Method for detecting content of glucan in raw sugar and related equipment

Abstract

The embodiment of the invention provides a method for detecting the content of glucan in raw sugar and related equipment, and belongs to the technical field of sample detection. The method comprises the following steps: obtaining a raw sugar sample, and pretreating the raw sugar sample to obtain a solution to be detected; measuring the absorbance of the glucan standard substance solutions with different concentrations and the to-be-measured solution under a specific wavelength by adopting an alcohol haze method; establishing a third-order polynomial fitting model of the solution concentration and the absorbance based on the concentrations of the glucan standard substance solutions with different concentrations and the corresponding absorbance measured values; and substituting the absorbance measurement value of the solution to be detected into the third-order polynomial fitting model, and calculating to obtain the content of glucan in the raw sugar sample. According to the embodiment of the invention, the content of the glucan in the raw sugar sample can be directly and accurately calculated, and the robustness and the result reliability of the whole detection process are improved by introducing high-order polynomial fitting, so that rapid and accurate quantitative analysis of the glucan in the raw sugar can be realized.

Description

Technical Field

[0001] This application relates to the field of sample testing technology, and in particular to a method and related equipment for detecting the glucan content in raw sugar. Background Technology

[0002] In related technologies, the measurement of glucan content in raw sugar has several significant drawbacks in practical applications, including extremely strict requirements for measurement time, lack of clear detection and quantitation limits, unclear definition of standards, and difficulty in achieving ideal linearity in standard curves. These factors together lead to many difficulties in the laboratory testing process. Summary of the Invention

[0003] The main objective of this application is to propose a method and related equipment for detecting the glucan content in raw sugar, which can achieve rapid and accurate quantitative analysis of glucan in raw sugar.

[0004] To achieve the above objectives, one aspect of this application proposes a method for detecting the dextran content in raw sugar, the method comprising: A raw sugar sample was obtained and pretreated to obtain a test solution; The absorbance of dextran standard solutions of different concentrations and the test solution at a specific wavelength was measured using the alcohol haze method. Based on the concentrations of the dextran standard solutions of different concentrations and the absorbance measurements corresponding to the dextran standard solutions of different concentrations, a third-order polynomial fitting model of solution concentration and absorbance is established. The absorbance measurement value of the test solution is substituted into the third-order polynomial fitting model to calculate the glucan content in the raw sugar sample.

[0005] In some embodiments, the pretreatment of the raw sugar sample to obtain the test solution includes: The raw sugar sample was dissolved and amylase was added for enzymatic hydrolysis to obtain the hydrolyzed solution. Add trichloroacetic acid solution to the enzymatically hydrolyzed solution and make up to volume. After mixing, add acidic diatomaceous earth and filter to obtain the filtrate. Denatured alcohol or anhydrous ethanol is added to the filtrate to carry out an alcohol precipitation reaction, forming a suspension, which is used as the test solution.

[0006] In some embodiments, the dextran standard is dextran T500 with an average molecular weight of about 500 kDa.

[0007] In some embodiments, the establishment of a third-order polynomial fitting model between solution concentration and absorbance based on the concentrations of the different concentrations of the dextran standard solutions and the absorbance measurements corresponding to the different concentrations of the dextran standard solutions includes: Prepare multiple standard solutions of dextran T500 with different concentrations; The absorbance of dextran standard solutions of different concentrations was measured at a wavelength of 720 nm. Using the concentration of the solution as the independent variable The absorbance is the dependent variable. Using a third-order polynomial function Perform fitting, where , , , The coefficients are determined by fitting, and .

[0008] In some embodiments, after establishing a third-order polynomial fitting model of solution concentration and absorbance based on the concentrations of the different concentrations of the dextran standard solutions and the absorbance measurements corresponding to the different concentrations of the dextran standard solutions, the method further includes: Using a set of verification samples with known concentrations, the absorbance of the verification samples is measured and substituted into the third-order polynomial fitting model to calculate the predicted concentration of the verification samples; the verification samples have corresponding known concentrations. Calculate the sum of squared relative errors or residuals between the predicted concentration and the known concentration of the verification sample; If the average value of the relative error exceeds a preset average threshold, or the sum of squared residuals exceeds a preset residual threshold, then the third-order polynomial fitting model needs to be recalibrated or the experimental conditions need to be checked, and a corresponding prompt message is generated.

[0009] In some embodiments, the method of measuring the absorbance of dextran standard solutions of different concentrations and the test solution at a specific wavelength using the alcohol haze method includes: Timing begins after the test solution or the dextran standard solutions of different concentrations are mixed with the alcohol precipitation reagent; The absorbance was measured at a wavelength of 720 nm within a time window from the 30th to the 40th minute after mixing.

[0010] In some embodiments, the alcohol precipitation reagent used in the alcohol precipitation reaction is anhydrous ethanol; and / or, the amylase is a common amylase or a thermostable amylase; and / or, the amount of acidic diatomaceous earth added is three to five grams per 100 milliliters of final diluted solution.

[0011] To achieve the above objectives, another aspect of this application provides a device for detecting the dextran content in raw sugar, the device comprising: The sample acquisition module is used to acquire raw sugar samples and preprocess the raw sugar samples to obtain the test solution; The solution measurement module is used to measure the absorbance of dextran standard solutions of different concentrations and the test solution at a specific wavelength using the alcohol haze method. The model building module is used to establish a third-order polynomial fitting model of solution concentration and absorbance based on the concentrations of the dextran standard solutions of different concentrations and the absorbance measurements corresponding to the dextran standard solutions of different concentrations. The content calculation module is used to substitute the absorbance measurement value of the test solution into the third-order polynomial fitting model to calculate the dextran content in the raw sugar sample.

[0012] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0013] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0014] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a method, apparatus, electronic device, storage medium, and program product for detecting the dextran content in raw sugar. This method involves obtaining a raw sugar sample and preprocessing the raw sugar sample to obtain a test solution; using the alcohol haze method, measuring the absorbance of dextran standard solutions of different concentrations and the test solution at a specific wavelength; based on the concentration of dextran standard solutions of different concentrations and their corresponding absorbance measurements, establishing a third-order polynomial fitting model of solution concentration and absorbance; substituting the absorbance measurement of the test solution into the third-order polynomial fitting model to calculate the dextran content in the raw sugar sample. By implementing the embodiments of this application, a third-order polynomial fitting model between solution concentration and absorbance is constructed based on the concentration of dextran standard solutions of different concentrations and their corresponding absorbance measurements. The absorbance measurements of the test solution are then substituted into this third-order polynomial fitting model to directly and accurately calculate the dextran content in the raw sugar sample. By introducing high-order polynomial fitting, the limitations of traditional linear calibration are effectively overcome, improving the robustness and reliability of the entire detection process and enabling rapid and accurate quantitative analysis of dextran in raw sugar. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application; Figure 2 This is a flowchart of a method for detecting the glucan content in raw sugar provided in an embodiment of this application; Figure 3 This is a schematic diagram of the device for detecting the dextran content in raw sugar provided in the embodiments of this application; Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0019] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0022] 1) The alcohol haze method is a classic analytical chemistry scheme specifically used to determine the content of high molecular weight polysaccharides (such as dextran) in sugar products. Its core principle is to use the solubility characteristics of dextran in aqueous alcohol solutions of a specific concentration, and the resulting light scattering phenomenon for quantitative analysis.

[0023] In related technologies, the modified alcohol haze method faces significant challenges in actual laboratory testing. This is primarily due to its extremely strict requirements on measurement time, the lack of clearly defined limits of detection and quantitation, the absence of clearly defined standards, and the difficulty in achieving ideal linearity in the standard curve. These factors collectively contribute to the numerous difficulties in the testing process. Building upon previous research, this application establishes a rapid and accurate method for detecting dextran content. By performing nonlinear polynomial fitting between dextran content and absorbance, the mathematical relationship function between the two is accurately derived. This function is then used to calculate the dextran content in the sample. Further detailed analysis of the fitting conditions and potential interferences of this method is conducted, and the precision and accuracy of the method are systematically evaluated.

[0024] In view of this, this application provides a method for detecting the dextran content in raw sugar, which can achieve rapid and accurate quantitative analysis of dextran in raw sugar.

[0025] The method for detecting the glucan content in raw sugar provided in this application relates to the field of sample detection technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application that implements the method for detecting the glucan content in raw sugar, but is not limited to the above forms.

[0026] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0027] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0028] like Figure 1The diagram shown is a schematic representation of an implementation environment provided in an embodiment of this application. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0029] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0030] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0031] Terminal 102 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the application does not impose any limitations.

[0032] For example, based on Figure 1 The implementation environment shown in this application embodiment provides a method for detecting the glucan content in raw sugar. The following description uses the application of this method for detecting the glucan content in raw sugar in server 101 as an example. It can be understood that this method for detecting the glucan content in raw sugar can also be applied to terminal 102.

[0033] Figure 2 This is an optional flowchart of a method for detecting the glucan content in raw sugar provided in this application embodiment. The subject executing this method for detecting the glucan content in raw sugar can be any of the aforementioned electronic devices (including servers or terminals). Figure 2 The method may include, but is not limited to, steps S201 to S204.

[0034] Step S201: Obtain raw sugar sample and preprocess the raw sugar sample to obtain the test solution.

[0035] In some embodiments, a carefully selected solvent, such as hot water at a specific temperature, is typically used to ensure complete dispersion of the raw sugar sample and thorough extraction of the target component. Subsequently, physical separation techniques, such as filtration using a membrane with a specific pore size or timely centrifugation, are employed to thoroughly remove any particulate impurities, insoluble substances, and suspended solids present in the sample, resulting in a clear and transparent preliminary extract. This clarification process is crucial to effectively avoid background interference from non-target particles during subsequent turbidity measurements. The extract can be diluted or brought to a suitable concentration range applicable to the alcohol haze method and matching the standard matrix, ultimately yielding a homogeneous, stable, and ready-to-use analytical solution. Prior to analysis, all raw sugar samples are stored in plastic containers in a cool, dark place.

[0036] As an optional implementation method, the raw sugar sample is dissolved and amylase is added for enzymatic hydrolysis to obtain a hydrolyzed solution; trichloroacetic acid solution is added to the hydrolyzed solution and the volume is adjusted, mixed well, and then acidic diatomaceous earth is added for filtration to obtain a filtrate; denatured alcohol or anhydrous ethanol is added to the filtrate for alcohol precipitation to form a suspension, which is used as the test solution.

[0037] The raw sugar sample was completely dissolved, and then amylase was added selectively for enzymatic hydrolysis. This step aims to utilize the biocatalytic specificity of amylase to efficiently hydrolyze starch polysaccharides that may coexist in the sample and interfere with the determination, converting them into soluble small-molecule sugars. This removes the main substances that positively interfere with the subsequent specific detection of dextran at the source, ensuring the specificity and accuracy of the subsequent measurement signal. After enzymatic hydrolysis, trichloroacetic acid solution was added to the hydrolyzed solution and the volume was adjusted. Trichloroacetic acid, as a highly efficient protein precipitant, can denature the protein components mixed in the raw sugar sample and form flocculent precipitates by changing the pH of the hydrolyzed solution and the solvent environment of the proteins, thereby eliminating the turbidity background interference that proteins may cause.

[0038] After thoroughly mixing the mixed solution with acidic diatomaceous earth, the solution is filtered. Due to its large specific surface area and surface activity, the acidic diatomaceous earth effectively adsorbs fine particles, pigments, and some impurities in the solution, further clarifying the eluent and obtaining a clear and transparent filtrate. Finally, excess denatured ethanol or anhydrous ethanol is added to this pure filtrate. In this specific concentration of alcohol-water system, the target analyte, dextran, rapidly precipitates due to its high molecular weight characteristics and solubility changes, forming a uniform and stable suspension of fine particles, thus obtaining the test solution. Alternatively, 50g of diatomaceous earth is added to 1L of distilled water, stirred, and then 50mL of concentrated hydrochloric acid (density 1.19 g / mL) is added. The mixture is stirred for 5 minutes, filtered using a Buchner funnel, and washed with distilled water until the filtrate is no longer acidic. The washed diatomaceous earth is then dried in an oven at 96℃~100℃ for 6 hours and stored in a sealed container to obtain acidic diatomaceous earth.

[0039] By progressively purifying the analytical system through enzymatic hydrolysis to remove starch, acid precipitation to remove protein, adsorption and filtration to purify the matrix, and alcohol precipitation to specifically enrich the target analyte, the matrix effect was greatly reduced. This laid a crucial foundation for obtaining a stable, reliable, and highly specific measurement signal using the subsequent alcohol haze method.

[0040] Step S202: The absorbance of dextran standard solutions and test solutions of different concentrations at a specific wavelength is measured using the alcohol haze method.

[0041] In some embodiments, under strictly controlled and identical reaction conditions, a series of dextran standard solutions of known concentrations and the final prepared test solution are mixed with alcohol reagents of specific concentrations to induce the specific precipitation of dextran and form a stable, homogeneous micro-suspension system. Subsequently, the absorbance of this series of standard suspensions and sample suspensions is measured at a specific wavelength using a spectrophotometer. This specific wavelength is typically selected in the near-infrared or visible light region. This selection aims to maximize the capture of light scattering effects caused by suspended particles while minimizing potential absorption interference from the solution's own color or other components, thereby ensuring that the measured signal specifically and sensitively reflects the turbidity intensity generated by dextran. This measurement process essentially converts the chemical quantity of different dextran concentrations into the physical quantity of accurately readable absorbance values, establishing a direct correlation between concentration and signal.

[0042] Specifically, the preparation of dextran standard solutions of different concentrations can be as follows: Weigh approximately 0.2000 g of dextran standard T500 into a beaker, add approximately 2 mL of water to dissolve it into a paste, let it stand for about 10 minutes while stirring occasionally, transfer it to a 200 mL volumetric flask, wash it with water to below the mark, place the volumetric flask in a boiling water bath for 30 minutes, remove it and cool it to room temperature, add water to the mark, and shake well. Each mL of this solution contains 1 mg of dextran. The dextran standard is dextran T500 with an average molecular weight of approximately 500 kDa.

[0043] Step S203: Based on the concentrations of dextran standard solutions of different concentrations and the corresponding absorbance measurements of dextran standard solutions of different concentrations, a third-order polynomial fitting model of solution concentration and absorbance is established.

[0044] In some embodiments, the third-order polynomial fitting model belongs to high-order regression analysis. Its mathematical expression includes first-order, second-order, and third-order terms of concentration, thus enabling flexible description of complex nonlinear trends such as bending and saturation that may exist in the calibration curve. Specifically, the electronic device can use the known concentration value as the independent variable and the corresponding instrument response value, i.e., absorbance, as the dependent variable. Using algorithms such as the least squares method, curve fitting is performed to solve for the optimal coefficients of each term of the polynomial, thereby determining a continuous functional relationship that best fits all standard data points. Compared to the ideal linear calibration proven difficult to achieve in the traditional alcohol haze method, this nonlinear model is inherently more consistent with the complex changes that may occur in the scattering behavior of dextran particles on light as the concentration of dextran increases in the actual reaction system.

[0045] Furthermore, multiple dextran T500 standard solutions of different concentrations were prepared; the absorbance of each dextran standard solution of different concentrations was measured at a wavelength of 720 nm; with solution concentration as the independent variable... Absorbance is the dependent variable Using a third-order polynomial function Perform fitting, where , , , The coefficients are determined by fitting, and .

[0046] First, several standard solutions of dextran T500 with precisely known concentrations, covering the expected measurement range, were prepared. This series of standard solutions constituted the benchmark for quantitative analysis. Subsequently, turbidity systems for each standard were prepared strictly according to uniform alcohol haze method reaction conditions, and their absorbance values ​​were measured individually using a spectrophotometer at a specific wavelength of 720 nm. The 720 nm wavelength was chosen because dextran alcohol suspensions exhibit significant light scattering characteristics near this wavelength, while effectively reducing background absorption interference from coexisting pigments or other components in the sugar solution, thus ensuring that the captured signal primarily originates from the turbidity generated by the target dextran particles. After acquiring the paired datasets of concentration and absorbance, the concentration of the dextran standard solution can be used as the independent variable. The corresponding absorbance measurement value is used as the dependent variable. Using third-order polynomial function relations Perform nonlinear regression fitting. Where, the coefficients... , , , The optimal parameters are uniquely determined by fitting experimental data using mathematical optimization algorithms such as the least squares method, and the coefficients of the cubic terms are required. The non-zero value mathematically ensures that what is constructed is a true, curved third-order polynomial model, rather than a degenerate low-order function.

[0047] Furthermore, in four 25mL volumetric flasks already containing 8.0mL of sucrose-TCA solution, add 0.5, 1.0, 2.0, and 4.0mL of 0.2mg / mL dextran standard solution, respectively. Then, in another four 25mL volumetric flasks already containing 8.0mL of sucrose-TCA solution, add 1.2, 1.6, 2.0, and 2.40mL of 1mg / mL dextran standard solution, respectively. Add distilled water to each flask until the total volume is 12.5mL. Then, add denatured ethanol to the mark, gently shaking the volumetric flask while adding the ethanol. The addition time should be between 30 and 60 seconds. After reaching the mark, gently invert the volumetric flask three times to mix the solutions. Immediately start a stopwatch after mixing. This prepares standard solutions with dextran concentrations of 25, 50, 100, 200, 300, 400, 500, and 600 mg / kg. Take two 25mL volumetric flasks containing 8.0mL of sucrose-TCA solution. Add distilled water to the mark in one flask, shake well, and use it as a blank for zeroing. Add 4.5mL of distilled water to the other flask, then add denatured alcohol to the mark, shake gently, and measure its absorbance to be no more than 0.003.

[0048] After mixing each solution, the absorbance was measured at 720 nm using a 2cm cuvette with the blank zeroing setting 35 minutes later. A working curve or regression equation was plotted with the dextran content on the x-axis and the corresponding absorbance value on the y-axis.

[0049] A third-order polynomial fitting model for solution concentration and absorbance was established, that is, fitting the calibration curve with a higher-order polynomial. Its fundamental advantage lies in its ability to adapt to the objectively existing nonlinear relationship between concentration and absorbance. By introducing curvature parameters, the model can more accurately fit all the data points actually observed. Compared with forced linear fitting, this third-order polynomial model significantly improves the goodness of fit of the calibration curve and can provide more accurate and reliable concentration predictions over a wider concentration range. This fundamentally overcomes the technical obstacle of the original national standard method, which makes it difficult for the standard curve to meet the linearity requirement, and ensures the scientificity and accuracy of subsequent quantitative calculations of the test sample.

[0050] The parameters to be fitted, dextran content and absorbance, were analyzed. Dextran produces turbidity under the influence of denatured alcohol, and the corresponding absorbance was determined using turbidimetry. The theoretical basis for this is light scattering theory, such as Rayleigh scattering (for particles much smaller than the wavelength of light) and Mie scattering (more applicable when particle size is comparable to the wavelength of light). In other words, when a beam of light passes through a suspension, the particles will: scatter light, causing the light to change direction and preventing it from reaching the detector; absorb light, as particles with color will absorb specific wavelengths of light; reflect / reflect, which occurs on the particle surface. Therefore, the reduced light intensity received by the detector is the result of both scattering and absorption. In most suspensions, especially white or opaque suspensions, light scattering is the primary factor affecting absorbance readings. The measured data shows that at low concentrations (low content), absorbance usually exhibits a good linear relationship with concentration with a small slope. At high concentrations, absorbance also shows a good linear relationship with concentration, but with a larger slope. According to literature data, the content of dextran in raw sugar ranges from 0-600 mg / kg. However, we found that the single linear calibration curve commonly used in existing techniques exhibits significant errors when detecting dextran in raw sugar over a wide concentration range (0-600 mg / kg). Specifically, the absorbance-concentration relationship shows different linear slopes in the low and high concentration regions, indicating an inherent nonlinear response. Those skilled in the art are accustomed to establishing linear relationships within local concentration ranges to simplify operations, but this approach is unsuitable for high-precision measurements across the entire range. Therefore, providing a turbidimetric calibration scheme applicable to a wide concentration range of dextran in raw sugar and achieving high-precision measurement is crucial. To overcome these technical challenges, we attempted various nonlinear fitting models, including exponential, power, and logarithmic functions commonly considered in chemical analysis. Unexpectedly, these models failed to fit the experimental data well. Through in-depth research and extensive experiments, we creatively discovered and verified that a polynomial model perfectly describes the concentration-absorbance relationship in this specific system, achieving an extremely high correlation coefficient close to 0.99, far superior to any other attempted models and simple linear models.

[0051] After establishing a third-order polynomial fitting model for solution concentration and absorbance, a set of verification samples with known concentrations can be used. The absorbance of the verification samples is measured and substituted into the third-order polynomial fitting model to calculate the predicted concentration of the verification samples. The verification samples have corresponding known concentrations. The relative error or residual sum of squares between the predicted concentration and the known concentration of the verification samples is calculated. If the average value of the relative error exceeds the preset average threshold, or the residual sum of squares exceeds the preset residual threshold, the third-order polynomial fitting model needs to be recalibrated or the experimental conditions need to be checked, and corresponding prompt information is generated.

[0052] The absorbance of these validation samples at a wavelength of 720 nm was measured using the alcohol haze method, which is completely consistent with the modeling. These measured absorbance values ​​were then used as input into the constructed third-order polynomial fitting model to calculate the predicted concentration for each validation sample. Since these validation samples themselves have known concentrations that serve as a true reference, the predicted concentrations calculated by the model can be systematically compared and evaluated with their known concentrations. The specific evaluation process involves calculating the relative error between the two, or using a more comprehensive statistic, the residual sum of squares, to quantify the overall prediction bias of the model across the entire validation set. The residual sum of squares refers to the sum of the squares of the differences between the predicted and known concentrations of all validation samples; it can sensitively reflect the degree of systematic deviation in the model's predictions.

[0053] After determining to use polynomial fitting, the choice of fitting order is crucial to the accuracy and robustness of the model. Too low an order may lead to underfitting, failing to accurately reflect the nonlinear relationship between concentration and absorbance; too high an order will cause overfitting, making the function model overly complex and producing unwanted fluctuations in certain intervals, thus reducing the model's generalizability and predictive accuracy. To determine the optimal fitting order, this invention uses the residual sum of squares (RSS) as the primary evaluation metric. Generally, the smaller the residual sum of squares, the closer the fitted curve is to the original data, and the better the fitting effect. The calibration data were fitted using polynomial functions of orders 3 to 6, and their residual sums of squares were calculated. To further verify the predictive accuracy of models of each order, eight pre-configured samples with known concentrations were selected using T500 as an example. Their concentrations were calculated by substituting them into the functions of each order and compared with the true values.

[0054] As the order of fit increases, the residual sum of squares (RSS) gradually decreases, indicating that higher-order models fit the training data better. However, when using 5th and 6th order functions, the predicted values ​​show significant positive biases for samples with high concentrations, and some samples even have no solution. This suggests that higher-order models learn noise in the data, resulting in poorer generalization ability. In contrast, although the third-order polynomial model does not have the absolute smallest residual sum of squares on the training set, its prediction results are the most robust and reliable. It provides reasonable predictions across the entire concentration range with minimal deviation from known values, effectively avoiding the instability and systematic overestimation exhibited by higher-order models.

[0055] By validating a third-order polynomial fitting model using a set of validation samples with known concentrations, an objective and quantitative standard is provided to evaluate the model's reliability. By setting reasonable average relative error thresholds or residual sum of squares thresholds, a clear judgment can be made on the model's state. If the calculated average error exceeds the acceptable average threshold, or the residual sum of squares exceeds the preset residual threshold, it indicates that the predictive performance of the current third-order polynomial fitting model has not met the established requirements. This will trigger a systematic diagnostic process, prompting analysts to check the preparation and measurement process of the standards, recalibrate the model parameters, and even examine the stability of the entire experimental conditions. Therefore, this validation technique is not only a touchstone for the performance of the third-order polynomial fitting model, but also an essential quality assurance measure to ensure the long-term reliable operation of the entire analytical technique and the accuracy and reliability of all analytical results.

[0056] Step S204: Substitute the absorbance measurement value of the solution to be tested into the third-order polynomial fitting model to calculate the glucan content in the raw sugar sample.

[0057] In some embodiments, the preparation of the test solution may involve weighing 32.0 g of the sample and transferring it into a 200 mL Erlenmeyer flask, adding 50 mL of distilled water to dissolve the sample, adding 0.10 g of α-amylase, shaking well, sealing the flask, shaking in a 55°C water bath for 15 min, removing it and cooling it to room temperature in the water bath, and then transferring it into a 100 mL Erlenmeyer flask. In a 150 mL volumetric flask, add 10.0 mL of trichloroacetic acid solution, add water to the mark, shake well, and pour into a 150 mL beaker. Add 4 g of acid-washed diatomaceous earth, mix well, filter, and discard the initial 10 mL filtrate. Take two 25 mL volumetric flasks and add 12.5 mL of filtrate to each. Add distilled water to the mark of one flask and mix well, then zero the flask with a blank. In the other flask, slowly add denatured alcohol to the mark while gently stirring. The addition time should be between 30 and 60 seconds. After adding to the mark, gently invert the volumetric flask three times to mix the solutions. Immediately after mixing, start the stopwatch and zero the flask with a blank within 35 minutes. Measure the absorbance of the solution at a wavelength of 720 nm using a 2 cm cuvette.

[0058] Optionally, timing begins after the test solution or dextran standard solutions of different concentrations are mixed with the alcohol precipitation reagent; absorbance is measured at a wavelength of 720 nm within a time window of 30 to 40 minutes after mixing. Timing begins immediately upon the moment the test solution or a series of dextran standard solutions of different concentrations are uniformly mixed with the specified concentration of alcohol precipitation reagent to ensure that all reaction systems have a completely consistent starting point. Subsequently, the absorbance of the reaction system is measured at a wavelength of 720 nm using a spectrophotometer within a specific time window of 30 to 40 minutes after the start of the mixing reaction. This time window is based on the dynamic equilibrium principle of dextran precipitation to form suspended particles in an alcohol-water system, i.e., the turbidity changes over time after mixing, requiring a defined reaction period for the formation and distribution of particles to reach a stable state. Choosing the 30th minute as the measurement starting point ensures that the vast majority of dextran particles have completely precipitated and uniformly dispersed; while limiting the measurement endpoint to the 40th minute prevents uncertainties introduced by excessively long settling time, which may lead to particle sedimentation or continued growth of aggregates.

[0059] By standardizing the reaction and measurement intervals, measurement fluctuations caused by differences in reaction time are effectively eliminated. Its beneficial effect is that it can significantly improve the comparability and repeatability of absorbance data between different batches of experiments and between standards and samples, thus providing a stable and consistent signal basis for subsequent construction of accurate quantitative models and obtaining reliable dextran content calculation results.

[0060] Optionally, the alcohol precipitation reagent used in the alcohol precipitation reaction is anhydrous ethanol; and / or, the amylase is a common amylase or a thermostable amylase; and / or, the amount of acidic diatomaceous earth added is three to five grams per 100 milliliters of final diluted solution.

[0061] As the core process for generating the final specific detection signal, the alcohol precipitation reaction uses anhydrous ethanol as the precipitation reagent. Anhydrous ethanol can provide an extremely high and stable alcohol concentration, which can rapidly and thoroughly reduce the dielectric constant of the reaction system, thereby efficiently destroying the hydration layer of dextran molecules. This causes dextran molecules to rapidly aggregate and precipitate through hydrogen bonds, forming uniform and fine suspended particles, which is the basis for obtaining a stable and sensitive turbidity signal.

[0062] In the enzymatic hydrolysis step, two types of amylase are provided: ordinary amylase and thermostable amylase. Ordinary amylase is suitable for enzymatic hydrolysis at normal temperatures, while thermostable amylase can maintain activity at higher temperatures and is suitable for scenarios requiring accelerated reactions or processing certain special matrix samples. The core function of both is to specifically catalyze the cleavage of glycosidic bonds in starch molecules, hydrolyzing them into smaller molecules such as dextrin and maltose, thereby eliminating the potential positive interference of starch on subsequent dextran-specific turbidity determination at the source. Regarding the dosage of acidic diatomaceous earth in the purification step, a precise range of three to five grams is specified for every 100 ml of final diluted solution. Acidic diatomaceous earth here mainly plays a dual role of adsorption and filtration aid. Its addition must be sufficient to fully adsorb protein precipitates, fine colloids, pigments, and other impurities in the solution, forming a loose filter cake to ensure smooth filtration. The above dosage range is the optimal range verified through systematic experiments, ensuring excellent purification results to obtain a clear filtrate while avoiding the loss of the target analyte, dextran, due to non-specific adsorption caused by excessive addition. These precisely defined technical parameters together constitute a stable, efficient, and flexible pretreatment scheme, ensuring the quality of the test solution from multiple dimensions and laying a solid matrix foundation for subsequent accurate measurements.

[0063] To simplify operation and reduce costs, key parameters such as reaction time, reagents, amylase type, and the amount of acidic diatomaceous earth were systematically optimized. Results showed that using T500 as the dextran standard extended the reaction time to 35 minutes, significantly reducing the stringent time requirements of the standard method while ensuring sufficient reaction. Simultaneously, it was verified that low-cost anhydrous ethanol can replace reactive alcohol, and there was no significant difference in effectiveness between ordinary and thermostable amylases. The optimal amount of acidic diatomaceous earth was determined to be 4g to achieve the best balance between filtration efficiency and assay stability. For dark-colored samples, appropriate dilution was confirmed to be a more reliable solution than activated carbon decolorization. Based on these optimizations, a more efficient, economical, and robust experimental procedure was finally established.

[0064] In the precision test, standard solutions of dextran T110, T500, and T2000 were prepared according to the method in Section 1.2.3, with a concentration of 400 mg / kg. The absorbance was measured at a wavelength of 720 nm, with six consecutive measurements, and the relative standard deviation (RSD) of the absorbance at each concentration point was calculated. The results showed that the RSD of the precision of the T500 and T2000 standard solutions was less than 5%, while the RSD of T110 was relatively large at 17.7%. Experimental observations indicated that T500 and T2000 produced more noticeable turbidity, while T100 produced less, especially at low concentrations, and the turbidity was not linear. Table 1 shows the precision test results for different dextran standards. Table 1

[0065] Accurately weigh the raw sugar sample, add three levels of reference standard, and prepare the test solution under conditions 1.2.4. Perform the test in triplicate and calculate the recovery rate of dextran. The results are shown in Table 2. Table 2

[0066] Table 2 shows that the recoveries of T-110 dextran spiked with spiking were 60.0%–71.7%, those of T-500 dextran spiked with spiking were 82.4%–96.7%, and those of T-2000 dextran spiked with spiking were 84.6%–97.7%. The results indicate that this method has good accuracy for the determination of different types of dextran.

[0067] Substitute each measurement result into the standard curve to convert it into sample content, calculate the standard deviation of 21 parallel determinations, repeat the blank test n times (n≥7 times) according to all the steps of sample analysis, convert each measurement result into concentration or content in the sample, calculate the standard deviation of n parallel determinations, and calculate the detection limit according to the following formula:

[0068] In the formula, MDL is the method detection limit; It is an indicator used to measure the difference between sample statistics and population parameters; For degrees of freedom, the confidence level is 99%. Distribution (one-sided); for The standard deviation of the second parallel determination; when the degrees of freedom are At a confidence level of 99% The values ​​can be found in Table 3: Table 3

[0069] The experimental data shown in Table 4 indicate that the detection limits for dextran solutions of different molecular weights obtained using this analytical method are 14.6, 1.53, and 0.803 mg / kg, respectively. It can also be observed that the detection limit decreases as the molecular weight of dextran increases.

[0070] Table 4

[0071] Fifteen batches of raw sugar samples from different origins were taken and their content was determined under optimized extraction conditions. The content of dextran T500 was calculated using the polynomial regression equation in section 2.4.1. The results showed that the dextran content of T500 ranged from 8.0 to 59.7 mg / kg. Table 5 shows the determination results of the 15 batches of samples. Table 5

[0072] Steps S201 to S204, as illustrated in the embodiments of this application, involve obtaining a raw sugar sample and pre-treating it to obtain a test solution; using the alcohol haze method, measuring the absorbance of dextran standard solutions of different concentrations and the test solution at a specific wavelength; establishing a third-order polynomial fitting model between solution concentration and absorbance based on the concentrations of the dextran standard solutions of different concentrations and their corresponding absorbance measurements; and substituting the absorbance measurement of the test solution into the third-order polynomial fitting model to calculate the dextran concentration in the raw sugar sample. Sugar content is determined by constructing a third-order polynomial fitting model between solution concentration and absorbance based on the concentration of dextran standard solutions at different concentrations and their corresponding absorbance measurements. By substituting the absorbance measurements of the test solution into this third-order polynomial fitting model, the dextran content in the raw sugar sample can be calculated directly and accurately. By introducing high-order polynomial fitting, the limitations of traditional linear calibration are effectively overcome, improving the robustness and reliability of the entire detection process and enabling rapid and accurate quantitative analysis of dextran in raw sugar.

[0073] Please see Figure 3 This application also provides a device for detecting the dextran content in raw sugar, which can implement the above method. The device includes: The sample acquisition module 301 is used to acquire raw sugar samples and preprocess the raw sugar samples to obtain the test solution; Solution measurement module 302 is used to measure the absorbance of dextran standard solutions and test solutions of different concentrations at a specific wavelength using the alcohol haze method; The model building module 303 is used to establish a third-order polynomial fitting model of solution concentration and absorbance based on the concentration of dextran standard solutions of different concentrations and the absorbance measurement values ​​corresponding to the dextran standard solutions of different concentrations. The content calculation module 304 is used to substitute the absorbance measurement value of the test solution into the third-order polynomial fitting model to calculate the dextran content in the raw sugar sample.

[0074] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0075] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0076] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0077] Please see Figure 4 , Figure 4 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 401 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 402 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 402 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 402 and is called and executed by the processor 401 using the methods described in the embodiments of this application. Input / output interface 403 is used to implement information input and output; The communication interface 404 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 405 transmits information between various components of the device (e.g., processor 401, memory 402, input / output interface 403, and communication interface 404); The processor 401, memory 402, input / output interface 403 and communication interface 404 are connected to each other within the device via bus 405.

[0078] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0079] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0080] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0081] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0082] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0083] The method, apparatus, electronic device, storage medium, and program product for detecting dextran content in raw sugar provided in this application embodiment obtain a raw sugar sample and preprocess it to obtain a test solution; use the alcohol haze method to measure the absorbance of dextran standard solutions of different concentrations and the test solution at a specific wavelength; based on the concentration of dextran standard solutions of different concentrations and their corresponding absorbance measurements, establish a third-order polynomial fitting model of solution concentration and absorbance; substitute the absorbance measurement of the test solution into the third-order polynomial fitting model to calculate the dextran content in the raw sugar sample, enabling rapid and accurate quantitative analysis of dextran in raw sugar.

[0084] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0085] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0086] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0087] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0088] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0089] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0090] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0091] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0092] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0093] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for detecting the glucan content in raw sugar, characterized in that, The method includes the following steps: A raw sugar sample was obtained and pretreated to obtain a test solution; The absorbance of dextran standard solutions of different concentrations and the test solution at a specific wavelength was measured using the alcohol haze method. Based on the concentrations of the dextran standard solutions of different concentrations and the absorbance measurements corresponding to the dextran standard solutions of different concentrations, a third-order polynomial fitting model of solution concentration and absorbance is established. The absorbance measurement value of the test solution is substituted into the third-order polynomial fitting model to calculate the glucan content in the raw sugar sample.

2. The method according to claim 1, characterized in that, The pretreatment of the raw sugar sample to obtain the test solution includes: The raw sugar sample was dissolved and amylase was added for enzymatic hydrolysis to obtain the hydrolyzed solution. Add trichloroacetic acid solution to the enzymatically hydrolyzed solution and make up to volume. After mixing, add acidic diatomaceous earth and filter to obtain the filtrate. Denatured alcohol or anhydrous ethanol is added to the filtrate to carry out an alcohol precipitation reaction, forming a suspension, which is used as the test solution.

3. The method according to claim 1 or 2, characterized in that, The dextran standard is dextran T500 with an average molecular weight of approximately 500 kDa.

4. The method according to claim 3, characterized in that, Based on the concentrations of the dextran standard solutions at different concentrations and the corresponding absorbance measurements, a third-order polynomial fitting model between solution concentration and absorbance is established, including: Prepare multiple standard solutions of dextran T500 with different concentrations; The absorbance of dextran standard solutions of different concentrations was measured at a wavelength of 720 nm. Using the concentration of the solution as the independent variable The absorbance is the dependent variable. Using a third-order polynomial function Perform fitting, where , , , The coefficients are determined by fitting, and .

5. The method according to claim 1, characterized in that, After establishing a third-order polynomial fitting model of solution concentration and absorbance based on the concentrations of the dextran standard solutions at different concentrations and the absorbance measurements corresponding to the dextran standard solutions at different concentrations, the method further includes: Using a set of verification samples with known concentrations, the absorbance of the verification samples is measured and substituted into the third-order polynomial fitting model to calculate the predicted concentration of the verification samples; the verification samples have corresponding known concentrations. Calculate the sum of squared relative errors or residuals between the predicted concentration and the known concentration of the verification sample; If the average value of the relative error exceeds a preset average threshold, or the sum of squared residuals exceeds a preset residual threshold, then the third-order polynomial fitting model needs to be recalibrated or the experimental conditions need to be checked, and a corresponding prompt message is generated.

6. The method according to claim 1, characterized in that, The method employs the alcohol haze method to measure the absorbance of dextran standard solutions of different concentrations and the test solution at a specific wavelength, including: Timing begins after the test solution or the dextran standard solutions of different concentrations are mixed with the alcohol precipitation reagent; The absorbance was measured at a wavelength of 720 nm within a time window from the 30th to the 40th minute after mixing.

7. The method according to any one of claims 1 to 6, characterized in that, The alcohol precipitation reagent used in the alcohol precipitation reaction is anhydrous ethanol; and / or, the amylase is a common amylase or a thermostable amylase; and / or, the amount of acidic diatomaceous earth added is three to five grams per 100 milliliters of final diluted solution.

8. A device for detecting the glucan content in raw sugar, characterized in that, The device includes: The sample acquisition module is used to acquire raw sugar samples and preprocess the raw sugar samples to obtain the test solution; The solution measurement module is used to measure the absorbance of dextran standard solutions of different concentrations and the test solution at a specific wavelength using the alcohol haze method. The model building module is used to establish a third-order polynomial fitting model of solution concentration and absorbance based on the concentrations of the dextran standard solutions of different concentrations and the absorbance measurements corresponding to the dextran standard solutions of different concentrations. The content calculation module is used to substitute the absorbance measurement value of the test solution into the third-order polynomial fitting model to calculate the dextran content in the raw sugar sample.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

Images