CN108195895B - Tea tree leaf nitrogen content rapid detection method based on electronic nose and spectrocolorimeter - Google Patents
Tea tree leaf nitrogen content rapid detection method based on electronic nose and spectrocolorimeter Download PDFInfo
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Abstract
The invention relates to a tea tree leaf nitrogen content rapid detection method based on an electronic nose and a spectrocolorimeter, which optimizes and screens 5 sensors with higher contribution rate through response difference and overload analysis of an electronic nose sensor to tea tree leaf odor substances. And secondly, 3 main factors (the volume of the gas collection container, the headspace time and the headspace temperature) of the electronic nose detection system are optimized. B in the spectrocolorimeter was then determined by linear regression analysis*The value may be used as a marker to determine the nitrogen content of the leaf. 5 main sensors of an electronic nose and a spectrocolorimeter b*And establishing a tea tree leaf nitrogen prediction model combining an electronic nose and a spectrocolorimeter for the value as a characteristic value. Through calculation, the model building module accuracy can reach 95%, and the verification module accuracy can reach 92.5%.
Description
Technical Field
The invention relates to a tea tree leaf nitrogen content rapid detection method based on an electronic nose and a spectrocolorimeter, belongs to the field of rapid detection of plant nutrition, and particularly relates to a method for rapidly detecting the nitrogen content of tea tree leaves by combining the electronic nose and the spectrocolorimeter.
Background
The electronic nose utilizes the characteristic that each gas sensitive device responds to the gas with complex components but is different from each other, and simulates a human olfactory system to identify various odors by a data processing method, so that the quality of the odors is analyzed and evaluated. The electronic nose has unique advantages in quality monitoring, quality evaluation and safety detection in the fields of meat, tea and wine, fruits and vegetables and the like, can track the processing technology and the detection process in the whole process on line, and has no damage to products, and is rapid and sensitive. The spectrocolorimeter technology has the characteristics of high detection speed, high precision, suitability for off-line operation in a laboratory environment and a production environment and the like, and accurate color data can be obtained by adopting a spectral analysis method to measure the color of an object. The electronic nose is combined with other analysis instruments to perform data fusion analysis, which is a popular analysis method at present. However, at present, no report is available for identifying the nitrogen content of tea leaves by combining an electronic nose with a spectrocolorimeter.
Nitrogen is one of the most important nutrient elements of tea trees. The traditional tea tree nitrogen detection methods such as plant external morphology diagnosis, plant total nitrogen diagnosis, plant nitrate diagnosis and the like have complex operation process, consume more financial resources and lack timeliness. With the continuous emergence of new detection technologies, nondestructive diagnosis technologies such as chlorophyll meter method, chlorophyll fluorescence technology, remote sensing technology and the like are gradually applied to nitrogen diagnosis of tea trees. In addition, a remote sensing technology for identifying the object based on the spectral reflection characteristics of the object also becomes a possible means for real-time monitoring and rapid diagnosis of the plant nitrogen.
Disclosure of Invention
The invention provides a tea tree leaf nitrogen content rapid detection method based on an electronic nose and a spectrocolorimeter.
The inventor firstly optimizes and screens 5 sensors (W5S, W1S, W1W, W2S and W2W) with high contribution rate through response difference and overload analysis of an electronic nose (German PEN3 type) sensor to tea leaf odor substances, and uses the sensors as main sensors for subsequent detection. And then, a detection system of the tea tree leaves by the electronic nose is optimized. Second, b in the spectrocolorimeter was determined by linear regression analysis*The value may be used as a marker to determine the nitrogen content of the leaf. With response values (G/G0) and b of W5S, W1S, W1W, W2S, W2W*And (4) establishing a tea tree leaf nitrogen prediction model combining an electronic nose and a spectrocolorimeter. Meanwhile, the model is verified and predicted, the model building accuracy can reach 95%, and the verification set accuracy can reach 92.5%.
A tea tree leaf nitrogen content rapid detection method based on an electronic nose and a spectrocolorimeter comprises the following steps:
(1) preparing materials: selecting 20 undamaged mature tea tree leaves (3-4 th leaves with downward terminal buds), cleaning, and wiping;
(2) and (3) detecting by an electronic nose: putting the prepared leaves in the step 1) into a 50mL gas collecting bottle, and sealing the gas collecting bottle by using tinfoil paper; and (3) carrying out gas detection by using an electronic nose by adopting a headspace sampling method. And (3) placing the gas collecting bottle in an environment of 30 ℃ for headspace preheating, wherein the headspace time is 30 min. The detection parameters are as follows: the sensor cleaning time is 100s, 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, the sample injection flow rate is 300mL/min, signals at 80-83s are taken as time points of sensor signal analysis, and response values G/G0 of 5 sensors, namely W5S (sensitive to oxynitride), W1S (sensitive to methyl compounds), W1W (sensitive to inorganic sulfides), W2S (sensitive to alcohols and aldehydes and ketones) and W2W (aromatic components and sensitive to organic sulfides), are respectively extracted;
(3) detecting by a spectrocolorimeter: fixing the blade prepared in the step 1) by using a sample clamp; respectively detecting b of 20 blades by using a CM-5 spectral color difference meter and an LAB color system under a D65 light source (simulated sunlight)*Value, take b*Substituting the average value into the formula in the step 4);
(4) the response values (G/G0) of W5S, W1S, W1W, W2S and W2W and b*Substituting the values into the following formula to obtain the maximum group value of the Y value, namely judging the nitrogen content of the leaf.
In the formula: G/G02、G/G06、G/G07、G/G08、G/G09Respectively represent the signal response values G/G0 of the sensors W5S, W1S, W1W, W2S and W2W at the detection time of 80-83 s.
The electronic nose is a portable electronic nose of PEN3 type from AIRSENSE of Germany.
The tea tree leaf nitrogen content prediction model established by the invention can accurately predict the nitrogen content of the tea tree leaves. The accuracy rate of the experimental module building can reach 95%, the accuracy rate of the verification module can reach 92.5%, and the nitrogen content of the tea tree leaves can be well predicted.
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FIG. 1 is a graph of different sensor responses and overload analysis
The graphs show that S2, S6, S7, S8 and S9 have high response values to gas substances and high contribution rates. (the German PEN3 type portable electronic nose sensors have 10 sensors, W1C, W5S, W3C, W6S, W5C, W1S, W1W, W2S, W2W and W3S, wherein the marked S1-S10 represent the 10 sensors in turn)
FIG. 2 is the optimization of the detection conditions of the electronic nose
The figure shows that the volume of a gas collector is 50mL, the headspace preheating temperature is 30 ℃, and the headspace time is 30min as an optimal detection system.
FIG. 3 is a graph showing the analysis of the nitrogen content of tea leaves by linear regression with the values of L, a and b of the electric noses
In the figure, L is shown*The linear relationship between the value and the total nitrogen content of the leaf is the lowest, a*The value shows a certain linearity, but the coefficient of determination is lower than b*The value is obtained. b*The value may be used as a marker to determine the nitrogen content of the leaf.
Detailed Description
The invention takes the conventional green tea tree variety (non-whitened and purple variety) as the background, and combines a color difference meter and an electronic nose to establish a model. The whitening variety and the purple variety belong to a leaf color variation variety, the leaf color is white, yellow or purple, the chromatic aberration variation degree is large, and the whitening variety and the purple variety do not belong to a conventional tea tree variety. Therefore, the leaf color variation of these two broad varieties is not within the scope of this model.
A tea tree leaf nitrogen content rapid detection method based on an electronic nose and a spectrocolorimeter technology comprises the following steps:
(1) detecting the nitrogen content of tea leaves: taking the intact mature leaf of the conventional green tea tree (the 3 rd to 4 th leaf with the lower terminal bud), deactivating enzyme at 105 deg.C for 5-10 minutes, and oven drying at 80 deg.C. Weighing 0.3000-0.5000 g of dried and ground (0.25mm) sample, and placing the sample in a digestion tube. 8mL of concentrated sulfuric acid was added, the mixture was gently shaken, and the mixture was left overnight. And (3) placing the digestion tube on a digestion furnace for heating, taking down the digestion tube when the solution is uniform brownish black, adding 10 drops of hydrogen peroxide, shaking up, heating to slightly boil, digesting for about 5 minutes, taking down, repeatedly dropwise adding 10 drops of hydrogen peroxide, and digesting. Repeating the steps for 3-5 times, and taking down and filtering after the solution is colorless or clear. And (5) sucking 2-5 mL of filtrate, and determining the nitrogen content by using a Kjeldahl azotometer.
(2) Optimizing an electronic nose detection system: placing 20 tea leaf samples in a container containing absorbent paper and Na2CO3(absorbent paper and Na)2CO3All dried for use) in a triangular flask, sealed and sealed by tinfoil paper. Gas detection was performed using a PEN3 model portable electronic nose using headspace sampling and the response G/G0 (representing the ratio of the amount of resistance G of sensor No. n after exposure to sample volatiles to the amount of resistance G0 of the sensor after filtration through standard activated carbon) for each sensor was recorded. The system is optimized by setting three factors of the volume of the gas collecting bottle, the headspace preheating temperature and the headspace time. Wherein the gas collecting bottle volume test is designed to carry out 3 treatments of 50, 100 and 150 mL; the headspace preheating temperature test is carried out by 6 treatments at 30, 40, 50, 60, 70 and 80 ℃; the headspace time test was performed with 5, 10, 15, 20, 25, 30min 6 treatments. Optimization experiments were performed for each treatment. The detection parameters of the electronic nose are that the cleaning time of the sensor is 100s, 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 80-83s is taken as the time point of the signal analysis of the sensor. The sensors were cleaned and standardized before and after each measurement. And (3) carrying out Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and overload analysis by utilizing WinMuster software, and screening out the optimal volume of the gas collecting bottle, the headspace preheating temperature and the headspace time so as to carry out subsequent experiments.
Early tests show that the concentration of gas substances volatilized by the blades is too low, and the sensors of the electronic nose are not enough to effectively identify the gas; if the number of blades is too large, the blades transpiration can generate a large amount of water vapor as the headspace collects the gas, and the resolution of the sensor can be reduced. Therefore, 20 leaves were selected for the measurement in the present invention.
(3) Screening color difference characteristic values: tea tree leaves are cleaned and wiped dry, and are fixed by a sample clamp (provided with equipment). The values of L, a and b of the blades are detected by a CM-5 spectral colorimeter under a D65 light source (simulating sunlight) by using an LAB color system (L indicates brightness; positive value of a is red, negative value is green; positive value of b is yellow, and negative value is blue). And (3) combining the L, a and b values with the nitrogen content of the tea leaves in the step (1) to perform linear regression analysis, and screening out the optimal color difference characteristic value.
(4) Establishing a nitrogen content model of tea leaves: the response values (G/G0) and b of W5S, W1S, W1W, W2S and W2W (the optimal sensors screened in step 2) are used*And (4) establishing a prediction model by using Matlab software according to the value (the optimal color difference characteristic value screened in the step (3)) and the nitrogen content of the tea leaf.
And extracting response values (G/G0) and b values of the 5 optimal sensors at 80-83s to establish a prediction model. The leaf nitrogen content prediction model based on the smell and the color is as follows:
in the formula, G/G02、G/G06、G/G07、G/G08、G/G09Respectively represent the signal response values G/G0 of the sensors W5S, W1S, W1W, W2S and W2W at the detection time of 80-83 s.
G/G02、G/G06、G/G08、G/G07、G/G09And b is substituted into the above formula, and the obtained group with the maximum Y value is judged as the nitrogen content of the leaf.
Example 1 optimization of an electronic nose detection System
The total number of PEN3 type universal sensors is 10, and W1C, W5S, W3C, W6S, W5C, W1S, W1W, W2S, W2W and W3S are respectively. The 10 sensors are denoted in sequence by reference numbers S1-S10 in FIG. 1. Since 10 sensors are focused on detecting different gas components, the response of the electronic nose sensor to the tea tree leaf odor substance is different. As shown in FIG. 1, the response values of the S2, S7 and S9 sensors are high, and the response values of the S6 and S8 sensors are also certain, but are lower than those of the S2, S7 and S9. The response values of S1, S3, S4, S5 and S105 sensors are all around 1.0, which shows that the sensors are not sensitive to the response of gas substances. As shown by the overload analysis, the sensor contributing the most in the leaf odor recognition is S6, followed by S9, S7, S2, S8, and the other sensors contribute less. Therefore, S2(W5S), S6(W1S), S7(W1W), S8(W2S), S9(W2W) were selected as optimal sensors for subsequent experiments.
As shown in FIG. 2, the 50, 100, 150mL processed PCA data are overlaid two by two. By linear angle LDA analysis (fig. 2a-2) it was found that there was overlap between 100mL and 150mL treatments, whereas the 50mL treatment spots could be clearly distinguished. Therefore, the experiment chose 50mL as the final gas collector volume. As shown in FIG. 2b-1, the sample spots at 30 ℃ were relatively concentrated, and the other samples were separated into a large group of sample spots and overlapped with the treatments at 50, 60, 70 and 80 ℃. As can be seen from the analysis of the 2b-2LDA data, the spots at 30 ℃ and 40 ℃ can be clearly distinguished. Based on the PCA and LDA data analysis, the headspace preheat temperature was finally selected to be 30 ℃. From the data analysis of FIGS. 2c-1 and 2c-2, the data of the sample points processed at 30min are small in dispersion and have no overlap with other processes, and other groups of processes have overlapping regions. Finally selecting the headspace time to be 30 min.
Example 2 chroma testing of tea leaves
Taking off mature leaves from non-yellowing variety of tea tree, quickly washing with deionized water, and wiping to dry. And (4) carrying out chromaticity detection by using a CM-5 type light splitting color difference meter. The tea leaf gradually changes its surface color from green to yellow with the increase of nitrogen deficiency. Fig. 3 shows that L is the lowest linear with respect to the total nitrogen content of the blade, and a is somewhat linear but has a lower coefficient of determination than b. In the LAB color system + b indicates yellow, and a larger value of + b indicates a heavier degree of yellow in the sample measured. The values of b in this test are all greater than 0 and show a linear correlation with total nitrogen content and the coefficient of determination is 0.9204, indicating that the values of b correlate best with leaf yellowing compared to the other values. Therefore, the b value can be used as a mark for judging the nitrogen content of the blade.
Example 3 leaf nitrogen content prediction model based on odor-associated color
And extracting response values (G/G0) and b values of the 5 odor sensors at 80-83s to build a prediction model. The leaf nitrogen content prediction model based on the smell and the color is as follows:
in the formula, G/G02、G/G06、G/G07、G/G08、G/G09Respectively represent the signal response values G/G0 of the sensors W5S, W1S, W1W, W2S and W2W at the detection time of 80-83 s.
G/G02、G/G06、G/G08、G/G07、G/G09And b is substituted into the above formula, and the obtained group with the maximum Y value is judged as the nitrogen content of the leaf.
Test example 1, verification and evaluation of a model for predicting the nitrogen content in leaves
The model building set and the verification set each had 80 samples. The sample selection standard of the building module group is the same as that of the verification group, the building module group and the verification group belong to the same type of samples and have the same treatment, one part is used for experiments, and the other part is used for verification. The following table is used primarily to verify the accuracy of the model for analyzing nitrogen content. As shown in Table 1, the accuracy of the modeling group is 95%, and the accuracy of the verification group is 92.5%, which shows that the model can accurately predict the nitrogen content of the blade.
TABLE 1 Back-judgment and verification results of leaf nitrogen content prediction model based on odor and color
Claims (3)
1. A tea tree leaf nitrogen content rapid detection method based on an electronic nose and a spectrocolorimeter is characterized by comprising the following steps: the method comprises the following steps:
(1) selecting 20 undamaged mature tea tree leaves, cleaning and wiping;
(2) putting the prepared leaves in the step 1) into a 50mL gas collecting bottle, and sealing the gas collecting bottle by using tinfoil paper; gas detection is carried out by using an electronic nose by adopting a headspace sampling method; placing the gas collecting bottle in an environment of 30 ℃ for headspace preheating, wherein the headspace time is 30 min; the detection parameters are as follows: the sensor cleaning time is 100s, 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 signals at 80-83s are used as the time points for sensor signal analysis; response values G/G0 of five sensors including W5S, W1S, W1W, W2S and W2W are extracted respectively;
(3) detecting by a spectrocolorimeter: fixing the blade prepared in the step 1) by using a sample clamp; respectively detecting b of 20 blades by using a CM-5 spectral color difference meter and an LAB color system under a D65 light source*Value, take b*Substituting the average value into the formula in the step 4);
(4) the response values G/G0 and b of W5S, W1S, W1W, W2S and W2W are set*Substituting the value into the following formula to obtain the maximum group value of the Y value, namely judging the maximum group value as the leaf nitrogen content;
in the formula: G/G02、G/G06、G/G07、G/G08、G/G09Respectively represent the signal response values G/G0 of the sensors W5S, W1S, W1W, W2S and W2W at the detection time of 80-83 s.
2. The method for rapidly detecting the nitrogen content of the tea leaves based on the electronic nose and the spectrocolorimeter as claimed in claim 1, wherein the method comprises the following steps: the electronic nose is a PEN3 type portable electronic nose.
3. The method for rapidly detecting the nitrogen content of the tea leaves based on the electronic nose and the spectrocolorimeter as claimed in claim 1, wherein the method comprises the following steps: the tea tree is a conventional green tea tree variety.
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