CN111595907A - Method for identifying organophosphorus pesticide in tea tree leaves and diagnosing content of organophosphorus pesticide in tea tree leaves based on electronic nose technology - Google Patents
Method for identifying organophosphorus pesticide in tea tree leaves and diagnosing content of organophosphorus pesticide in tea tree leaves based on electronic nose technology Download PDFInfo
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Abstract
The invention discloses a method for identifying and diagnosing the content of organophosphorus pesticides in tea tree leaves based on an electronic nose technology, which comprises the following steps: (1) placing tea tree leaves which are not sprayed with pesticide in a gas collector, and uniformly spraying glufosinate-ammonium and glyphosate to the tea tree leaves respectively to stabilize the concentration of volatile matters in the gas collector; (2) gas detection is carried out on the volatile matter by using an electronic nose, and an optimal sensor combination is screened out according to a response value; (3) extracting response values of the optimal sensor combination at 145-147s, and establishing a smell-based prediction model of the concentrations of glufosinate and glyphosate pesticides in the tea leaves by combining the concentrations of glufosinate and glyphosate pesticides in the tea leaves; (4) and detecting the contents of glufosinate and glyphosate in the tea tree leaves to be detected by using a prediction model. By adopting the method, the contents and types of glufosinate-ammonium and glyphosate in the tea tree leaves can be identified quickly and accurately.
Description
Technical Field
The invention belongs to the technical field of plant pesticide residue detection, and particularly relates to a method for identifying and diagnosing the content of glufosinate-ammonium and glyphosate pesticides in tea tree leaves based on an electronic nose technology.
Background
The electronic nose is a novel bionic detection system simulating the working principle of biological olfaction, can compare and analyze the whole information of volatile components in a sample, establishes a database by collecting standard sample information, and carries out qualitative and quantitative analysis on an unknown sample by utilizing a statistical analysis method of chemometrics.
The structure of the electronic nose mainly comprises: the gas sensor array comprises a gas sensor array, a signal preprocessing system and a pattern recognition system. The gas sensor array is equivalent to olfactory cells in a human olfactory system and is the basis of the detection performance of the electronic nose; the signal preprocessing method is properly selected according to the type of the actually used gas sensor, the mode recognition method and the final recognition task; the pattern recognition system is equivalent to the brain of an animal, and qualitatively recognizes the component information of the single gas and the mixed gas and quantitatively analyzes the concentration by properly processing the output signals of the sensor array.
Tea growers can often use pesticides in the tea tree planting process to reduce the occurrence and harm of diseases and pests. Unreasonable use of pesticide can cause pesticide residue in tea to exceed standard, which not only seriously affects the quality safety of tea, but also restricts the output of high-quality tea in China and trade of export. At present, the traditional methods for detecting the tea pesticide residues, such as gas chromatography, high performance liquid chromatography, ultra-high performance liquid chromatography-tandem mass spectrometry, hydroxylated multi-walled carbon nanotube dispersed solid phase extraction/combined gas chromatography-mass spectrometry combined method and the like, have complex operation process and higher requirements on detection instruments, and cannot meet the requirements of rapid detection.
Different kinds of pesticides have different physicochemical properties and different volatile odors. Different specific pesticides in the same class of pesticides have different molecular structures, so that volatilized gases are different, some types of odors are different, and some degrees of odors are different. Since odor is an important property of various chemical substances and is an expression of quality characteristics thereof, qualitative and quantitative analysis of pesticide residues can be performed starting from odor volatilized from a sample. The electronic nose has the unique advantages of no damage to products, rapidness, sensitivity, simplicity and convenience in operation, low price and the like, is widely applied to detection of food quality and fruit and vegetable pesticide residues at present, but no report of applying the electronic nose to rapid detection of tea tree pesticide residues is available.
Disclosure of Invention
In view of the problems in the prior art, the invention aims to provide a method for identifying and diagnosing the contents of glufosinate-ammonium and glyphosate pesticides in tea tree leaves based on an electronic nose technology. By adopting the method, the contents and types of the glufosinate-ammonium and glyphosate pesticides in the tea tree leaves can be identified quickly and accurately.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for identifying and diagnosing the content of organophosphorus pesticides in tea tree leaves based on an electronic nose technology comprises the following steps:
(1) placing tea tree leaves which are not sprayed with pesticide in a gas collector after drying treatment, uniformly spraying glufosinate-ammonium and glyphosate to the tea tree leaves respectively, sealing the tea tree leaves with tin foil paper, and standing for 10-15 minutes to stabilize the concentration of volatile matters in the gas collector;
(2) carrying out gas detection on the volatile matter by using an electronic nose, generating a response value after a sensor in the electronic nose is contacted with the volatile matter, and screening out an optimal sensor combination according to the response value;
(3) extracting response values of the optimal sensor combination screened in the step (2) at the position of 145-147s, and establishing a tea tree leaf glufosinate-ammonium and glyphosate pesticide concentration gradient content prediction model based on odor in the tea tree leaf;
(4) and (4) detecting the contents of glufosinate and glyphosate in the tea tree leaves to be detected by using the prediction model established in the step (3).
Preferably, in the step (1), the drying treatment specifically includes: placing the gas collector in an oven for drying at 120 deg.C for 30 min; after drying, the absorbent paper is mixed with Na2CO3The dried product is put into a gas collector to absorb water in the gas collector.
Preferably, in the step (1), the tea leaves are fresh tea leaves with 1 bud and 2 leaves.
Preferably, in the step (1), the spraying amount of glufosinate-ammonium and glyphosate is as follows: 3mL of the fertilizer is sprayed in every 5g of tea leaves.
Preferably, in the step (2), the electronic nose is a PEN3 type portable electronic nose.
Preferably, in the step (2), the gas detection of the volatile by using the electronic nose specifically comprises: parameters of a headspace sampling method: preheating at 25 ℃ for 10 minutes in headspace; electronic nose detection parameters: the cleaning time of the sensor is 120s, the automatic zero setting time is 10s, the sample preparation time is 5s, the sample measurement interval time is 1s, the internal flow rate is 300mL/min, and the sample injection flow rate is 300 mL/min; the signal at 145-147s is taken as the point in time for sensor signal analysis. The laboratory temperature was 25 ℃, the humidity was about 50%, and the atmospheric pressure was 101.325 kpa.
Preferably, the step (2) of screening out the optimal sensor combination includes: W5S, W1S, W1W, W2S and W2W sensors.
Preferably, in the step (3), the established model (model I) for predicting the concentration of the glufosinate-ammonium pesticide in the tea tree leaves based on the odor is as follows:
Y＝2.992-1.539G/G0 W5S+4.491G/G0 W1S+3.070G/G0 W1W-10.009G/G0 W2S+0.054G/G0 W2W；
the established model (model II) for predicting the concentration and the content of the glyphosate pesticide in the tea leaves based on the smell is as follows:
Y＝7.480-6.060G/G0 W5S+10.287G/G0 W1S-0.925G/G0 W1W-6.278G/G0 W2S-1.326G/G0 W2W；
wherein G/G0 W5S、G/G0 W1S、G/G0 W1W、G/G0 W2S、G/G0 W2WRespectively represent the signal response values of the sensors W5S, W1S, W1W, W2S and W2W at the detection time of 145-147 s.
The invention has the beneficial effects that:
the method establishes an organophosphorus pesticide content prediction model in tea tree leaves, performs concentration content prediction on the established models I and II, performs linear fitting on the predicted concentration value and the actual value of each pesticide, and tests the accuracy of the models, wherein the correlation coefficients between the predicted concentration values and the actual values of glufosinate-ammonium and glyphosate are respectively 0.991 and 0.984, and the estimation standard errors are respectively 0.19 and 0.55.
Drawings
FIG. 1: load Analysis (LA) of different kinds of pesticides by 10 sensors; wherein, a is the load loading analysis of the glufosinate-ammonium pesticide, and b is the load loading analysis of the glyphosate pesticide.
FIG. 2: analyzing the main components of the odor data of the electronic nose 145-147s with different concentrations of a single pesticide; wherein, a is a PCA analysis chart of glufosinate ammonium with different concentrations, and b is a PCA analysis chart of glyphosate with different concentrations.
FIG. 3: a fitting graph of a tea organophosphorus pesticide content prediction model on the predicted concentration and the actual concentration; wherein, a is a fitting graph of the predicted concentration and the actual concentration of the glufosinate-ammonium, and b is a fitting graph of the predicted concentration and the actual concentration of the glyphosate.
FIG. 4: and (3) analyzing the main components of the odor data of the electronic noses 145-147s of different pesticides.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
As described in the background section, an electronic nose is an instrument that consists of an array of partially selective chemical sensors and a suitable pattern recognition system, capable of recognizing simple or complex odors. The sensor of the electronic nose converts the chemical input of the odor into an electric signal, the sensor has different emphasis on gas components, a broad-spectrum response spectrum of the odor is formed, and the gas can be identified and analyzed by a data processing method according to the characteristic response spectrum of the gas.
With the increasing emphasis on food safety, organophosphorus pesticide residues in vegetables are the key point of safety detection, and at present, the electronic nose is applied to the detection of pesticide residues in vegetables, and achieves certain results. However, tea leaves are different from vegetables, tea aroma is one of indexes of tea quality, tea aroma is very complex in composition, and any tea aroma is the comprehensive expression of different aromatic substances contained in the tea leaves in different concentrations and types. Therefore, due to the existence and interference of the aroma of the tea, the difficulty of detecting and identifying the organophosphorus pesticide in the tea leaves by using the electronic nose is greatly increased.
Because the gas sensor almost has different degrees of sensitivity to all gases, a single sensor has a serious 'broad spectrum effect' problem during measurement, and the accuracy of quantitative detection is low.
Based on the above, the invention firstly optimizes and screens 5 PEN3 type general sensors (W5S, W1S, W1W, W2S and W2W) with high contribution rate through the load overload analysis of the response difference of an electronic nose (PEN 3 type portable electronic nose of AIRSENSE company, Germany) sensor on the leaf of the tea tree to glufosinate and glyphosate odor substances respectively, so as to be used as the main sensors for detecting different concentration contents of the same pesticide in the follow-up process. The sensor combination screened by the invention has cross sensitivity, and can be used for specific identification of glufosinate-ammonium and glyphosate pesticides in tea tree leaves.
In one embodiment of the invention, a method for identifying and diagnosing the contents of glufosinate-ammonium and glyphosate pesticides in tea tree leaves based on an electronic nose technology is provided, which comprises the following steps:
(1) and (3) processing of a gas collector: the flask, which served as a gas collector, was placed in an oven for drying. The drying temperature is 120 ℃, and the drying time is 30 minutes. After drying, the absorbent paper is mixed with Na2CO3The dried product was used in an Erlenmeyer flask to absorb water in the Erlenmeyer flask.
(2) Detecting the content of organophosphorus pesticides in tea tree leaves: weighing 5g of 1 bud and 2 leaf tea fresh leaves which are not sprayed with pesticide, placing the leaves in a 50mL gas collector, uniformly spraying 3mL of glufosinate-ammonium and glyphosate respectively, and then sealing the leaves with tinfoil paper. After standing for 10 minutes at the room temperature of 25 ℃, the solution is measured by an electronic nose, and data are recorded by WinMuster software.
(3) Optimizing an electronic nose sensor: carrying out gas detection on the sample to be detected after standing for 10 minutes by adopting a headspace sampling method and utilizing a PEN3 type portable bionic electronic nose system, and recording a sensor response value G/G0(shows the resistance G of the sensor n after contacting with the sample volatile and the resistance G of the sensor after filtering with standard activated carbon0Ratio of (d). Parameters of the headspace sampling method are preheating temperature 25 ℃ and headspace time 10 minutes. The detection parameters of the electronic nose are that the cleaning time of the sensor is 120s, the automatic zero setting time is 10s, the sample preparation time is 5s, the sample determination interval time is 1s, the internal flow rate is 300mL/min, the sample injection flow rate is 300mL/min, and the signal at the position of 145-147s is taken as the time point of the signal analysis of the sensor. The laboratory temperature was 25 ℃, the humidity was about 50%, and the atmospheric pressure was 101.325 kpa. The sensors were cleaned and standardized before and after each measurement. And carrying out principal component analysis by using SPSS 22.0 software and carrying out load loading analysis by using WinMuster software, and screening out the optimal gas detection optimal sensor for subsequent experiments.
(4) Establishing a tea tree leaf organophosphorus pesticide concentration content prediction model: the test takes the maximum residual quantity of the tea pesticide specified by the national standard (GB2763-2019) as the standard, and each pesticide is set to be 5 concentrations. Glufosinate ammonium is 0, 0.2, 0.5, 1, 4, 8mg/kg, glyphosate is 0, 0.5, 1, 4, 8, 16 mg/kg. In the test, distilled water is used as a control group to replace pesticides, and 0.5mg/kg (glufosinate-ammonium) and 1mg/kg (glyphosate) are used as the maximum residual limit concentration of the pesticides in the tea specified by the national standard (GB 2763-2019). And (3) establishing a concentration content prediction model of the organophosphorus pesticide by using SPSS 22.0 software and a partial least squares regression (PLS), and predicting and verifying. And establishing a prediction model by combining the responses of W5S, W1S, W1W, W2S and W2W with the concentration gradient content of organophosphorus pesticides in tea leaves.
And extracting response values of the 4 odor sensors at 145-147s to establish a prediction model. The tea tree leaf glufosinate ammonium and glyphosate pesticide concentration content prediction models based on odor are respectively as follows:
a model for predicting the concentration and content of glufosinate-ammonium organophosphorus pesticide (model I):
Y＝2.992-1.539G/G0 W5S+4.491G/G0 W1S+3.070G/G0 W1W-10.009G/G0 W2S+0.054G/G0 W2W
glyphosate pesticide concentration content prediction model (model ii):
Y＝7.480-6.060G/G0 W5S+10.287G/G0 W1S-0.925G/G0 W1W-6.278G/G0 W2S-1.326G/G0 W2W
wherein G/G0 W5S、G/G0 W1S、G/G0 W1W、G/G0 W2S、G/G0 W2WRespectively represent the signal response values of the sensors W5S, W1S, W1W, W2S and W2W at the detection time of 145-147 s.
G/G0 W5S、G/G0 W1S、G/G0 W1W、G/G0 W2S、G/G0 W2WAnd (4) substituting the formula, and judging the obtained Y value as the concentration content of the organophosphorus pesticide corresponding to the model in the blade.
(5) Qualitative analysis of glufosinate and glyphosate species: 5 optimal sensors (W5S, W1S, W1W, W2S and W2W) are used as analysis sensors, and the response characteristic values of the glufosinate-ammonium and the glyphosate are subjected to dimensionality reduction and classification by a Principal Component Analysis (PCA) through SPSS 22.0.
In order to make the technical solutions of the present application more clearly understood by those skilled in the art, the technical solutions of the present application will be described in detail below with reference to specific embodiments.
The test materials used in the examples of the present invention, which were not specifically described, were all those conventional in the art and commercially available.
Example 1: take optimization of electronic nose detection system as an example
The total number of PEN3 type universal sensors is 10, and W1C, W5S, W3C, W6S, W5C, W1S, W1W, W2S, W2W and W3S are respectively. Since 10 sensors are focused on detecting different gas components, the response of the electronic nose sensor to the pesticide smell on the tea trees is different. 1a-1b, the sensors W5S, W1S, W1W, W2S and W2W have larger differential contribution rates to the concentration gradient of glufosinate and glyphosate; thus, sensors W5S, W1S, W1W, W2S, W2W were selected for analysis in this test.
Example 2: taking the main component analysis of the concentration contents of glufosinate-ammonium and glyphosate of tea tree leaves based on odor as an example
The response values of W5S, W1S, W1W, W2S and W2W 5 odor sensors at 145-147s were extracted for Principal Component Analysis (PCA) (FIGS. 2a-2 b). The analytical values of the first main component and the second main component of each concentration gradient of the glufosinate-ammonium pesticide and the glyphosate pesticide are approximately in the range of-1-3 and-2-2, the analytical values of the first main component of each concentration also have a linear relation, but the analytical value of the first main component of 0mg/kg (treated by clear water) is obviously greater than other concentration values; the linear relation of the analytical values of the second main component with 4 concentrations of glyphosate except 0mg/kg is also more obvious.
Example 3: taking the model for predicting the concentration and content of glufosinate-ammonium and glyphosate in tea tree leaves based on odor as an example
And extracting response values of W5S, W1S, W1W, W2S and W2W 5 odor sensors at 145-147s to establish a prediction model. The tea tree leaf glufosinate ammonium and glyphosate concentration content prediction models based on odor are respectively as follows:
a model for predicting the concentration and content of glufosinate-ammonium organophosphorus pesticide (model I):
Y＝2.992-1.539G/G0 W5S+4.491G/G0 W1S+3.070G/G0 W1W-10.009G/G0 W2S+0.054G/G0 W2W
glyphosate pesticide concentration content prediction model (model ii):
Y＝7.480-6.060G/G0 W5S+10.287G/G0 W1S-0.925G/G0 W1W-6.278G/G0 W2S-1.326G/G0 W2W
wherein G/G0 W5S、G/G0 W1S、G/G0 W1W、G/G0 W2S、G/G0 W2WRespectively represent the signal response values of the sensors W5S, W1S, W1W, W2S and W2W at the detection time of 145-147 s.
G/G0 W5S、G/G0 W1S、G/G0 W1W、G/G0 W2S、G/G0 W2WAnd (4) substituting the formula, and judging the obtained Y value as the concentration content of the organophosphorus pesticide corresponding to the model in the blade.
Test example 1: taking the verification and evaluation of the tea tree leaf glufosinate ammonium and glyphosate concentration content prediction model as an example
The modeling group for each model had 50 samples, and the validation group had 25 samples. The same treatment is adopted for the samples of the building group and the verification group. In the test, the concentration of glufosinate ammonium is set to be 0, 0.2, 0.5, 1, 4 and 8mg/kg, and the concentration of glyphosate is set to be 0, 0.5, 1, 4, 8 and 16 mg/kg. Wherein 0mg/kg is that distilled water replaces pesticides as a control group in the test, 0.5mg/kg (glufosinate-ammonium) and 1mg/kg (glyphosate) are maximum residual limit concentrations of the pesticides in the tea specified by the national standard (GB 2763-2019). As shown in fig. 3, the correlation coefficients between the predicted values and the actual values of the concentrations of glufosinate and glyphosate are 0.991 and 0.984 respectively, and the estimated standard errors are 0.19 and 0.55 respectively, which indicates that the model can accurately predict the concentrations of glufosinate and glyphosate in the leaves.
Test example 2: taking the analysis of variance of the prediction model of the glufosinate-ammonium and glyphosate concentration contents of tea tree leaves as an example
A tea tree leaf glufosinate-ammonium organophosphorus pesticide concentration content prediction model based on smell (model I):
Y＝2.992-1.539G/G0 W5S+4.491G/G0 W1S+3.070G/G0 W1W-10.009G/G0 W2S+0.054G/G0 W2W
glyphosate pesticide concentration content prediction model (model ii):
Y＝7.480-6.060G/G0 W5S+10.287G/G0 W1S-0.925G/G0 W1W-6.278G/G0 W2S-1.326G/G0 W2W
wherein G/G0 W5S、G/G0 W1S、G/G0 W1W、G/G0 W2S、G/G0 W2WRespectively represent the signal response values of the sensors W5S, W1S, W1W, W2S and W2W at the detection time of 145-147 s.
G/G0 W5S、G/G0 W1S、G/G0 W1W、G/G0 W2S、G/G0 W2WAnd (4) substituting the formula, and judging the obtained Y value as the concentration content of the organophosphorus pesticide corresponding to the model in the blade.
As can be seen from the analysis of variance in Table 1, the P values of both models I and II are less than 0.001, indicating that the PLS regression model has great significance.
Table 1: variance analysis of tea tree leaf glufosinate-ammonium and glyphosate pesticide concentration content prediction model
Test example 3: qualitative analysis of glufosinate and glyphosate species
Dimensionality reduction and classification are carried out on the response characteristic value through SPSS 22.0 by utilizing a Principal Component Analysis (PCA), and stronger discrimination is shown on 2 pesticide modes of glufosinate-ammonium and glyphosate (figure 4); wherein, the distinction degree of glufosinate-ammonium and glyphosate is higher. From the discriminatory analysis (table 2) of the first principal component (90.745%) and the second principal component (5.1014%), glufosinate and glyphosate differed very significantly (p < 0.01). The mixing degree of the 2 organophosphorus pesticides in the tea leaves is better by utilizing the electronic nose.
Table 2: discrimination of electronic nose odor data Principal Component Analysis (PCA) of glufosinate-ammonium and glyphosate pesticides (146-
Note: indicates that the principal component analysis was poor at the 0.01 level between pesticides.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (8)
1. A method for identifying and diagnosing the content of organophosphorus pesticides in tea tree leaves based on an electronic nose technology is characterized by comprising the following steps:
(1) placing tea tree leaves which are not sprayed with pesticide in a gas collector after drying treatment, uniformly spraying glufosinate-ammonium and glyphosate to the tea tree leaves respectively, sealing the tea tree leaves with tin foil paper, and standing for 10-15 minutes to stabilize the concentration of volatile matters in the gas collector;
(2) carrying out gas detection on the volatile matter by using an electronic nose, generating a response value after a sensor in the electronic nose is contacted with the volatile matter, and screening out an optimal sensor combination according to the response value;
(3) extracting response values of the optimal sensor combination screened in the step (2) at the position of 145-147s, and establishing a tea tree leaf glufosinate-ammonium and glyphosate pesticide concentration gradient content prediction model based on odor in the tea tree leaf;
(4) and (4) detecting the contents of glufosinate and glyphosate in the tea tree leaves to be detected by using the prediction model established in the step (3).
2. The method according to claim 1, wherein in step (1), the drying treatment is specifically: placing the gas collector in an oven for drying at 120 deg.C for 30 min; after drying, the absorbent paper is mixed with Na2CO3Putting the water-absorbing material into a gas collector and absorbing the water in the gas collector.
3. The method according to claim 1, wherein in step (1), the tea plant leaves are fresh tea plant leaves having 1 shoot and 2 leaves.
4. The method according to claim 1, wherein in the step (1), the spraying amount of the glufosinate-ammonium and the glyphosate is as follows: 3mL of the fertilizer is sprayed in every 5g of tea leaves.
5. The method according to claim 1, wherein in step (2), the electronic nose is a PEN3 type portable electronic nose.
6. The method according to claim 1, wherein in the step (2), the gas detection of the volatile substance by the electronic nose is specifically as follows: parameters of a headspace sampling method: preheating at 25 ℃ for 10 minutes in headspace; electronic nose detection parameters: the cleaning time of the sensor is 120s, the automatic zero setting time is 10s, the sample preparation time is 5s, the sample measurement interval time is 1s, the internal flow rate is 300mL/min, and the sample injection flow rate is 300 mL/min; the signal at 145-147s is taken as the point in time for sensor signal analysis.
7. The method of claim 1, wherein the step (2) of screening out the optimal sensor combination comprises: W5S, W1S, W1W, W2S and W2W sensors.
8. The method according to claim 1, wherein in the step (3), the established model for predicting the concentration of the glufosinate pesticide in the leaves of the tea tree based on the smell is as follows:
Y＝2.992-1.539G/G0W5S+4.491G/G0W1S+3.070G/G0W1W-10.009G/G0W2S+0.054G/G0W2W；
the established model for predicting the concentration and the content of the glyphosate pesticide in the tea leaves based on the smell is as follows:
Y＝7.480-6.060G/G0W5S+10.287G/G0W1S-0.925G/G0W1W-6.278G/G0W2S-1.326G/G0W2W；
wherein G/G0W5S、G/G0W1S、G/G0W1W、G/G0W2S、G/G0W2WRespectively represent the signal response values of the sensors W5S, W1S, W1W, W2S and W2W at the detection time of 145-147 s.
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| CN114388905A (en) * | 2021-12-16 | 2022-04-22 | 上万清源智动车有限公司 | Novel battery box battery leakage sensor model selection method |
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| XIAOYANTANG 等: "An Electronic Nose Technology to Quantify Pyrethroid Pesticide Contamination in Tea", 《CHEMOSENSORS》 * |
| 丁玉勇: "基于电子鼻和多种模式识别算法的不同种食用香辛料的鉴别", 《食品科学》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114113471A (en) * | 2021-11-08 | 2022-03-01 | 滁州怡然传感技术研究院有限公司 | Method and system for detecting food freshness of artificial nose refrigerator based on machine learning |
| CN114388905A (en) * | 2021-12-16 | 2022-04-22 | 上万清源智动车有限公司 | Novel battery box battery leakage sensor model selection method |
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