Rapid detection method and system for water activity

Abstract

The invention belongs to the technical field of detection, and particularly relates to a rapid detection method and system for water activity, and the method comprises the following steps: sensing and measuring water activity values of a sample at different time points in real time by adopting a water activity sensor or a measuring device; the timestamps corresponding to the obtained real-time water activity data and the water activity values are stored in an associated mode; carrying out denoising, smoothing and formatting processing on the collected original data, and carrying out data caching according to a set time interval or a real-time updating mode; according to the discrete water activity data, performing real-time fitting on the water activity time sequence curve based on a water activity prediction model to obtain a fitted curve, and deducing a water activity value at a future moment according to the fitted curve; and performing difference value comparison according to the two continuous fitting curves to obtain a difference value sequence. According to the method, fitting prediction can be carried out through an algorithm on the premise that a small amount of measured data is obtained in the initial stage, and the final stable value of the water activity is rapidly obtained in combination with continuous data updating and stability judgment.

Description

Technical Field

[0001] The present invention belongs to the field of detection technology, and in particular relates to a rapid detection method and system for water activity. Background Art

[0002] Water activity is an important indicator for measuring the free water content in a substance and is widely used in the food, pharmaceutical, cosmetics, and chemical industries. Water activity not only affects a product's physical properties, chemical stability, and microbial growth, but also directly impacts its quality, safety, and shelf life. Therefore, accurate and rapid water activity measurement is crucial for production process control, product development, and quality management.

[0003] Currently, water activity measurement relies primarily on traditional physical measurement methods. This process involves sample balancing, equipment calibration, and data recording. This process is often time-consuming, often requiring hours or even days to obtain stable results. This long measurement cycle is particularly inconvenient in applications requiring rapid production and immediate quality control, limiting its application in real-time monitoring and rapid response.

[0004] Furthermore, traditional water activity measurement methods have high requirements for environmental conditions during operation. Improper control of temperature and humidity, for example, can lead to inaccurate or unstable measurement results. This not only increases measurement uncertainty but can also affect the continuity and efficiency of production processes.

[0005] For production processes that require rapid feedback and immediate quality control, low measurement efficiency not only reduces production benefits but also makes it difficult to detect and correct product quality issues in a timely manner. Therefore, a method that can quickly predict the final stable value of water activity at the initial measurement stage is urgently needed to significantly shorten testing time and improve real-time monitoring capabilities of the production process. Summary of the Invention

[0006] The purpose of the present invention is to provide a rapid water activity detection method and system, which can quickly obtain the final stable value of water activity by fitting and predicting through an algorithm based on the premise of obtaining a small amount of measurement data in the early stage, and combining continuous data updating and stability judgment.

[0007] The technical solutions adopted by the present invention are as follows: A rapid water activity detection method and system, comprising: Using a water activity sensor or measuring device to sense and measure the water activity value of the sample at different time points in real time; The corresponding timestamp of the acquired real-time water activity data is associated with the water activity value and stored; De-noise, smooth, and format the collected raw data, and cache the data according to the set time interval or real-time update mode; According to the discrete water activity data and based on the water activity prediction model, a water activity time series curve is fitted in real time to obtain a fitting curve, and the water activity value at a future moment is inferred based on the fitting curve; Comparing the difference between two consecutive fitting curves to obtain a difference sequence, and performing statistical analysis based on the difference sequence to obtain a determination result of the stability of the fitting curve; Combine the stability judgment results with the latest fitting curve to output the final water activity measurement value and related data.

[0008] As a preferred solution, the measuring device includes a dew point method, a resistance method or a microwave absorption method for water activity measurement.

[0009] As a preferred embodiment, the dew point method specifically comprises the following steps: Crush solid samples to a particle size no larger than 5 mm. Liquid or semi-fluid samples need to be stirred evenly, and the sample filling volume should be controlled to two-thirds of the sample cup capacity; The sample cup is quickly placed in the measurement chamber. After sealing, the semiconductor module is used to rapidly reduce the mirror temperature from ambient temperature to below the expected dew point under a constant temperature and humidity environment. The temperature is then raised back up at a rate of 0.1°C / second. The mirror reflectivity is scanned in real time by an infrared laser to capture the moment of condensation. The system automatically records the critical temperature for condensation and calculates the water activity based on the critical temperature and the Clausius-Clapeyron equation.

[0010] As a preferred solution, the resistance method specifically includes the following steps: Crush solid samples to a particle size no larger than 3 mm. Liquid or semi-fluid samples must be stirred evenly, and the sample filling volume must be controlled to two-thirds of the sample cup capacity; The sample cup is quickly placed into the measuring chamber, sealed, and the sample is absorbed by the hygroscopic material under a constant temperature and humidity environment. The real-time resistance value is measured using a sensor, and the steady-state resistance value is recorded. Based on the steady-state resistance value, the instrument's built-in algorithm is used to convert the steady-state resistance value into a water activity value.

[0011] As a preferred embodiment, the microwave absorption method specifically comprises the following steps: Solid samples should be crushed to a particle size of no more than 3mm. Liquid or semi-fluid samples should be stirred evenly, and the sample filling thickness should be controlled within 2mm-5mm. The sample is placed in a sample dish made of microwave-transmitting material. Microwaves of a fixed frequency are emitted, penetrate the sample, and the attenuation signal is received. The energy loss value of the microwave after passing through the sample is recorded by the sensor. Based on the energy loss value and the correlation between the absorption intensity of microwaves by water and molecular polarity, a mathematical model of loss factor and water activity was established to calculate the water activity.

[0012] As a preferred solution, the specific steps of the associated storage are as follows: Data reception and labeling: Collect and receive water activity measurement values ​​and record the timestamp of the measurement moment. Each data point is composed of a timestamp and a water activity value so that its time series characteristics can be identified later. Data storage or caching: The received data points are stored in the system cache or database in chronological order. At the same time, the system is networked and transmits the data to the host computer or cloud for backup through the communication interface; Abnormal data filtering: Mark or remove data whose values ​​are outside the reasonable range; or use historical data comparison or statistical methods to identify abnormal points.

[0013] As a preferred solution, the specific method for denoising, smoothing and formatting the raw data includes a moving average method or a Kalman filter method; in, The moving average method specifically comprises the following steps: Determine the window size and weight distribution rules based on data characteristics; Arrange the data in ascending order according to the time series by timestamp. Missing values ​​need to be interpolated or removed, and the moving average is calculated after removing obvious outliers. The Kalman filtering method specifically comprises the following steps: Define the initial state vector and its error covariance matrix according to the data type. If the initial state is unknown, it can be set as a diagonal matrix; Predict the state at the next moment based on the system dynamic model and update the uncertainty of the predicted state; The weight coefficients of the fusion prediction and observation are used to correct the predicted state using the actual observation value, and the uncertainty of the corrected state is updated at the same time; The predicted state and state uncertainty are used as input for the next moment, and the prediction and correction process is repeated to achieve real-time recursive filtering.

[0014] As a preferred solution, the specific fitting steps in the water activity prediction model are as follows: Initial fitting: After obtaining a small number of discrete data points at the beginning of the measurement, the first fitting curve is generated; Dynamic update: As the test progresses, new data points are continuously added, and the existing water activity prediction model is updated or refitted; Real-time output of prediction results: Based on the latest fitting curve, the water activity prediction value after the current time point is obtained.

[0015] As a preferred solution, the specific steps of outputting the determination result are as follows: Comparison of continuous fitting curves: After obtaining two continuous fitting curves, select several corresponding water activity values ​​y at the same time point x for comparison to obtain a difference sequence; Calculate difference statistics: perform statistical analysis on the difference series, specifically calculate the standard deviation or maximum difference; Threshold determination: When the difference statistic is lower than the preset threshold, the fitting curve is considered to be stable, indicating that the final value of water activity no longer changes significantly; Output results: Once it is determined to be stable, a "measurement stable" signal is sent to the result output module for final result extraction and presentation.

[0016] A rapid water activity detection system, applying the above-mentioned rapid water activity detection method, comprises: The sensing detection module includes a water activity sensor or a measuring device for sensing and measuring the water activity value of the sample at different time points in real time; a data acquisition module, configured to obtain real-time water activity data from the sensing detection module and associate corresponding timestamps with water activity values ​​for storage; A data processing module, configured to perform denoising, smoothing, and formatting on the raw data collected by the data acquisition module, and to cache the data according to a set time interval or real-time update mode; A data fitting and prediction module is used to perform real-time fitting of the water activity time series curve using a water activity prediction model based on the discrete water activity data output by the data processing module to obtain a fitting curve, and to infer the water activity value at a future moment based on the fitting curve; a stability judgment module, configured to compare the differences between two consecutive fitting curves to obtain a difference sequence, and to perform statistical analysis based on the difference sequence to obtain a judgment result on the stability of the fitting curve; The result output module is used to combine the stability judgment result with the latest fitting curve and output the final water activity measurement value and related data.

[0017] The technical effects achieved by the present invention are: By utilizing a water activity prediction model, the present invention enables rapid fitting and continuous updating of a small amount of initial data, eliminating the need to wait for the sample to fully equilibrate with the measurement environment. This allows accurate water activity predictions to be obtained at an earlier stage. Compared to existing technologies, this significantly improves measurement efficiency. Furthermore, in areas where water activity is closely related to product quality, the present invention can more quickly detect anomalies or trend changes, enabling timely action to be taken, thereby improving overall quality control and safety management.

[0018] This invention introduces "continuous updating" and "stability assessment" into the testing process, creating essential new steps. This allows the system to continuously modify the fitting model as new data is acquired in real time, and to make timely judgments when the fitting curve shows signs of stabilization, ensuring the accuracy and stability of the test results.

[0019] Because the present invention shortens the measurement cycle, the production line can quickly obtain relevant water activity information, allowing for more flexible adjustments and reducing losses or rework caused by water activity deviations from target values. At the same time, the occupancy time of the testing equipment is reduced, thereby reducing operating costs.

[0020] The method of the present invention does not rely on a specific type of sensor, as long as it has the corresponding precise measurement and data communication capabilities. It is applicable to a variety of scenarios in industries such as food, medicine, cosmetics, and chemicals, with minimal requirements for production conditions and good versatility and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the process structure of the rapid detection method of water activity in the present invention; Figure 2 It is a schematic diagram of the process structure of the rapid detection system of water activity in the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose and advantages of the present invention more clearly understood, the present invention is described in detail below with reference to the following examples. It should be understood that the following text is only used to describe one or more specific embodiments of the present invention and does not strictly limit the scope of protection of the present invention.

[0023] like Figure 1 As shown in the figure, a rapid detection method for water activity is proposed. Its core idea is: on the premise of obtaining a small amount of measurement data in the initial stage, fitting prediction is performed through an algorithm, and combined with continuous data updates and stability judgment, the final stable value of water activity is quickly obtained.

[0024] The specific steps are as follows: Step 1: Using a water activity sensor or measuring device to sense and measure the water activity value of the sample at different time points in real time; Step 2: Associate and store the corresponding timestamp of the acquired real-time water activity data with the water activity value; Step 3: De-noise, smooth, and format the collected raw data, and cache the data according to the set time interval or real-time update mode; Step 4: Based on the discrete water activity data and the water activity prediction model, the water activity time series curve is fitted in real time to obtain a fitting curve, and the water activity value at a future moment is inferred based on the fitting curve; Step 5: Compare the difference between the two consecutive fitting curves to obtain a difference sequence, and perform statistical analysis based on the difference sequence to obtain a determination result of the stability of the fitting curve; Step 6: Combine the stability judgment result with the latest fitting curve to output the final water activity measurement value and related data.

[0025] like Figure 1-Figure 2 As shown, a rapid water activity detection system is provided, to which the above-mentioned rapid water activity detection method is applied. The system specifically includes the following functional modules and steps: 1. Sensing Detection Module Function and effect: This module mainly includes a water activity sensor or measuring device, which is used to sense and measure the water activity value of the sample at different time points in real time.

[0026] Implementation: Existing water activity measurement methods such as dew point, electrical resistance, and microwave absorption can be used, or other suitable hardware devices can be selected based on the specific application. This module must ensure that the measurement environment is relatively stable in terms of temperature, humidity, and other conditions to reduce measurement noise and errors.

[0027] It should be noted that different water activity sensors or measuring devices can be selected depending on the type of experiment and product. In this example, the Decagon Aqualab 4TE water activity sensor is selected. Of course, the specific steps for water activity measurement using the dew point method, electrical resistance method, or microwave absorption method are as follows: First, the dew point method specifically includes the following steps: 1. Sample pretreatment 1.1 Sample preparation Solid samples need to be crushed to a particle size of ≤5mm, and liquid samples need to be evenly stirred to ensure uniform water distribution.

[0028] The sample filling volume should be controlled at 2 / 3 of the sample cup capacity to avoid excessive squeezing that affects the steam balance.

[0029] 1.2 Environmental Calibration The laboratory needs to maintain a constant temperature (25±0.5℃) and humidity of 50%-80%. The instrument should be preheated for 30 minutes to eliminate temperature drift.

[0030] 2. Start the closed balance system Seal the sample chamber: quickly place the sample cup into the measuring chamber and ensure that the chamber door is airtight after closing to prevent interference from ambient humidity.

[0031] Note: If the sample contains volatile components (such as alcohol), a special sealing film must be used to block it.

[0032] 3. Mirror cooling and dew point detection 3.1 Dynamic cooling mirror control The mirror temperature is rapidly reduced from ambient temperature to below the expected dew point (usually -10°C) through the semiconductor module, and then raised back up at a rate of 0.1°C / second.

[0033] Infrared laser scans the mirror light reflectivity in real time to capture the moment of condensation (reflectivity drops by ≥15%).

[0034] 3.2 Dew point temperature lock The system automatically records the critical condensation temperature Td (dew point temperature) with an accuracy of ±0.1°C.

[0035] 4. Water activity calculation 4.1 Vapor pressure conversion Calculate water activity based on the Clausius-Clapeyron equation : ; in, is the water activity, is the sample vapor pressure, is the saturated vapor pressure of pure water, is the latent heat of vaporization, is the water vapor gas constant, is the reference temperature, is the dew point temperature.

[0036] Second, the resistance method specifically includes the following steps: 1. Instrument calibration (core preprocessing) 1.1 Calibration with standard salt solution Fill the calibration cup with a saturated salt solution (such as sodium chloride aw = 0.753 or magnesium chloride aw = 0.328) to cover the sensor surface.

[0037] Set the calibration mode according to the instrument manual and wait for the reading to stabilize (usually 10-15 minutes). The error should be ≤±0.01aw. It is important to note that the calibration frequency should be once a week or immediately after a significant fluctuation in ambient temperature and humidity.

[0038] 2. Sample preparation 2.1 Homogenization Solid samples should be crushed to a particle size of ≤3mm (such as milk powder and pharmaceutical coatings) to avoid the influence of particle gaps on vapor balance.

[0039] Liquid / semi-fluid samples (such as sauces) need to be stirred thoroughly to eliminate stratification.

[0040] 2.2 Filling the sample cup The filling volume should account for 2 / 3 of the cup volume to ensure sufficient contact with the sensor. Excessive compaction may result in obstruction of vapor diffusion.

[0041] 3. Sensor loading and balancing 3.1 Closed cabin operation Place the sample cup into the measuring chamber, seal it, and start the air circulation (flow rate 1-2 L / min) to accelerate the humidity balance in the chamber.

[0042] 3.2 Temperature balance control The sample temperature must be consistent with the sensor temperature (temperature difference ≤ 0.5°C), and the equilibration time is usually 20 minutes to 2 hours.

[0043] It is worth noting that high-fat / high-sugar samples require an extended equilibration time of more than 1.5 hours.

[0044] 4. Resistance measurement and data acquisition 4.1 Electrical Signal Conversion When the sensor's hygroscopic material (such as lithium chloride) absorbs moisture, its resistance decreases and the instrument records the steady-state resistance value.

[0045] 4.2 Water activity conversion The instrument's built-in algorithm converts the resistance value into The value is as follows: ; in, is the water activity value, is the measured resistance, 、 are the calibration curve parameters.

[0046] Third, the microwave absorption method specifically includes the following steps: 1. Instrument calibration and benchmark establishment 1.1 Calibration with standard substances Fill the sample chamber with a standard salt solution of known water activity (such as sodium chloride aw = 0.753) or a standard humidity tablet.

[0047] Record the corresponding relationship between microwave energy absorption rate and standard value and construct a calibration curve (usually 3-5 standard points are required).

[0048] 2. Sample preparation and loading 2.1 Sample pretreatment Solid samples need to be crushed to a particle size of ≤3 mm, and liquid samples need to be evenly stirred to avoid stratification that affects microwave penetration.

[0049] The sample thickness should be controlled between 2mm and 5mm (too thick will result in signal attenuation, too thin will result in insufficient sensitivity).

[0050] 2.2 Sample chamber loading Place the sample in a special sample dish made of microwave-transparent material (such as polytetrafluoroethylene) and ensure that the surface is flat.

[0051] 3. Microwave parameter setting and measurement 3.1 Microwave Transmission and Reception The microwaves of a fixed frequency (common frequency band: 2.45GHz or 5.8GHz) are emitted, penetrate the sample and receive the attenuated signal.

[0052] Control the microwave power within the range of 10mW-50mW to avoid thermal effects interfering with the measurement.

[0053] 3.2 Real-time monitoring of energy loss The energy loss value after the microwave passes through the sample is recorded by the sensor (Unit: dB), calculation formula: ; in, is the energy loss value, is the incident power, is the transmitted power.

[0054] 4. Water activity calculation 4.1 Loss factor related to water activity The absorption intensity of water to microwaves is related to the molecular polarity, and the loss factor is established and Mathematical model: ; in, is the water activity value, is the energy loss value, 、 are the calibration curve parameters.

[0055] Output results: The real-time measured water activity value (and the corresponding timestamp) is sent to the data acquisition module.

[0056] 2. Data Acquisition Module Function and role: This module is responsible for obtaining real-time water activity data from the sensor detection module and associating the corresponding timestamp with the water activity value for storage.

[0057] Implementation method: A data acquisition card or built-in communication interface can be used to communicate with the sensor through serial port, USB, network interface, etc., and at the same time, preliminary filtering or anomaly detection can be performed on the data (such as eliminating obviously distorted data).

[0058] Output results: Continuously output discrete data points of time-water activity for subsequent data processing and model fitting.

[0059] 3. Data processing module Function and effect: This module performs denoising, smoothing and formatting on the collected raw data, and caches the data according to the set time interval or real-time update mode.

[0060] Implementation method: Based on experience or specific application requirements, data can be processed using methods such as moving average method and Kalman filter method to reduce random errors and maintain data authenticity.

[0061] It is worth noting that when using the moving average method to process data, it is necessary to determine the window size and weight distribution rules based on the data characteristics; then the data is arranged in ascending order by timestamp according to the time series, missing values ​​need to be interpolated or eliminated, and the moving average is calculated after eliminating obvious outliers.

[0062] Secondly, when using the Kalman filter method, it is necessary to define the initial state vector and its error covariance matrix according to the data type. If the initial state is unknown, it can be set as a diagonal matrix; then the state at the next moment is predicted based on the system dynamic model, and the uncertainty of the predicted state is updated; then the weight coefficients of the prediction and observation are integrated, and the actual observation value is used to correct the predicted state, and the uncertainty of the corrected state is updated at the same time; finally, the predicted state and state uncertainty are used as the input of the next moment, and the prediction and update process is repeated to realize real-time recursive filtering.

[0063] Output: Generates high-quality, structured time-water activity data streams, providing reliable data input for fitting and prediction modules.

[0064] 4. Data Fitting and Prediction Module Function and Effect: This module is the core of the present invention. Its primary purpose is to use mathematical models (such as nonlinear least squares, polynomial fitting, logarithmic function fitting, or machine learning-based prediction models) to perform real-time fitting of water activity time series curves based on the discrete water activity data output by the data processing module, and to infer water activity values ​​at future moments.

[0065] Implementation: Initial fitting: After obtaining a small number of discrete data points at the beginning of the measurement, the first fitting curve is generated; Dynamic update: As the test progresses, new data points are continuously added and the existing model is updated or refitted; Real-time output of prediction results: Based on the latest fitting curve, the water activity prediction value after the current time point is obtained.

[0066] Output results: real-time updated fitting curve and predicted value, providing input basis for the stability judgment module.

[0067] 5. Stability Judgment Module Function and effect: It is used to determine whether the water activity prediction results are stable, avoiding the defects of the traditional method of waiting for a long time to confirm the final results.

[0068] Implementation: Comparison of continuous fitting curves: After obtaining two continuous fitting curves, select the corresponding water activity values ​​(y) at the same time point (x) for comparison to obtain a difference sequence; Calculate difference statistics: Perform statistical analysis on the difference series, typically calculating the standard deviation or maximum difference; Threshold determination: When the difference statistic is lower than the preset threshold (set in advance according to different experimental scenarios), the fitting curve is considered to have stabilized, indicating that the final value of water activity no longer changes significantly.

[0069] Output results: Once it is determined to be stable, a "measurement stable" signal is sent to the result output module for final result extraction and presentation.

[0070] 6. Result Output Module Function and effect: Combine the stability judgment result with the latest fitting curve to output the final water activity measurement value and related data.

[0071] Implementation method: The final predicted water activity value can be displayed on the instrument panel or software interface, and a measurement report (including measurement time, sensor information, prediction model type, stability judgment curve, etc.) can be recorded and generated at the same time.

[0072] Output: Provide the final water activity value to the user or production control system for subsequent quality control or process management.

[0073] like Figure 1-Figure 2 As shown, a method and system for rapid detection of water activity is specifically applied. Its main application areas are as follows: 1. Food industry Raw material and formula evaluation: such as coffee beans, grains, condiments, baking materials, dairy products, meat products, aquatic products, etc., the control of water activity can effectively prevent the growth of microorganisms and ensure the flavor and taste.

[0074] Production process control: Real-time monitoring of water activity during food processing allows for timely adjustments to drying, moisturizing, or sterilization processes to improve product consistency and production efficiency.

[0075] Storage and shelf life studies: By measuring water activity, we can evaluate changes in food quality under different storage conditions, optimize packaging and storage and transportation methods, and extend shelf life.

[0076] 2. Pharmaceutical and bioengineering industries Pharmaceutical R&D and quality control: For tablets, capsules, powders for injection, lyophilized preparations, etc., accurate control of water activity helps to avoid drug failure, deterioration or microbial contamination.

[0077] Production of strains and enzyme preparations: In microbial fermentation and enzyme preparation production, the stability and effectiveness of bioactive substances are ensured by monitoring water activity.

[0078] 3. Cosmetics and personal care products industry Product stability assessment: For products such as creams, facial masks, and skin care products, water activity has a significant impact on microbial growth and product texture. Monitoring and controlling water activity can extend shelf life and maintain product efficacy.

[0079] New Product Development: Monitor water activity during the formulation design phase and optimize the formulation to ensure product stability and safety in different environments.

[0080] 4. Research institutions and university laboratories Basic research: In scientific research experiments in fields such as food science, pharmacy, microbiology, and materials science, water activity is an important factor affecting various reactions and processes. Accurate measurement helps to reveal the interaction mechanism between water and materials or microorganisms.

[0081] 5. Other potential areas Electronic device and lithium battery manufacturing: Some high-end electronic components and lithium battery electrolytes have strict requirements on humidity. Water activity monitoring can prevent quality or safety risks.

[0082] In the application process in the above fields, the specific implementation methods are as follows: Step 1: Sensor detection module 1.1 Sample preparation and placement Place the sample to be tested in the sample chamber or corresponding measurement position of the water activity tester, and ensure that the basic conditions required by the instrument (such as temperature, humidity, etc.) meet or are close to the test requirements.

[0083] Check that the sensor is calibrated and that the equipment meets the measurement standards.

[0084] 1.2 Start sensor measurement Start the water activity sensor to collect data through the water activity detector or control system interface.

[0085] The sensor can adopt continuous acquisition mode or interval acquisition mode (for example, measurement every few seconds / minutes), and the specific frequency can be configured according to actual needs.

[0086] 1.3 Measuring water activity At each sampling moment, the sensor will instantly measure the water activity value of the sample and temporarily store the measured value in the buffer area inside the detector or directly output it to the next module.

[0087] During this process, it is necessary to ensure that the measurement environment is relatively stable to minimize data fluctuations or noise caused by external interference.

[0088] Key points of operation Ensure that the sensor sensitivity meets the standards. If it is a dew point sensor, resistance sensor or microwave absorption sensor, it should be calibrated in advance.

[0089] The sampling frequency should be set to match the expected detection speed; a higher sampling frequency can capture water activity changes more quickly, but may introduce more noise or increase system load.

[0090] Step 2: Data Acquisition Module 2.1 Data Reception and Marking The data acquisition module receives the water activity measurement value from the sensing detection module and records the timestamp of the measurement moment.

[0091] Each data point consists of (timestamp, water activity value) so that subsequent modules can recognize its time series characteristics.

[0092] 2.2 Data Storage or Caching The received data points are stored in the system cache or database in chronological order.

[0093] The system is networked and transmits data to a host computer or cloud for backup through communication interfaces (such as serial port, USB, network interface, etc.).

[0094] Abnormal data filtering To prevent the subsequent analysis process from being disturbed by obviously erroneous data, preliminary outlier determination can be added at this step, such as marking or eliminating data with values ​​outside a reasonable range.

[0095] This operation can be designed as a simple threshold judgment, or historical data comparison or statistical methods can be used to identify abnormal points.

[0096] Key points of operation The synchronization and timing accuracy of data acquisition are very important for subsequent fitting and analysis.

[0097] The acquisition frequency should be consistent with the sensor detection module, and the system delay should be reduced as much as possible.

[0098] Step 3: Data processing module 3.1 Denoising The collected raw data is filtered or smoothed. Common methods include moving average filtering, Kalman filtering or wavelet noise reduction, etc., the purpose of which is to remove high-frequency noise.

[0099] This step can significantly improve the usability of the original data and avoid excessive data jitter affecting subsequent fitting.

[0100] 3.2 Anomaly Detection and Correction For discrete data points outside the normal fluctuation range of water activity values, re-evaluation is performed. If the data point is confirmed to be abnormal, it can be eliminated or corrected by interpolation or other methods.

[0101] In addition, if the sensor encounters significant interference during measurement (such as sudden vibration, sample contamination, etc.), it is necessary to perform corrections by repeated measurements or referring to auxiliary sensor data.

[0102] 3.2 Data Formatting After the above denoising and anomaly detection, relatively accurate and stable water activity time series data are obtained.

[0103] The data is packaged or cached as a "processed time series data stream" in a specific structure (such as a two-dimensional array or key-value pairs) for use by the next fitting and prediction modules.

[0104] Key points of operation The selection of filtering method should take into account both real-time performance and smoothness to avoid loss of effective information due to excessive filtering.

[0105] This step can be regarded as a key link in improving data quality and preventing invalid data from affecting the algorithm.

[0106] Step 4: Fitting and prediction module 4.1 Preliminary fitting Obtain processed initial data (usually data from several minutes or tens of minutes in the initial stage of the test) and perform preliminary fitting using the selected mathematical model.

[0107] Obtain the processed water activity data and construct the initial fitting model using the following formula: ; in: : humidity value; :time; : slope; :intercept.

[0108] The fitting formula estimates the parameters using the following method: ; in: : Design matrix (containing the logarithm of time and constant terms); : Weight matrix, updated by BISQUARE method; : Logarithmic value of humidity.

[0109] The power function model obtained by fitting is: .

[0110] 4.2 Continuous Data Update and Dynamic Fitting Over time, newly collected water activity data is continuously fed into this module. Rolling updates or incremental learning can be used to revise or retrain the existing fitted model. The addition of new data allows the model to gradually correct for biases introduced by initial estimates, better aligning with actual water activity trends.

[0111] 4.3 Real-time prediction output After obtaining the latest fitting curve, the fitting This module can give the predicted value of water activity at the current moment and in the short future, which can be provided to the next module for stability judgment or directly used for display.

[0112] When the measurement time is still in its early stages, the prediction results may have certain errors, but as new data are continuously injected, the prediction accuracy will improve.

[0113] Key points of operation Different fitting models have different applicability to data scale and variation characteristics. Logarithmic curves often provide a good fit for many scenarios where water activity balances over time.

[0114] The update strategy of dynamic fitting should be designed based on sampling frequency, model complexity, etc., to ensure both prediction accuracy and computational efficiency.

[0115] Step 5: Stability judgment module 5.1 Comparison of continuous fitting curves Each time the fitting curve is updated, the new curve is compared with the previous (or previous) fitting curves.

[0116] Assume that the predicted value of the nth fitting curve is , the predicted value of the n-1th fitting curve is , then the two curves at a certain time point The difference is: ; The difference sequence of all time points is: ; in is the total number of time points.

[0117] 5.2 Calculating Difference Statistics Perform statistical analysis on the obtained difference sequence.

[0118] The mean square error is a measure of the overall deviation between the two fitted curves: ; The standard deviation measures how widely the forecast differences are spread out: ; in: is the average value of the difference series, calculated as: ; The maximum deviation is the largest absolute value in the sequence of differences: ; 5.3 Determining Stability When the difference statistic meets the threshold requirement: and ; in: : mean square error threshold; : Maximum deviation threshold.

[0119] The current fitted curve is considered to have largely reflected the true water activity equilibrium trend. At this point, the predicted value fluctuates slightly, and the addition of new data will only result in minor corrections.

[0120] If the statistic is still significantly high, it means that the prediction curve is still in the process of convergence and it is necessary to continue collecting more data and repeat the "fitting and prediction" steps.

[0121] Key points of operation Choosing an appropriate threshold , is the key and can be determined based on experience or through preliminary experiments.

[0122] Step 6: Final result output module 6.1 Extraction of stable water activity value Once the fitting curve is judged to be stable, the predicted water activity value at the current moment or theoretically at a subsequent moment can be read from the curve as the final measurement result.

[0123] Ideally, the water activity value will be in the flat or asymptotic region of the curve and close to the result obtained by traditional measurements after long-term equilibrium.

[0124] 6.2 Data Display and Interaction The current water activity value, current fitting curve, and stability determination status are displayed in real time on the detector's display screen or upper system interface.

[0125] In an intelligent production environment, the results can be pushed to the factory execution system (MES) or quality management system (QMS) to facilitate rapid production control decisions.

[0126] 6.3 Records and Archives The final test results, fitted model parameters, stability determination time, and other relevant information are recorded in the tester or backend database for subsequent traceability, analysis, or review. If necessary, data collection and parallel log monitoring can be continued to track abnormal situations or the determination of new formulas / products over the long term.

[0127] 6.4 End Complete the rapid measurement process of the current batch or current sample. If a new sample needs to be measured, the process can be reset or restarted for the next test.

[0128] Key points of operation For situations where extremely high accuracy is required, it is recommended to conduct a short-term supplementary measurement based on the predicted results to ensure that there are no mistakes.

[0129] The foregoing is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art may make various improvements and modifications without departing from the principles of the present invention, and such improvements and modifications are also within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained herein shall, unless otherwise specified or limited, be implemented in accordance with conventional means in the art.

Claims

1. A rapid detection method for water activity, characterized in that: include: Using a water activity sensor or measuring device to sense and measure the water activity value of the sample at different time points in real time; The corresponding timestamp of the acquired real-time water activity data is associated with the water activity value and stored; De-noise, smooth, and format the collected raw data, and cache the data according to the set time interval or real-time update mode; According to the discrete water activity data and based on the water activity prediction model, a water activity time series curve is fitted in real time to obtain a fitting curve, and the water activity value at a future moment is inferred based on the fitting curve; Comparing the difference between two consecutive fitting curves to obtain a difference sequence, and performing statistical analysis based on the difference sequence to obtain a determination result of the stability of the fitting curve; Combine the stability judgment results with the latest fitting curve to output the final water activity measurement value and related data.

2. A rapid detection method for water activity according to claim 1, characterized in that: The measuring device comprises a dew point method, a resistance method or a microwave absorption method for measuring water activity.

3. A rapid detection method for water activity according to claim 2, characterized in that: The dew point method specifically comprises the following steps: Crush solid samples to a particle size no larger than 5 mm. Liquid or semi-fluid samples need to be stirred evenly, and the sample filling volume should be controlled to two-thirds of the sample cup capacity; The sample cup is quickly placed in the measurement chamber. After sealing, the semiconductor module is used to rapidly reduce the mirror temperature from ambient temperature to below the expected dew point under a constant temperature and humidity environment. The temperature is then raised back up at a rate of 0.1°C / second. The mirror reflectivity is scanned in real time by an infrared laser to capture the moment of condensation. The system automatically records the critical temperature for condensation and calculates the water activity based on the critical temperature and the Clausius-Clapeyron equation.

4. A rapid detection method for water activity according to claim 2, characterized in that, The resistance method specifically comprises the following steps: Crush solid samples to a particle size no larger than 3 mm. Liquid or semi-fluid samples must be stirred evenly, and the sample filling volume must be controlled to two-thirds of the sample cup capacity; The sample cup is quickly placed into the measuring chamber, sealed, and the sample is absorbed by the hygroscopic material under a constant temperature and humidity environment. The real-time resistance value is measured using a sensor, and the steady-state resistance value is recorded. Based on the steady-state resistance value, the instrument's built-in algorithm is used to convert the steady-state resistance value into a water activity value.

5. A rapid detection method for water activity according to claim 2, characterized in that: The microwave absorption method specifically comprises the following steps: Solid samples should be crushed to a particle size of no more than 3mm. Liquid or semi-fluid samples should be stirred evenly, and the sample filling thickness should be controlled within 2mm-5mm. The sample is placed in a sample dish made of microwave-transmitting material. Microwaves of a fixed frequency are emitted, penetrate the sample, and the attenuation signal is received. The energy loss value of the microwave after passing through the sample is recorded by the sensor. Based on the energy loss value and the correlation between the absorption intensity of microwaves by water and molecular polarity, a mathematical model of loss factor and water activity was established to calculate the water activity.

6. A rapid water activity detection method according to claim 1, characterized in that: The specific steps of the associated storage are as follows: Data reception and marking: Collect and receive water activity measurement values ​​and record the timestamp of the measurement moment. Each data point is composed of a timestamp and a water activity value to identify its time series characteristics. Data storage or caching: The received data points are stored in the system cache or database in chronological order. At the same time, the system is networked and transmits the data to the host computer or cloud for backup through the communication interface; Abnormal data filtering: Mark or remove data whose values ​​are outside the reasonable range; Or use historical data comparison or statistical methods to identify anomalies.

7. A rapid water activity detection method according to claim 1, characterized in that: The specific method for performing denoising, smoothing and formatting on the raw data includes a moving average method or a Kalman filter method; in, The moving average method specifically comprises the following steps: Determine the window size and weight distribution rules based on data characteristics; Arrange the data in ascending order according to the time series by timestamp. Missing values ​​need to be interpolated or removed, and the moving average is calculated after removing obvious outliers. The Kalman filtering method specifically comprises the following steps: Define the initial state vector and its error covariance matrix according to the data type. If the initial state is unknown, it can be set as a diagonal matrix; Predict the state at the next moment based on the system dynamic model and update the uncertainty of the predicted state; The weight coefficients of the fusion prediction and observation are used to correct the predicted state using the actual observation value, and the uncertainty of the corrected state is updated at the same time; The predicted state and state uncertainty are used as input for the next moment, and the prediction and correction process is repeated to achieve real-time recursive filtering.

8. A rapid water activity detection method according to claim 1, characterized in that: The specific fitting steps in the water activity prediction model are as follows: Initial fitting: After obtaining a small number of discrete data points at the beginning of the measurement, the first fitting curve is generated; Dynamic update: As the test progresses, new data points are continuously added, and the existing water activity prediction model is updated or refitted; Real-time output of prediction results: Based on the latest fitting curve, the water activity prediction value after the current time point is obtained.

9. A rapid water activity detection method according to claim 1, characterized in that: The specific output steps of the determination result are as follows: Comparison of continuous fitting curves: After obtaining two continuous fitting curves, select several corresponding water activity values ​​y at the same time point x for comparison to obtain a difference sequence; Calculate difference statistics: perform statistical analysis on the difference series, specifically calculate the standard deviation or maximum difference; Threshold determination: When the difference statistic is lower than the preset threshold, the fitting curve is considered to be stable, indicating that the final value of water activity no longer changes significantly; Output results: Once it is determined to be stable, a "measurement stable" signal is sent to the result output module for final result extraction and presentation.

10. A rapid water activity detection system, using the rapid water activity detection method according to any one of claims 1 to 9, characterized in that: include: The sensing detection module includes a water activity sensor or a measuring device for sensing and measuring the water activity value of the sample at different time points in real time; a data acquisition module, configured to obtain real-time water activity data from the sensing detection module and associate corresponding timestamps with water activity values ​​for storage; A data processing module, configured to perform denoising, smoothing, and formatting on the raw data collected by the data acquisition module, and to cache the data according to a set time interval or real-time update mode; A data fitting and prediction module is used to perform real-time fitting of the water activity time series curve using a water activity prediction model based on the discrete water activity data output by the data processing module to obtain a fitting curve, and to infer the water activity value at a future moment based on the fitting curve; a stability judgment module, configured to compare the differences between two consecutive fitting curves to obtain a difference sequence, and to perform statistical analysis based on the difference sequence to obtain a judgment result on the stability of the fitting curve; The result output module is used to combine the stability judgment result with the latest fitting curve and output the final water activity measurement value and related data.

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