Method and system for evaluating taste information based on specific digitalization of biological perception

Abstract

The present invention discloses a method and system for evaluating taste information based on specific digitalization of biological perception, which uses a taste sensor chip based on different types of taste receptor cells, and includes the following steps: using basic taste substances to test the response of the taste sensor chip to different basic tastes (sour, sweet, bitter, and salty), finding a specific channel that responds to a single basic taste substance, using different concentrations of basic taste substances to calibrate the basic taste intensity detection range of the specific channel, and adding a test sample to identify the taste information. The present invention simulates the taste recognition process of mammals, and uses the characteristics of different types of taste receptor cells that specifically recognize a single taste substance to quickly and accurately evaluate the intensity of different basic tastes in the same test sample, thereby realizing the digital evaluation of different taste information in the sample. Compared with traditional taste recognition methods based on taste receptor cells, this method has high recognition specificity and accuracy.

Description

Technical Field

[0001] The present invention relates to the field of taste sensing technology, and in particular to a method and system for evaluating taste information by specific digitalization based on biological perception. Background Art

[0002] The quality control of traditional food and tobacco and alcohol products and the development of new products are mainly carried out through manual evaluation by professionally trained tasters. This method is costly and is greatly affected by subjective factors. In vitro taste sensing technology can assist in the manual evaluation of food, greatly reducing the influence of subjective factors. In the mammalian taste system, taste receptor cells are important components responsible for extracting taste information. Using taste receptor cells as the taste sensitive elements of the electronic tongue can reflect the real mammalian taste to a certain extent. Taste receptor cells contain a variety of taste receptors (some of which have been discovered, but some are still unknown). These receptors recognize taste substances alone or together and trigger taste signal transduction in cells, causing the opening or closing of ion channels on the cell membrane to cause changes in membrane potential, ultimately leading to the generation of action potentials and the release of neurotransmitters (acetylcholine, ATP, etc.). Taste substances can be identified by detecting changes in cell membrane potential and changes in the content of extracellular ions and neurotransmitters. The bioelectronic tongue based on taste receptor cells reflects the taste information in the sample more comprehensively (there are many types of taste receptors) and the generated taste information is more real (after undergoing the intracellular signal transduction process). Currently, a variety of bioelectronic tongues based on taste receptor cells have been developed by combining microelectrode arrays (MEA) and light-addressable potentiometric sensors (LAPS).

[0003] Studies have shown that in the process of taste recognition in mammals, different taste receptor cells are responsible for the recognition of different basic tastes and transmit different taste information to the corresponding neurons, and specific neural clouds are responsible for the transmission of specific taste information. However, the traditional taste recognition method used by the bioelectronic tongue system (based on tongue epithelium, taste buds or primary taste receptor cells) does not distinguish the taste specificity of the channel, and directly analyzes the waveform of the obtained taste signal to achieve the classification and recognition of different taste substances. This recognition method can obtain relatively good taste substance recognition results in a simple detection environment (with fewer types of taste substances). However, when used in a complex detection environment, since there may be multiple taste sensitive cells or nerves responsible for transmitting multiple taste information near the electrode point of the detection chip, a single channel may respond to multiple basic taste substances. Therefore, the waveform information of the obtained taste signal will be subject to mutual interference between multiple taste substances, making it difficult to perform specific taste information recognition. In addition, since the types and numbers of taste sensitive cells near the electrode point of the detection chip are unknown, it is difficult to calibrate when using a new chip for measurement, making the repeatability of taste recognition using this method poor. Summary of the invention

[0004] The purpose of the present invention is to address the deficiencies of the prior art and to propose a method and system for evaluating taste information based on specific digitalization of biological perception.

[0005] To achieve the above object, the present invention adopts the following technical solution: a method for evaluating taste information based on specific digitalization of biological perception, comprising the following steps:

[0006] (1) Testing taste response: Using a variety of basic taste substances to test the response of the taste sensor chip to different basic tastes;

[0007] (2) Selecting specific channels: Selecting specific channels based on the responses of different basic tastes;

[0008] (3) Detection range calibration: Use different concentrations of basic taste substances to calibrate the basic taste intensity detection range of the corresponding specific channels and calculate the standard curve;

[0009] (4) Taste information identification and evaluation: Add the test sample to identify the taste information, and calculate the intensity of different basic tastes in the sample based on the response of each channel and the standard curve.

[0010] Furthermore, the different basic tastes in step (1) refer to sourness, sweetness, bitterness and saltiness.

[0011] Furthermore, in step (1), 25 mg / mL glacial acetic acid is used to test the response of the taste sensor chip to sour taste, 1 g / mL sucrose is used to test the response of the taste sensor chip to sweet taste, 10 mM phenylthiourea is used to test the response of the taste sensor chip to bitter taste, and 6 mM sodium chloride is used to test the response of the taste sensor chip to salty taste.

[0012] Furthermore, in step (1), the criterion for generating a taste response is that the power spectral density of the response signal exceeds a set threshold compared with a control without taste stimulation.

[0013] Furthermore, in step (2), the specific channel refers to a channel that produces a response signal only to one basic taste substance.

[0014] Furthermore, in step (3), 25 mg / mL, 12.5 mg / mL, 6.25 mg / mL, 3.125 mg / mL and 1.5625 mg / mL glacial acetic acid are used to calibrate the sourness intensity detection range, 1 g / mL, 0.75 g / mL, 0.5 g / mL, 0.25 g / mL and 0.125 g / mL sucrose are used to calibrate the sweetness intensity detection range, 10 mM, 1 mM, 0.1 mM, 10 μM and 1 μM phenylthiourea are used to calibrate the bitterness intensity detection range, and 6 M, 4.5 M, 3 M, 1.5 M and 0.75 M sodium chloride are used to calibrate the saltiness intensity detection range.

[0015] Furthermore, in step (3), the standard curve is calculated by calculating the size of each characteristic value of the response signal (maximum peak amplitude, average peak amplitude, peak occurrence frequency, 0-4 Hz frequency band power spectrum energy, 4-8 Hz frequency band power spectrum energy, 8-13 Hz frequency band power spectrum energy, 13-30 Hz frequency band power spectrum energy and 30-40 Hz frequency band power spectrum energy), using the principal component analysis algorithm to calculate the weight of each characteristic and calculate the comprehensive score of each response signal; the comprehensive score of the response signal is matched with the concentration of the taste substance and a linear regression algorithm or a nonlinear regression algorithm is used to fit each basic taste intensity standard curve.

[0016] Furthermore, in step (4), the calculation method for different basic tastes in the sample is to calculate the size of each characteristic value of the response signal (maximum peak amplitude, average peak amplitude, peak occurrence frequency, 0-4 Hz frequency band power spectrum energy, 4-8 Hz frequency band power spectrum energy, 8-13 Hz frequency band power spectrum energy, 13-30 Hz frequency band power spectrum energy and 30-40 Hz frequency band power spectrum energy), and calculate the comprehensive score of each response signal using the principal component analysis algorithm according to the characteristic weights calculated above; the comprehensive score of the response signal is substituted into the basic taste intensity standard curve to calculate the corresponding taste intensity.

[0017] Furthermore, the taste sensor chip includes taste receptor cells as taste sensitive elements and a microelectrode array chip for sensing extracellular potential changes; the taste receptor cells include receptor cells for different basic tastes, which can generate extracellular action potentials under the action of taste substances, and potential detection is performed through the microelectrode array chip, which is used to identify the information of different basic tastes in the detection sample.

[0018] On the other hand, the present invention also provides a system for evaluating taste information based on biological perception and specificity, the system comprising:

[0019] A taste response module is used to test the response of the taste sensor chip to different basic tastes using a variety of basic taste substances;

[0020] A specific channel selection module is used to select specific channels corresponding to basic tastes based on responses of different basic tastes;

[0021] A range calibration module, used to calibrate the basic taste intensity detection range of the corresponding specific channels using basic taste substances of different concentrations;

[0022] The taste information recognition and evaluation module is used to identify the taste information of the test sample and calculate the intensity of different basic tastes in the sample based on the response of each channel.

[0023] The beneficial effects of the present invention are:

[0024] (1) The method of the present invention simulates the recognition process of the mammalian taste system and more closely reflects the taste of real mammals;

[0025] (2) Compared with traditional taste recognition methods, the present invention can distinguish and screen the taste specificity of the detection channel, and can respectively identify and quantify the intensity of different basic tastes in the sample, that is, the method of the present invention can quantify the acidity, sweetness, bitterness, and saltiness in the sample;

[0026] (3) At the same time, the present invention uses elements for taste recognition in mammals, thereby being able to obtain accurate taste information of the sample;

[0027] (4) Due to the specificity of taste recognition, even if there are multiple taste substances in the sample, the interference to the detection is small. Therefore, the method of the present invention can obtain relatively good taste substance recognition results even in a complex detection environment (there are often multiple taste stimuli in real samples);

[0028] (5) The method of the present invention does not require separate calibration of different basic tastes, which reduces the difficulty of calibration and helps improve the repeatability of different batches of chips. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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.

[0030] Figure 1 A schematic diagram of the method for digitally evaluating taste information in a sample according to the present invention.

[0031] Figure 2 Schematic diagram of specific channel selection.

[0032] Figure 3 Schematic diagram of the fitted standard curve for different basic taste recognition.

[0033] Figure 4 This is a schematic diagram of the results of identifying lemon juice using the method of the present invention. DETAILED DESCRIPTION

[0034] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings. It should be pointed out that the specific implementation methods are only detailed descriptions of the present invention and should not be regarded as limitations of the present invention.

[0035] The taste sensitive element of the taste sensor chip used in the present invention is a taste organoid, but is not limited to a taste organoid. It can also be tongue epithelial tissue, taste bud tissue, taste receptor cells, gene-edited cells and other cells or tissues with taste function.

[0036] The extracellular potential detection device in the taste sensor chip used in the present invention is a planar microelectrode array chip, but is not limited to a planar microelectrode array chip, and can also be a 3D microelectrode array chip, a flexible microelectrode array chip and other devices for detecting extracellular potential.

[0037] The signal analysis method used in the present invention is signal feature extraction and principal component analysis, but is not limited to signal feature extraction and principal component analysis, and can also be a machine learning or deep learning algorithm.

[0038] The signal features extracted by the present invention are the maximum peak amplitude, the average peak amplitude, the peak occurrence frequency, the power spectrum energy in the 0-4 Hz frequency band, the power spectrum energy in the 4-8 Hz frequency band, the power spectrum energy in the 8-13 Hz frequency band, the power spectrum energy in the 13-30 Hz frequency band and the power spectrum energy in the 30-40 Hz frequency band, but are not limited to these 8 features and can also be features extracted by a deep learning algorithm or other features that can reflect taste information.

[0039] The specific embodiments of the present invention are as follows:

[0040] The taste sensing system used in the present invention includes a taste sensitive element and a response signal acquisition device;

[0041] The taste sensitive element is used to sense taste substances and generate specific taste response signals. In this embodiment, taste organoids are selected as taste sensitive elements. The taste organoids contain a variety of taste receptor cells and taste stem cells, which can sense a variety of taste information in the sample and have a good service life.

[0042] The response signal acquisition device is used to detect the response signal of the taste sensitive element. In this embodiment, an extracellular potential acquisition device is selected to collect the response signal of the taste sensitive element, which is composed of a taste sensor chip, a chip adapter, a transmission line, a signal collector and a computer.

[0043] The taste sensor chip is used for detecting taste response signals, and is composed of a cell culture chamber, a 64-channel microelectrode array, and a printed circuit board.

[0044] The cell culture chamber is fixed to the PCB board by gluing with epoxy resin and is used for culturing taste organoids of taste sensitive elements;

[0045] In some preferred embodiments, the taste organoids are fixed to the surface of the microelectrode array using Matrigel, so that the taste organoids and the microelectrode array fit closely;

[0046] The microelectrode array is connected to the PCB board via gold wires, and the exposed gold wires are insulated by epoxy resin.

[0047] The present invention proposes a method for evaluating taste information based on biological perception and specific digitalization. Taking the above taste sensor system as an example, the principle is as follows: Figure 1 As shown, by utilizing the different specificities of different taste receptor cells in taste organoids to different tastes, channels that respond to different basic taste substances are removed, thereby achieving specific digital evaluation of the sample's taste information.

[0048] The specific detection process includes the following steps:

[0049] (1) Testing taste response: Add phosphate buffered saline (PBS) to the cell culture chamber, let the taste sensor chip stand at room temperature for 5 min, remove the PBS, and use the same taste sensor chip to detect taste substances in turn (add 25 mg / mL glacial acetic acid to test the taste sensor chip's response to sour taste, use 1 g / mL sucrose to test the taste sensor chip's response to sweet taste, use 10 mM phenylthiourea to test the taste sensor chip's response to bitter taste, and use 6 mM sodium chloride to test the taste sensor chip's response to salty taste). Each test records the extracellular potential signal for 3 min. After the test, remove the taste substance from the cell culture chamber and add PBS. Let the chip stand at room temperature for 5 min to restore the taste, and then perform the next taste test.

[0050] (2) Selecting specific channels: In some preferred methods, power spectrum density is used to determine the response of a channel to basic taste. The size of the power spectrum density can intuitively reflect the strength of the taste response and requires little calculation. The power spectrum density of the extracellular potential signal recorded by each channel is calculated. The present invention has two judgment criteria. The first is to use the power spectrum density and threshold of the electrical signal to determine the response of the channel to basic taste and only determine whether it responds. The second judgment criterion is to further extract features and perform algorithm judgment on the response signals of different concentrations. This is used when calculating the standard curve and identifying the taste information of the sample. The results are as follows: Figure 2 As shown, when the power spectral density of the response signal of a channel exceeds a specific threshold when a certain taste substance is added compared with the control (no taste stimulation), and the threshold is set based on experience, it is considered that the channel has produced a taste response to the taste substance; since the taste receptor cells distributed around each electrode point are different, each channel may respond to multiple basic tastes. Channels that respond to different basic taste substances are removed, and channels that respond only to one basic taste substance are selected for subsequent identification of different basic tastes.

[0051] (3) Detection range calibration: Before the test, PBS was added to the cell culture chamber to immerse the taste organoids, and then the chip was placed at room temperature for 5 min. Different concentrations of different types of specific basic taste substances were used to calibrate the basic taste intensity detection range of the corresponding specific channels. PBS was aspirated, and basic taste substances of various concentrations were added to the cell culture chamber in turn. The extracellular potential signal was recorded for 3 min each time. After the test, the taste substances were aspirated from the cell culture chamber and PBS was added. The chip was placed at room temperature for 5 min to restore the taste, and then the next taste test was performed. 25, 12.5, 6.25, 3.125, and 1.5625 mg / mL glacial acetic acid was used to calibrate the sourness intensity detection range. 1, 0.75, 0.5, 0.25, and 0.125 g / mL sucrose was used to calibrate the sweetness intensity detection range. 10 mM, 1 mM, 0.1 mM, 10 μM, 1 The bitterness intensity detection range was calibrated using μM phenylthiourea, and the saltiness intensity detection range was calibrated using 6, 4.5, 3, 1.5, and 0.75 M sodium chloride.

[0052] (4) Calculate the standard curve: Calculate the size of each characteristic value of the response signal (maximum peak amplitude, average peak amplitude, peak frequency, 0-4 Hz frequency band power spectrum energy, 4-8 Hz frequency band power spectrum energy, 8-13 Hz frequency band power spectrum energy, 13-30 Hz frequency band power spectrum energy and 30-40 Hz frequency band power spectrum energy), use the principal component analysis algorithm to calculate the weight of each characteristic and calculate the comprehensive score of each response signal; Figure 3 As shown, the comprehensive score of the response signal is matched with the concentration of the taste substance and the standard curve of each basic taste intensity is fitted using a linear regression algorithm or a nonlinear regression algorithm.

[0053] (5) Taste information recognition and evaluation: Before sample testing, add PBS to the cell culture chamber to immerse the taste organoids, and then place the chip at room temperature for 5 min; aspirate the PBS and add the test sample for taste information recognition; each test records the extracellular potential signal for 3 min; after the test, aspirate the taste substance from the cell culture chamber and add PBS, place the chip at room temperature for 5 min to restore the taste, and then perform the next taste test; then calculate the size of each eigenvalue based on the response signal of each channel, and calculate the comprehensive score of the response signal based on the weight of each feature; finally, calculate the intensity of different basic tastes in the sample based on the standard curve of each basic taste intensity.

[0054] The characteristics of the present invention are: (1) Compared with the traditional taste recognition method, the present invention can distinguish and screen the taste specificity of the detection channel, and can respectively identify and quantify the intensity of different basic tastes in the sample, that is, the method of the present invention can quantify the acidity, sweetness, bitterness, and saltiness in the sample, which is in line with the characteristics of mammalian taste recognition and reflects the real biological taste perception; (2) At the same time, the present invention uses the elements of mammalian taste recognition, so it can obtain accurate taste information of the sample; (3) Due to the specificity of taste recognition, even if there are multiple taste substances in the sample, the interference to the detection is small, so the method of the present invention can obtain relatively good taste substance recognition results in a complex detection environment (there are often multiple taste stimuli in real samples). (4) The method of the present invention does not need to calibrate different basic tastes separately, which reduces the difficulty of calibration and helps to improve the repeatability of different batches of chips.

[0055] The taste sensor system and taste recognition method are used to identify the taste information of lemon samples. The results are as follows: Figure 4 As shown in Table 1, the specific taste information of the lemon sample can be obtained.

[0056] Table 1 Results of identifying taste information in lemon juice samples using the method of the present invention

[0057]

[0058] Note: 1. The acidity of the sample is measured with 25 mg / mL glacial acetic acid as acidity 1 and PBS as acidity 0;

[0059] 2. Measure the sweetness of the sample with 1 g / mL sucrose as sweetness 1 and PBS as sweetness 0;

[0060] 3. The salinity of the sample was measured with 6M sodium chloride as salinity 1 and PBS as salinity 0;

[0061] 4. Measure the bitterness of the sample with 10 mM phenylthiourea as bitterness 1 and PBS as bitterness 0.

[0062] On the other hand, corresponding to the embodiment of the method for evaluating taste information based on specific digitalization of biological perception, the present invention also provides an embodiment of a system for evaluating taste information based on specific digitalization of biological perception. The system includes: a taste response module, a specific channel selection module, a range calibration module, and a taste information identification and evaluation module. For the specific implementation process of each module, please refer to the specific implementation steps of the embodiment of the method for evaluating taste information based on specific digitalization of biological perception.

[0063] A taste response module is used to test the response of the taste sensor chip to different basic tastes using a variety of basic taste substances;

[0064] A specific channel selection module is used to select specific channels corresponding to basic tastes based on responses of different basic tastes;

[0065] A range calibration module, used to calibrate the basic taste intensity detection range of the corresponding specific channels using basic taste substances of different concentrations;

[0066] The taste information recognition and evaluation module is used to identify the taste information of the test sample and calculate the intensity of different basic tastes in the sample based on the response of each channel.

[0067] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating taste information based on biological perception and specificity, characterized in that: The following steps are involved: (1) Testing taste response: using a variety of basic taste substances to test the response of the taste sensor chip to sourness, sweetness, bitterness and saltiness; the standard for generating a taste response is: the power spectrum density of the response signal exceeds a set threshold compared with a control without taste stimulation; the taste sensor chip includes taste receptor cells as taste sensitive elements and a microelectrode array chip for sensing extracellular potential changes; the taste receptor cells include receptor cells of different basic tastes, which can generate extracellular action potentials under the action of taste substances, and the potential is detected by the microelectrode array chip, which is used to identify the information of different basic tastes in the test sample; (2) Selecting specific channels: Selecting specific channels based on the responses of different basic tastes. Specific channels refer to channels that only produce response signals to one basic taste substance. (3) Detection range calibration: Use different concentrations of basic taste substances to calibrate the basic taste intensity detection range of the corresponding specific channels and calculate the standard curve; the standard curve calculation method is to calculate the various characteristic values ​​of the response signal, including the maximum peak amplitude, the average peak amplitude, the peak frequency, the power spectrum energy of the 0-4 Hz frequency band, the power spectrum energy of the 4-8 Hz frequency band, the power spectrum energy of the 8-13 Hz frequency band, the power spectrum energy of the 13-30 Hz frequency band and the power spectrum energy of the 30-40 Hz frequency band, and use the principal component analysis algorithm to calculate the weight of each feature and calculate the comprehensive score of each response signal; the comprehensive score of the response signal is matched with the concentration of the taste substance and the linear regression algorithm or the nonlinear regression algorithm is used to fit the standard curve of each basic taste intensity; (4) Taste information identification and evaluation: Add the test sample to identify the taste information, and calculate the intensity of different basic tastes in the sample based on the response of each channel and the standard curve; The calculation method for different basic tastes in the sample is to calculate the size of each characteristic value of the response signal, and use the principal component analysis algorithm to calculate the comprehensive score of each response signal according to the characteristic weight calculated above; the comprehensive score of the response signal is substituted into the basic taste intensity standard curve to calculate the corresponding taste intensity.

2. The method for evaluating taste information based on specific digitalization of biological perception according to claim 1, characterized in that: In step (1), 25 mg / mL glacial acetic acid is used to test the response of the taste sensor chip to sour taste, 1 g / mL sucrose is used to test the response of the taste sensor chip to sweet taste, 10 mM phenylthiourea is used to test the response of the taste sensor chip to bitter taste, and 6 mM sodium chloride is used to test the response of the taste sensor chip to salty taste.

3. The method for evaluating taste information based on specific digitalization of biological perception according to claim 1, characterized in that: In step (3), 25 mg / mL, 12.5 mg / mL, 6.25 mg / mL, 3.125 mg / mL and 1.5625 mg / mL glacial acetic acid are used to calibrate the sourness intensity detection range, 1 g / mL, 0.75 g / mL, 0.5 g / mL, 0.25 g / mL and 0.125 g / mL sucrose are used to calibrate the sweetness intensity detection range, 10 mM, 1 mM, 0.1 mM, 10 μM and 1 μM phenylthiourea are used to calibrate the bitterness intensity detection range, and 6 M, 4.5 M, 3 M, 1.5 M and 0.75 M sodium chloride are used to calibrate the saltiness intensity detection range.

4. A system for realizing the specific digital evaluation of taste information based on biological perception as described in any one of claims 1 to 3, characterized in that: The system includes: A taste response module is used to test the response of the taste sensor chip to different basic tastes using a variety of basic taste substances; the taste sensor chip includes taste receptor cells as taste sensitive elements and a microelectrode array chip for sensing extracellular potential changes; the taste receptor cells include receptor cells of different basic tastes, which can generate extracellular action potentials under the action of taste substances, and the potential is detected by the microelectrode array chip, which is used to identify the information of different basic tastes in the test sample; A specific channel selection module is used to select specific channels corresponding to basic tastes based on responses of different basic tastes; A range calibration module, used to calibrate the basic taste intensity detection range of the corresponding specific channels using basic taste substances of different concentrations; The taste information recognition and evaluation module is used to identify the taste information of the test sample and calculate the intensity of different basic tastes in the sample based on the response of each channel.

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