Sweetness perception threshold detection method and detection system based on dynamic gradient feedback

Abstract

The present application relates to a method and system for detecting the sweetness perception threshold based on dynamic gradient feedback, and belongs to the technical field of animal behavior detection. The method comprises: configuring sucrose solutions with gradient concentrations; collecting the time and pressure of the animal contacting the bottle mouth respectively; calculating the contact proportion of the animal to the sucrose solution under each concentration; constructing a mathematical model to fit the relationship curve between the contact proportion and the concentration of the sucrose solution; determining whether there is a significant change in the animal's perception of the sugar concentration according to the slope, and determining the sweetness perception threshold; and inducing sugar solution aversion reaction to verify whether the threshold result is reliable. The present application introduces the L w slope of the value-concentration curve to analyze the sensitivity of the animal to sucrose solutions with different concentrations, which can effectively distinguish between physiological perception and random preference, and effectively improve the accuracy of sweetness perception threshold detection; and the calculation of L w integrates the double parameter indicators of contact time and licking pressure, further improving the accuracy of sweetness perception threshold detection.

Description

Technical Field

[0001] This invention relates to a method and system for detecting the sweetness perception threshold based on dynamic gradient feedback, belonging to the field of animal behavior detection technology. Background Technology

[0002] In animal taste perception research, sweetness threshold detection is a key step in revealing the neural mechanisms of metabolic diseases and evaluating sensory characteristics. However, current technologies are significantly inadequate and cannot meet the requirements for precision and efficiency in research.

[0003] Traditional two-bottle preference tests typically employ a fixed concentration gradient design. This method cannot dynamically capture real-time changes in the sweetness threshold of animals during disease development or treatment, leading to significant errors in the test results. Because animals may be affected by factors such as satiety or exploratory behavior during the test, multiple trials are often required to confirm the threshold, which is not only inefficient but also increases experimental costs. Furthermore, existing methods mainly rely on counting the number of licks or measuring the duration of preference to determine an animal's sweetness preference. These behavioral indicators are easily affected by environmental noise, animal stress, or learning and memory, lacking objectivity and stability. In addition, these methods usually only compare a single concentration gradient, making it difficult to quickly cover a wide concentration range, and they lack quantitative mathematical models analyzing the relationship between sweetness and concentration, limiting in-depth analysis of taste perception mechanisms.

[0004] In the validation phase, traditional conditional taste aversion (CTA) experiments require the establishment of a new animal model, which cannot be directly correlated with the original threshold detection data, resulting in a longer experimental cycle and low efficiency. Summary of the Invention

[0005] To provide a rapid and highly sensitive method for assessing animal sweetness sensitivity and accurately quantifying the sweetness threshold, this invention provides a sweetness perception threshold detection method and system based on dynamic gradient feedback. The technical solution is as follows:

[0006] The first objective of this invention is to provide a method for detecting a sweetness perception threshold, comprising:

[0007] Step 1: Prepare sucrose solutions of different concentrations according to a gradient;

[0008] Step 2: Place sucrose solutions of different concentrations and pure water in different test cages, and let the animals come into contact with the sucrose solutions and pure water. Use contact sensors and pressure sensors to collect the time and pressure of the animals contacting the bottle openings, respectively.

[0009] Step 3: Calculate the percentage of animal contact with sucrose solution at each concentration. The calculation method is as follows:

[0010]

[0011] Among them, T Ai and T Bi P represents the duration of a single exposure of an animal to sucrose solution and pure water, respectively. Ai and P Bi These represent the pressure weights for the corresponding contact events;

[0012] Step 4: Based on the concentration range of the sucrose solution, construct a segmented mathematical model to fit the contact ratio L. w The curve showing the relationship between the concentration of the sucrose solution and the sucrose concentration;

[0013] Step 5: Determine whether there is a significant change in the animal's perception of the sugar concentration based on the slope K of the relationship curve. When L w If the sucrose concentration is greater than the preset contact ratio threshold and the slope K fluctuation is less than the preset slope fluctuation threshold, then the corresponding sucrose concentration is determined to be the sweetness perception threshold.

[0014] Step 6: Inject the same batch of model animals with a chemical nausea-inducing drug to induce a sugar aversion response. If the original sweetness perception threshold concentration is lower than L... w If the value decreases by ≥30%, the verification threshold result is reliable.

[0015] Optionally, step 4 includes:

[0016] Step 41: For the low concentration range, use the least squares method to fit the contact ratio L. w Relationship with sucrose solution concentration;

[0017] Step 42: For the high concentration range, use a Logistic regression model to fit the contact ratio L. w Relationship with sucrose solution concentration;

[0018] Step 43: Determine the critical value between the low concentration range and the high concentration range using the residual sum of squares minimization method;

[0019] Step 44: Fit the low concentration range and the high concentration range based on the critical value to obtain a piecewise fitting hybrid model.

[0020] Optionally, step 5 includes:

[0021] Step 51: Based on the initial concentration C0, conduct multiple animal tests and take the average L. w value;

[0022] Step 52: Calculate the slope K between the adjacent concentration C1 and the initial concentration C0. Dynamically adjust the step size of the sucrose concentration based on the slope K. If K > 0.5, it is determined to be a sensitive area, and the step size is reduced to ΔC × 0.5; if K < 0.3, it is determined to be a desensitized area, and the step size is increased to ΔC × 2; otherwise, the step size remains unchanged.

[0023] Step 53: Update concentration: C1' = C0 ± ΔC', where ΔC' represents the step size after updating in Step 2;

[0024] Step 54: Based on the concentration C1', conduct multiple animal tests and take the average L. w Value, if the average L w If the value is greater than the preset contact ratio threshold, then the current sucrose concentration is the sweetness perception threshold.

[0025] Optionally, step 52 further includes: when the fluctuation of the slope K is >20%, re-measuring the previous concentration.

[0026] A second objective of this invention is to provide a sweetness perception threshold detection system, the system comprising:

[0027] The data acquisition device includes a test cage, a contact sensor, and a pressure sensor. The test cage contains sucrose solutions of different concentrations configured in a gradient and pure water. The contact sensor and pressure sensor are used to collect the time and pressure of the animal contacting the bottle opening.

[0028] The contact percentage calculation module is used to calculate the contact percentage of animals with sucrose solution at each concentration. The calculation method is as follows:

[0029]

[0030] Among them, T Ai and T Bi P represents the duration of a single exposure of an animal to sucrose solution and pure water, respectively. Ai and P Bi These represent the pressure weights for the corresponding contact events;

[0031] The curve fitting module is used to construct a mathematical model segmented according to the concentration range of the sucrose solution to fit the contact ratio L. w The curve showing the relationship between the concentration of the sucrose solution and the sucrose concentration;

[0032] The sweetness perception threshold determination module is used to determine whether there is a significant change in the animal's perception of the sugar concentration based on the slope K of the relationship curve. When L... w If the sucrose concentration is greater than the preset contact ratio threshold and the slope K fluctuation is less than the preset slope fluctuation threshold, then the corresponding sucrose concentration is determined to be the sweetness perception threshold.

[0033] The verification module is used to determine the magnitude of the decrease in the sweetness perception threshold. If the original sweetness perception threshold concentration is lower than L... w If the value decreases by ≥30%, the verification threshold result is reliable. The condition for the decrease in the sweet taste perception threshold is: injecting the same batch of model animals with a chemical nausea drug to induce a sugar aversion response.

[0034] Optionally, the curve fitting process of the relationship curve in the curve fitting module includes:

[0035] For the low concentration range, the least squares method is used to fit the contact ratio L. w Relationship with sucrose solution concentration;

[0036] For the high concentration range, a Logistic regression model was used to fit the contact ratio L. w Relationship with sucrose solution concentration;

[0037] The critical values ​​between the low concentration range and the high concentration range are determined by minimizing the sum of squared residuals.

[0038] Based on the critical values, the low concentration range and the high concentration range are fitted separately to obtain a piecewise fitting hybrid model.

[0039] Optionally, the process of obtaining the sweetness perception threshold in the sweetness perception threshold determination module includes:

[0040] Based on the initial concentration C0, the average L was obtained after multiple animal tests. w value;

[0041] Calculate the slope K between the adjacent concentration C1 and the initial concentration C0, and dynamically adjust the step size of the sucrose concentration according to the slope K. If K > 0.5, it is determined to be a sensitive area and the step size is reduced to ΔC × 0.5; if K < 0.3, it is determined to be a desensitized area and the step size is increased to ΔC × 2; otherwise, the step size remains unchanged.

[0042] Update concentration: C1' = C0 ± ΔC', where ΔC' represents the step size after the update in step 2;

[0043] Based on the concentration C1', the average L was obtained after multiple animal tests. w Value, if the average L w If the value is greater than the preset contact ratio threshold, then the current sucrose concentration is the sweetness perception threshold.

[0044] Optionally, if the slope K fluctuates by more than 20%, the previous concentration should be remeasured.

[0045] Optionally, the contact sensor may be an infrared photoelectric probe.

[0046] Optionally, the two water bottles in the data acquisition device are of the same color and model, and the positions of the two solutions are randomly swapped to avoid animal position preference.

[0047] Optionally, the sucrose solution is prepared with deionized water and freshly prepared within 24 hours before the test to avoid microbial contamination; the sucrose concentration gradient can be extended to 0.05%-30% to accommodate the sensitivity differences of different animal strains.

[0048] Optional animals can be mice or rats of different strains; animals need to be trained in advance, that is: animals need to be deprived of water for 16-24 hours in advance to induce their motivation to drink water; give two bottles of pure water first to train the animals to adapt to drinking two solutions; by replacing one of the pure water bottles with an empty bottle or sugar solution, the animals can be encouraged to taste the two solutions in turn.

[0049] Optionally, the animal training and testing environment should maintain a light intensity of ≤50 lux and an ambient noise level of ≤40 dB to reduce external interference.

[0050] Optionally, the contact sensor uses an infrared photoelectric probe (contact response) and a capacitive touch layer (contact pressure response), with a sensitivity adjustment range of 0.1 to 1.0 s to avoid accidental recording; each sugar concentration gradient is tested at least 3 times to avoid single-shot errors (such as licking contact caused by brief curiosity of animals and other errors).

[0051] Optionally, the chemical nausea-inducing agent is a lithium salt or morphine, and the type and concentration of the drug can be selected according to the degree of the animal's behavioral response; the administration method is intraperitoneal injection or subcutaneous injection.

[0052] Optionally, the aversion induction response involves simultaneously administering a sweet-tasting solution (1% or higher) at a perception threshold concentration with a chemical nausea-inducing agent, with an interval of 0–10 minutes, to ensure that the animal establishes a taste-nausea association; the induction cycle is 1–2 times per day for 3–5 consecutive days.

[0053] Optionally, a control group, i.e., a solvent control of the same volume, should be set up during aversion experiments to eliminate the influence of the injection operation itself.

[0054] The beneficial effects of this invention are:

[0055] This invention involves preparing sucrose solutions of varying concentrations using a gradient method, conducting multiple data acquisition experiments, and using the percentage of animal contact with the sucrose solution (L) as the basis for the data collection. w To evaluate animals' preference for sucrose solutions, L was introduced. wThe slope (K-value) of the concentration curve can be used to analyze the sensitivity of animals to different concentrations of sucrose solution. This method can effectively distinguish between physiological perception (K>0.5) and random preference (K≤0.5), providing a dynamic sensitivity indicator for taste degeneration or drug intervention studies and effectively improving the accuracy of sweet taste perception threshold detection.

[0056] The contact ratio L proposed in this invention w The calculation integrates the dual parameters of contact time and licking pressure, assigning different pressure weights to different licking behaviors in animals. This effectively distinguishes between stable licking, light licking, and ineffective contact. It not only weights and quantifies the preference represented by different licking behaviors but also eliminates the interference of ineffective behaviors on experimental results. Therefore, the contact ratio L... w The introduction of this technology further improves the accuracy of sweetness perception threshold detection.

[0057] This invention employs two-way verification for reliability, combined with conditional aversion experiments for cross-validation, upgrading the traditional one-way judgment method to two-way verification, reducing the false positive rate by more than 50%. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a flowchart of the adaptive gradient adjustment algorithm provided in Embodiment 1 of the present invention.

[0060] Figure 2 This is a schematic diagram of the testing device provided in Embodiment 1 of the present invention.

[0061] Figure 3 This is a schematic diagram of the structure of the bottle mouth sensor of the present invention.

[0062] Figure 4 This is a fitting curve of the segmented hybrid model provided in Embodiment 1 of the present invention.

[0063] Figure 5 This is a diagram illustrating the effect of conditional taste aversion provided in Embodiment 1 of the present invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0065] Example 1:

[0066] This embodiment provides a sweetness perception threshold detection method based on dynamic gradient feedback, including the following steps:

[0067] Step 1: Prepare sucrose solutions of different concentrations according to a gradient, specifically including:

[0068] (1) Preparation of mother liquor:

[0069] Weigh 5.0g of sucrose (analytical grade) and dissolve it in 100mL of distilled water to prepare a 5% stock solution. Store the stock solution in a sealed brown glass bottle.

[0070] According to the formula Calculate the required volumes of mother liquor (V1) and distilled water (V2), and mix them manually (example: when preparing a 0.5% concentration, take 0.1 mL of mother liquor + 0.9 mL of distilled water). Prepare 3 portions for each concentration and dispense them into bottles A1 / A2 / A3. Label the bottles with the concentration and batch number.

[0071] (2) Concentration verification:

[0072] The refractive index of the sugar solution was measured using a handheld refractometer (ATAGO PAL-1) and compared with the standard sucrose refractive index table (accuracy ±0.1%). If the deviation exceeded ±0.2%, the batch was reconstituted.

[0073] Step 2: Place sucrose solutions of different concentrations and pure water in different test cages, allowing the animals to come into contact with the sucrose solutions and pure water. Use contact sensors and pressure sensors to collect the time and pressure of the animals contacting the bottle openings.

[0074] (1) Set up a data acquisition device.

[0075] A schematic diagram of the testing device is shown below. Figure 2 As shown, the device includes a test cage and a signal recording unit: the test cage is made of acrylic material (30cm*20cm*20cm) with two bottle mouth slots (10cm apart) on the side; the signal recording unit includes a contact sensor and data transmission, with an infrared photoelectric probe (response time <1ms) and a capacitive touch probe (pressure sensitivity 0.1N) installed at the bottle mouth, synchronously recording the contact time (accuracy 0.1s) and licking pressure (unit: N; distinguishing between "light touch" <0.3N and "stable lick" >0.5N).

[0076] (2) Test the animals and record the test data.

[0077] After the animals were deprived of water for 12 hours, they were placed in a test cage and given two 10-minute acclimatization training sessions daily (bottle A contained 0.5% sugar solution, and bottle B contained pure water). Bottles A (sugar solution) and B (pure water) were inserted at the current test concentration. Each test lasted 10 minutes. When the animal came into contact with bottle A, the timer switch on channel A was activated; when the animal came into contact with bottle B, the timer switch on channel B was activated. The contact time was recorded as T. A With T B (Unit: seconds) Calculate the percentage of animal contact with sucrose solution at each concentration:

[0078]

[0079] Among them, T Ai T Bi These represent the duration (in seconds) of a single contact between the animal and bottle A (sugar solution) and bottle B (pure water), respectively; P Ai P Bi These represent the pressure weights for the corresponding contact events: a stable lick (e.g., >0.5N) has a weight of 1, a light touch (e.g., 0.3~0.5N) has a weight of 0.3, and an ineffective contact (e.g., <0.3N) has a weight of 0.

[0080] Calculate L at each concentration w The value is the average of three tests conducted at the same concentration.

[0081] (3) Calibration steps and effect verification

[0082] ① Establish a strain-stress threshold mapping table through preliminary experiments:

[0083] C57BL / 6 mice: stable licking threshold = 0.5N;

[0084] BALB / c mice: stable licking threshold = 0.4N.

[0085] ② Comparison of accidental touch rates:

[0086] Traditional single-sensor systems have a 15% false touch rate (brief contact is misinterpreted as licking);

[0087] The false touch rate of the multimodal sensor has been reduced to 5% (data only includes stable licking).

[0088] Data consistency: Weighted L w The correlation coefficient R between the value and the actual amount of licking 2 =0.92.

[0089] Step 3: Based on the concentration range of the sucrose solution, construct a segmented mathematical model to fit the contact ratio L. w The curve showing the relationship between the concentration of the sucrose solution and the concentration of the sucrose solution.

[0090] (1) Construct a mathematical model.

[0091] Within the low concentration range, the least squares method was used to analyze L. w The value was fitted to the concentration of the sucrose solution:

[0092] L w =aC 3 +bC 3 +cC 3 +d

[0093] Where: C is the sugar concentration (%); L w denoted as the percentage of sucrose solution in contact at this concentration; a, b, c, and d are the least squares fitting parameters.

[0094] Logistic regression model was used in the high concentration range:

[0095]

[0096] Where: L max For the maximum L w Value (theoretically 100%); C0 is L w The inflection point where the value increases the fastest (can be regarded as the critical point for the perception of sweetness); k is the slope parameter, which measures the steepness of the curve.

[0097] The method for determining the critical values ​​of high and low concentration ranges is the residual sum of squares (RSS) minimization method.

[0098] Iterate through the candidate critical points (e.g., concentrations of 0.5%, 1%, and 1.5%), calculate the total RSS at each critical point, and select the concentration corresponding to the minimum value as the critical point.

[0099] Comparative experiments show that the average fitting error of the piecewise hybrid model constructed in this embodiment is 6%, which is significantly lower than the 12% of the single linear model, effectively reducing the model error.

[0100] Step 4: Determine whether there is a significant change in the animal's perception of the sugar concentration based on the slope K of the relationship curve. When L w If the sucrose concentration is greater than a preset contact ratio threshold and the slope K fluctuation is less than a preset slope fluctuation threshold, then the corresponding sucrose concentration is determined to be the sweetness perception threshold. This specifically includes the following steps:

[0101] (1) Dynamically adjust the sugar concentration step size based on the real-time calculated slope (K value):

[0102] ① If the K value is greater than 0.5, it is determined to be a sensitive area for perception, and the step size is reduced to △C×0.5 (e.g., 1%→0.5%).

[0103] ② If the K value is < 0.3, it is determined to be a sensory desensitization zone, and the step size is increased to △C×2 (e.g., 1% → 2%).

[0104] ③ If the volatility of K value (standard deviation / mean) > 20%, the rollback mechanism is triggered, and the previous concentration is retested.

[0105] (2) Real-time data stream processing:

[0106] Data acquisition frequency: 10Hz (contact time data updated every 0.1 seconds)

[0107] Algorithm response latency: <0.5s, real-time calculation is achieved through multi-threaded programming.

[0108] (3) Calculate the sweetness perception threshold. The calculation process is as follows: Figure 1 As shown, it includes the following steps:

[0109] Based on the initial concentration C0, the average L was obtained after three animal tests. w value;

[0110] Calculate the slope K between the adjacent concentration C1 and the initial concentration C0, and dynamically adjust the step size of the sucrose concentration according to the slope K. If K > 0.5, it is determined to be a sensitive area and the step size is reduced to ΔC × 0.5; if K < 0.3, it is determined to be a desensitized area and the step size is increased to ΔC × 2; otherwise, the step size remains unchanged.

[0111] Update concentration: C1' = C0 ± ΔC', where ΔC' represents the updated step size;

[0112] Based on the concentration C1', the average L was taken after three animal tests. w Value, if the average L w If the value is greater than the preset contact ratio threshold, then the current sucrose concentration is the sweetness perception threshold (3 consecutive L). w Value ≥ 60% and K value fluctuation < 5%.

[0113] Comparative experimental data shows that this embodiment uses an adaptive step size, with an average of 8 tests and a time consumption of 160 minutes; the traditional method uses a fixed step size of 1%, with 12 tests and a time consumption of 240 minutes; this embodiment improves efficiency by 33%.

[0114] Furthermore, regarding the stability of threshold determination, the standard deviation of traditional methods is ±15%, while that of this invention is reduced to ±5%, indicating that the sweetness perception threshold obtained by this invention has high stability.

[0115] Step 5: Inject the same batch of model animals with a chemical nausea-inducing drug to induce a sugar aversion response. If the original sweetness perception threshold concentration is lower than L... w If the value decreases by ≥30%, the verification threshold result is reliable.

[0116] Taking mice as an example, a threshold concentration glucose solution was prepared in the above manner. The mice were given the glucose solution at the training time point. After 10 minutes, the water bottle was removed. After 30 minutes, the intervention group was injected intraperitoneally with 0.15M LiCl solution (0.64g LiCl dissolved in 50mL physiological saline) using a 1mL syringe (0.01mL graduation). The dosage was calculated based on the mouse body weight (0.01mL / g). The control group was injected with an equal volume of physiological saline.

[0117] 24 hours after injection, the contact test was repeated using glucose solution at the original threshold concentration, and L was calculated. w Value decline rate:

[0118]

[0119] Judgment criteria: The experimental group showed a decrease of ≥30% and the control group showed a fluctuation of ≤±5%. The threshold results were considered valid, and the effect was illustrated in the figure below. Figure 4 As shown.

[0120] This embodiment innovatively constructs a sweetness perception threshold detection system based on dynamic gradient feedback, and for the first time integrates gradient solution progressive testing, behavioral dynamic parameter analysis and conditional aversion verification in multiple dimensions, providing a standardized technical framework for animal taste perception research.

[0121] This embodiment effectively reduces the interference of individual differences through three repeated tests using the gradient solution module, combined with the contact time ratio (L) of the behavior acquisition module. w The quantitative index (value) significantly improves the detection sensitivity of sweetness preference; the dynamic threshold determination module adopts L... w The dual verification mechanism of the value-concentration curve slope (K value) and continuous response criterion ensures the timeliness of threshold determination (≥60% for 3 consecutive times) and identifies the perception inflection point through mathematical modeling (K>0.5), improving the detection accuracy by about 40% compared to the traditional fixed concentration method. The conditional aversion verification module successfully achieves reverse verification of the threshold result by establishing a negative correlation between sugar solution and nausea reaction (L). w The value reduction is ≥30%, forming a closed-loop verification system. This technical system has advantages such as short detection cycle (single test <30 minutes), strong repeatability of results (inter-module verification consistency >85%), and wide applicability (can be extended to other taste modalities). However, it has high requirements for sensor accuracy (requires <0.1-second response) and requires 5-7 days of animal behavioral training beforehand. Overall, this embodiment solves the technical bottlenecks of strong subjectivity and single verification method in traditional threshold detection, providing a high-precision detection tool for the study of taste neural mechanisms and the construction of animal models of metabolic diseases such as diabetes. It has important application value in the fields of food science, neurobiology, and drug development.

[0122] Example 2

[0123] This embodiment provides a sweetness perception threshold detection system for implementing the sweetness perception threshold detection method described in Embodiment 1, specifically including the following structure:

[0124] The data acquisition device includes a test cage, a contact sensor, and a pressure sensor. The test cage contains sucrose solutions of different concentrations configured in a gradient and pure water. The contact sensor and pressure sensor are used to collect the time and pressure of the animal contacting the bottle opening.

[0125] The contact percentage calculation module is used to calculate the contact percentage of animals with sucrose solution at each concentration. The calculation method is as follows:

[0126]

[0127] Among them, T Ai and T Bi P represents the duration of a single exposure of an animal to sucrose solution and pure water, respectively. Ai and P Bi These represent the pressure weights for the corresponding contact events;

[0128] The curve fitting module is used to construct a mathematical model segmented according to the concentration range of the sucrose solution to fit the contact ratio L. w The curve showing the relationship between the concentration of the sucrose solution and the sucrose concentration;

[0129] The sweetness perception threshold determination module is used to determine whether there is a significant change in the animal's perception of the sugar concentration based on the slope K of the relationship curve. When L... w If the sucrose concentration is greater than the preset contact ratio threshold and the slope K fluctuation is less than the preset slope fluctuation threshold, then the corresponding sucrose concentration is determined to be the sweetness perception threshold.

[0130] The verification module is used to determine the decrease in the sweetness perception threshold after injecting the same batch of model animals with a chemical nausea-inducing drug to induce a sugar aversion response. If the original sweetness perception threshold concentration is lowered by L... w If the value decreases by ≥30%, the verification threshold result is reliable.

[0131] Some steps in the embodiments of the present invention can be implemented using software, and the corresponding software program can be stored in a readable storage medium, such as an optical disc or a hard disk.

[0132] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A sweetness perception threshold detection system, characterized in that, The system includes: The data acquisition device includes a test cage, a contact sensor, and a pressure sensor. The test cage contains sucrose solutions of different concentrations configured in a gradient and pure water. The contact sensor and pressure sensor are used to collect the time and pressure of the animal contacting the bottle opening. The contact percentage calculation module is used to calculate the contact percentage of animals with sucrose solution at each concentration. The calculation method is as follows: L w = in, and These represent the duration of a single exposure of the animal to sucrose solution and pure water, respectively. P Ai and P Bi These represent the pressure weights for the corresponding contact events; The curve fitting module is used to construct a mathematical model segmented according to the concentration range of the sucrose solution to fit the contact ratio. L w The curve showing the relationship between the concentration of the sucrose solution and the sucrose concentration; The sweetness perception threshold determination module is used to determine the sweetness based on the slope of the relationship curve. K To determine whether there is a significant change in an animal's perception of the sugar concentration, when L w Greater than the preset contact ratio threshold and slope K If the fluctuation is less than the preset slope fluctuation threshold, the corresponding sucrose concentration is determined to be the sweetness perception threshold. The verification module is used to determine the magnitude of the decrease in the sweetness perception threshold. If the original sweetness perception threshold concentration is lower than the threshold value, the module will determine the sweetness perception threshold. L w If the value decreases by ≥30%, the verification threshold result is reliable. The condition for the decrease in the sweetness perception threshold is: injecting the same batch of model animals with a chemical nausea drug to induce a sugar aversion response.

2. The sweetness perception threshold detection system according to claim 1, characterized in that, The curve fitting module includes the following process for fitting the relationship curve: For the low concentration range, the least squares method was used to fit the contact ratio. L w Relationship with sucrose solution concentration; For the high concentration range, a Logistic regression model was used to fit the contact ratio. L w Relationship with sucrose solution concentration; The critical values ​​between the low concentration range and the high concentration range are determined by minimizing the sum of squared residuals. Based on the critical values, the low concentration range and the high concentration range are fitted separately to obtain a piecewise fitting hybrid model.

3. The sweetness perception threshold detection system according to claim 1, characterized in that, The process of obtaining the sweetness perception threshold in the sweetness perception threshold determination module includes: Based on the initial concentration C0, the average value was obtained after multiple animal tests. L w value; Calculate the slope between adjacent concentration C1 and the initial concentration C0. K According to the slope K Dynamically adjust the step size of sucrose concentration, if K If the value is greater than 0.5, it is determined to be a sensitive area for perception, and the step size is reduced to ΔC × 0.5; if K If the value is less than 0.3, it is determined to be a sensory desensitization zone, and the step size is increased to △C×2; otherwise, the step size remains unchanged. Update concentration: C1' = C0 ± ΔC', where ΔC' represents the updated step size; Based on the concentration C1', the average value was obtained after multiple animal tests. L w Value, if average L w If the value is greater than the preset contact ratio threshold, then the current sucrose concentration is the sweetness perception threshold.

4. The sweetness perception threshold detection system according to claim 3, characterized in that, When the slope K If the fluctuation is greater than 20%, the previous concentration should be measured again.

5. The sweetness perception threshold detection system according to claim 1, characterized in that, The contact sensor uses an infrared photoelectric probe.

6. The sweetness perception threshold detection system according to claim 1, characterized in that, The data acquisition device uses two water bottles of the same color and model, and the positions of the two solutions are randomly swapped to avoid animal position preference.

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