Method for optimizing fresh mulberry twig processing parameters by response surface method

Abstract

The invention relates to the field of medicinal material processing, and particularly discloses a method for optimizing fresh mulberry twig processing parameters by using a response surface method.The method comprises the following steps that the drying degree, the slice thickness and the slice drying temperature before cutting are tested in sequence through a control variable method, and the corresponding mulberroside A content is obtained; then, a response surface horizontal parameter table is designed according to the state of the mulberroside A with the maximum content, a model is fitted through a computer, and optimal processing parameters are reversely deduced through the model. According to the scheme, the influence of the drying degree, the slice thickness and the slice drying temperature of the mulberry twigs before cutting on the quality of the fresh-cut mulberry twig decoction pieces is investigated through a single factor experiment, and the production place primary processing and processing integrated process of the mulberry twigs is optimized by utilizing Box-Behnken design and combining a response surface method.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of medicinal material processing, and particularly relates to a method for optimizing processing parameters of fresh mulberry branch by using a response surface method. BACKGROUND

[0002] Mulberry branch is a deciduous shrub or small tree, 3-15 m high. The bark is grayish white, with shallow and latticed furrows; the root bark is yellowish brown or reddish yellow, and is fibrous. The leaves are alternate; the leaf stalk is 1-2.5 cm long; the leaf blade is ovate or broad ovate, 5-20 cm long and 4-10 cm wide, acute or acuminate at the apex, rounded or nearly cordate at the base, with coarse serrations or rounded teeth on the margin, sometimes irregularly lobed, glabrous on the upper surface, shiny, sparsely pubescent on the lower surface, with interlacing axillary and basal veins, and with 3 distinct basal veins and reticulate venation on the lower surface; the stipules are needle-shaped and early deciduous.

[0003] The mulberry branch is listed as a good medicine for expelling wind and dampness, dredging meridians and joints in traditional Chinese medicine, and the fresh processing mulberry branch has significant medicinal value. According to the traditional concept, the mulberry branch is harvested in late spring and early summer, and then is cut into slices and dried, so that the effective components such as flavonoids and polysaccharides can be better preserved. After processing, the mulberry branch can also avoid moldy and metamorphic problems in the storage and transportation process. Direct fresh processing in the original habitat is the development direction of future Chinese medicinal material processing.

[0004] The Chinese Pharmacopoeia and the processing specification generally introduce that the medicinal materials are cut into thin slices, thick slices, segments, blocks and dings, but the drying degree before cutting, the thickness of the slices and the drying temperature of the slices all have an impact on the quality of the cut slices, and how to explore the optimal fresh processing method of the mulberry branch is worth discussing. SUMMARY

[0005] The application aims to provide a method for optimizing processing parameters of fresh mulberry branch by using a response surface method.

[0006] To achieve the above-mentioned purpose, the application provides a method for optimizing processing parameters of fresh mulberry branch by using a response surface method, which comprises the following steps:

[0007] a. Set the core parameters to be optimized, including the drying degree before cutting, the drying temperature of the slices and the thickness of the slices, and then perform single-factor tests on the drying degree before cutting, the thickness of the slices and the drying temperature of the slices respectively;

[0008] b. Only change the drying degree before cutting of the mulberry branch, and set four groups of mulberry branches with decreasing drying degrees, i.e. fresh mulberry branches, mulberry branches naturally dried by 70%, mulberry branches naturally dried by 50% and completely dried mulberry branches; perform slicing operation on the four groups of mulberry branches, and dry them at 50℃ until the weight is constant, and record the changes of the end face of the slices and the changes of the cutting difficulty;

[0009] c. Only change the drying temperature of mulberry branch section, set five groups of drying temperature of mulberry branch section, respectively 50℃, 60℃, 70℃, 80℃, 90℃, and dry the mulberry branch section in the air drying oven until the constant mass;

[0010] d. Only change the thickness of mulberry branch section, set four groups of thickness of mulberry branch section, respectively 2mm, 3mm, 4mm, 5mm, and then dry at 50℃ until the constant weight;

[0011] e. Determine the content of mulberryin A in the nine groups of mulberry branch section in step c and step d by chromatograph, find that the content of mulberryin is the largest when the drying temperature of mulberry branch section is 60℃; the content of mulberryin is the largest when the thickness of mulberry branch section is 4mm;

[0012] f. The drying degree before cutting is denoted as A, the drying temperature of section is denoted as B, the thickness of section is denoted as C, and the mulberryin A is denoted as Y, according to the conclusion of step e, design the response surface factor level table with A, B, C as independent variables and Y as dependent variable, and perform the Box-Behnken experiment of 3 factors and 3 levels by using Design-Expert13 software;

[0013] g. Fit the "polynomial regression equation" of the drying degree before cutting (A), the drying temperature of section (B), the thickness of section (C) and the content of mulberryin A (Y) in the mulberry branch by computer, and induce the optimal processing parameters A, B, C by the "polynomial regression equation";

[0014] h. After obtaining the optimal processing parameters A, B, C, modify the single variable respectively to perform multiple verification tests, and obtain the corresponding Y; if Y is greater than the optimal value, adjust the response surface factor level table in step f, and re-fit the polynomial regression equation.

[0015] As an improvement of the above scheme, in step f, the response surface factor level table is four rows and four columns, the content of the first row is "level", "drying degree before cutting A", "drying temperature of section B", "thickness of section C", the content of the second row is "-1", "fresh", "50℃", "3", the content of the third row is "0", "seventy percent dry", "60℃", "4", and the content of the fourth row is "1", "half dry", "70℃", "5".

[0016] As an improvement of the above scheme, in step g, the computer also obtains the "response surface experiment analysis table" when obtaining the "polynomial regression equation", if the fitting model P, the lack of fitting term F and the signal-to-noise ratio are within the theoretical range, it is determined that the polynomial regression equation is reasonable.

[0017] As an improvement of the above scheme, the multiple regression equation obtained in step g is Y = 1.168 + 0.07375A - 0.08875B + 0.1425C - 0.1AB + 0.1825AC + 0.1025BC - 0.3265A 2 -0.2765B 2 -0.214C 2 .

[0018] As an improvement of the above scheme, in step g, the drying degree before cutting is 70%, the slice drying temperature is 58.7℃, and the slice thickness is 4.4mm, and the above parameters are the optimal processing parameters.

[0019] The present application has the following beneficial effects: using the response surface method to systematically optimize the fresh processing parameters of mulberry branches can accurately determine the optimal process conditions in a scientific and rigorous manner, covering key elements such as drying degree before cutting, slice drying temperature, and slice thickness. This optimization strategy not only significantly improves the processing efficiency of mulberry branches, but also effectively improves the utilization rate of raw materials, achieving efficient use of resources. At the same time, this method reveals the internal relationship between each processing parameter and the content of morusin A, helping researchers to better understand the parameter influence mechanism in the processing of mulberry branches. The response surface method can be combined with various scientific research projects to fully utilize its advantages, avoid the blindness and inefficiency of traditional trial-and-error methods, significantly reduce research costs, and significantly improve research efficiency, opening up a new efficient path for research and practice in the field of mulberry branch processing. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a response surface graph of the drying temperature before cutting and the drying temperature under an embodiment;

[0021] Figure 2 is a response surface graph of the drying temperature before cutting and the slice thickness under an embodiment;

[0022] Figure 3 is a response surface graph of the drying temperature and the slice thickness under an embodiment. DETAILED DESCRIPTION

[0023] In the description of the present application, several meanings are one or more, and multiple meanings are two or more. Greater than, less than, more than, etc. are understood as not including the number, and above, below, etc. are understood as including the number. If the terms "first", "second", "third" are described, they are only for the purpose of description and distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or the sequence of indicated technical features.

[0024] The application discloses a method for optimizing fresh processing parameters of mulberry branches by using a response surface method.

[0025] The instruments used in the test include: LC-20ADXR high performance liquid chromatograph (Japan Shimadzu Corporation); BSA124S-CW type one ten-thousandth electronic balance (Germany SARTORIUS Corporation); 200A type high-speed multifunctional pulverizer (Shanghai Senai Machinery Co., Ltd.); KQ-500E type ultrasonic cleaner (Kunshan Ultrasonic Instrument Co., Ltd.); DHG-9075A type electric heating constant-temperature air drying oven (Shanghai Yiheng Scientific Instrument Co., Ltd.).

[0026] The materials used in the test include: mulberry branches, which are collected on October 12, 2024, and the collection place is the Guangxi Zhuang Autonomous Region Guilin Academy of Agricultural Sciences, and the tree age is 2 years; mulbarb skin glycoside A (batch number MUST-24110718, content is 99.28%), Chengdu Manster Biological Technology Co., Ltd.; methanol (chromatographic pure), phosphoric acid (analytical pure), Xilong Scientific Co., Ltd.; acetonitrile (chromatographic pure), Tianjin Kemio Chemical Reagent Co., Ltd.; and water is pure water (Hangzhou Wahaha Group).

[0027] 1. Preparation of mulberry branches with different water contents: the same batch of fresh mulberry branches with uniform thickness are divided into four groups, one group is fresh mulberry branches, and the other three groups are naturally dried to 70%, 50% and completely dry at room temperature, respectively, and then sliced and dried at 50 DEG C to constant weight. With the decrease of water content, the surface properties of the mulberry branches gradually change from fullness to slight wrinkles to obvious wrinkles; the cross section gradually changes from smooth to slightly stubble to unsmooth; the water content of fresh mulberry branches is 60%-75%, the texture is crisp and easy to cut; the water content of 70% dried mulberry branches is 40%-50%, the texture is slightly soft and easy to cut; when the water content is reduced to 40% or below, the texture is hard and difficult to cut; the completely dried mulberry branches are hard and difficult to cut. Therefore, fresh mulberry branches, 70% dried mulberry branches and semi-dried mulberry branches are selected as the suitable water content before cutting for subsequent investigation.

[0028] 2. Investigation of slicing and drying temperature: the same batch of fresh mulberry branches with uniform thickness are cut into uniform slices by using a straight knife at an angle, and the slices are dried by using five drying methods at 50 DEG C, 60 DEG C, 70 DEG C, 80 DEG C and 90 DEG C respectively (see Table 1 below), so that the water content of the slices is less than 11.0%.

[0029] Number Treatment method Z1 Dried in a 50°C forced air drying oven to constant mass; Z2 Dried in a 60°C forced air drying oven to constant mass; Z3 Dried in a 70°C forced air drying oven to constant mass; Z4 Dried in an 80°C forced air drying oven to constant mass; Z5 Dried in a 90°C forced air drying oven to constant mass;

[0030] Table 1 Drying method of mulberry branch slices

[0031] 3. Investigation of slice thickness: The thickness of the sliced mulberry branch is 0.2-0.5 cm according to the Chinese Pharmacopoeia 2020 edition, so the same batch of fresh mulberry branches with uniform thickness were taken, and the straight knife was obliquely cut into pieces with thicknesses of 2 mm, 3 mm, 4 mm, and 5 mm (see Table 2 below), and dried at 50°C to constant weight.

[0032] Number Thickness S1 Dried in a 50°C forced air drying oven to constant mass; S2 Dried in a 60°C forced air drying oven to constant mass; S3 Dried in a 70°C forced air drying oven to constant mass; S4 Dried in an 80°C forced air drying oven to constant mass;

[0033] Table 2 Slice thickness of mulberry branch

[0034] 4. The contents of mulbarbion AS in nine samples Z1-Z5 and S1-S4 were determined.

[0035] 4.1, Chromatographic conditions: The chromatographic column was BOR1124-C18 column (250 mm x 4.6 mm, 5 μm); the mobile phase was water (A)-0.1% phosphoric acid aqueous solution (B), gradient elution (0-5 min, 10% B; 5-10 min, 10% B→17% B; 10-20 min, 17% B; 20-25 min, 17% B→10% B); the flow rate was 1.0 mL / min; the detection wavelength was 303 nm; the column temperature was 30°C; and the injection volume was 20 μL.

[0036] 4.2, Preparation of mixed reference solution: 2.9 mg of mulbarbion A standard was accurately weighed into a 10 mL volumetric flask, and methanol was added to the mark to obtain a 0.29 mg / mL mulbarbion A reference solution. 1 mL of the above solution was accurately taken and diluted to 10 mL in a volumetric flask to obtain a 29 μg / mL mulbarbion A reference solution. Store at 4°C away from light for future use.

[0037] 4.3, Preparation of test solution: 0.5 g of mulberry branch powder (passed through 40 mesh) was accurately weighed into a 50 mL conical flask, 10 mL of 70% methanol was accurately measured, tightly capped, weighed, ultrasonically extracted for 40 min (power: 40 kHz, frequency: 500 W), cooled, weighed, supplemented with 70% methanol, shaken, filtered, and the filtrate was filtered through a 0.22 μm microporous filter to obtain the test solution.

[0038] 4.4, Experimental results

[0039] Number Slice drying temperature (°C) Mulberrosidase A, % Z1 50℃ 0.27 Z2 60℃ 0.43 Z3 70℃ 0.39 Z4 80℃ 0.37 Z5 90℃ 0.37

[0040] Table 3 Effect of slice drying temperature on the content of mulbarbion A in mulberry branch

[0041] As shown in Table 3, when the slice drying temperature of the mulberry branch is 60°C, the content of mulbarbion A in the mulberry branch reaches the maximum value, and as the drying temperature increases, the content of mulbarbion A does not increase with the increase of temperature.

[0042]

[0043]

[0044] Table 4 Effect of slice thickness on content of Astringin A in mulberry branch and bark

[0045] As shown in Table 4, when the slice thickness of the mulberry branch is 4 mm, the content of Astringin A in the mulberry branch reaches the maximum value, and the content of Astringin A decreases with the increase of the slice thickness.

[0046] 5. Response surface optimization method

[0047] According to the above single factor test results, taking the drying degree (A) before cutting, the drying temperature (B) of the slice, and the slice thickness (C) as the independent variables, and Astringin A as the dependent variable (Y), a Box-Behnken experimental design of 3 factors and 3 levels was carried out by using Design-Expert 13 software. The factors and levels are shown in Table 5.

[0048]

[0049] Table 5 Response surface factor level design

[0050] 6. Response surface experiment results (see Table 6 and Table 7): The effects of the drying degree before cutting, the drying temperature (℃) of the slice, and the slice thickness (mm) on the content of Astringin A in the mulberry branch were investigated by using Design-Expert 13 software, and the results are shown in Table 6. The obtained data were analyzed by quadratic multinomial regression fitting, and a multinomial regression equation of the drying degree before cutting (A), the drying temperature (B) of the slice, and the slice thickness (C) and the content of Astringin A (Y) was fitted as follows: Y = 1.168 + 0.07375A - 0.08875B + 0.1425C - 0.1AB + 0.1825AC + 0.1025BC - 0.3265A 2 - 0.2765B 2 - 0.214C 2 .

[0051]

[0052]

[0053] Table 6 Response surface experiment results

[0054]

[0055] Table 7 Analysis of response surface experiment

[0056] The fitting model P<0.0001 indicates that the model is highly significant; the F value of the lack-of-fit term is 1.43, P=0.3585>0.05, and there is no significant difference, indicating that the model fits the experiment well and the predicted value is consistent with the experimental results. The results are shown in Table 7. In this model, R 2 =0.9757, indicating that it can reflect the relationship between the various factors and their response values ​​in the fresh-cut mulberry branch processing process. Furthermore, the signal-to-noise ratio of this model is 14.453, which is greater than 4, further indicating that the model has a certain degree of reliability and can be used for the analysis and prediction of the fresh-cut mulberry branch processing process. The variance analysis results show that there is a certain degree of interaction between the factors of drying degree before cutting (A), slice drying temperature (B), and slice thickness (C), and A, B, and C all have a significant impact on the content of morusin A.

[0057] Figures 1-3 The response surface plot is a graph of the interaction between the two factors, and the contour plot is its projection. The shape of the contour plot reflects the strength of the interaction, with an ellipse indicating a strong interaction between the two factors, and a circle indicating a weak interaction. The steepness of the response surface also reflects the strength of the interaction. According to the simulation results, the optimal conditions determined by the model are a drying degree of 70% before cutting, a slice drying time of 58.7°C, and a slice thickness of 4.4 mm. Under these conditions, the content of morusin A in mulberry branches is maximized.

[0058] The response surface methodology was used to systematically optimize the processing parameters of fresh mulberry branches. This method can accurately determine the optimal process conditions in a scientific and rigorous manner, covering key factors such as the degree of drying before cutting, the drying temperature of the slices, and the thickness of the slices. This optimization strategy can not only significantly improve the processing efficiency of mulberry branches, but also effectively improve the utilization rate of raw materials and achieve efficient use of resources. At the same time, this method deeply reveals the intrinsic relationship between various processing parameters and the content of mulberry glycoside A, helping researchers to have a deeper and more comprehensive understanding of the parameter influence mechanism in the mulberry branch processing process. The combination of response surface methodology and various scientific research projects can give full play to its advantages, avoid the blindness and inefficiency of traditional trial and error methods, greatly reduce scientific research costs, and significantly improve scientific research efficiency, opening up a new and efficient path for research and practice in the field of mulberry branch processing.

[0059] The foregoing descriptions of specific exemplary embodiments of the present invention are for purposes of illustration and description. These descriptions are not intended to limit the invention to the precise forms disclosed, and it is apparent that many variations and modifications are possible in light of the foregoing teachings. The exemplary embodiments have been selected and described for the purpose of explaining the specific principles of the invention and their practical application, thereby enabling those skilled in the art to realize and utilize a variety of exemplary embodiments of the invention and various options and modifications. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A method for optimizing processing parameters of fresh mulberry branches using response surface methodology, characterized in that The following steps are involved: a. Set the core parameters that need to be optimized, including the degree of dryness before cutting, slice drying temperature, and slice thickness, and then conduct single-factor experiments on the degree of dryness before cutting, slice thickness, and slice drying temperature; b only change the degree of drying before cutting mulberry branches, set four groups of decreasing dryness of mulberry branches, namely fresh mulberry branches, naturally dried 70% mulberry branches, naturally dried 50% mulberry branches, all dry mulberry branches; four groups of mulberry branches were sliced ​​and dried to constant weight at 50 ° C, and the changes in the end surface of the slices and the difficulty of cutting were recorded; c only change the drying temperature of the mulberry slices, set five groups of increasing drying temperature mulberry, respectively, 50 ℃, 60 ℃, 70 ℃, 80 ℃, 90 ℃, mulberry were dried in a blast drying oven to a constant mass; d only change the thickness of the slices of mulberry branches, set four groups of slices with increasing thickness of mulberry branches, namely 2mm, 3mm, 4mm, 5mm, and then dried at 50 ℃ to constant weight; e by chromatograph determination step c and step d in the nine groups of mulberry mulberry glycoside A content, found that mulberry slices at 60 ℃ drying temperature, the maximum mulberry glycoside content; mulberry slices at 4mm thickness, the maximum mulberry glycoside content; f. The degree of drying before cutting is code A, the slice drying temperature is code B, the slice thickness is code C, mulberry glycoside A is code Y, according to the conclusion of step e, with A, B, C as independent variables, Y as the dependent variable, the response surface factor level table is designed, and the 3-factor 3-level Box-Behnken experiment is performed using Design-Expert13 software; g. A multinomial regression equation was developed by computer to determine the optimal processing parameters A, B, and C, based on the relationship between the degree of drying of the medicinal material before cutting (A), the slice drying temperature (B), the slice thickness (C), and the morusin A content in the mulberry branch (Y). h. After obtaining the optimal processing parameters A, B, and C, modify each individual variable separately and conduct multiple verification tests to obtain its corresponding Y; if Y is greater than the optimal value, adjust the response surface factor level table in step f and refit the polynomial regression equation.

2. The method for optimizing processing parameters of fresh mulberry branches using response surface methodology according to claim 1, characterized in that: In step f, the response surface factor level table has four rows and four columns, the first row contains "level", "drying degree before cutting A", "slice drying temperature B", and "slice thickness C", the second row contains "-1", "fresh", "50°C", and "3", the third row contains "0", "70% dry", "60°C", and "4", and the fourth row contains "1", "half dry", "70°C", and "5".

3. The method for optimizing processing parameters of fresh mulberry branches using response surface methodology according to claim 2, characterized in that: When the "multinomial regression equation" is obtained in step g, the computer also obtains a "response surface experimental analysis table". If the fitting model P, the lack-of-fit term F, and the signal-to-noise ratio are all within the theoretical range, the multinomial regression equation is considered reasonable.

4. The method for optimizing processing parameters of fresh mulberry branches using response surface methodology according to claim 3, characterized in that: In step g, the degree of drying before cutting is 70% dry, the slicing drying temperature is 58.7° C., and the slicing thickness is 4.4 mm. The above parameters are the optimal processing parameters.

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