Rapid nondestructive detection method for quality of lycium barbarum root bark based on hue angle

Abstract

The invention relates to the technical field of traditional Chinese medicine detection, and discloses a lycium barbarum root bark quality rapid nondestructive detection method based on hue angle, which comprises the following steps: collecting reflection spectrum chromaticity signals of the surface of lycium barbarum root bark to be detected in a standard light source environment, and calculating the average hue angle; and substituting the average hue angle as an input variable into a preset functional component prediction model for calculation, and directly outputting the predicted contents of kukoamine A, kukoamine B and total flavonoids in the sample to be detected. The prediction model is a unary linear regression equation constructed based on a large amount of experimental data, and a quantitative mapping relation between the appearance hue angle and the internal chemical component content is determined. According to the method, optical detection is used for replacing traditional chemical analysis, the physical form of a sample does not need to be damaged, no chemical reagent is consumed, rapid, low-cost and objective evaluation on the internal quality of the boxthorn root bark is achieved, and the method is suitable for batch screening and grading of a medicinal material circulation site.

Description

Technical Field

[0001] This invention relates to the field of traditional Chinese medicine testing technology, specifically a rapid and non-destructive method for detecting the quality of wolfberry root bark based on hue angle. Background Technology

[0002] As a commonly used heat-clearing and blood-cooling medicine in clinical practice, the intrinsic quality of wolfberry root bark directly affects the stability of its clinical efficacy. Lycium bark A, Lycium bark B, and total flavonoids are the key pharmacodynamic material basis for evaluating the quality of wolfberry root bark. Accurate and efficient determination of the above-mentioned active ingredients in the planting, purchasing, and circulation of medicinal materials is a prerequisite for achieving quality grading and precise control of medicinal materials.

[0003] Currently, the quality evaluation of wolfberry root bark mainly relies on the physicochemical testing methods specified in standards such as the Pharmacopoeia of the People's Republic of China, or on the traditional sensory experience of practitioners. For the quantitative analysis of chemical components, high performance liquid chromatography is usually used to determine the content of lycopene A and lycopene B, and ultraviolet-visible spectrophotometry is used to determine the content of total flavonoids. For preliminary on-site screening, the sensory characteristics such as the appearance and cross-sectional color of the medicinal material are mostly judged based on experience.

[0004] Although existing physicochemical testing technologies have high analytical sensitivity and accuracy, they still have shortcomings in practical large-scale on-site testing applications. Chromatographic and spectroscopic analyses require the crushing, grinding, and organic solvent extraction of medicinal samples. This irreversible physical damage makes the tested samples unusable, limiting these methods to small-scale destructive sampling and failing to meet the need for comprehensive non-destructive screening of high-value medicinal materials. At the same time, standard chemical testing procedures involve cumbersome pretreatment steps, long single testing cycles, and continuous consumption of chemical reagents such as methanol and acetonitrile, resulting in high testing costs and a burden of waste disposal. This makes them unsuitable for the real-time, low-cost grading requirements of large batches of samples in the production area acquisition or warehousing and logistics stages. While traditional sensory identification is simple, it lacks objective quantitative data support. Relying solely on visual observation is easily affected by ambient light and subjective differences in human experience, making it difficult to guarantee the consistency and reproducibility of quality judgment results. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a rapid and non-destructive method for detecting the quality of wolfberry root bark based on hue angle. This method solves the problems of existing wolfberry root bark quality detection technologies, such as the need to destroy the physical morphology of the sample, long detection time, high reagent costs, and the strong subjectivity and lack of quantitative standards in human sensory evaluation.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a rapid and non-destructive method for detecting the quality of Lycium barbarum root bark based on hue angle, comprising the following steps: selecting dried Lycium barbarum root bark samples for testing, placing them under a D65 standard light source environment for photothermal equilibrium treatment until the sample surface condition stabilizes; acquiring the reflectance spectral chromaticity signal of the root bark surface of the sample using a color measurement device, and converting the reflectance spectral chromaticity signal into a CIE colorimetric value. The color space values ​​are used to calculate the average hue angle of the sample to be tested; the average hue angle is used as a single input variable and substituted into a preset efficacy component prediction model for calculation; the predicted content of lycium chinense A, lycium chinense B, and total flavonoids in the sample to be tested is output.

[0007] In a preferred embodiment, the photothermal balancing treatment specifically includes: using a soft brush to remove adhering mud and impurities from the surface of the sample to be tested, ensuring that there is no obvious mold or physical damage on the surface of the outer root bark; placing the cleaned sample to be tested in a D65 standard light source box and letting it stand for more than 5 minutes, so that the thermodynamic state and moisture distribution of the surface of the medicinal material can physically adapt to the photothermal environment.

[0008] In a preferred embodiment, acquiring the reflectance spectral colorimetric signal specifically includes: setting the measurement mode of the color measurement device to CIE. The color space was set to 10 degrees, and the measurement light source was set to D65. For each sample, a region with a smooth outer bark and uniform color was selected as the measurement point. The measurement probe was placed vertically against the sample surface for multi-point scanning. When calculating the average hue angle, the arithmetic mean of the multiple measurement readings for the same sample was taken to eliminate local color differences.

[0009] A method for constructing the above-mentioned efficacy component prediction model and a specific quantitative calculation logic are also provided.

[0010] The construction method includes: selecting 30 or more representative samples of dried wolfberry root bark to form a standard sample set; obtaining the average hue angle of each sample in the standard sample set; using chemical analysis methods to determine the true content of lycium bark A, lycium bark B, and total flavonoids in each sample in the standard sample set; using the average hue angle as the abscissa and the true content of each component as the ordinate, performing univariate linear regression fitting using the least squares method, calculating the slope and intercept of the regression line, thereby establishing a predictive model for efficacy components.

[0011] The efficacy ingredient prediction model comprises three independent linear regression equations, each representing a quantitative mapping relationship between the average hue angle and the content of each efficacy ingredient. The specific mathematical expressions are as follows:

[0012] The prediction logic for Lycium chinense root extract is as follows: ; The prediction model for Lycium barbarum ethylstilbestrol is as follows: 10.892; The prediction model for total flavonoids is as follows: ; In the formula, The average hue angle of the wolfberry root bark sample to be tested is expressed in degrees. , , The values ​​represent the predicted contents of lycopene, lycopene B, and total flavonoids, respectively, all in milligrams per gram.

[0013] In constructing the model, the actual content was determined as follows: the actual content of lycium chinense A and B was determined by high performance liquid chromatography; the actual content of total flavonoids was determined by ultraviolet-visible spectrophotometry. The correlation coefficients and significance tests of the above regression equations all indicate a statistically significant linear correlation between hue angle and the content of each target component.

[0014] This invention provides a rapid and non-destructive method for detecting the quality of wolfberry root bark based on hue angle. It has the following beneficial effects: 1. This invention replaces the crushing, grinding, and organic solvent extraction steps that must be performed in traditional physicochemical testing by collecting surface reflectance spectra and calculating hue angles. Since this testing process does not change the physical form and internal chemical properties of the medicinal materials, the samples after testing can directly enter the subsequent circulation, sales, or processing of medicinal slices, avoiding sample loss caused by destructive sampling. It is particularly suitable for screening high-value medicinal materials one by one.

[0015] 2. This invention utilizes a univariate linear regression prediction model to transform the complex chemical analysis process into a mathematical calculation process for data. It eliminates the need for time-consuming pretreatment and does not consume chemical reagents such as methanol and acetonitrile. Compared with liquid chromatography or spectroscopy, this method significantly shortens the detection cycle and eliminates the economic and environmental burden of reagent procurement and waste liquid treatment. It is suitable for rapid and low-cost initial quality testing of large batches of samples at the production site.

[0016] 3. Based on the experimentally calibrated regression equation, this invention establishes a quantitative correspondence between the appearance color angle of wolfberry root bark and the contents of lycium bark A, lycium bark B, and total flavonoids. This method transforms the traditional subjective sensory judgment that relies on human experience into a repeatable and quantifiable objective value, overcoming the judgment error caused by differences in the observer's vision or fluctuations in ambient light, and ensuring the consistency of quality grading results. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a detailed flowchart of the present invention; Figure 3 This is a bar chart showing the true values ​​of the high performance liquid chromatography method of the present invention. Detailed Implementation

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See attached document Figure 1 , Figure 1 This is a flowchart illustrating the prediction of the content of active ingredients in wolfberry root bark based on colorimetric parameters according to an embodiment of the present invention. The present invention provides a method for quality evaluation and component prediction of wolfberry root bark, comprising the following steps: S1. In order to eliminate the influence of ambient light fluctuations and instantaneous instability of the sample surface on the measurement accuracy, firstly, a dried wolfberry root bark sample was selected and placed in a D65 standard light source environment for more than 5 minutes to perform photothermal equilibrium treatment until the sample surface was stable. Then, using a precision color measurement device (such as a spectrophotometer), while maintaining the integrity of the sample's physical morphology, the reflectance spectrum was scanned on the flat area of ​​its outer root bark to collect the original colorimetric signal characterizing its appearance optical properties. S2, convert the raw chromaticity signal acquired in step S1 into a CIE converter. The average hue angle of the sample is further calculated based on the coordinates in the international standard color space. In this embodiment, the average hue angle of the sample to be tested... The feature variable selected as the most sensitive to the relationship between the appearance attributes and internal quality of medicinal materials was used as the sole independent input parameter for the subsequent quantitative calculation model.

[0020] S3, the construction process of the prediction model is as follows: Select representative wolfberry root bark from the same batch to construct a standard sample set, on the one hand to obtain the hue angle of the standard sample set. On the other hand, classical chemical analysis methods were used to determine their true content (lycium chinense A and B were determined by high performance liquid chromatography, and total flavonoids were determined by ultraviolet-visible spectrophotometry). The hue angle of the standard sample set... Using the independent variable and the actual content as the dependent variable, the least squares method was used to fit a large amount of sample data, thereby calibrating the specific slope coefficient and intercept constant in the regression equation, establishing the above prediction model, and thus constructing a univariate linear regression prediction model containing three target components: Lycium chinense glycoside A, Lycium chinense glycoside B, and total flavonoids.

[0021] S4, the average hue angle of the sample to be tested extracted in step S2. Substituting into the pre-set efficacy component prediction model, which is constructed based on a univariate linear regression prediction model, the model contains three independent sets of operational logic, which are used to characterize the linear dependence between hue angle and the contents of Lycium chinense A, Lycium chinense B, and total flavonoids, respectively. Through one calculation process, the system simultaneously calculates the predicted content values ​​of the above three target components. , , ), output the results and complete the non-destructive testing.

[0022] See attached document Figure 2 , Figure 2 This is a flowchart illustrating the construction and detection of a predictive model for effective components according to an embodiment of the present invention. The present invention provides a method for determining the internal components of wolfberry root bark based on colorimetric parameters, comprising the following steps: S11. First, prepare the samples. If you are in the model building stage, select representative samples of dried wolfberry root bark. Set To meet the approximation requirements of large statistical samples and ensure sufficient freedom and universality for subsequent regression analysis, a soft-bristled brush was used to gently remove adhering mud and impurities, ensuring the outer root bark surface was free of obvious mold or physical damage, preventing random noise interference to the spectral signal caused by foreign objects. The sample was then placed in a D65 standard light source box for static equilibration, with the settling time strictly set to at least 5 minutes. This process aims to allow the thermodynamic state and moisture distribution of the medicinal material surface to physically adapt to the photothermal environment, eliminating microscopic surface deformation or unstable gloss reflection caused by environmental temperature differences, thereby ensuring the reproducibility of measurement data. Subsequently, appearance colorimetric data were collected. A portable colorimeter or hyperspectral imaging device was used as the detection terminal, with the instrument measurement mode set to CIE. Color space, viewing angle is The light source was set to D65 for each sample. The measurement points were selected from areas with smooth outer root bark and uniform color, avoiding scars, bifurcation, and fracture surfaces. The probe was placed vertically against the sample surface for multi-point scanning measurements, and the number of measurements per sample was set. The values ​​are taken from 3 to 5 times to cover the spatial heterogeneity of the sample surface.

[0023] S12, according to the definition of colorimetry, convert the original colorimetric signal obtained in step S1 into a hue angle value, and calculate the average hue angle of the sample to be tested. The formula is ; In the formula The average hue angle of the sample to be tested is expressed in degrees (°). For this sample number The hue angle reading of the second measurement The total number of measurements was used; the average hue angle of the sample was calculated by arithmetic averaging. This eliminates potential local biases in single-point measurements, resulting in a comprehensive feature value that characterizes the overall appearance attributes of the sample. In this embodiment, the... The value is determined as the sole independent variable for subsequent mathematical model operations, in order to simplify the computational dimension and improve the robustness of the model; S13, firstly, obtain the average hue angle h of the standard sample set (as the independent variable), and simultaneously determine the true content of each target component (as the dependent variable) using classical chemical analysis: the true content of Lycium chinense root extract. The actual content of Lycium bark extract The total flavonoid content was determined using high-performance liquid chromatography (HPLC). The ground sample powder was accurately weighed, extracted with solvent using ultrasound, filtered, and diluted to a final volume before being injected into the HPLC instrument. The content was calculated based on the standard curve method. The content was determined by ultraviolet-visible spectrophotometry using an aluminum nitrate-sodium nitrite colorimetric system at a specific wavelength; all actual content units were standardized to milligrams per gram (mg / g). Based on acquisition Group of data pairs, with average hue angle As the independent variable, the actual content of each chemical component is used. Using as the dependent variable, a univariate linear regression analysis is performed using the least squares method. The prediction model for Lycium chinense dermal extract is as follows: ; In the formula, the correlation coefficient Significance The regression slope was 0.245, and the intercept was -9.929. The prediction model for Lycium bark extract is as follows: 10.892; In the formula, the correlation coefficient Significance The regression slope was 0.396, and the intercept was -10.892. The total flavonoid prediction model is as follows: ; In the formula, the correlation coefficient Significance The regression slope was 1.755, and the intercept was -87.508. In the above equation The values ​​are all less than 0.05, indicating that there is a statistically significant linear relationship between the hue angle and the content of each component; S14: Obtain the sample to be tested and perform light balance and colorimetric measurements according to the conditions in steps S11 and S12, and calculate its average hue angle. This Substitute the values ​​into the three prediction equations established in step S13, calculate and output the lycopene in the sample. Lycium bark B and total flavonoids The predicted content value; this step does not require physical crushing or chemical extraction of unknown samples, and the prediction results are directly used to determine the quality grading of medicinal materials, based on the quality grading standards preset at the acquisition site.

[0024] To further illustrate the technical solution of the present invention, the present invention will be described in detail below with reference to specific on-site testing scenarios.

[0025] Test Example: Rapid on-site quality grading of a batch of wolfberry root bark medicinal materials Sample background At a certain Chinese medicinal herb purchasing site, a batch of dried wolfberry root bark with Ningxia origin and intact appearance needs to be initially screened and graded.

[0026] Detection steps Step A: Sample Preparation Approximately 50 grams of the herbal sample was randomly selected from this batch, and surface dust was removed. The sample was placed in a portable testing box, and a D65 standard light source was turned on. The sample was left to stand for 5 minutes to allow for photothermal equilibrium.

[0027] Step B: Data Acquisition Using a handheld colorimeter (model: X-RiteCi6x series), set the parameters to CIE. D65 light source, 10° viewing angle. Measurements were taken on a flat area of ​​the sample's outer surface, and instrument readings were recorded at five randomly selected points as follows (automatically converted to hue angle h): Measurement point 1: 54.8°; Measurement point 2: 55.5°; Measurement point 3: 55.1°; Measurement point 4: 56.0°; Measurement point 5: 55.1°; Step C: Calculate the input variables and calculate the average hue angle of the sample.

[0028] ; Step D: Model Calculation and Result Output. Substitute h=55.3 into the pre-set regression equation in S15 of this invention: Lycium barbarum dermal osseoinjection prediction: ; Lycium bark B-type prediction: ; Total flavonoids prediction: ; Judgment Conclusion Based on the pre-set quality grading standards at the acquisition site (e.g., for premium grade products, Lycium barbarum extract must be >10.0 mg / g and total flavonoids >9.0 mg / g), the predicted results for this batch of samples all exceeded the thresholds, thus classifying it as a premium grade product and allowing for acquisition at the premium grade price. The entire assessment process took approximately 2 minutes, and the samples were returned to their storage location intact.

[0029] To comprehensively evaluate the accuracy and practicality of the colorimetry-based prediction model proposed in this invention, 10 independent external validation samples (numbered T01 to T10) that were not involved in the modeling were selected. For the core fingerprint component, Lycium barbarum glycoside, the calculated predicted values ​​of the method of this invention were compared one by one with the actual values ​​determined by high-performance liquid chromatography.

[0030] The predicted values ​​are calculated based on the regression equation determined in the aforementioned embodiments: 10.892; Comparative verification data table of the method of this invention and standard chemical detection methods for the content of Lycium barbarum glycoside.

[0031] Data analysis conclusions: In the above 10 sets of verification experiments, the absolute values ​​of the relative deviations between the predicted values ​​of this invention and the true values ​​of high performance liquid chromatography ranged from 3.22% to 7.55%, with an average relative deviation of 4.63%. This indicates that the colorimetric prediction results maintain a high degree of agreement with the chemical true values ​​in terms of numerical values.

[0032] Using 10.0 mq / g as the critical threshold for distinguishing between qualified and superior products, the grade determination results of the method of this invention for all 10 samples were completely consistent with the results of traditional chemical testing; the data shows that, with the hue angle... With the increase of , the content of Lycium barbarum glycoside showed a clear upward trend, which verified the effectiveness of the aforementioned linear regression model. This method can meet the actual technical needs of large-scale on-site rapid screening and quality grading.

[0033] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A rapid and non-destructive method for detecting the quality of Lycium barbarum root bark based on hue angle, characterized in that, Includes the following steps: The dried wolfberry root bark samples were subjected to photothermal equilibrium treatment to eliminate ambient light fluctuations and surface instability. Then, the original colorimetric signals characterizing the appearance and optical properties of the outer root bark were collected using a color measurement device while maintaining the integrity of the physical morphology of the samples. The original chromaticity signal is converted into CIELab color space values, and the average hue angle of the sample under test is derived based on the numerical calculation. The average hue angle is used as the only independent variable characterizing the overall appearance attributes and internal quality of the sample. A pre-defined univariate linear regression prediction model was constructed, which includes three target components: lycium chinense root extract A, lycium chinense root extract B, and total flavonoids. The model was established using least squares regression analysis based on the linear dependence between the average hue angle of the standard sample set and the actual content of each target component. Substitute the average hue angle of the sample to be tested obtained in step S2 into the preset univariate linear regression prediction model described in step S3, and calculate the predicted content values ​​of Lycium chinense A, Lycium chinense B, and total flavonoids in the sample to be tested, thereby completing the non-destructive testing.

2. The rapid and non-destructive testing method for the quality of Lycium barbarum root bark based on hue angle according to claim 1, characterized in that, The photothermal balance treatment in step S1 specifically includes: Use a soft brush to remove the attached mud and impurities from the surface of the sample to be tested, ensuring that there is no obvious mold or physical damage on the surface of the outer root bark. After cleaning, the sample to be tested is placed in a D65 standard light source box and left to stand for more than 5 minutes until the surface of the medicinal material reaches a photothermal equilibrium state, so as to eliminate microscopic surface deformation or unstable gloss reflection caused by environmental temperature difference.

3. The rapid and non-destructive method for detecting the quality of Lycium barbarum root bark based on hue angle according to claim 1, characterized in that, The specific conditions for acquiring the raw chromaticity signal in step S1 are as follows: The color measuring device is set to the CIELab color space, the viewing angle is set to 10 degrees, and the measurement light source is set to D65. For each of the test samples, a region with a smooth outer root bark and uniform color is selected as the measurement point, avoiding scars, bifurcation and fracture surfaces. The measurement probe is placed vertically and tightly against the sample surface to perform multi-point scanning measurement, and the number of measurements for a single sample is set to 3 to 5.

4. The rapid and non-destructive method for detecting the quality of Lycium barbarum root bark based on hue angle according to claim 3, characterized in that, The method for calculating and deriving the average hue angle in step S2 is as follows: The chromaticity signals obtained from multiple measurements are converted into hue angle readings. The arithmetic mean of all hue angle readings of the same sample is calculated to obtain the average hue angle of the sample, in degrees. The average hue angle is used to eliminate local deviations in single-point measurements and serves as a comprehensive feature value characterizing the overall appearance attributes of the sample.

5. The rapid and non-destructive method for detecting the quality of Lycium barbarum root bark based on hue angle according to claim 1, characterized in that, The process of constructing a pre-defined univariate linear regression prediction model in step S3 includes: A standard sample set was constructed by selecting 30 or more representative samples of dried wolfberry root bark. Obtain the average hue angle of each sample in the standard sample set; Chemical analysis methods were used to determine the true content of Lycium chinense root bark A, Lycium chinense root bark B, and total flavonoids in each sample of the standard sample set. Using the average hue angle as the abscissa and the actual content of each component as the ordinate, a univariate linear regression analysis is performed using the least squares method to calculate the regression slope and intercept constant of the best-fit line, thereby establishing the preset univariate linear regression prediction model.

6. The rapid and non-destructive method for detecting the quality of wolfberry root bark based on hue angle according to claim 5, characterized in that, The specific chemical analysis method is as follows: The true contents of Lycium chinense root bark A and B were determined by high performance liquid chromatography. The sample powder was accurately weighed, extracted with solvent by ultrasonication, filtered and diluted to volume before being injected into the liquid chromatograph, and the contents were calculated according to the standard curve method. The total flavonoid content was determined by ultraviolet-visible spectrophotometry, using an aluminum nitrate-sodium nitrite colorimetric system to measure absorbance at a specific wavelength and calculate the result.

7. The rapid and non-destructive method for detecting the quality of Lycium barbarum root bark based on hue angle according to claim 5, characterized in that, The calculation logic for the predicted content value of Lycium chinense in step S4 is as follows: Multiply the input average hue angle by the first regression slope, and then subtract the first intercept constant to obtain the predicted content value of Lycium chinense dermatitis. The first regression slope is set to 0.245, the first intercept constant is set to 9.929, and the unit of the predicted content value is milligrams per gram.

8. The rapid and non-destructive method for detecting the quality of wolfberry root bark based on hue angle according to claim 5, characterized in that... Multiply the input average hue angle by the second regression slope, and then subtract the second intercept constant to obtain the predicted content of Lycium barbarum ethylsin. The second regression slope was set to 0.396, the second intercept constant was set to 10.892, and the predicted content was expressed in milligrams per gram.

9. The rapid and non-destructive method for detecting the quality of Lycium barbarum root bark based on hue angle according to claim 5, characterized in that, The calculation logic for the predicted content value of Lycium barbarum ethylstilbestrol in step S4 is as follows: Multiply the input average hue angle by the second regression slope, and then subtract the second intercept constant to obtain the predicted content value of Lycium barbarum glycoside B. The second regression slope was set to 0.396, the second intercept constant was set to 10.892, and the unit of the predicted content value was milligrams per gram.

10. The rapid and non-destructive method for detecting the quality of Lycium barbarum root bark based on hue angle according to claim 1, characterized in that, Based on the preset quality grading standards, the predicted content results of Lycium chinense A, Lycium chinense B, and total flavonoids are compared with the corresponding grading thresholds to determine the quality grade of the sample to be tested. The entire testing process does not involve physical pulverization or chemical extraction of the sample to be tested.

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