A rapid identification method of atractylodes rhizome origin and quality
By using a specific electronic nose sensor array and OPLS-DA model, the problems of strong subjectivity and long time consumption in the identification method of Atractylodes macrocephala have been solved, realizing rapid identification of the origin of Atractylodes macrocephala and prediction of its efficacy, which is suitable for efficient quality control in the Chinese medicine industry.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHEJIANG CHINESE MEDICAL UNIV MEDICAL PIECES
- Filing Date
- 2026-01-30
- Publication Date
- 2026-05-29
AI Technical Summary
Existing methods for identifying Atractylodes macrocephala are highly subjective and have poor reproducibility. Traditional methods are time-consuming and complex. The application of electronic nose technology in traditional Chinese medicine lacks in-depth correlation and cannot achieve rapid and accurate identification of origin and prediction of efficacy.
A specific electronic nose sensor array (W1W, W2W, and W5S) was used to acquire information on the volatile odor of Atractylodes macrocephala. The place of origin was distinguished by the OPLS-DA model, and the efficacy was indirectly reflected by the response value of the W5S sensor. A "component-sensor-efficacy" correlation model was established.
It enables rapid and accurate identification of the origin of Atractylodes macrocephala and prediction of its efficacy. The testing process is non-destructive and efficient, and is suitable for rapid screening and quality control in the Chinese medicine industry.
Smart Images
Figure CN122109464A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of traditional Chinese medicine quality control technology, specifically to a rapid identification method for the origin and quality of Atractylodes macrocephala. Background Technology
[0002] Atractylodes macrocephala is a commonly used bulk Chinese medicinal herb, and its quality is closely related to its traditional place of origin, hence the saying "authentic medicinal material." Among them, Zhejiang is considered the best, and it is one of the famous "Eight Zhejiang Herbs." Currently, the quality evaluation of Atractylodes macrocephala relies mainly on appearance, microscopic identification, and determination of the content of individual chemical components. These methods are either highly subjective or cumbersome and time-consuming, making it difficult to meet the needs of the modern Chinese medicine industry for rapid and high-throughput quality monitoring of raw materials.
[0003] Electronic nose technology is an analytical instrument that simulates the biological olfactory system and has been initially applied to quality identification in the food and tobacco industries. Although some research has attempted to apply it to traditional Chinese medicine, it has mostly remained at the level of simply distinguishing different herbs or roughly judging authenticity. Current technology has not yet solved the following key problems: 1. How to select sensor combinations that are most specifically responsive to the differences in origin of specific herbs such as Atractylodes macrocephala; 2. How to scientifically correlate the macroscopic response signal of the electronic nose with the microscopic chemical composition differences of the herbs; 3. How to establish a quantitative relationship between the electronic nose signal and the final efficacy, achieving rapid prediction from "odor" to "efficacy".
[0004] Chinese patent with publication number CN112036482A relates to a method for classifying Chinese medicinal materials based on electronic nose sensor data, but it cannot solve the above-mentioned technical problems; Chinese patent with publication number CN109948676A relates to a method for identifying the planting origin of Chinese medicinal materials based on artificial intelligence, which takes pictures of each type of Chinese medicinal material from different origins for analysis and identification, but the analysis and identification still remains at the surface level. Summary of the Invention
[0005] This invention solves the problems of existing Atractylodes macrocephala identification methods being highly subjective, having poor reproducibility, and relying on personal experience. It proposes a rapid identification method for the origin and quality of Atractylodes macrocephala, which can not only accurately distinguish Atractylodes macrocephala from different origins, but also predict the intrinsic quality related to the "spleen-strengthening and diarrhea-relieving" effects of Atractylodes macrocephala through specific electronic nose sensor signals.
[0006] To achieve the above objectives, the present invention adopts the following technical solution: a rapid identification method for the origin and quality of Atractylodes macrocephala, comprising the following steps: S1, using an electronic nose to acquire volatile odor information of Atractylodes macrocephala powder, wherein the sensor array of the electronic nose includes at least W1W, W2W and W5S sensors; acquiring response value data of W1W, W2W and W5S sensors; S2, based on the response value data of W1W and W2W sensors, outputs the origin information of Atractylodes macrocephala using the first discrimination method; S3, based on the response value data of the W5S sensor, uses a second discrimination method to output the efficacy and quality information of Atractylodes macrocephala in improving diarrhea.
[0007] The technical solution of this invention analyzes and predicts the differences in origin of Atractylodes macrocephala and its efficacy in improving diarrhea through an electronic nose sensor array. It can not only accurately distinguish Atractylodes macrocephala from different origins, but also predict the intrinsic quality related to the "spleen-strengthening and diarrhea-relieving" effect of Atractylodes macrocephala through specific electronic nose sensor signals, providing core technical support for the raw material procurement acceptance, grading and premium pricing of Chinese herbal medicine enterprises.
[0008] The present invention is further configured such that: the first discrimination method includes: The response data of W1W and W2W sensors are input into the constructed and compliant OPLS-DA model. The scatter plot of the OPLS-DA model is used to distinguish Atractylodes macrocephala from different origins and obtain the origin information of Atractylodes macrocephala.
[0009] In this technical solution, the first discrimination method described above is used to accurately distinguish Atractylodes macrocephala from Zhejiang Province from other producing areas.
[0010] The present invention is further configured such that the response values of the W1W and W2W sensors are significantly positively correlated with the contents of eight volatile components, and the response values of the W1W sensor are also significantly positively correlated with the contents of L-carvone.
[0011] In this technical solution, the response value data of the W1W sensor is significantly positively correlated with the content of nine volatile components, and the response value data of the W2W sensor is significantly positively correlated with the content of eight volatile components.
[0012] The present invention is further configured such that: the OPLS-DA model is obtained according to the orthogonal partial least squares discriminant analysis method; after the model is constructed, model evaluation and model verification are performed to obtain an OPLS-DA model that meets the requirements.
[0013] In this technical solution, the response values of the two sensors obtained from Atractylodes macrocephala from different origins are imported into SIMCA software, and the OPLS-DA model can be obtained through orthogonal partial least squares discriminant analysis (OPLS-DA).
[0014] The present invention is further configured such that: the second discrimination method includes: Correlation analysis was conducted between the response data of the W5S sensor and the efficacy of the drug in improving diarrhea in zebrafish. The results showed that the response data of the W5S sensor was significantly negatively correlated with the efficacy. Furthermore, the response data of the W5S sensor was significantly positively correlated with the response data of the W2W sensor. The response data of the W5S sensor can be used to indirectly reflect the overall abundance of volatile odor components, thereby enabling the prediction of the efficacy and quality of Atractylodes macrocephala in improving diarrhea.
[0015] In this technical solution, the first discrimination method described above is used to achieve rapid prediction of the efficacy and quality of Atractylodes macrocephala.
[0016] The present invention is further configured such that the eight volatile components include cinnamate, 2-hexanone, isopentenal, trans-2-pentenal, 4-ethylphenol, 2-acetyl-2-thiazoline, 4-hexen-3-one, and butylcaprolactone.
[0017] The present invention is further configured such that the lower the response value of the Atractylodes macrocephala W5S sensor, the better the efficacy in improving diarrhea, and the lower the content of the eight volatile components.
[0018] The present invention is further configured such that: the model evaluation is achieved by evaluating the model’s R2 and Q2. The closer the model’s R2 is to 1 and the greater the Q2 is to 0.5, the better the model’s fit and predictability.
[0019] The present invention is further configured such that: the model test is performed by evaluating the intercepts of R2 and Q2 to test whether the model has the risk of overfitting.
[0020] In this technical solution, the model is subjected to 200 permutation tests to evaluate the intercepts of R² and Q². The slopes of both regression lines are greater than 0, and the R² and Q² on the left side are smaller than the original R² and Q² on the right side. Both R² and Q² intersect the y-axis on the left side. The intercept of R² is less than 0.3, and the intercept of Q² is less than 0, with larger absolute values being better, indicating that the model does not have the risk of overfitting.
[0021] The present invention is further configured such that the eight volatile components are selected from fifteen volatile components, specifically by gas chromatography-ion mobility spectrometry analysis.
[0022] In this technical solution, the results were verified by gas chromatography-ion mobility spectrometry (GC-IMS). Among the 15 differentially volatile components screened by GC-IMS analysis, 9 of them showed a significant positive correlation with the W1W and W2W sensors (P<0.05), and the W5S sensor showed a significant positive correlation with the W2W sensor (P<0.05). These 9 components constitute the material basis for the response of the electronic nose sensor.
[0023] The present invention provides a rapid identification method for the origin and quality of Atractylodes macrocephala, which can bring the following beneficial effects: 1. The entire testing process takes only a few minutes, requires no complex sample pretreatment, and enables rapid, non-destructive, and high-throughput screening of Atractylodes macrocephala samples; 2. It was clarified that the responses of the W2W and W1W sensors originated from nine specific differentially volatile components, providing a solid chemical basis for the identification of Atractylodes macrocephala from Zhejiang and other producing areas; 3. The W5S sensor, as an indirect yet efficient predictive indicator of core drug efficacy, indirectly characterizes the overall level of characteristic components through linkage with the W2W sensor, and ultimately establishes a negatively correlated quantitative relationship with drug efficacy, forming a "component-sensor-drug efficacy" correlation model. This correlation model is highly flexible in practical applications. Enterprises can use only W2W / W1W for rapid origin verification, or only W5S for rapid drug efficacy and quality analysis, or combine both for comprehensive evaluation, greatly enhancing the practical value and applicability of the method of this invention. Attached Figure Description
[0024] Figure 1 The electronic nose odor response curves for Atractylodes macrocephala from different origins are shown.
[0025] Figure 2 Radar chart of electronic nose odor response values for Atractylodes macrocephala from different origins.
[0026] Figure 3 The image shows the OPLS analysis results of Atractylodes macrocephala from Zhejiang and Anhui.
[0027] Figure 4 The image shows the OPLS analysis results of Atractylodes macrocephala from Zhejiang and Henan provinces.
[0028] Figure 5 The results of OPLS analysis of Atractylodes macrocephala from Zhejiang and Hebei are presented.
[0029] Figure 6 GC-IMS spectra of volatile organic compounds in Atractylodes macrocephala from different origins.
[0030] Figure 7 A graph showing the differences in volatile organic compounds in Atractylodes macrocephala from within and outside Zhejiang Province.
[0031] Figure 8 This is a correlation analysis diagram between the electronic nose sensor and volatile components.
[0032] Figure 9 Results of the efficacy of Atractylodes macrocephala from different origins in improving diarrhea in zebrafish.
[0033] Figure 10 A heatmap showing the correlation between performance and sensor performance. Detailed Implementation
[0034] Example 1 Existing technologies have the following drawbacks: 1. Traditional methods for identifying Atractylodes macrocephala are highly subjective, have poor reproducibility, and rely on personal experience; 2. Conventional instrumental analysis methods, such as HPLC and GC-MS, involve complex pretreatment, long analysis cycles, and high costs, making them unsuitable for rapid screening of large batches of samples; 3. Existing electronic nose technology has a superficial application in traditional Chinese medicine, lacking in-depth correlation with specific chemical components and pharmacodynamics, failing to elucidate its scientific implications, and unable to achieve accurate quality prediction. To address these shortcomings, this embodiment proposes a rapid identification method for the origin and quality of Atractylodes macrocephala, which mainly includes the following steps.
[0035] Step S1: Use an electronic nose to obtain volatile odor information of Atractylodes macrocephala powder. The sensor array of the electronic nose includes at least W1W, W2W and W5S sensors; obtain the response value data of W1W, W2W and W5S sensors.
[0036] Step S1 mainly includes sample preparation and detection using an electronic nose.
[0037] For sample preparation, in this embodiment, 2g of Atractylodes macrocephala powder (passed through a No. 2 sieve) was accurately weighed, placed in a 200mL butyl rubber stopper headspace bottle, and sealed.
[0038] In this embodiment, the sampling interval is set to 1 second, the rinsing time to 100 seconds, the zeroing time to 10 seconds, the pre-sampling time to 5 seconds, and the measurement time to 120 seconds.
[0039] Gas flow rate settings: Chamber flow rate is 300 mL / min, initial injection flow rate is 300 mL / min. The electronic nose sampling needle is precisely inserted into the fixed position of the sample vial and connected to an activated carbon filter to maintain pressure equilibrium within the vial. Each batch of samples is sampled three times to record and capture the odor signal of the sample.
[0040] The sensor array includes at least a W1W sensor sensitive to inorganic sulfides, a W2W sensor sensitive to organic sulfides, and a W5S sensor sensitive to nitrogen oxides. Signal values from the stable phase of the sensor response are collected as raw data.
[0041] Step S2: Based on the response value data of W1W and W2W sensors, the origin information of Atractylodes macrocephala is output using the first discrimination method.
[0042] For step S2, the main task is to distinguish the origin of Atractylodes macrocephala according to the first discrimination method. Specifically, the first discrimination method includes the following: constructing an OPLS-DA model, inputting the response value data of the W1W and W2W sensors obtained in step S1 into the constructed model, distinguishing Atractylodes macrocephala from different origins according to the scatter plot of the OPLS-DA model, and obtaining the origin information of Atractylodes macrocephala.
[0043] In this technical solution, the first discrimination method described above is used to accurately distinguish Atractylodes macrocephala from Zhejiang Province from other producing areas.
[0044] The response values of the W1W and W2W sensors showed a significant positive correlation with the content of eight volatile components (P<0.05), and the response values of the W1W sensor also showed a significant positive correlation with the content of L-carvone (P<0.05). The eight volatile components included cinnamyl butyrate, 2-hexanone, isopentenal, trans-2-pentenal, 4-ethylphenol, 2-acetyl-2-thiazoline, 4-hexen-3-one, and butylcaprolactone. The above nine components constituted the chemical markers of the characteristic odor of Atractylodes macrocephala origin.
[0045] The OPLS-DA model described above is obtained using the orthogonal partial least squares discriminant analysis method. After the model is constructed, it is evaluated and tested to obtain an OPLS-DA model that meets the requirements.
[0046] More specifically, the response values of the two sensors obtained from Atractylodes macrocephala from different origins were imported into the SIMCA software, and the OPLS-DA model was obtained through orthogonal partial least squares discriminant analysis (OPLS-DA).
[0047] Evaluate the model's R² and Q²; the closer the model's R² is to 1 and the greater its Q² is to 0.5, the better the model's fit and predictability.
[0048] Model validation assesses the risk of overfitting by evaluating the intercepts of R² and Q². In this embodiment, the model undergoes 200 permutation tests to evaluate the intercepts of R² and Q². The slopes of both regression lines are greater than 0, and the R² and Q² values on the left side are smaller than the original R² and Q² values on the right side. Both R² and Q² intersect the y-axis on the left side. The intercept of R² is less than 0.3, and the intercept of Q² is less than 0, with larger absolute values being better, indicating that the model does not have a risk of overfitting.
[0049] The model is evaluated and tested in the above way to determine whether it meets the requirements.
[0050] Step S3: Based on the response value data of the W5S sensor, the second discrimination method is used to output the efficacy and quality information of Atractylodes macrocephala in improving diarrhea.
[0051] Step S3 mainly involves predicting and evaluating the efficacy of Atractylodes macrocephala in improving diarrhea based on the second discriminant method. Specifically, the second discriminant method includes the following: Correlation analysis was conducted between the response data of the W5S sensor and the efficacy of the drug in improving diarrhea in zebrafish. The results showed that the response data of the W5S sensor was significantly negatively correlated with the efficacy (P<0.05). Furthermore, the response data of the W5S sensor was significantly positively correlated with the response data of the W2W sensor. The response data of the W5S sensor can be used to indirectly reflect the overall abundance of volatile odor components, thereby enabling the prediction of the efficacy and quality of Atractylodes macrocephala in improving diarrhea.
[0052] More specifically, the experiment found that Atractylodes macrocephala from different origins could improve diarrhea in zebrafish. Further analysis of the response values of W5S, W2W, and W1W sensors obtained from Atractylodes macrocephala from different origins and their correlation with the efficacy in improving zebrafish diarrhea revealed a significant negative correlation between the W5S sensor response value and the efficacy; that is, the lower the W5S sensor response value, the better the effect in improving diarrhea. Analysis of the W5S sensor response value and volatile components showed no correlation between the W5S sensor response value and any of the components, but a significant positive correlation was found between the W5S sensor response value and the W2W sensor response value, and the W2W sensor was positively correlated with the aforementioned eight characteristic components (P<0.05). Therefore, it is believed that the W5S sensor response value can indirectly reflect the overall abundance of characteristic volatile components of Atractylodes macrocephala; that is, the lower the W5S sensor response value, the better the effect in improving diarrhea.
[0053] Eight volatile components were selected from fifteen volatile components, specifically through gas chromatography-ion mobility spectrometry analysis.
[0054] In this technical solution, the results were verified by gas chromatography-ion mobility spectrometry (GC-IMS). Among the 15 differentially volatile components screened by GC-IMS analysis, 9 of them showed a significant positive correlation with the W1W and W2W sensors (P<0.05), and the W5S sensor showed a significant positive correlation with the W2W sensor (P<0.05). These 9 components constitute the material basis for the response of the electronic nose sensor.
[0055] The technical solution described in this embodiment can bring about the following technical effects, which are mainly reflected in the following aspects: 1. Fast and efficient: The entire detection process takes only a few minutes, requires no complicated sample pretreatment, and can achieve rapid, non-destructive and high-throughput screening of Atractylodes macrocephala samples; 2. Origin identification: It was clarified that the response of W2W and W1W sensors originated from 9 specific differentially volatile components, providing a solid chemical basis for the identification of Atractylodes macrocephala from Zhejiang and Atractylodes macrocephala from other origins; 3. Drug Efficacy Prediction: The W5S sensor was found to be an indirect yet efficient predictor of core drug efficacy. Through its linkage with the W2W sensor, it indirectly characterizes the overall level of characteristic components and ultimately establishes a negatively correlated quantitative relationship with drug efficacy, forming a component-sensor-drug efficacy correlation model. 4. High practicality: The above-mentioned correlation models are highly flexible in practical applications. Enterprises can use W2W / W1W alone for rapid screening of origin authenticity, or use W5S alone for rapid analysis of efficacy and quality, or combine both for comprehensive evaluation, greatly enhancing the practical value and applicability of the method.
[0056] Example 2 This embodiment provides a specific implementation method that can achieve the technical solution of Embodiment 1 above, mainly including three main processes: electronic nose detection of differences in volatile odor of Atractylodes macrocephala from different origins, qualitative analysis of volatile components of Atractylodes macrocephala by gas chromatography-ion mobility spectrometry (GC-IMS), and comparison of the efficacy of Atractylodes macrocephala from different origins in improving diarrhea in zebrafish.
[0057] The main steps for using an electronic nose to detect differences in the volatile odor of Atractylodes macrocephala from different origins include the following detailed procedures.
[0058] Accurately weigh 2.0 g of Atractylodes macrocephala powder and place it in a 200 mL butyl rubber stoppered headspace vial, then seal it. Measure using a PEN3 electronic nose. Set the sampling interval to 1 s, rinsing time to 100 s, zeroing time to 10 s, pre-sampling time to 5 s, and measurement time to 120 s. Gas flow rate settings: chamber flow rate 300 mL / min, initial injection flow rate 300 mL / min; accurately insert the electronic nose sampling needle into the fixed position of the sample vial and connect it to an activated carbon filter to maintain pressure equilibrium within the vial.
[0059] The electronic nose sensor response values are as follows: Figure 1 As shown, the horizontal axis represents time, and the vertical axis represents the sensor response value. Each curve represents the odor response change of a sensor within 120 seconds. A represents Zhejiang Province, B represents Anhui Province, C represents Henan Province, and D represents Hebei Province. The radar chart shows the response values of various sensors for the odor of Atractylodes macrocephala from within and outside Zhejiang Province when the response value is stable (100 seconds). Figure 2 As shown, ZJ represents Zhejiang, AH represents Anhui, HeH represents Henan, and HeB represents Hebei. The results showed that the differences in the aroma of Atractylodes macrocephala from different producing areas were mainly reflected in sensors W2W, W1W, and W5S, with Zhejiang Atractylodes macrocephala showing higher response values in W2W and W1W than Atractylodes macrocephala from other provinces. A search of the PEN3 electronic nose sensor's response types to substances (Table 1) revealed that the aroma sources in Atractylodes macrocephala are mainly nitrogen oxides, aromatic components, and inorganic and organic sulfides; Table 1 is as follows: Table 1. Substance Response Types Corresponding to PEN3 Electronic Nose Sensor .
[0060] To demonstrate the reliability of the electronic nose detection method, the precision of the instrument was examined. Five repeated measurements were performed on randomly selected samples from the same batch from Henan province, and the relative standard deviation (RSD) of each sensor's response value was calculated. The results are shown in Table 2. The RSD of all sensor response values was <4%, indicating that the instrument has good accuracy and data stability.
[0061] Table 2 Precision results of each sensor in the electronic nose .
[0062] Orthogonal partial least squares discriminant analysis (OPLS-DA) using SIMCA14.1 was performed to analyze the odor of Atractylodes macrocephala from different origins. The results showed that Atractylodes macrocephala from Zhejiang was significantly different from Atractylodes macrocephala from other origins. (Reference) Figure 3 An OPLS-DA model based on the aroma of Atractylodes macrocephala from Zhejiang and Anhui provinces revealed significant differences in aroma between the two regions, clearly classifying them into two categories ( Figure 3 A, Figure 3 A is the scatter plot of the OPLS-DA model. Generally, the closer the R² is to 1 and the Q² is greater than 0.5, the better the model's fit and predictability. This model has an R²Y of 0.944 and a Q² of 0.912, indicating good interpretability and predictive ability. Further verification of the model's predictive ability using a 200-permutation test is shown in the following results. Figure 3 As shown in B (permutation test), the slopes of both regression lines are greater than 0, and the R² and Q² on the left are both smaller than the original R² and Q² on the right. Both R² and Q² intersect the y-axis on the left. The intercept of R² is less than 0.3 (R² = 0.176), and the intercept of Q² is less than 0, with a larger absolute value being better (Q² = -0.604). This indicates that the model does not have the risk of overfitting and is considered capable of distinguishing between Atractylodes macrocephala from Zhejiang and Anhui.
[0063] The odor of Atractylodes macrocephala from Zhejiang and Henan, as well as Atractylodes macrocephala from Zhejiang and Hebei, was analyzed using the same method as described above. An OPLS-DA model was constructed, revealing that Atractylodes macrocephala from Zhejiang was significantly different from Atractylodes macrocephala from Henan and Hebei. The R²Y of the OPLS-DA model constructed for Atractylodes macrocephala from Zhejiang and Henan was 0.974, and the Q² was 0.958; the R² of the permutation test was 0.131, and the Q² was -0.533. The results are as follows: Figure 4 As shown. The OPLS-DA model constructed from Zhejiang Atractylodes macrocephala and Hebei Atractylodes macrocephala has an R²Y of 0.975 and a Q² of 0.962; the permutation test has an R² of 0.147 and a Q² of -0.594, as shown in the figure. Figure 5 As shown.
[0064] The qualitative analysis of volatile components in Atractylodes macrocephala using gas chromatography-ion mobility spectrometry (GC-IMS) mainly includes the following detailed steps.
[0065] Accurately weigh 0.5 g of Atractylodes macrocephala powder (passed through a No. 2 sieve) and place it in a 20 mL stoppered empty vial. Incubate at 80℃ for 15 min before injection. System conditions: column temperature 60℃; carrier gas N2; ion migration detector temperature 45℃; analysis time 56 min. Injection volume 200 μL; incubation time 15 min; incubation temperature 80℃; injection needle temperature 85℃; incubation rotation speed 500 r / min. E1 drift gas flow rate 75 mL / min; E2 carrier gas flow rate: 0–2 min, 2 mL / min; 2–10 min, 2–10 mL / min; 10–20 min, 10–100 mL / min; 20–25 min, 100–150 mL / min; 25–50 min, 150 mL / min.
[0066] The analytical software accompanying the instrument, including VOCal and the accompanying Reporter plugin, was used to analyze the spectral differences among Atractylodes macrocephala from different origins. The results are as follows: Figure 6 As shown in A, Figure 6 A shows the GC-IMS spectrum. The background is blue, the horizontal axis represents ion migration time, the red vertical line at 1.0 represents the reaction ion peak, and each point on either side of the peak represents a volatile organic compound (VOC). Darker colors indicate higher VOC concentrations. The vertical axis represents retention time. The results show differences in VOC concentrations among Atractylodes macrocephala samples from different origins. To clearly compare these differences, a comparative spectrum was used, and the results are shown below. Figure 6 B, Figure 6 B represents the differential comparison spectrum. The spectrum of Atractylodes macrocephala from Zhejiang Province was selected as the reference spectrum. Spectra of Atractylodes macrocephala from other origins were subtracted from the reference spectrum. If the volatile organic compounds (VOCs) of the two spectra were identical, the subtracted background was white. Red indicated that the VOC concentration was higher than the reference spectrum, and blue indicated that the VOC concentration was lower than the reference spectrum. The results showed that the VOCs of Atractylodes macrocephala from different origins were basically consistent.
[0067] The application software's built-in NIST and IMS databases and the accompanying analysis software VOCal were used to perform qualitative analysis of the substances. Information on volatile organic compounds, retention index, and relative migration time data are shown in Table 3.
[0068] Table 3. Identification of the components of volatile organic compounds Where RI is the retention index; Rt is the retention time; Dt is the relative migration time; M is superscript for monomer; and D is superscript for dimer.
[0069] The peak height information of volatile organic compounds of Atractylodes macrocephala from different origins was imported into SIMCA14.0 software for OPLS-DA analysis. Figure 7 A) The results showed that Atractylodes macrocephala from within Zhejiang Province was distributed in the second and third quadrants, while Atractylodes macrocephala from outside Zhejiang Province was distributed in the first and fourth quadrants, indicating a significant difference in the volatile organic compounds of Atractylodes macrocephala from within and outside Zhejiang Province. The model's R²Y was 0.910 and Q² was 0.824, indicating good interpretability and predictive ability. The model's reliability was validated by 200 permutation tests, with an R² intercept of 0.389 and a Q² of -0.491, indicating model validity. Figure 7 B).
[0070] Generally, differential components are screened based on criteria such as VIP > 1.00, -log10(P-Value) > 1.30, and Fold change > 2.00 or Fold change < 0.50. The VIP value results are shown below. Figure 7 As shown in C, 54 components were screened based on VIP>1; 66 components were screened based on P-value<0.05; and 15 components were selected based on Fold change>2 or Fold change<0.5. The intersection of these criteria yielded 15 components with different volatile values, including aldehydes, esters, ketones, alcohols, pyridines, pyrazines, and carboxylic acids. The results are shown in Table 4. Table 4. Table of Differential Components .
[0071] To further analyze the intrinsic relationship between the electronic nose sensor and volatile components, Pearson correlation analysis was performed on the main differential sensors W2W, W1W, and W5S with 15 differentially expressed volatile components screened by GC-IMS. The results are shown in [Figure number missing]. Figure 8 P < 0.05 indicates a strong correlation between the differentially expressed compounds and the sensor; red indicates a positive correlation, blue indicates a negative correlation, and darker colors represent stronger correlations, with correlation coefficients closer to 1. The results showed that the W2W and W1W sensors were significantly positively correlated with nine volatile components, such as cinnamyl butyrate (C1), L-carvone (C5), 2-hexanone (C6), isopentenal (C9), trans-2-pentenal (C10), 4-ethylphenol (C11), 2-acetyl-2-thiazoline (C12), 4-hexen-3-one (C14), and butylcaprolactone (C15) (P < 0.05), which could serve as the main material basis for the aroma of Atractylodes macrocephala from different origins. Figure 8In the mean squares, *P<0.05, **P<0.01, and ***P<0.001.
[0072] The comparison of the efficacy of Atractylodes macrocephala from different origins in improving diarrhea in zebrafish mainly includes the following detailed steps.
[0073] Wild-type AB strain zebrafish (4 dpf) were randomly selected and placed in 6-well plates, with 30 zebrafish treated in each well (experimental group). A normal control group and a model control group were set up, with a volume of 3 mL per well. Except for the normal control group, all other experimental groups were given senna leaves to establish a zebrafish diarrhea model. After treatment at 28℃ for 1 day, each experimental group was fed Nile red in water for 3 hours. After washing off the Nile red, the samples were given in water. Taking Atractylodes macrocephala from Zhejiang as an example, samples were administered at gradient concentrations (125, 250, 500, 1000, 2000 μg / mL). The number of dead zebrafish in each experimental group was counted and removed promptly. After treatment at 28℃ for 18 hours, the maximum detectable concentration of the sample in the model zebrafish was determined. It was found that under the experimental conditions, the maximum detectable concentration of Atractylodes macrocephala from Zhejiang for improving diarrhea in zebrafish was 500 μg / mL. This concentration was used for the formal experiments.
[0074] The positive control group was treated with montmorillonite powder at a concentration of 250 μg / mL at 28℃ for 18 h. Ten zebrafish were randomly selected from each experimental group and photographed under a fluorescence microscope. Data were analyzed and collected using NIS-Elements D 3.20 advanced image processing software. The intestinal fluorescence intensity (S) was analyzed, and the statistical analysis results of this index were used to evaluate the efficacy of the samples in improving diarrhea.
[0075] The percentage of diarrhea-improving efficacy is equal to the difference between S (sample group) and S (model control group), divided by the difference between S (normal control group) and S (model control group). The results are as follows: Figure 9 As shown, compared with the model group, the intestinal fluorescence intensity of zebrafish treated with Atractylodes macrocephala from different origins was significantly different (P<0.05), indicating that Atractylodes macrocephala from different origins could improve the diarrhea status of zebrafish. Comparison at the same concentration revealed that although the efficacy of Atractylodes macrocephala from different origins in improving diarrhea varied, there was no statistical significance (P>0.05), indicating that the ability of Atractylodes macrocephala from different origins to improve zebrafish diarrhea was consistent. Figure 9 In the image, A represents the intestinal fluorescence intensity result, and B represents the effect of improving diarrhea.
[0076] Further Pearson correlation analysis was performed on the sensitive sensors W2W, W1W, and W5S with the drug efficacy results, and the results are as follows: Figure 10 As shown in the figure. The results showed that the efficacy of Atractylodes macrocephala from different origins in improving diarrhea was significantly negatively correlated with the W5S sensor, but not with the W2W and W1W sensors.
Claims
1. A rapid method for identifying the origin and quality of Atractylodes macrocephala, characterized in that, Includes the following steps: S1, using an electronic nose to acquire volatile odor information of Atractylodes macrocephala powder, wherein the sensor array of the electronic nose includes at least W1W, W2W and W5S sensors; acquiring response value data of W1W, W2W and W5S sensors; S2, based on the response value data of W1W and W2W sensors, outputs the origin information of Atractylodes macrocephala using the first discrimination method; S3, based on the response value data of the W5S sensor, uses a second discrimination method to output the efficacy and quality information of Atractylodes macrocephala in improving diarrhea.
2. The rapid identification method for the origin and quality of Atractylodes macrocephala according to claim 1, characterized in that, The first discrimination method includes: The response data of W1W and W2W sensors are input into the constructed and compliant OPLS-DA model. The scatter plot of the OPLS-DA model is used to distinguish Atractylodes macrocephala from different origins and obtain the origin information of Atractylodes macrocephala.
3. The rapid identification method for the origin and quality of Atractylodes macrocephala according to claim 2, characterized in that, The response values of the W1W and W2W sensors showed a significant positive correlation with the content of eight volatile components, and the response values of the W1W sensor also showed a significant positive correlation with the content of L-carvone.
4. A rapid identification method for the origin and quality of Atractylodes macrocephala according to claim 2 or 3, characterized in that, The OPLS-DA model is obtained based on the orthogonal partial least squares discriminant analysis method. After the model is constructed, it is evaluated and tested to obtain an OPLS-DA model that meets the requirements.
5. The rapid identification method for the origin and quality of Atractylodes macrocephala according to claim 3, characterized in that, The second discrimination method includes: Correlation analysis was conducted between the response data of the W5S sensor and the efficacy of the drug in improving diarrhea in zebrafish. The results showed that the response data of the W5S sensor was significantly negatively correlated with the efficacy. Furthermore, the response data of the W5S sensor was significantly positively correlated with the response data of the W2W sensor. The response data of the W5S sensor can be used to indirectly reflect the overall abundance of volatile odor components, thereby enabling the prediction of the efficacy and quality of Atractylodes macrocephala in improving diarrhea.
6. The rapid identification method for the origin and quality of Atractylodes macrocephala according to claim 3, characterized in that, The eight volatile components include cinnamyl butyrate, 2-hexanone, isopentenal, trans-2-pentenal, 4-ethylphenol, 2-acetyl-2-thiazoline, 4-hexen-3-one, and butylcaprolactone.
7. The rapid identification method for the origin and quality of Atractylodes macrocephala according to claim 5, characterized in that, The lower the response value of the Atractylodes macrocephala W5S sensor, the better the efficacy in improving diarrhea, and the lower the content of the eight volatile components.
8. The rapid identification method for the origin and quality of Atractylodes macrocephala according to claim 4, characterized in that, The model evaluation is achieved by evaluating the model's R² and Q². The closer the model's R² is to 1 and the greater its Q² is to 0.5, the better the model's fit and predictability.
9. The rapid identification method for the origin and quality of Atractylodes macrocephala according to claim 4, characterized in that, The model test examines whether the model is at risk of overfitting by evaluating the intercepts of R² and Q².
10. A rapid identification method for the origin and quality of Atractylodes macrocephala according to claim 3, characterized in that, The eight volatile components were selected from fifteen volatile components, specifically through gas chromatography-ion mobility spectrometry analysis.