH free radical scavenging rate was constructed using the calibration set, and the model was validated using the validation set; (6) Obtaining a near-infrared spectrum of the unknown Dendrobium officinale sample according to the method of step (3), and importing the established quantitative calibration model to obtain the DPPH free radical scavenging rate value of the unknown Dendrobium officinale medicinal material sample.

2. The method for rapid detection of antioxidant activity of Dendrobium officinale medicinal material according to claim 1, characterized in that: In the step (2) of determining the ABTS free radical scavenging rate, a chemometric method is used to determine the ABTS free radical scavenging rate in the Dendrobium officinale medicinal material; in the step (2) of determining the iron ion reducing ability, a chemometric method is used to determine the iron ion reducing ability in the Dendrobium officinale medicinal material; in the step (2) of determining the DPPH free radical scavenging rate, a chemometric method is used to determine the DPPH free radical scavenging rate in the Dendrobium officinale medicinal material.

3. The method for rapid detection of antioxidant activity of Dendrobium officinale medicinal material according to claim 1 or 2, characterized in that: In the step (3) of the ABTS free radical scavenging rate determination, the iron ion reducing ability and the DPPH free radical scavenging rate determination, the specific process of collecting the near-infrared original spectrum of the Dendrobium officinale sample is as follows: accurately weigh the Dendrobium officinale medicinal material powder and place it in a weighing bottle, and use the diffuse reflectance method to collect the near-infrared original spectrum, with air as the reference; spectrum collection conditions: scanning range 4000-10000 cm -1 , the number of scans is 64, and the resolution is 8 cm -1 , three spectra were collected for each sample and the average spectrum was calculated.

4. The method for rapid detection of antioxidant activity of Dendrobium officinale medicinal material according to claim 1 or 2, characterized in that: In the step (4) of the ABTS free radical scavenging rate determination, the iron ion reducing ability and the DPPH free radical scavenging rate determination, the ratio of the number of samples in the calibration set and the number of samples in the validation set is 2:

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5. The method for rapid detection of antioxidant activity of Dendrobium officinale medicinal material according to claim 1 or 2, characterized in that: In the step (4) of the ABTS free radical scavenging rate determination, the iron ion reducing ability and the DPPH free radical scavenging rate determination, a quantitative calibration model is established using calibration set samples, and the prediction accuracy of the model is evaluated using a validation set.

6. The method for rapid detection of antioxidant activity of Dendrobium officinale medicinal material according to claim 1, characterized in that: In the step (5) of the ABTS free radical scavenging rate determination, competitive adaptive weighted sampling combined with partial least squares regression algorithm is used to obtain the optimal wavelength subset, and the RMSECV value is 0.44%; in the step (5) of the iron ion reducing ability determination, competitive adaptive weighted sampling combined with partial least squares regression algorithm is used to obtain the optimal wavelength subset, and the RMSECV value is 2.64 μmol / L; in the step (5) of the DPPH free radical scavenging rate determination, competitive adaptive weighted sampling combined with partial least squares regression algorithm is used to obtain the optimal wavelength subset, and the RMSECV value is 2.06%.

7. The method for rapid detection of antioxidant activity of Dendrobium officinale medicinal material according to claim 1, 2 or 6, characterized in that: In the step (5) of the ABTS free radical scavenging rate determination, the iron ion reducing ability and the DPPH free radical scavenging rate determination, when using the CARS-partial least squares regression method to find the optimal wavelength subset, 80% of the random samples are used as the calibration set of the partial least squares regression model, and the RMSECV value is used as the criterion for parameter optimization. The wavelength subset with the smallest value is considered to be the optimal wavelength subset.

8. The method for rapid detection of antioxidant activity of Dendrobium officinale medicinal material according to claim 1, 2 or 6, characterized in that: The step (5) of measuring the ABTS free radical scavenging rate, the iron ion reducing ability and the DPPH free radical scavenging rate also includes a step of evaluating the prediction performance of the established calibration model, and the evaluation index includes the calibration set correlation coefficient (R C ), prediction set correlation coefficient (R P ), root mean square error of the calibration set (RMSEC), root mean square error of the validation set (RMSEP), and relative deviation of prediction (RSEP).

9. Use of the detection method according to any one of claims 1 to 8 in the field of quality detection and control of Dendrobium officinale medicinal materials.

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