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

Abstract

The invention relates to a sweet perception threshold detection method and detection system based on dynamic gradient feedback, and belongs to the technical field of animal behavior detection. The method comprises the following steps: preparing a sucrose solution with gradient concentration; respectively collecting the time and the pressure when the animal contacts the bottle mouth; calculating the contact ratio of the animal to the sucrose solution under each concentration; constructing a mathematical model section by section to fit a relation curve between the contact ratio and the concentration of the sucrose solution; judging whether the perception of the animal on the sugar concentration is obviously changed according to the slope, and determining a sweetness perception threshold value; and inducing a sugar solution aversion reaction to verify whether a threshold result is reliable or not. According to the method, the Lw value-concentration curve slope is introduced to analyze the sensitivity of animals to sucrose solutions with different concentrations, physiological perception and random preference can be effectively distinguished, and the accuracy of sweet perception threshold detection is effectively improved; and the calculation of Lw integrates two parameter indexes of contact time and licking pressure, so that the accuracy of sweet taste sensing threshold detection is further improved.

Description

Technical Field

[0001] The present invention relates to a method and a detection system for detecting the sweet taste perception threshold based on dynamic gradient feedback, belonging to the technical field of animal behavior detection. Background Art

[0002] In the study of animal taste perception, the detection of the sweet taste threshold is a key link to reveal the neural mechanism of metabolic diseases and evaluate sensory characteristics. However, the current technical means have obvious deficiencies and are difficult to meet the requirements of precision and efficiency in research.

[0003] The traditional two-bottle preference test usually adopts a fixed concentration gradient design. This method cannot dynamically capture the real-time change of the sweet taste threshold of animals during the disease development or treatment process, resulting in a large error in the detection result. Since animals may be affected by interference factors such as satiety or exploratory behavior during the test, it is often necessary to repeat the experiment many times to confirm the threshold, which not only has low efficiency but also increases the experimental cost. At the same time, the existing methods mainly rely on counting the licking times or measuring the preference duration to judge the sweet taste preference of animals. These ethological indicators are easily interfered by environmental noise, the stress state of animals or learning and memory, lacking objectivity and stability. In addition, these methods usually can only compare a single concentration gradient, are difficult to quickly cover a wide concentration range, and lack a quantitative mathematical model analysis of the relationship between sweetness and concentration, restricting the in-depth analysis of the taste perception mechanism.

[0004] In the verification link, the traditional conditioned taste aversion (CTA) experiment needs to establish a new animal model separately and cannot be directly associated with the original threshold detection data, resulting in an extended experimental period and low efficiency. Summary of the Invention

[0005] In order to provide a method for evaluating the sweet taste sensitivity of animals with high speed and high sensitivity and accurately quantifying the sweet taste threshold, the present invention provides a method and a detection system for detecting the sweet taste perception threshold based on dynamic gradient feedback, and the technical solutions are as follows:

[0006] The first object of the present invention is to provide a method for detecting the sweet taste perception threshold, including:

[0007] Step 1: Configure sucrose solutions with different concentrations according to a gradient;

[0008] Step 2: Place the sucrose solutions with different concentrations and pure water in different test cages respectively, let the animals contact the sucrose solutions and pure water, and use a contact sensor and a pressure sensor to collect the time and pressure when the animals contact the bottle mouth respectively;

[0009] Step 3: Calculate the contact ratio of the animals to the sucrose solution at each concentration, and the calculation method is:

[0010]

[0011] Among them, T Ai and T Bi respectively represent the single contact time of the animal with the sucrose solution and pure water, and P Ai and P Bi respectively represent the pressure weights corresponding to the contact events;

[0012] Step 4: According to the concentration range of the sucrose solution, construct a mathematical model in segments to fit the relationship curve between the contact ratio L w and the sucrose solution concentration;

[0013] Step 5: Judge whether there is a significant change in the animal's perception of the sugar concentration according to the slope K of the relationship curve. When L w is greater than the preset contact ratio threshold and the fluctuation of the slope K is less than the preset slope fluctuation threshold, then judge the corresponding sucrose concentration as the sweet perception threshold;

[0014] Step 6: Inject a chemical nausea drug into the same batch of model animals to induce a sugar solution aversion reaction. If the L w value drops by ≥ 30% at the original sweet perception threshold concentration, then verify that the threshold result is reliable.

[0015] Optionally, the said Step 4 includes:

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

[0017] Step 42: For the high concentration range, use the Logistic regression model to fit the relationship between the contact ratio L w and the sucrose solution concentration;

[0018] Step 43: Use the method of minimizing the sum of squared residuals to determine the critical value between the low concentration range and the high concentration range;

[0019] Step 44: Based on the critical value, fit the low concentration range and the high concentration range respectively to obtain a piecewise fitting mixed model.

[0020] Optionally, the said Step 5 includes:

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

[0022] Step 52: 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 as the sensitive perception area, and the step size is reduced to △C × 0.5; if K < 0.3, it is determined as the insensitive perception area, and the step size is enlarged to △C × 2; otherwise, the step size remains unchanged.

[0023] Step 53: Update the concentration: C1’ = C0 ± △C’, where △C’ represents the step size updated in Step 2.

[0024] Step 54: Based on the concentration C1’, conduct multiple animal tests and take the average value of L w value. If the average L w value is greater than the preset contact ratio threshold, the current sucrose concentration is the sweet taste perception threshold.

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

[0026] The second object of the present invention is to provide a sweet taste perception threshold detection system, and the system includes:

[0027] A data acquisition device, including a test cage, a contact sensor, and a pressure sensor. Different concentrations of sucrose solutions and pure water configured in gradients are placed in the test cage, and the contact sensor and the pressure sensor are used to collect the time and pressure of the animal contacting the bottle mouth.

[0028] A contact ratio calculation module, used to calculate the contact ratio of the animal to the sucrose solution at each concentration. The calculation method is:

[0029]

[0030] where T Ai and T Bi respectively represent the single contact time of the animal with the sucrose solution and pure water, and P Ai and P Bi respectively represent the pressure weights corresponding to the contact events.

[0031] A curve fitting module, used to segmentally construct a mathematical model to fit the relationship curve between the contact ratio L w and the sucrose solution concentration according to the concentration range of the sucrose solution.

[0032] A sweet taste perception threshold judgment module, used to judge whether there is a significant change in the animal's perception of the sugar concentration according to the slope K of the relationship curve. When L w is greater than the preset contact ratio threshold and the fluctuation of the slope K is less than the preset slope fluctuation threshold, it is judged that the corresponding sucrose concentration is the sweet taste perception threshold.

[0033] A verification module, used to judge the decline range of the sweet taste perception threshold. If the L value drops by ≥30% at the original sweet taste perception threshold concentration, the verification threshold result is reliable. The condition for the decline of the sweet taste perception threshold is: injecting a chemical nausea drug into the same batch of model animals to induce a sugar solution aversion reaction. w

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

[0035] For the low concentration range, use the least squares method to fit the relationship between the contact ratio L and the sucrose solution concentration; w

[0036]

[0037] For the high concentration range, use the Logistic regression model to fit the relationship between the contact ratio L and the sucrose solution concentration; w

[0038] Use the method of minimizing the sum of squared residuals to determine the critical value between the low concentration range and the high concentration range;

[0039] Based on the critical value, fit the low concentration range and the high concentration range respectively to obtain a piecewise fitting hybrid model.

[0040] Optionally, the process of obtaining the sweet taste perception threshold in the sweet taste perception threshold judgment module includes:

[0041] Based on the initial concentration C0, take the average value after multiple animal tests; w

[0042]

[0043] 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 as the perception sensitive area, and the step size is reduced to △C×0.5; if K<0.3, it is determined as the perception dulling area, and the step size is increased to △C×2; otherwise, the step size remains unchanged;

[0044] Update the concentration: C1' = C0 ± △C', where △C' represents the step size updated in step 2;

[0045] Based on the concentration C1', take the average value after multiple animal tests. If the average value is greater than the preset contact ratio threshold, the current sucrose concentration is the sweet taste perception threshold. w w

[0046]

[0044] Optionally, when the fluctuation of the slope K > 20%, re-measure the previous concentration.

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

[0046] Optionally, the two water bottles in the data acquisition device have the same color and model, and the positions of the two bottles of solution 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 adapt to the sensitivity differences of different strains of animals.

[0048] Optionally, the animals are different strains of mice or rats; the animals need to be trained in advance before the test, that is: the animals need to be water-deprived for 16-24 hours in advance to induce the animal's drinking motivation; two bottles of pure water are given preferentially to train the animals to adapt to drinking the two bottles of solution; by replacing one of the bottles of pure water with an empty bottle or sugar solution, the habit of the animals to taste the two bottles of solution in turn is stimulated.

[0049] Optionally, the light intensity in the animal training and test environment needs to be ≤50 lux, and the environmental noise ≤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), and the sensitivity adjustment range is 0.1-1.0 s to avoid false touch records; each sugar concentration gradient is tested at least 3 times to avoid single-time contingency (such as errors caused by the animal's brief curiosity resulting in licking contact and false touch, etc.).

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

[0052] Optionally, the aversion-induced response is to synchronize the sweet solution (above 1%) at the perception threshold concentration with the chemical nausea drug, and the interval time is 0-10 minutes to ensure that the animal establishes a taste-nausea association; the induction period is 1-2 times a day for 3-5 consecutive days.

[0053] Optionally, a control group needs to be set up during the aversion experiment, that is, a solvent control of the same volume is used to exclude the influence of the injection operation itself.

[0054] The beneficial effects of the present invention are:

[0055] The present invention configures sucrose solutions with different concentrations in gradients and conducts multiple data acquisition experiments respectively, and uses the contact ratio L of the animals to the sucrose solution w to evaluate the preference of the animals for the sucrose solution, and at the same time introduces L wThe slope of the sweetness-concentration curve (K value) analyzes the sensitivity of animals to sucrose solutions of different concentrations, which can effectively distinguish physiological perception (K>0.5) from random preference (K≤0.5), provide a dynamic sensitivity index for taste degeneration or drug intervention research, and effectively improve the accuracy of sweetness perception threshold detection.

[0056] The contact ratio L proposed in the present invention w The calculation integrates the dual parameter indicators of contact time and licking pressure, assigns different pressure weights to different licking behaviors of animals, and can effectively distinguish between stable licking, light licking and invalid contact. It can not only quantify the preference represented by different licking behaviors, but also eliminate the interference of invalid behaviors on the experimental results. Therefore, the contact ratio L w The introduction of further improves the accuracy of sweetness perception threshold detection.

[0057] The present invention adopts two-way verification reliability and combines it with conditioned aversion experiment for cross-validation, upgrading the one-way judgment of the traditional method to two-way verification, thereby reducing the false positive rate by more than 50%. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0059] Figure 1 This is a logic flow chart of the adaptive gradient adjustment algorithm provided in Example 1 of the present invention.

[0060] Figure 2 Schematic diagram of the test device provided in Example 1 of the present invention.

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

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

[0063] Figure 5 This is a diagram of the conditioned taste aversion effect provided by Example 1 of the present invention. DETAILED DESCRIPTION

[0064] To make the objectives, technical solutions and advantages of the present invention more clear, 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 method for detecting the sweet taste perception threshold based on dynamic gradient feedback, including the following steps:

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

[0068] (1) Preparation of the mother liquor:

[0069] Weigh 5.0 g of sucrose (analytical grade) and dissolve it in 100 mL of distilled water to prepare a 5% mother liquor, which is sealed and stored in a brown glass bottle.

[0070] According to the formula Calculate the volumes of the required mother liquor (V1) and distilled water (V2), and manually mix them (example: when preparing a 0.5% concentration, take 0.1 mL of the mother liquor + 0.9 mL of distilled water). Repeat the preparation of each concentration in 3 portions, and sub-pack them into bottles A1 / A2 / A3. Mark the concentration and batch on the bottle body.

[0071] (2) Concentration verification:

[0072] Use a handheld refractometer (ATAGO PAL-1) to measure the refractive index of the sugar solution, and compare it with the standard sucrose refractive index table (accuracy ±0.1%). If the deviation exceeds ±0.2%, re-prepare this batch.

[0073] Step 2: Place the sucrose solutions with different concentrations and pure water in different test cages respectively, let the animals contact the sucrose solutions and pure water, and use a contact sensor and a pressure sensor to collect the time and pressure when the animals contact the bottle mouth respectively.

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

[0075] The schematic diagram of the test device is as shown in Figure 2 The device includes a test cage and a signal recording unit: The test cage is made of acrylic (30 cm * 20 cm * 20 cm), with two bottle mouth slots (spacing 10 cm) on the side; the signal recording unit includes a contact sensor and data transmission. An infrared optoelectronic probe (response time < 1 ms) and a capacitive touch probe (pressure sensitivity 0.1 N) are installed at the bottle mouth to synchronously record the contact time (accuracy 0.1 s) and the licking pressure (unit: N; distinguish "light touch" < 0.3 N and "stable licking" > 0.5 N).

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

[0077] After the animals were deprived of water for 12 h, they were placed in a test cage and underwent two 10-min adaptation trainings per day (bottle A was filled with 0.5% sugar solution and bottle B was filled with pure water); the bottles A (sugar solution) and B (pure water) with the current test concentration were inserted, and the single test duration was 10 min. When the animal contacted bottle A, the timing switch of channel A was activated, and when the animal contacted bottle B, the timing switch of channel B was activated. The contact times were recorded as T A and T B (unit: second), and calculate the contact proportion of the animals to the sucrose solution at each concentration:

[0078]

[0079] Among them, T Ai and T Bi respectively represent the single contact times of the animals with bottle A (sugar solution) and bottle B (pure water) (unit: second); P Ai and P Bi respectively represent the pressure weights of the corresponding contact events. The weight of stable licking (such as > 0.5 N) = 1, the weight of light touch (such as 0.3 - 0.5 N) = 0.3, and the weight of ineffective contact (such as < 0.3 N) = 0.

[0080] Calculate the L w value at each concentration, and take the average of the three test results at the same concentration.

[0081] (3) Calibration steps and effect verification

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

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

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

[0085] ② False touch rate comparison:

[0086] The false touch rate of the traditional single sensor is 15% (brief contact misjudged as licking);

[0087] The false touch rate of the multi-modal sensor is reduced to 5% (only stable licking is included in the data).

[0088] Data consistency: the correlation coefficient R w between the weighted L 2 value and the true licking amount = 0.92.

[0089] Step 3: According to the concentration range of the sucrose solution, segmentally construct a mathematical model to fit the relationship curve between the contact proportion L w and the concentration of the sucrose solution.

[0090] (1) Construct a mathematical model.

[0091] In the low concentration range, the least squares method is used to fit the L w value and the sucrose solution concentration:

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

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

[0094] In the high concentration range, the Logistic regression model is used:

[0095]

[0096] where: L max is the maximum L w value (theoretically 100%); C0 is the inflection point where the L w value increases fastest (which can be regarded as the critical point of sweet taste perception); k is the slope parameter, measuring the steepness of the curve.

[0097] Among them, the judgment method for the critical value of the high and low concentration ranges is: the method of minimizing the sum of squared residuals (RSS).

[0098] Traverse the candidate critical points (such as concentrations of 0.5%, 1%, 1.5%), calculate the total RSS under each critical point, and select the concentration corresponding to the minimum value as the critical point.

[0099] The comparative experiment shows that the fitting average error of the piecewise mixed model constructed in this embodiment is 6%, which effectively reduces the model error compared with 12% of the single linear model.

[0100] Step 4: Judge whether there is a significant change in the animal's perception of the sugar concentration according to the slope K of the relationship curve. When L w is greater than the preset contact percentage threshold and the slope K fluctuates less than the preset slope fluctuation threshold, then judge the corresponding sucrose concentration as the sweet taste perception threshold, which specifically includes the following steps:

[0101] (1) Dynamically adjust the sugar concentration step size according to the slope (K value) calculated in real time:

[0102] ① If the K value > 0.5, it is determined as the perception sensitive area, and the step size is reduced to △C × 0.5 (such as 1% → 0.5%);

[0103] ② If the K value < 0.3, it is determined as the perception dulling area, and the step size is enlarged to △C × 2 (such as 1% → 2%);

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

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

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

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

[0108] (3) Calculate the sweet taste perception threshold, and the calculation process is as Figure 1 shown, including the following steps:

[0109] Based on the initial concentration C0, take the average L value after 3 animal tests; w value;

[0110] Calculate the slope K between the adjacent concentration C1 and the initial concentration C0, dynamically adjust the step size of the sucrose concentration according to the slope K. If K > 0.5, it is determined as the sensitive perception area, and the step size is reduced to △C × 0.5; if K < 0.3, it is determined as the dull perception area, and the step size is enlarged to △C × 2; otherwise, the step size remains unchanged;

[0111] Update the concentration: C1’ = C0 ± △C’, where △C’ represents the updated step size;

[0112] Based on the concentration C1’, take the average L value after 3 animal tests; w value, if the average L w value is greater than the preset contact ratio threshold, then the current sucrose concentration is the sweet taste perception threshold (the L w value ≥ 60% for 3 consecutive times and the K value fluctuation < 5%).

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

[0114] In addition, regarding the stability of the threshold determination, the standard deviation of the traditional method is ±15%, while that of the present invention is reduced to ±5%. It can be seen that the sweet taste perception threshold obtained by the present invention has high stability.

[0115] Step 5: Inject a chemical nausea drug into the same batch of model animals to induce a sugar solution aversion reaction. If the L w value drops ≥ 30% at the original sweet taste perception threshold concentration, then the reliability of the threshold result is verified.

[0116] Taking mice as an example, prepare the sugar solution at the threshold concentration in the above manner. Give the sugar solution to the mice at the training time point. Remove the water bottle 10 minutes later. After 30 minutes, the intervention group uses a 1 mL syringe (calibrated to 0.01 mL) to intraperitoneally inject 0.15 M LiCl solution (weigh 0.64 g of LiCl and dissolve it in 50 mL of normal saline). The dose is calculated according to the body weight of the mice (0.01 mL / g), and the control group injects the same volume of normal saline.

[0117] Twenty-four hours after the injection, repeat the contact test using the original threshold concentration sugar solution and calculate the w value decrease rate:

[0118]

[0119] Judgment criterion: If the decrease in the experimental group is ≥ 30% and the fluctuation in the control group is ≤ ±5%, the threshold result is valid. The effect diagram is as Figure 4 shown.

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

[0121] In this example, the interference of individual differences is effectively reduced through three repeated tests of the gradient solution module. Combining the quantification index of the contact time ratio ( w value) of the behavior acquisition module significantly improves the detection sensitivity of sweet taste preference; the dynamic threshold judgment module adopts a dual verification mechanism of the slope (K value) of the w value-concentration curve and the continuous response criterion, which not only ensures the timeliness of threshold judgment (continuous 3 times ≥ 60%), but also identifies the perception inflection point through mathematical modeling (K > 0.5), and the detection accuracy is improved by about 40% compared with the traditional fixed concentration method; the conditional aversion verification module successfully realizes the reverse verification of the threshold result ( w value decrease ≥ 30%) by establishing a negative association between the sugar solution and the nausea reaction, forming a closed-loop verification system. This technical system has the advantages of a short detection period (single test < 30 minutes), strong result repeatability (verification consistency between modules > 85%), and wide application scenarios (can be extended to other taste modalities), but it has higher requirements for the sensor accuracy (response required < 0.1 second level), and animal behavior training for 5 - 7 days is required in advance. Generally speaking, this example solves the technical bottlenecks of strong subjectivity and single verification means in traditional threshold detection, provides a high-precision detection tool for taste nerve mechanism research and the construction of animal models for metabolic diseases such as diabetes, and has important application value in the fields of food science, neurobiology, and drug research and development.

[0122] Example Two

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

[0124] A data acquisition device, including a test cage, a contact sensor, and a pressure sensor. Different concentrations of sucrose solutions and pure water configured in gradients are placed in the test cage. The contact sensor and the pressure sensor are used to collect the time and pressure when the animal touches the bottle mouth.

[0125] A contact ratio calculation module for calculating the contact ratio of the animal to the sucrose solution at each concentration. The calculation method is:

[0126]

[0127] where T Ai and T Bi respectively represent the single contact time of the animal with the sucrose solution and pure water, and P Ai and P Bi respectively represent the pressure weights corresponding to the contact events.

[0128] A curve fitting module for segmentally constructing a mathematical model to fit the relationship curve between the contact ratio L w and the concentration of the sucrose solution according to the concentration range of the sucrose solution.

[0129] A sweet taste perception threshold judgment module for judging whether there is a significant change in the animal's perception of the sugar concentration according to the slope K of the relationship curve. When L w is greater than the preset contact ratio threshold and the fluctuation of the slope K is less than the preset slope fluctuation threshold, it is judged that the corresponding sucrose concentration is the sweet taste perception threshold.

[0130] A verification module for judging the decline amplitude of the sweet taste perception threshold after injecting a chemical nausea drug into the same batch of model animals to induce a sugar solution aversion reaction. If the L w value at the original sweet taste perception threshold concentration drops by ≥30%, it is verified that the threshold result is reliable.

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

[0132] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for detecting the sweet taste perception threshold, characterized in that The method includes: Step 1: Configure sucrose solutions with different concentrations according to a gradient; Step 2: Place the sucrose solutions with different concentrations and pure water in different test cages respectively, let the animals contact the sucrose solutions and pure water, and use a contact sensor and a pressure sensor to collect the time and pressure when the animals contact the bottle mouth respectively; Step 3: Calculate the contact proportion of the animals to the sucrose solution at each concentration. The calculation method is: Among them, T Ai and T Bi respectively represent the single contact time of the animal with the sucrose solution and pure water, and P Ai and P Bi respectively represent the pressure weights of the corresponding contact events; Step 4: Segmentedly construct a mathematical model to fit the contact ratio L w and the relationship curve of the sucrose solution concentration; 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 is greater than a preset contact ratio threshold and the fluctuation of the slope K is less than a preset slope fluctuation threshold, then determine that the corresponding sucrose concentration is the sweet taste perception threshold; Step 6: Inject a chemical nausea drug into the same batch of model animals to induce a sugar aversion response. If the L value decreases by ≥30% at the original sweet taste perception threshold concentration, the threshold result is verified to be reliable. w ​ 2. The method for detecting the sweetness perception threshold according to claim 1, characterized in that The said Step 4 includes: Step 41: For the low-concentration range, use the least squares method to fit the relationship between the contact ratio L w and the concentration of the sucrose solution; Step 42: For the high-concentration range, use the Logistic regression model to fit the relationship between the contact ratio L w and the concentration of the sucrose solution; Step 43: Use the method of minimizing the sum of squared residuals to determine the critical value between the low-concentration interval and the high-concentration interval; Step 44: Based on the critical value, fit the low-concentration interval and the high-concentration interval respectively to obtain a piecewise fitting hybrid model.

3. The method for detecting the sweetness perception threshold according to claim 1, wherein, The said Step 5 includes: Step 51: Based on the initial concentration C0, take the average L value after conducting multiple animal tests; w value 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 according to the slope K. If K > 0.5, it is determined as the sensitive perception area, and the step size is reduced to △C×0.5; if K < 0.3, it is determined as the insensitive perception area, and the step size is enlarged to △C×2; otherwise, the step size remains unchanged; Step 53: Update the concentration: C1’ = C0 ± △C’, where △C’ represents the step size updated in Step 52; Step 54: Based on the concentration C1’, take the average L value after conducting multiple animal tests. If the average L value is greater than the preset contact ratio threshold, then the current sucrose concentration is the sweetness perception threshold. w value. If the average L w value is greater than the preset contact ratio threshold, then the current sucrose concentration is the sweetness perception threshold.

4. The method for detecting the sweetness perception threshold according to claim 3, wherein The said Step 52 also includes: when the fluctuation of the slope K > 20%, re-measure the previous concentration.

5. A sweetness perception threshold detection system, characterized in that [[ID=II]]The system includes: A data acquisition device, including a test cage, a contact sensor and a pressure sensor. Different concentrations of sucrose solutions configured according to a gradient and pure water are placed in the test cage. The contact sensor and the pressure sensor are used to collect the time and pressure when the animals contact the bottle mouth; A contact proportion calculation module, used to calculate the contact proportion of the animals to the sucrose solution at each concentration. The calculation method is: Among them, T Ai and T Bi respectively represent the single contact time of the animal with the sucrose solution and pure water, and P Ai and P Bi respectively represent the pressure weights of the corresponding contact events; A curve fitting module, which is used to segmentally construct a mathematical model to fit the contact ratio L according to the concentration range of the sucrose solution w and the relationship curve of the sucrose solution concentration; The sweet taste perception threshold judgment module is used to judge whether there is a significant change in the animal's perception of the sugar concentration according to the slope K of the relationship curve. When L w is greater than the preset contact ratio threshold and the fluctuation of the slope K is less than the preset slope fluctuation threshold, it is determined that the corresponding sucrose concentration is the sweet taste perception threshold; A verification module, which is used to judge the decline range of the sweet taste perception threshold. If the L value at the original sweet taste perception threshold concentration decreases by ≥ 30%, the verification threshold result is reliable. The condition for the decline of the sweet taste perception threshold is: injecting a chemical nausea drug into the same batch of model animals to induce a sugar solution aversion reaction. w If the value decreases by ≥ 30%, the verification threshold result is reliable. The condition for the decline of the sweet taste perception threshold is: injecting a chemical nausea drug into the same batch of model animals to induce a sugar solution aversion reaction.

6. The sweetness perception threshold detection system according to claim 5, characterized in that, The fitting process of the relationship curve in the curve fitting module includes: For the low concentration range, the least squares method is used to fit the contact ratio L w and its relationship with the concentration of the sucrose solution; For the high-concentration range, a Logistic regression model is used to fit the relationship between the contact ratio L w and the concentration of the sucrose solution; Use the method of minimizing the sum of squared residuals to determine the critical value between the low-concentration interval and the high-concentration interval; Based on the critical value, fit the low-concentration interval and the high-concentration interval respectively to obtain a piecewise fitting hybrid model.

7. The sweetness perception threshold detection system according to claim 5, characterized in that, The process of obtaining the sweet taste perception threshold in the sweet taste perception threshold judgment module includes: Based on the initial concentration C0, the average L value is obtained after conducting multiple animal tests; w value Calculate the slope K between the adjacent concentration C1 and the initial concentration C0, dynamically adjust the step size of the sucrose concentration according to the slope K. If K > 0.5, it is determined as the sensitive perception area, and the step size is reduced to △C×0.5; if K < 0.3, it is determined as the insensitive perception area, and the step size is enlarged to △C×2; otherwise, the step size remains unchanged; Update the concentration: C1’ = C0 ± △C’, where △C’ represents the step size updated in Step 2; Based on the concentration C1’, the average value of L is obtained after multiple animal tests. If the average value of L is greater than the preset contact ratio threshold, the current sucrose concentration is the sweetness perception threshold. w value, if the average L w value is greater than the preset contact ratio threshold, then the current sucrose concentration is the sweetness perception threshold.

8. The sweetness perception threshold detection system according to claim 7, wherein When the fluctuation of the slope K > 20%, re-measure the previous concentration.

9. The sweetness perception threshold detection system according to claim 5, wherein The contact sensor uses an infrared photoelectric probe.

10. The sweetness perception threshold detection system according to claim 5, wherein In the data acquisition device, the two water bottles have the same color and model, and the positions of the two bottles of solutions are randomly swapped to avoid the animal's position preference.

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