Chemical property control method for honey refining of large traditional Chinese medicine honeyed bolus and online moisture detection device

Abstract

The invention provides an online detection method and device based on a near-infrared (NIR) spectrum technology. The online detection method and device are used for monitoring and controlling the content of water and 5-hydroxymethylfurfural (5-HMF) in the honey refining process of a traditional Chinese medicine large honeyed pill in real time. According to the method disclosed by the invention, by optimizing a spectrum preprocessing technology and a modeling wave band and combining a support vector regression (SVR) algorithm, rapid and accurate detection of key chemical attributes in a honey refining process is realized. The device adopts a bypass external circulation design, is suitable for normal-pressure and reduced-pressure honey refining processes, ensures that the relative deviation of a detection result is less than 5%, and has the characteristics of high precision, high reliability and nondestructive detection. According to the technical scheme, the digitization level of the honey refining process is remarkably improved, the stability and consistency of product quality are guaranteed, and effective technical support is provided for quality control of traditional Chinese medicine preparations.

Description

Technical Field

[0001] The present invention relates to an online detection technology for the moisture and 5-HMF content in the process of refining honey for traditional Chinese medicine large honey pills. The technology realizes real-time, rapid and non-destructive detection of key chemical properties in the process of refining honey by applying near infrared spectroscopy (NIR) analysis method, thereby improving the production efficiency and product quality of traditional Chinese medicine large honey pills. Background Art

[0002] With the rapid development of the Chinese medicine pharmaceutical industry, the digitization and quality control of the manufacturing process of honey pills, a traditional Chinese medicine preparation, has become the key to the development of the industry. The preparation of honey pills involves several key steps, including honey refining, pilling, and pill making, which have a decisive influence on the quality of the final product. In particular, in the honey refining process, moisture control is a critical quality attribute (CQA), which plays a vital role in the quality and stability of honey pills.

[0003] Honey refining is an important step in the preparation of large honey pills. It removes part of the water in the honey through thermal processing to increase the viscosity to meet the needs of subsequent assembling and pill making. The traditional honey refining process relies on manual experience and judgment, such as vague standards such as "water drops form beads", and lacks precise scientific control methods, resulting in product quality fluctuations and inconsistencies.

[0004] Moisture is the core indicator in the honey refining process, and its content directly affects the molding and storage stability of large honey pills. The Chinese Pharmacopoeia stipulates that the limit of 5-HMF in honey is 40ppm, and the content of 5-HMF is closely related to the moisture control in the honey refining process. Therefore, establishing an accurate moisture measurement and control method is crucial to ensure the quality of large honey pills.

[0005] At present, the moisture detection in the honey refining process mainly adopts offline methods, such as the refractive index method. These methods are complicated and time-consuming to operate, and cannot achieve real-time online monitoring, which is difficult to meet the real-time and accuracy requirements of modern Chinese medicine manufacturing processes. In addition, the traditional honey refining process lacks standardization, resulting in low production efficiency and inconsistent quality.

[0006] In order to improve the digital level and quality control of the honey refining process, it is urgent to introduce online detection technology. NIR spectroscopy technology is widely used in quality control in the food and pharmaceutical industries due to its advantages of rapidity, non-destructiveness and online detection. However, there is no unified specification and standard for the online detection method of NIR spectroscopy in the process of traditional Chinese medicine honey refining, which limits its transformation efficiency in the production, education and research of pharmaceutical companies.

[0007] Therefore, it is urgent to develop an accurate, real-time and easy-to-operate moisture measurement technology to effectively monitor key quality attributes in the honey refining process. This technology can not only significantly improve the control accuracy of the honey refining process, but also ensure the quality and safety of honey in key production links, thereby ensuring the product quality of traditional Chinese medicine honey pills and enhancing their market competitiveness and consumer trust. Summary of the invention

[0008] The present invention relates to a method for measuring and controlling the chemical properties of a honey refining process in the production process of large honey pills of traditional Chinese medicine, in particular, real-time measurement and control of chemical key quality attributes (CQAs) such as moisture, sugar components (such as fructose, glucose, maltose, sucrose) and 5-HMF in the honey refining process. The invention also relates to an online moisture detection device based on near-infrared spectroscopy technology, which is used for real-time monitoring and control of moisture content in the process of refining honey for large honey pills of traditional Chinese medicine. Through the method and device of the present invention, digital, precise and standardized control of the honey refining process can be achieved, thereby effectively improving the product quality and consistency of large honey pills of traditional Chinese medicine.

[0009] The technical solution of the present invention is as follows:

[0010] 1. Methods for controlling the chemical properties of honey

[0011] (1) Moisture content determination method: The present invention uses the Abbe refractometer method to determine the moisture content of the refined honey sample. The specific method is: heating the sample to 40°C, determining the refractive index of the honey, and calculating the moisture content by a predetermined formula. The moisture control standards include:

[0012] Tender honey: 18% to 20%

[0013] Medium honey: 16% to 18%

[0014] Old honey: 14%~16%

[0015] (2) Tetrasaccharide content determination method: The present invention uses high performance liquid chromatography (HPLC) technology with evaporative light scattering detector (ELSD) to analyze the fructose, glucose, sucrose and maltose content in honey. The chromatographic conditions of the HPLC method are as follows:

[0016] Chromatographic column: DIkmapolyamino HILIC (4.6mm×250mm, 5μm)

[0017] Mobile phase: water: acetonitrile = 30%: 70%

[0018] Flow rate: 1.0 mL min-1

[0019] Temperature: 30℃

[0020] Detection method: Evaporative light scattering detector (ELSD)

[0021] A linear regression equation was established using standard solutions to ensure the accuracy and reliability of quantitative analysis.

[0022] (3) 5-HMF content determination method: The present invention uses a Shimadzu LC-20AT HPLC instrument equipped with a diode array detector (DAD) to accurately determine the 5-HMF content. The HPLC chromatographic conditions are:

[0023] Chromatographic column: Agilent ZORBAX SB-C18 (4.6 mm × 250 mm, 5 μm)

[0024] Mobile phase: water: methanol = 85%: 15%

[0025] Flow rate: 1.0 mL min-1

[0026] Temperature: 35℃

[0027] Detection wavelength: 284nm

[0028] (4) Quality control method: By analyzing samples from multiple batches during the honey refining process, the present invention constructs a chemical property control method, establishes standard ranges for moisture, tetrasaccharides and 5-HMF, and proposes a quality control method based on a matrix scatter plot. By calculating the 95% confidence interval of the measurement results, the quality fluctuations of each batch of products can be effectively identified, outliers can be found, and adjustments can be made in a timely manner. For example, the 5-HMF content of some batches is significantly higher, and the process needs to be optimized; while the moisture content of some batches fluctuates greatly, and control and standardized management need to be strengthened.

[0029] (5) Stability and recovery rate investigation: The reliability and accuracy of the method used were verified through stability and recovery rate experiments. The experiments showed that the determination method of tetrasaccharides and 5-HMF has good repeatability and stability, and the recovery rate meets the requirements, which can meet the needs of high-precision measurement.

[0030] 2. Spectral measurement device for online moisture detection

[0031] 2.1 Equipment structure and composition

[0032] The online moisture detection device consists of the following main parts:

[0033] (1) Light source module:

[0034] The module uses a high-power near-infrared light source, which can provide stable near-infrared radiation, covering the near-infrared spectral band of 1,800 to 2,400nm. The selection of the light source is crucial to the measurement accuracy and stability. The light source of the present invention has the characteristics of low power consumption, long life and high stability.

[0035] (2) Spectral sensor:

[0036] The spectral sensor is the core part of the equipment, responsible for collecting the reflected spectrum information of the sample surface. The sensor can receive the near-infrared spectrum after being reflected by the sample surface and convert it into an electrical signal. The sensor uses high-precision photoelectric detectors and high-resolution spectral analysis technology to ensure high sensitivity and high-resolution detection of moisture content.

[0037] (3) Data processing and analysis module:

[0038] This module mainly consists of spectral signal processing, data preprocessing, correction and modeling algorithms. The raw spectral data collected by the sensor is optimized through signal preprocessing techniques such as Fourier transform (FT). Then, multivariate statistical methods such as support vector regression (SVR) are used to model and analyze the moisture content.

[0039] The present invention adopts grey correlation analysis (GCA) as a performance evaluation index, optimizes the preprocessing algorithm and regression model, and ensures the accuracy and reliability of the measurement results. By comparing with the laboratory analysis results, the model can effectively control the error of moisture content within ±5%.

[0040] (4) Control system and feedback module:

[0041] The control system of the equipment is connected to the automatic control system of the production process, which can feed back the detected moisture data to the production line in real time, and automatically adjust the temperature, pressure and other process parameters in the honey refining process through the feedback mechanism. For example, when the moisture content is close to the upper limit, the control system can automatically reduce the heating temperature or adjust the decompression state to prevent the moisture from exceeding the limit.

[0042] The control system can also be set with an alarm function. When the test results exceed the preset moisture range, an alarm will be immediately issued to notify the operator to make adjustments or inspections.

[0043] 2.2 Working Principle

[0044] The working principle of the online moisture detection device is based on near-infrared spectral reflection technology. The core idea is to irradiate the sample surface with near-infrared light, analyze the spectral data of sample absorption and reflection, and thus deduce the moisture content. Moisture has specific absorption wavelengths for the near-infrared spectrum, and the absorption characteristics of these wavelengths are used for quantitative analysis of moisture content.

[0045] (1) Spectral acquisition:

[0046] The near-infrared light emitted by the light source is irradiated onto the surface of the honey sample through the light guide system. When the light is irradiated onto the sample, the moisture in the sample absorbs the near-infrared light of a specific wavelength and reflects it back. The reflected light is received by the spectral sensor, which converts the reflected light into an electrical signal.

[0047] (2) Signal processing and analysis:

[0048] The sensor transmits the captured signal to the data processing module. The raw spectral data is preprocessed by Fourier transform to eliminate noise and other interfering signals. Then, a mathematical model of moisture content and spectral data is constructed using multivariate statistical methods (such as principal component analysis PCA and support vector regression SVR).

[0049] Through the calculation of the model, the precise value of the moisture content is output and displayed on the operation interface. At the same time, the system makes quality judgments according to the set standards. If the moisture content exceeds or falls below the set range, the adjustment feedback mechanism is triggered.

[0050] (3) Online real-time detection and feedback:

[0051] The moisture detection process is carried out in real time. After each round of near-infrared spectroscopy measurement, the data is immediately fed back to the control system. The control system adjusts the honey refining process according to the moisture data, such as heating temperature, heating time or decompression degree, so as to ensure that the moisture content of each batch of products is always maintained within a reasonable range to avoid excessive drying or wetting.

[0052] 3. Optimization and precision

[0053] The present invention adopts a variety of optimization technologies in online moisture detection to ensure the accuracy and stability of measurement:

[0054] (1) Fourier transform and wavelength selection:

[0055] Applying Fourier Transform to the original spectral signal effectively removes background noise and interference information. By selecting the wavelength range of 1,880-2,040nm for spectral analysis, this band is particularly sensitive to the absorption characteristics of water and can provide high-precision moisture detection.

[0056] (2) Grey relational optimization model:

[0057] The present invention introduces grey correlation analysis (GCA) into the traditional regression model to evaluate the influence of different pretreatment methods on moisture detection. By optimizing the spectral pretreatment process, unnecessary errors are reduced and the prediction accuracy of the model is improved.

[0058] (3) SVR regression algorithm:

[0059] The support vector regression (SVR) algorithm is used to model the moisture content. This algorithm can handle high-dimensional nonlinear problems and is suitable for complex moisture detection data. SVR converts the original data into a form suitable for regression analysis through nonlinear mapping, thereby improving the prediction accuracy.

[0060] Technical Effects

[0061] The method of the present invention can achieve accurate measurement and real-time control of key quality attributes (such as moisture, tetrasaccharide and 5-HMF content) in the honey refining process, thereby effectively ensuring the standardization and quality stability of the honey refining process. Specific technical effects include:

[0062] Accurately measure moisture and sugar content: Use high performance liquid chromatography technology to accurately quantify the tetrasaccharides in honey, combined with the determination of moisture and 5-HMF, to fully understand the changes in chemical composition during the honey refining process.

[0063] Improve the reliability of quality control: Through the established quality control method, combined with the matrix scatter plot and 95% confidence interval, the chemical properties of different batches of refined honey products can be monitored in real time to ensure that the products meet the set quality standards.

[0064] Reducing production fluctuations: Water and 5-HMF are key quality attributes in the honey refining process. The method of the present invention can effectively identify and adjust quality fluctuations in the production process, thereby reducing the occurrence of unqualified products.

[0065] Ensure safety: By strictly controlling the content of 5-HMF, the safety of honey is ensured to avoid potential harm to human health caused by high content of 5-HMF.

[0066] Optimize honey refining process: Through accurate measurement of chemical properties, we can provide a scientific basis for process optimization of honey refining, further improving production efficiency and product quality.

[0067] Real-time online detection: The device can monitor moisture content in real time and automatically adjust the production process through a feedback mechanism without human intervention, ensuring the automation and efficiency of the production process.

[0068] High-precision and non-destructive measurement: Through near-infrared spectroscopy technology, the equipment can provide high-precision moisture measurement, while being non-destructive and fast, avoiding sample loss and detection delays in traditional methods.

[0069] System integration and intelligent control: The device can not only monitor moisture independently, but also be connected to the automated control system of the TCM honey pill production line to achieve quality control and traceability throughout the entire process. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1: HPLC chromatograms of honey samples and standards are shown, with (a) showing the chromatogram of tetrasaccharides and (b) showing the chromatogram of 5-HMF. These graphs are used to characterize the chemical properties of real-world samples from honey production, including the content of fructose, glucose, sucrose, maltose, and 5-HMF.

[0071] Figure 2 : A scatter plot of the chemical properties of real-world samples of raw honey production is shown. This figure shows the correlation between different chemical properties and their distribution within 95% confidence intervals in the form of a matrix scatter plot for quality control and analysis.

[0072] Figure 3 :Shows the chemical properties matrix scatter plot of real-world samples produced by Laomi. This figure shows the correlation between different chemical properties and their distribution within 95% confidence intervals in the form of a matrix scatter plot, which is used for quality control and analysis.

[0073] Figure 4 :Shows a scatter plot of the chemical properties of real-world samples produced by Hetuo Honey. This figure shows the correlation between different chemical properties and their distribution within 95% confidence intervals in the form of a matrix scatter plot for quality control and analysis.

[0074] Figure 5 :The structure diagram of the NIR spectrum online measurement device in the honey refining process is shown. The figure shows in detail the structure and connection method of the honey refining tank, NIR spectrometer and bypass external circulation system, and explains the specific construction and layout of the online measurement device.

[0075] Figure 6 :The moisture content of honey in batch Y1 during the normal pressure process is shown. The figure shows the relationship between the refining time and the moisture content, reflecting the trend of moisture content over time, which is used to verify the reliability of the online measurement device.

[0076] Figure 7 :The original NIR spectra of the atmospheric pressure honey refining process are shown, where (a) is the original spectrum and (b) is the spectrum after SNV processing. These charts are used to demonstrate the reliability of NIR spectroscopy measurement and the inclusion of moisture information.

[0077] Figure 8 :The correlation diagram of the moisture reference value of the atmospheric pressure honey refining process and the NIR model prediction value is shown. This figure shows the correlation between the model prediction value and the actual reference value, which is used to evaluate the accuracy and reliability of the model.

[0078] Fig. 9:The moisture content of three batches (Y2, Y3 and Y4) of honey refining by vacuum process is shown, where (a), (b) and (c) correspond to the moisture content of different batches. These graphs are used to show the moisture content change trend during the vacuum process of honey refining and the reliability of the online measurement device.

[0079] Fig.10 :The original NIR spectra of the three batches of honey, Y2, Y3 and Y4, during the decompression process of honey refining are shown. This figure is used to show the characteristic absorption of the NIR spectra of different batches of honey during the decompression process of honey refining, as well as the inclusion of moisture information.

[0080] Fig.11 :The correlation diagram of the moisture reference value and the NIR model prediction value in the decompression process is shown. This figure shows the correlation between the model prediction value and the actual reference value, which is used to evaluate the accuracy and reliability of the model in the decompression process. DETAILED DESCRIPTION

[0081] Example 1: Determination of moisture content of refined honey samples

[0082] Sample preparation: Select honey samples from Beijing Tong Ren Tang Daxing production base, including raw honey, aged honey and honey for compound honey. Take 5 batches of raw honey (batch number A1A5), 30 batches of aged honey (batch number B1B30) and 30 batches of honey for compound honey (batch number C1~C30) for moisture determination.

[0083] Moisture content determination method: According to the provisions on moisture content determination in Appendix A of SNT0852-2012 "Regulations for the Inspection of Imported and Exported Honey", use an Abbe refractometer to determine the refractive index of honey at 40°C. The calculation formula is as follows:

[0084] X = 100 - [78 + 390.7 (n - 1.4768)]

[0085] Wherein, X is the moisture content of honey (%), and n is the refractive index of honey at 40°C.

[0086] Result analysis: The test results show that the moisture content of raw honey ranges from 23.99% to 27.20%, with an average moisture content of 24.77% and a standard deviation of 1.22%. The moisture content of old honey ranges from 14.46% to 18.01%, with an average moisture content of 15.92% and a standard deviation of 0.88%. The moisture content of honey for Hetuo ranges from 14.26% to 17.43%, with an average moisture content of 15.43% and a standard deviation of 0.68%. From the results, it can be seen that the moisture stability of old honey is poor and cannot meet the set quality control standard of 14.16%.

[0087] Example 2: Tetrasaccharide content determination

[0088] Sample preparation: An Agilent 1260 high performance liquid chromatograph equipped with an evaporative light scattering detector (ELSD) was used to determine the content of tetrasaccharides (fructose, glucose, sucrose and maltose) in the samples.

[0089] Preparation of standard solution: Accurately weigh D-fructose, glucose, sucrose and maltose standards and prepare them into mixed standard solutions with concentrations of 0.9670 mg·mL-1 fructose, 0.9572 mg·mL-1 anhydrous glucose, 0.3195 mg·mL-1 sucrose, and 0.3436 mg·mL-1 maltose.

[0090] Chromatographic conditions: Chromatographic column: DIkmapolyamino HILIC (4.6 mm×250 mm, 5 μm), mobile phase: water: acetonitrile = 30%: 70%, flow rate: 1.0 mL·min-1, column temperature: 30°C, detection method: evaporative light scattering detector (ELSD).

[0091] Result analysis: The samples from different batches were tested, such as Figure 1 As shown in Figure a, the results show that the fructose content of raw honey is between 32.90% and 36.14%, the glucose content is between 31.56% and 33.65%, and the sucrose and maltose contents are relatively low, which meets the requirements of the Chinese Pharmacopoeia (the total amount of fructose and glucose should be greater than 60%). The tetrasaccharide content of old honey and honey for Hetuo also basically meets the standards, among which the fructose and glucose contents of old honey are 39.29% to 42.14% and 33.20% to 37.96% respectively, and the fructose and glucose contents of honey for Hetuo are 39.04% to 42.18% and 34.78% to 38.67% respectively.

[0092] Example 3: Determination of 5-HMF content

[0093] Sample preparation: A Shimadzu LC-20AT high performance liquid chromatograph equipped with a diode array detector (DAD) was used to determine the 5-HMF content in the samples.

[0094] Preparation of standard solution: Accurately weigh the 5-HMF standard and prepare a 0.0166 mg·mL-1 5-HMF standard solution.

[0095] Chromatographic conditions: Chromatographic column used: Agilent ZORBAX SB-C18 (4.6 mm×250 mm, 5 μm), mobile phase: water: methanol = 85%: 15%, flow rate: 1.0 mL·min-1, column temperature: 35°C, detection wavelength: 284 nm.

[0096] Result analysis: The 5-HMF content of raw honey, old honey and Hetuo honey samples was determined. Figure 1As shown in Figure b, the results show that the 5-HMF content of raw honey is 5.99-53.74ppm, the 5-HMF content of old honey is 16.36-414.51ppm, and the 5-HMF content of honey for compounding is 51.04-243.07ppm. The 5-HMF content fluctuates greatly, with RSDs of 83.26%, 73.51%, and 36.32%, respectively. This result shows that 5-HMF is a key quality control indicator in the honey refining process, and its stability is poor.

[0097] Example 4: Quality Control and Optimization

[0098] Matrix scatter plot analysis: Matrix scatter plot analysis was performed on the moisture, tetrasaccharide and 5-HMF content of each batch in the refining honey production process to construct a 95% confidence interval for the measurement results and identify quality fluctuations and outliers of each batch, such as Figures 2 to 4 As shown in the scatter plot, it can be seen that the 5-HMF content of some batches (such as the old honey of batch B1 and its corresponding honey of batch C1) is significantly different from that of other batches, and the production process needs to be adjusted and optimized.

[0099] Optimization strategy: By analyzing the batches with high 5-HMF content, it was found that the temperature and time during the honey refining process are the key factors affecting the generation of 5-HMF. Optimizing the honey refining temperature to control it in the range of 50-60℃ and shortening the honey refining time can significantly reduce the generation of 5-HMF.

[0100] Summary: This example verifies that the whole process of honey refining can be controlled by accurate determination of water, tetrasaccharide and 5-HMF contents, thereby ensuring that the produced honey products meet the quality standards, especially in the production of traditional Chinese medicine preparations.

[0101] Example 5: Online moisture detection based on near infrared technology

[0102] Data acquisition unit operation:

[0103] Use a spectral acquisition unit (such as Figure 5 The (8) NIR spectrometer in the honey refining process is used to obtain NIR spectral data in real time during the honey refining process. The unit is equipped with an incident optical fiber (6) and an output optical fiber (7) for transmitting near-infrared light and receiving light reflected from the sample. The acquisition range of spectral data is set to 1880-2040nm, because the spectrum within this wavelength range can sensitively reflect the characteristic absorption of water.

[0104] Spectral preprocessing:

[0105] The collected raw NIR spectral data first passes through the data processing unit (such as Figure 5The preprocessing steps include Fourier transform (FT) denoising and baseline correction, as well as standard canonical transform (SNV) and first-order derivative processing to enhance the characteristic signal and improve the stability of the model.

[0106] Feature extraction and modeling:

[0107] The multivariate correction algorithm is used to extract spectral features related to moisture concentration from the preprocessed spectral data. Then, the moisture detection model is established based on these features using the support vector regression (SVR) algorithm. The model is verified by the calibration set and the prediction set to ensure that the relative deviation is less than 5% to meet the requirements of high-precision detection.

[0108] Online real-time monitoring and moisture control:

[0109] By bypassing the external circulation system (such as Figure 5 The system (1) from the honey refining tank to the (5) circulation pool) collects the honey refining sample in real time and uses the NIR spectrometer to perform online detection of the moisture content. The detection results are displayed in real time on the display and alarm module, and the honey refining process parameters such as temperature and pressure are automatically adjusted according to the preset moisture control standard to ensure that the moisture content is always maintained within a reasonable range.

[0110] System optimization and precision improvement:

[0111] In order to further improve the accuracy and robustness of the detection system, the data processing unit supports grey correlation analysis to evaluate the prediction accuracy of the detection model. By optimizing the preprocessing algorithm and regression model, the accuracy and reliability of the measurement results are ensured.

[0112] Technical effect verification:

[0113] By comparing with the laboratory analysis results, the effectiveness of the online moisture detection device of the present invention was verified. The results show that the device can effectively control the error of moisture content within ±5%, meeting the demand for precise control of moisture in the process of refining honey for large Chinese medicine honey pills. Example 6: Reliability evaluation of NIR spectroscopy online measurement equipment

[0114] Sample preparation: Four batches of honey (batch numbers Y1, Y2, Y3, and Y4), 15 L each, were real-world samples from the honey refining process at Beijing Tong Ren Tang’s Daxing production base, used to test moisture.

[0115] NIR spectrum measurement of honey refining process under normal pressure:

[0116] The moisture content of Y1 batch honey during the normal pressure refining process is as follows Figure 6As shown in the figure, during the whole process of refining honey under normal pressure, the moisture content decreases with time. The samples collected at the sampling port of the online measurement equipment can reflect the moisture changes during the refining process. The original NIR spectrum of the Y1 batch honey under normal pressure refining process is shown in the figure. Figure 7 In the 1400-1500nm, 1900-1950nm and 2050-2150nm bands, the NIR original spectrum has obvious characteristic absorption, which is affected by temperature changes, such as Figure 7 a. The spectrum was preprocessed using SNV. After preprocessing, the baseline difference of the spectrum was small and the characteristic absorption remained basically unchanged, as shown in Figure 7 As shown in b, it shows that the online measurement equipment can realize the NIR spectrum measurement of the normal pressure honey refining process, and the NIR spectrum collected in the flow cell contains the information of water.

[0117] The 27 refined honey samples were sorted according to the water reference value, and samples were taken every 2 samples, and the samples were divided into 18 calibration set samples and 9 prediction set samples. Figure 8 As shown, the R of the model calibration set samples 2 cal=0.9994,RMSEC=0.1026%,RMSECV=0.1555%,R of the prediction set samples 2 pre=0.9984,RMSEP=0.1786%,RPD=25.00. The relative deviation between the reference value and the predicted value of the calibration set samples ranged from 0.01% to 0.72%, and the relative deviation between the predicted value and the reference value of all samples of the atmospheric pressure honey refining process ranged from 0.08% to 2.24%. The relative deviation between the reference value and the predicted value of all samples of the atmospheric pressure honey refining process was less than 3.00%, indicating that the correlation between the moisture content of the atmospheric pressure honey refining process and the NIR spectrum was high, and the NIR spectrum online measurement equipment could be used for online detection of moisture content in the atmospheric pressure honey refining process.

[0118] NIR spectrum measurement of honey refining process by decompression technology:

[0119] The moisture content of three batches of honey during the decompression process of Y2, Y3 and Y4 is shown in the figure. Fig. 9 As shown. During the whole process of honey refining by decompression process, the moisture content decreases with time. The samples collected at the sampling port can reflect the moisture changes in the honey refining process. The honey refining process of decompression process is consistent with that of normal pressure process, such as Fig.10 As shown, the temperature of the decompression process is lower, and the baseline drift of the NIR original spectrum is not obvious. This shows that the online measurement equipment can realize the NIR spectrum measurement of the decompression process, and the NIR spectrum collected in the flow cell contains the information of water.

[0120] The KS method was used to divide the 79 samples into a calibration set and a prediction set, with 52 calibration sets and 27 prediction sets. Fig.11As shown, the R of the model calibration set samples 2 cal=0.9875,RMSEC=0.3740%,RMSECV=0.4514%,R of the prediction set samples 2 pre=0.9581,RMSEP=0.5145%,RPD=4.89. The relative deviation between the reference value and the predicted value of the calibration set samples ranged from 0.001% to 4.77%, and the relative deviation of the predicted set ranged from 0.50% to 5.19%. The comparison results showed that the model prediction performance of the decompression process was lower than that of the normal pressure process due to the variation between batches of honey refining samples, but the correlation between the moisture content and the NIR spectrum in the honey refining process was still high. This shows that the NIR spectroscopy online measurement equipment can be used for online detection of moisture content in the honey refining process of the decompression process.

[0121] Summarize

[0122] The present invention provides a method for measuring and controlling the chemical properties in the process of refining honey for traditional Chinese medicine large honey pills, and realizes online real-time monitoring of moisture in combination with near-infrared spectroscopy technology. Through the verification of the examples, the high precision and stability of the method in the determination of moisture, tetrasaccharide and 5-HMF content are demonstrated. For example, the moisture content is determined by the Abbe refractometer method, and the moisture range and fluctuation of different honey species are obtained, revealing the problem of poor moisture stability of old honey; high performance liquid chromatography (HPLC) technology is used to determine the tetrasaccharide content, and the results show that the original honey, old honey and honey for combined pills all meet the specified standards; and the determination of 5-HMF shows that its volatility is large during the refining process, pointing out the influence of temperature and time on its generation. Further through matrix scatter plot analysis, the refining process of honey is discovered and optimized to reduce the generation of 5-HMF. Finally, combined with the online moisture detection system based on near-infrared technology, real-time monitoring and automatic regulation of moisture are realized, ensuring the precise control of moisture in the refining process, and the error is controlled within ±5%, meeting the high requirements for quality stability and consistency in the production process of traditional Chinese medicine large honey pills. This invention significantly improves the digitalization, precision and standardization of the honey refining process, which helps to optimize the production process and improve product quality.

[0123] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for measuring and controlling the chemical properties of honey refining process in the production process of large honey pills of traditional Chinese medicine, characterized in that: The following steps are involved: The moisture content of the honey samples was determined using the Abbe refractometer method, where the moisture control standards were: 18% to 20% for young honey, 16% to 18% for medium honey, and 14% to 16% for old honey; The fructose, glucose, sucrose and maltose contents in honey were analyzed using high performance liquid chromatography (HPLC) with evaporative light scattering detector (ELSD); The 5-hydroxymethylfurfural (5-HMF) content in honey was accurately determined using HPLC instrument equipped with a diode array detector (DAD); The quality fluctuations of water content, sugar content and 5-HMF content in the honey refining process were analyzed by matrix scatter plot and 95% confidence interval, and the standard range was established for quality control. The accuracy and reliability of the determination method were verified by stability and recovery experiments.

2. The method for measuring and controlling the chemical properties of the honey refining process in the production process of the Chinese medicine honey pill according to claim 1, characterized in that: The moisture content determination method comprises heating a refined honey sample to 40° C., determining the refractive index of the honey, and calculating the moisture content by a predetermined formula.

3. The method for measuring and controlling the chemical properties of the honey refining process in the production process of the Chinese medicine honey pill according to claim 1, characterized in that: The HPLC column used in the tetrasaccharide content determination method is Dikmapolyamino HILIC, the mobile phase ratio is water: acetonitrile = 30%: 70%, the flow rate is 1.0 mL·min-1, and the temperature is 30°C. The HPLC column used in the 5-HMF content determination method is Agilent ZORBAX SB-C18, 4.6 mm×250 mm, 5 μm, the mobile phase is water: methanol = 85%: 15%, the flow rate is 1.0 mL·min-1, the temperature is 35°C, and the detection wavelength is 284 nm.

4. A spectral measurement device for online moisture detection, characterized in that: The device is used for real-time monitoring and control of the moisture content in the method for measuring and controlling the chemical properties of the honey refining process in the production process of the large honey pills of traditional Chinese medicine according to any one of claims 1 to 3, and the device comprises: The light source module uses a high-power near-infrared light source with a wavelength range of 1,800 to 2,400 nm; A spectral sensor is responsible for collecting the reflected spectral information of the sample surface, and the sensor has a high-precision photoelectric detector and high-resolution spectral analysis capability; The data processing and analysis module uses signal preprocessing techniques such as Fourier transform and combines multivariate statistical methods such as support vector regression (SVR) to model and analyze moisture content; The control system and feedback module feed back moisture data to the production control system in real time, automatically adjusting process parameters such as temperature and pressure during the honey refining process.

5. The spectral measurement device for online water detection according to claim 4, characterized in that: The data processing and analysis module further includes a grey correlation analysis (GCA) optimization algorithm for evaluating the preprocessing effect of spectral data and the accuracy of moisture content.

6. The spectral measurement device for online water detection according to claim 4, characterized in that: The spectral sensor can provide high-resolution near-infrared spectral data to improve the accuracy and sensitivity of moisture content measurement.

7. The spectral measurement device for online water detection according to claim 4, characterized in that: The control system is connected to the automated control system of the honey refining production line and can automatically adjust process parameters according to real-time detection data to ensure that the moisture content is maintained within a set range.

8. The spectral measurement device for online water detection according to claim 4, characterized in that: The control system includes an alarm function, which automatically triggers an alarm when the moisture content exceeds a set range.

9. A method for online detection of moisture in the process of making honey for Chinese medicine honey pills based on near infrared spectroscopy, characterized in that: Utilizing the principle of near-infrared spectral reflection, the moisture content is calculated in real time by analyzing the near-infrared spectral data of sample absorption and reflection, and the process parameters in the honey refining process are controlled through a feedback mechanism.

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