CN103472198B - Detection device and detection method for varieties of tea drinks - Google Patents
Detection device and detection method for varieties of tea drinks Download PDFInfo
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Abstract
The invention discloses a kind of tea beverage kind detection device and detection method, include the following steps: for n kind tea beverage to be respectively classified into m tea beverage samples identical in quality, sensor array successively detects each sample of each sample; Obtain n threshold value Thr1s, Thr2.., Thrn associated with tea beverage kind; Tea beverage W to be detected is detected, the signal to noise spectrum characteristic noise width value σ of tea beverage W is obtained; The dynamic cataloging parameter, Δ of tea beverage W is calculated using non-linear self-calibration dynamic cataloging model; Computer successively calculates
When
Then computer makes the judgement that the kind of detected tea beverage W is kind j. The present invention has the characteristics that fast and accurately distinguish the tea beverage kind of the kind of tea beverage.
Description
Technical field
The present invention relates to food quality detection technical field, especially relating to one can accurately detect tea beverage kind, the tea beverage kind pick-up unit providing authentic data to support for tea beverage that manufacturer production mass is stable and detection method.
Background technology
For a long time, people are judged by the local flavor of sense organ to drink of self, and this judgement is usually with very strong subjectivity, and evaluation analysis result, often along with the difference of age, experience, exists larger difference.Even if same person also can due to health, emotional change and draw Different Results.Moreover it is a volatile substance suction process that sense of smell is differentiated, long-term experiment can work the mischief to the health of human body, and some bad smell can make the personnel of judging responsive especially and make result wrong; In addition, often need have the composition of personnel of the experience of judging to judge group in a large number in sensory evaluation process, process is comparatively loaded down with trivial details, and evaluation result does not often have repeatability.
Chinese patent Authorization Notice No.: CN101769889A, authorized announcement date on July 7th, 2010, disclose the electric nasus system that a kind of quality of agricultural product detects, comprise one and mainly complete gas enrichment module to low concentration odor trap, one air chamber gas path module and the sensor array mainly olfactory signal being converted into electric signal, one mainly carries out filtering to sensor array output signal, analog to digital conversion, the Conditioning Circuits of Sensor of feature extraction and data preprocessing module, a pair signal carries out identifying and judging, and with the embedded system that data store, one display and result output module, described gas enrichment module is formed by being filled with the adsorption tube of adsorbent, heating wire and attemperating unit.This invention has function singleness, the deficiency that detection time is long.
Summary of the invention
The present invention is that food in order to overcome prior art judges that the length consuming time of method, cost are high, the deficiency of apparatus expensive, provides a kind of tea beverage kind pick-up unit and the detection method that can distinguish the kind of tea beverage fast and accurately.
To achieve these goals, the present invention is by the following technical solutions:
A kind of tea beverage kind pick-up unit, comprises gas extractor and surveys device of air; Described gas extractor comprises gas collecting chamber, sample cavity, the lower communicating tube being located at the upper communicating pipe between gas collecting chamber and sample cavity top and being located between gas collecting chamber and sample cavity bottom; Gas collecting chamber is provided with draft tube, and draft tube is provided with the first solenoid valve, and upper communicating pipe is provided with the first air pump, and upper communicating pipe is provided with gas outlet, and gas outlet is provided with the second solenoid valve, and sample cavity is positioned at the oblique below in gas collecting chamber;
Described survey device of air comprises sampling probe, excitation noise circuit, cleaning probe and sensor array; Sampling probe and cleaning probe are equipped with the second air pump,
Sensor array comprises several gas sensors, and each sensor lays respectively at independently in air chamber; First solenoid valve, the second solenoid valve, sensor array, excitation noise circuit, the first air pump and the second air pump are equipped with the data-interface for being electrically connected with computing machine.
N kind tea beverage is first divided into m tea beverage sample identical in quality by the present invention respectively, and sensor array detects each sample of each sample successively; Obtain n the threshold value Thr be associated with tea beverage kind
1, Thr
2..., Thr
n;
Tea beverage W to be detected is detected, obtains the signal to noise spectrum characteristic noise width cs of tea beverage W; Non-linear self-calibration dynamic cataloging model is utilized to calculate the dynamic cataloging parameter Δ of tea beverage W; Computing machine calculates successively
j=1 ..., n, when
then the kind of detected tea beverage W made by computing machine is the judgement of kind j.
The present invention is that food in order to overcome prior art judges that the length consuming time of method, cost are high, the deficiency of apparatus expensive, provides a kind of tea beverage kind pick-up unit and the detection method that can distinguish the kind of tea beverage fast and accurately.
As preferably, the T-shaped stirring pipe of column and lower ending opening is provided with in described sample cavity, the lower end of T-shaped stirring pipe is positioned at column, the lower end of T-shaped stirring pipe is provided with the annular edge of outside horizontal-extending, column inner peripheral surface top is provided with the bulge loop suitable with annular edge, and annular edge lower surface contacts with bulge loop upper surface;
Column upper end is provided with back-up ring, the xsect of back-up ring is in the U-shaped under shed, and back-up ring comprises interior annular ring and outer ring circle, and interior annular ring contacts with column inner peripheral surface, outer ring circle contacts with column outer peripheral face, the lower limb of interior annular ring and annular edge upper surface clearance fit; Column bottom is connected with lower communicating tube;
The outer peripheral face rear portion of T-shaped stirring pipe one end is provided with several ventholes, and the outer peripheral face front portion of the other end of T-shaped stirring pipe is provided with several ventholes.
As preferably, the circular in cross-section of described T-shaped stirring pipe, the outer peripheral face bottom of T-shaped stirring pipe is provided with several ventholes.
As preferably, described sensor array comprises 8 gas sensors, be respectively the first sensor for detecting sulfide, for detecting the second sensor of hydrogen, for detecting the four-sensor of alcohol, toluene, dimethylbenzene, for detecting the 5th sensor of hydrocarbon component gas, for detecting the 6th sensor of methane and propane, for detecting the 7th sensor of butane, for detecting the 8th sensor of oxides of nitrogen, for detecting the 3rd sensor of ammonia.
As preferably, described excitation noise circuit comprises oscillator and amplifier; The output terminal of oscillator is electrically connected with the input end of amplifier, and the output terminal of amplifier is electrically connected with digital signal processor.
A detection method for tea beverage kind pick-up unit, comprises the steps:
(6-1) be provided with non-linear self-calibration dynamic cataloging model in computing machine, non-linear self-calibration dynamic cataloging model comprises Nonlinear state space model, residual error variable and criteria for classification model;
Nonlinear state space model is
Wherein σ is signal to noise spectrum characteristic noise width, and ε is intermediate transfer parameter, and τ is initial phase,
for output variable, κ, η and Γ are real parameter;
Residual error variable is
wherein
for the actual output of spatial model,
for the theory of spatial model exports, it is preset value; Such as, for icy bleak tea sample,
after can being set as 100 measurements, calculate according to the σ detected
weighted mean value.
Criteria for classification model is:
wherein, L is mean data length, and N is maximum detection data length, and such as: detection by electronic nose data length L is 500, maximum detection data length N is 500 ~ 1000.Δ is dynamic cataloging parameter; Specification error threshold value p;
Be provided with in computing machine
wherein V (x) is non-linear symmetric potential function, and Adjust (t) is normalized signal, and ξ (t) is white Gaussian noise, and A is constant, f
0be frequency modulating signal, D is noise intensity,
for phase place, x is particle movement displacement, and t is the time;
(6-2) the first and second solenoid valves are opened by computing machine, to be passed in gas collecting chamber 20 to 30 minutes through the air of activated carbon filtration by draft tube;
(6-3) select n kind tea beverage, the kind sequence number of setting tea beverage is j, j=1;
(6-3-1) tea beverage j is divided into the individual tea beverage sample identical in quality of m, setting sample sequence number is i, i=1;
(6-3-2) the sample i of tea beverage j is put into sample cavity, computing machine controls first, second closed electromagnetic valve, and starts the first air pump; The escaping gas that first air pump drives tea beverage to produce is at upper and lower communicating pipe, gas collecting chamber and sample cavity Inner eycle 20 to 30 minutes;
(6-3-3) computing machine controls the second air pump work on cleaning probe, and pure air sucks in each air chamber by cleaning probe, cleans each sensor;
(6-3-4) the first solenoid valve is opened by computing machine, sampling probe inserts in gas collecting chamber by draft tube, computing machine controls the second air pump work on sampling probe, the escaping gas that tea beverage produces sucks in each air chamber by sampling probe, escaping gas and the sensor contacts be located in air chamber, each sensor produces analog response signal respectively; Each analog response signal is converted to digital response signal by analog to digital converter respectively, obtains digital response signal eNOSE (t) of sensor array;
(6-3-5) by eNOSE (t) composition data matrix, the columns of data matrix is equal with the quantity of sensor in sensor array, and the data rows in data matrix is respectively the digital response signal that each sensor detects; Each data rows is all handled as follows:
Choose the minimum value minvalue in data rows and maximal value maxvalue, utilize formula y (t)=(x (t)-MinValue)/(MaxValue-MinValue) to be normalized described data rows; Wherein, x (t) is the raw data of described data rows, the data of y (t) for obtaining after normalized;
Each data rows is normalized the data matrix after rear formation normalization, calculate the mean value of the y (t) of the data matrix after normalization, be normalized signal Adjust (t) by the mean value definition of y (t), excitation noise signal and Adjust (t) are inputted stochastic resonance system model
in, make stochastic resonance system model produce accidental resonance;
Computing machine utilizes formula
Calculate the signal to noise ratio snr of excitation noise signal, wherein ω is signal frequency, and Ω is angular frequency, and S (ω) is signal spectral density, S
n(Ω) be noise intensity in signal frequency range;
(6-3-6) computing machine draws the signal to noise ratio (S/N ratio) spectrogram of the excitation noise signal of stochastic resonance system model, chooses signal to noise spectrum characteristic noise width in signal to noise ratio (S/N ratio) spectrogram, and is stored in computing machine by signal to noise spectrum characteristic noise width;
(6-3-7) as i < m, make i value increase by 1, repeat step (6-3-2) to step (6-3-6), obtain m signal to noise spectrum characteristic noise width; The mean value of computer calculate signal to noise spectrum characteristic noise width, and be threshold value Thr by the mean value definition of signal to noise spectrum characteristic noise width;
(6-3-8) as j < n, make j value increase by 1, repeat step (6-3-1) to step (6-3-7), obtain n the threshold value Thr be associated with tea beverage kind
1, Th
r2 ..., Thr
n;
(6-4) selected tea beverage W to be detected, is divided into the individual tea beverage sample identical in quality of m by tea beverage W, setting sample sequence number is i, i=1; Repetition step (6-2), (6-3-2) to (6-3-6) detect successively to the m of a tea beverage W sample, obtain the signal to noise spectrum characteristic noise width cs of tea beverage W;
(6-5) non-linear self-calibration dynamic cataloging model is utilized to calculate the dynamic cataloging parameter Δ of tea beverage W;
The process calculating dynamic cataloging parameter Δ is optimized by the accidental resonance output signal-to-noise ratio spectrum signature noise width of unknown sample, object is the accidental error eliminated in measuring process and brings interference, add the accuracy of residual error, be conducive to the accuracy improving sample prediction.
(6-6) computing machine calculates successively
j=1 ..., n, when
then the kind of detected tea beverage W made by computing machine is the judgement of kind j.
As preferably, described error threshold value p is 0.02 to 0.12.
As preferably, described m is 5 to 16.
As preferably, described n is 5 to 200.
As preferably, also comprise the steps: escaping gas to suck in step (6-3-4) in each air chamber and detect 35 to 70 seconds.
Therefore, the present invention has following beneficial effect: (1) can accurately detect tea beverage kind; (2) for the tea beverage that manufacturer production mass is stable provides authentic data support.
Accompanying drawing explanation
Fig. 1 is a kind of process flow diagram of embodiments of the invention;
Fig. 2 is a kind of structural representation of gas extractor of the present invention;
Fig. 3 is a kind of theory diagram of the present invention;
Fig. 4 is a kind of structural representation of column of the present invention, back-up ring and T-shaped stirring pipe.
In figure: gas collecting chamber 1, sample cavity 2, upper communicating pipe 3, lower communicating tube 4, draft tube 5, first solenoid valve 6, second solenoid valve 7, first air pump 8, sampling probe 9, excitation noise circuit 10, cleaning probe 11, sensor array 12, second air pump 13, computing machine 14, column 15, T-shaped stirring pipe 16, bulge loop 17, back-up ring 18, venthole 19, screw 20, analog to digital converter 21.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention will be further described.
Embodiment is as shown in Figure 2 a kind of tea beverage kind pick-up unit, comprises gas extractor and surveys device of air; Gas extractor comprises gas collecting chamber 1, sample cavity 2, the lower communicating tube 4 being located at the upper communicating pipe 3 between gas collecting chamber and sample cavity top and being located between gas collecting chamber and sample cavity bottom; Gas collecting chamber is provided with draft tube 5, and draft tube is provided with the first solenoid valve 6, and be provided with the first air pump 8 upper communicating pipe, upper communicating pipe is provided with gas outlet, and gas outlet is provided with the second solenoid valve 7, and sample cavity is positioned at the oblique below in gas collecting chamber; The T-shaped stirring pipe 16 of column 15 and lower ending opening is provided with in sample cavity.
As shown in Figure 3, survey device of air and comprise sampling probe 9, excitation noise circuit 10, cleaning probe 11 and sensor array 12; Sampling probe and cleaning probe are equipped with the second air pump 13,
Sensor array comprises 8 gas sensors, and each sensor lays respectively at independently in air chamber; First solenoid valve, the second solenoid valve, sensor array, excitation noise circuit, the first air pump and the second air pump are electrically connected with computing machine 14 respectively.
As shown in Figure 4, the lower end of T-shaped stirring pipe is positioned at column, and the lower end of T-shaped stirring pipe is provided with the annular edge of outside horizontal-extending, and column inner peripheral surface top is provided with the bulge loop 17 suitable with annular edge, and annular edge lower surface contacts with bulge loop upper surface;
Column upper end is provided with back-up ring 18, the xsect of back-up ring is in the U-shaped under shed, and back-up ring comprises interior annular ring and outer ring circle, and interior annular ring contacts with column inner peripheral surface, outer ring circle contacts with column outer peripheral face, the lower limb of interior annular ring and annular edge upper surface clearance fit; Column bottom is connected with lower communicating tube; Back-up ring is connected with column by screw 20.Back-up ring is made up of nonrigid plastic.
The outer peripheral face rear portion of T-shaped stirring pipe one end is provided with 10 ventholes 19, and the outer peripheral face front portion of the other end of T-shaped stirring pipe is provided with 8 ventholes.
The circular in cross-section of T-shaped stirring pipe, the outer peripheral face bottom of T-shaped stirring pipe is provided with the venthole of arrangement.
8 gas sensors are respectively the first sensor for detecting sulfide, for detecting the second sensor of hydrogen, for detecting the four-sensor of alcohol, toluene, dimethylbenzene, for detecting the 5th sensor of hydrocarbon component gas, for detecting the 6th sensor of methane and propane, for detecting the 7th sensor of butane, for detecting the 8th sensor of oxides of nitrogen, for detecting the 3rd sensor of ammonia.
Excitation noise circuit comprises oscillator and amplifier; The output terminal of oscillator is electrically connected with the input end of amplifier, and the output terminal of amplifier is electrically connected with digital signal processor.
In the present embodiment, sample cavity is provided with the first gland bonnet, and lower communicating tube is provided with leakage fluid dram, and leakage fluid dram is provided with the second gland bonnet.When needs add sample in sample cavity, open the first gland bonnet, close the second gland bonnet, sample is loaded in sample cavity; When sample changed by needs, first, second gland bonnet is all opened, sample is discharged from leakage fluid dram, then clean sample cavity with pure water, and close the second gland bonnet, new sample is joined in sample cavity.
Embodiment is as shown in Figure 1 a kind of detection method of tea beverage kind pick-up unit, comprises the steps:
Be provided with non-linear self-calibration dynamic cataloging model in computing machine, non-linear self-calibration dynamic cataloging model comprises Nonlinear state space model, residual error variable and criteria for classification model;
Nonlinear state space model is
Wherein σ is signal to noise spectrum characteristic noise width, and ε is intermediate transfer parameter, and τ is initial phase,
for output variable, κ, η and Γ are real parameter;
Residual error variable is
wherein
for the actual output of spatial model,
for the theory of spatial model exports, it is preset value;
Criteria for classification model is:
wherein, L is mean data length, and N is maximum detection data length, and Δ is dynamic cataloging parameter; Specification error threshold value p=0.04;
Be provided with in computing machine
wherein V (x) is non-linear symmetric potential function, and Adjust (t) is normalized signal, and ξ (t) is white Gaussian noise, and A is constant, f
0be frequency modulating signal, D is noise intensity,
for phase place, x is particle movement displacement, and t is the time;
Step 100, the first and second solenoid valves are opened by computing machine, to be passed in gas collecting chamber 20 minutes through the air of activated carbon filtration by draft tube;
Step 200, selects 8 kinds of tea beverage samples, is respectively sample 1 to sample 8; The initial value of setting tea beverage sample sequence number j is 1; Often kind of tea beverage sample is divided into 6 tea beverage samples identical in quality, and setting sample sequence number is i, i=1; Each sample of each sample is detected and data processing as follows:
Tea beverage sample is as shown in table 1:
Table 1
Step 201, the sample i of tea beverage j is put into sample cavity, and computing machine controls first, second closed electromagnetic valve, and starts the first air pump; The escaping gas that first air pump drives tea beverage to produce is at upper and lower communicating pipe, gas collecting chamber and sample cavity Inner eycle 30 minutes;
Step 202, computing machine controls the second air pump work on cleaning probe, and pure air sucks in each air chamber by cleaning probe, cleans each sensor;
Step 203, first solenoid valve is opened by computing machine, sampling probe inserts in gas collecting chamber by draft tube, computing machine controls the second air pump work on sampling probe, the escaping gas that tea beverage produces sucks in each air chamber by sampling probe, escaping gas and the sensor contacts be located in air chamber, each sensor produces analog response signal respectively; Each analog response signal is converted to digital response signal by analog to digital converter respectively, obtains digital response signal eNOSE (t) of sensor array;
Step 204, by eNOSE (t) composition data matrix, the columns of data matrix is equal with the quantity of sensor in sensor array, and the data rows in data matrix is respectively the digital response signal that each sensor detects; Each data rows is all handled as follows:
Choose the minimum value minvalue in data rows and maximal value maxvalue, utilize formula y (t)=(x (t)-MinValue)/(MaxValue-MinValue) to be normalized described data rows; Wherein, x (t) is the raw data of described data rows, the data of y (t) for obtaining after normalized;
Each data rows is normalized the data matrix after rear formation normalization, calculate the mean value of the y (t) of the data matrix after normalization, be normalized signal Adjust (t) by the mean value definition of y (t), excitation noise signal and Adjust (t) are inputted stochastic resonance system model
stochastic resonance system model is made to produce accidental resonance;
Such as: 10 row data in data matrix are as shown in table 2:
Table 2
The columns of data matrix is equal with the quantity of sensor in sensor array, and the data rows in data matrix is respectively the digital response signal that 8 sensors detect; Each data rows is all handled as follows:
Such as: the minimum value minvalue in data rows 1 is 71, maximal value maxvalue is 76, formula y (t)=(x (t)-71)/(76-71) is utilized to be normalized data rows 1;
Minimum value minvalue in data rows 2 is 84, maximal value maxvalue is 89, utilizes formula y (t)=(x (t)-84)/(89-84) to be normalized data rows 1;
Minimum value minvalue in data rows 3 is 138, maximal value maxvalue is 144; Minimum value minvalue in data rows 4 is 112, maximal value maxvalue is 118; Minimum value minvalue in data rows 5 is 169, maximal value maxvalue is 175; Minimum value minvalue in data rows 6 is 144, maximal value maxvalue is 152; Minimum value minvalue in data rows 7 is 148, maximal value maxvalue is 156; Minimum value minvalue in data rows 8 is 81, maximal value maxvalue is 89; Respectively data rows 3 to 8 is normalized, obtains 10 row data in the data matrix after normalization as shown in table 3:
Table 3
Calculate the mean value of 8 data rows y (t) in the data matrix after normalization, namely the mean value of each row of data in reckoner 2, is normalized signal Adjust (t) by the mean value definition of y (t), be 0.35,0.52,0.03 according to the Adjust (t) that table 3 obtains in the present embodiment, 0.33,0.90,0.74,0.61,0.42,0.80,0.21,0.40.
Excitation noise signal and Adjust (t) are inputted stochastic resonance system model
in; Stochastic resonance system model is made to produce accidental resonance;
Computing machine utilizes formula
Calculate the signal to noise ratio snr of excitation noise signal, wherein ω is signal frequency, and Ω is angular frequency, and S (ω) is signal spectral density, S
n(Ω) be noise intensity in signal frequency range;
Step 205, computing machine draws the signal to noise ratio (S/N ratio) spectrogram of the excitation noise signal of stochastic resonance system model, chooses-73 decibels as signal to noise ratio (S/N ratio) eigenwert in signal to noise ratio (S/N ratio) spectrogram, finds the noise level value we corresponding with-73 decibels in signal to noise ratio (S/N ratio) spectrogram
1and we
2, will | we
1-we
2| as signal to noise spectrum eigenwert noise width, and signal to noise spectrum eigenwert noise width is stored in computing machine;
Step 206, as i < 6, makes i value increase by 1, repeats step 201 to step 205, obtains 6 signal to noise spectrum characteristic noise width; The mean value of computer calculate signal to noise spectrum characteristic noise width, and be threshold value Thr by the mean value definition of signal to noise spectrum characteristic noise width;
Step 300, as j < 8, makes j value increase by 1, repeats step 201 to step 206, obtains 8 threshold value Thr be associated with tea beverage kind
1, Thr
2..., Thr
8; Thr
1, Thr
2..., Thr
8be respectively 175,263,273,294,310,331,364,380.
Step 400, gets one bottle of tea beverage W to be detected, tea beverage W is divided into the tea beverage sample of 6 20 milliliters, and setting sample sequence number is i, i=1; 6 samples of repetition step 100,201 to 205 couples of tea beverage W detect successively, obtain the signal to noise spectrum characteristic noise width cs of tea beverage W; σ=290 in the present embodiment;
Step 500, utilizes non-linear self-calibration dynamic cataloging model to calculate the dynamic cataloging parameter Δ of tea beverage W; In the present embodiment, Δ=289;
Step 600, computing machine calculates successively
j=1 ..., n, when
then the kind of detected tea beverage W made by computing machine is the judgement of kind j
In the present embodiment,
Then the judgement that the kind of tea beverage W is Chef Kang's extra-strong tea made by computing machine.
Should be understood that the present embodiment is only not used in for illustration of the present invention to limit the scope of the invention.In addition should be understood that those skilled in the art can make various changes or modifications the present invention, and these equivalent form of values fall within the application's appended claims limited range equally after the content of having read the present invention's instruction.
Claims (9)
1. a tea beverage kind pick-up unit, is characterized in that, comprises gas extractor and surveys device of air; Described gas extractor comprises gas collecting chamber (1), sample cavity (2), the lower communicating tube (4) being located at the upper communicating pipe (3) between gas collecting chamber and sample cavity top and being located between gas collecting chamber and sample cavity bottom; Gas collecting chamber is provided with draft tube (5), draft tube is provided with the first solenoid valve (6), be provided with the first air pump (8) upper communicating pipe, upper communicating pipe be provided with gas outlet away from position, gas collecting chamber, gas outlet is provided with the second solenoid valve (7), and sample cavity is positioned at the oblique below in gas collecting chamber;
Described survey device of air comprises sampling probe (9), analog to digital converter (21), excitation noise circuit (10), cleaning probe (11) and sensor array (12); Sampling probe and cleaning probe are equipped with the second air pump (13), and sensor array is electrically connected with analog to digital converter;
Sensor array comprises several gas sensors, and each sensor lays respectively at independently in air chamber; First solenoid valve, the second solenoid valve, analog to digital converter, excitation noise circuit, the first air pump and the second air pump are equipped with the data-interface for being electrically connected with computing machine (14); The T-shaped stirring pipe (16) of column (15) and lower ending opening is provided with in described sample cavity, the lower end of T-shaped stirring pipe is positioned at column, the lower end of T-shaped stirring pipe is provided with the annular edge of outside horizontal-extending, column inner peripheral surface top is provided with the bulge loop (17) suitable with annular edge, and annular edge lower surface contacts with bulge loop upper surface;
Column upper end is provided with back-up ring (18), the xsect of back-up ring is in the U-shaped under shed, and back-up ring comprises interior annular ring and outer ring circle, and interior annular ring contacts with column inner peripheral surface, outer ring circle contacts with column outer peripheral face, the lower limb of interior annular ring and annular edge upper surface clearance fit; Column bottom is connected with lower communicating tube;
The outer peripheral face rear portion of T-shaped stirring pipe one end is provided with several ventholes (19), and the outer peripheral face front portion of the other end of T-shaped stirring pipe is provided with several ventholes.
2. tea beverage kind pick-up unit according to claim 1, is characterized in that, the circular in cross-section of described T-shaped stirring pipe, and the outer peripheral face bottom of T-shaped stirring pipe is provided with several ventholes.
3. tea beverage kind pick-up unit according to claim 1 and 2, it is characterized in that, described sensor array comprises 8 gas sensors, be respectively the first sensor for detecting sulfide, for detecting the second sensor of hydrogen, for detecting the four-sensor of alcohol, toluene, dimethylbenzene, for detecting the 5th sensor of hydrocarbon component gas, for detecting the 6th sensor of methane and propane, for detecting the 7th sensor of butane, for detecting the 8th sensor of oxides of nitrogen, for detecting the 3rd sensor of ammonia.
4. tea beverage kind pick-up unit according to claim 1, is characterized in that, described excitation noise circuit comprises oscillator and amplifier; The output terminal of oscillator is electrically connected with the input end of amplifier, and the output terminal of amplifier is electrically connected with digital signal processor.
5. be applicable to a detection method for tea beverage kind pick-up unit according to claim 1, it is characterized in that, comprise the steps:
(5-1) be provided with non-linear white demarcation dynamic cataloging model in computing machine, non-linear self-calibration dynamic cataloging model comprises Nonlinear state space model, residual error variable and criteria for classification model;
Nonlinear state space model is
wherein σ is signal to noise spectrum characteristic noise width, and ε is intermediate transfer parameter, and τ is initial phase,
for output variable, κ, η and Γ are real parameter;
Residual error variable is
wherein
for the actual output of spatial model,
for the theory of spatial model exports, it is preset value;
Criteria for classification model is:
wherein, L is mean data length, and N is maximum detection data length, and Δ is dynamic cataloging parameter; Specification error threshold value p;
Be provided with in computing machine
wherein V (x) is non-linear symmetric potential function, and Adjust (t) is normalized signal, and ξ (t) is white Gaussian noise, and A is constant, f
0be frequency modulating signal, D is noise intensity,
for phase place, x is particle movement displacement, and t is the time;
(5-2) the first and second solenoid valves are opened by computing machine, to be passed in gas collecting chamber 20 to 30 minutes through the air of activated carbon filtration by draft tube;
(5-3) select n kind tea beverage, the kind sequence number of setting tea beverage is j, j=1;
(5-3-1) tea beverage j is divided into the individual tea beverage sample identical in quality of m, setting sample sequence number is i, i=1;
(5-3-2) the sample i of tea beverage j is put into sample cavity, computing machine controls first, second closed electromagnetic valve, and starts the first air pump; The escaping gas that first air pump drives tea beverage to produce is at upper and lower communicating pipe, gas collecting chamber and sample cavity Inner eycle 20 to 30 minutes;
(5-3-3) computing machine controls the second air pump work on cleaning probe, and pure air sucks in each air chamber by cleaning probe, cleans each sensor;
(5-3-4) the first solenoid valve is opened by computing machine, sampling probe inserts in gas collecting chamber by draft tube, computing machine controls the second air pump work on sampling probe, the escaping gas that tea beverage produces sucks in each air chamber by sampling probe, escaping gas and the sensor contacts be located in air chamber, each sensor produces analog response signal respectively; Each analog response signal is converted to digital response signal by analog to digital converter respectively, obtains digital response signal eNOSE (t) of sensor array;
(5-3-5) by eNOSE (t) composition data matrix, the columns of data matrix is equal with the quantity of sensor in sensor array, and the data rows in data matrix is respectively the digital response signal that each sensor detects; Each data rows is all handled as follows:
Choose the minimum value minvalue in data rows and maximal value maxvalue, utilize formula y (t)=(x (t)-MinValue)/(MaxValue-MinValue) to be normalized described data rows; Wherein, x (t) is the raw data of described data rows, the data of y (t) for obtaining after normalized;
Each data rows is normalized the data matrix after rear formation normalization, calculate the mean value of the y (t) of the data matrix after normalization, be normalized signal Adjust (t) by the mean value definition of y (t), excitation noise signal and Adjust (t) are inputted stochastic resonance system model
in, make stochastic resonance system model produce accidental resonance;
Computing machine utilizes formula
calculate the signal to noise ratio snr of excitation noise signal, wherein ω is signal frequency, and Ω is angular frequency, and S (ω) is signal spectral density, S
n(Ω) be noise intensity in signal frequency range;
(5-3-6) computing machine draws the signal to noise ratio (S/N ratio) spectrogram of the excitation noise signal of stochastic resonance system model, chooses signal to noise spectrum characteristic noise width in signal to noise ratio (S/N ratio) spectrogram, and is stored in computing machine by signal to noise spectrum characteristic noise width;
(5-3-7) as i < m, make i value increase by 1, repeat step (5-3-2) to step (5-3-6), obtain m signal to noise spectrum characteristic noise width; The mean value of computer calculate signal to noise spectrum characteristic noise width, and be threshold value Thr by the mean value definition of signal to noise spectrum characteristic noise width;
(5-3-8) as j < n, make j value increase by 1, repeat step (5-3-1) to step (5-3-7), obtain n the threshold value Thr be associated with tea beverage kind
1, Thr
2..., Thr
n;
(5-4) selected tea beverage W to be detected, is divided into the individual tea beverage sample identical in quality of m by tea beverage W, setting sample sequence number is i, i=1; Repetition step (5-2), (5-3-2) to (5-3-6) detect successively to the m of a tea beverage W sample, obtain the signal to noise spectrum characteristic noise width cs of tea beverage W;
(5-5) non-linear self-calibration dynamic cataloging model is utilized to calculate the dynamic cataloging parameter Δ of tea beverage W;
(5-6) computing machine calculates successively
when
then the kind of detected tea beverage W made by computing machine is the judgement of kind j.
6. the detection method of tea beverage kind pick-up unit according to claim 5, is characterized in that, described error threshold value p is 0.02 to 0.12.
7. the detection method of tea beverage kind pick-up unit according to claim 5, is characterized in that, described m is 5 to 16.
8. the detection method of tea beverage kind pick-up unit according to claim 5, is characterized in that, described n is 5 to 200.
9. the detection method of the tea beverage kind pick-up unit according to claim 5 or 6 or 7 or 8, is characterized in that, also comprises the steps: escaping gas to suck in each air chamber to detect 35 to 70 seconds in step (5-3-4).
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| 基于SRSAnose系统的茶饮料智能感官分析方法研究;何阳春 等;《中国食品学报》;20120831;第12卷(第8期);第1.1节至2.2节 * |
| 基于随机共振的电子鼻系统构建及在谷物霉变程度检测中的应用;惠国华 等;《传感技术学报》;20110228;第24卷(第2期);159页-164页 * |
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