CN103512920A - Intelligent electronic nose system based device and method for analyzing quality of lemon tea beverage - Google Patents

Abstract

The invention relates to an intelligent electronic nose system based device and method for analyzing quality of a lemon tea beverage and solves the technical problems of subjective differences and harm to human body in the existing detection on volatile gas of a beverage sample by human organs. The device comprises a gas gathering unit, a gas recovery unit, a processing unit and a control unit. The gas gathering unit comprises a gas chamber, a sample chamber, a first communicating pipe and a second communicating pipe, which constitute a return type circulation path. The gas recovery unit is connected to the gas chamber; the processing unit is connected with the gas recovery unit; and an air inlet mechanism, an air outlet solenoid valve and a collection unit are respectively connected with the control unit. The invention has the advantages that the electronic nose system realizes more comprehensive and objective test results and also avoids harm of the sample gas to human health; and the gas is circulating during gas recovery, so that the gas is more uniformly mixed and the detection data is more accurate.

Description

A kind of lemon tea beverage attributional analysis device and method based on smart electronics nasus system

Technical field

The present invention relates to a kind of technology to beverage Quality Detection, especially relate to a kind of lemon tea beverage attributional analysis device based on smart electronics nasus system, and the lemon tea beverage Quality Analysis Methods based on smart electronics nasus system.

Background technology

Beverage is people's current consumption product, consumer choose and drinking process in, the fragrance of beverage and taste have larger impact to it.If beverage taste is not suitable for consumer or unstable, in fact can directly affect the market sale of product, relate to manufacturer's benefit.Therefore, manufacturer, in beverage research and development and production run, can carry out sensory evaluation evaluation to it.

For a long time, people judge the quality of beverage etc. by the sense organ of self, and this judgement is usually with very strong subjectivity, and evaluation analysis result tends to along with differences such as age, experiences, has sizable individual difference.Even if same person also can be because health, emotional change draw Different Results.Moreover sense of smell differentiates it is a volatile substance suction process, and 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, in sensory evaluation process, often needing has the composition of personnel of the experience of judging to judge group in a large number, and process is comparatively loaded down with trivial details, and evaluation result does not often have repeatability, therefore day by day urgent for novel analytical technology demand.

Publication No. is the Chinese patent application of CN101769889A, the electric nasus system that a kind of quality of agricultural product detects is disclosed, its structure comprises that one mainly completes the gas enrichment module that low concentration smell is collected, one is mainly converted into olfactory signal casing gas path module and the sensor array of electric signal, the one Conditioning Circuits of Sensor numeric field data pretreatment module of mainly sensor array output signal being carried out to filtering, analog to digital conversion, feature extraction, the embedded system that a pair of signal is identified and judged and store with data, one shows and result output module; Described gas enrichment module consists of the adsorption tube, heating wire and the attemperating unit that are filled with adsorbent.This invention also can be collected gas and be identified, but Shortcomings part is gone back in this invention: the one, and function is more single, can not identify agricultural product other samples in addition; But sensor exists randomness to sample collection method, affect test result; The 3rd, the system that do not propose is processed the data of sensor collection, to obtain the method for precise results.

Summary of the invention

The present invention solves existing human body self organ that leans on drink sample volatilization gas is detected, there is subjective differences, and harmful technical matters, a kind of lemon tea beverage attributional analysis device and method based on smart electronics nasus system is provided.

Above-mentioned technical matters of the present invention is mainly solved by following technical proposals: a kind of lemon tea beverage attributional analysis device based on smart electronics nasus system, comprise gas collection unit, gas production unit, processing unit and control module, described gas collection unit comprises air chamber, sample chamber, the first communicating pipe and the second communicating pipe, air chamber and sample chamber all have import and outlet, be connected to described the first communicating pipe between air chamber outlet and sample chamber entrance, be connected to described the second communicating pipe between air chamber entrance and sample chamber outlet, make air chamber and sample chamber form the circulation path of a hollow, in described gas compartment, be provided with admission gear, on the second communicating pipe, be provided with gas outlet, on gas outlet, be provided with the solenoid valve of giving vent to anger, described collecting unit is connected to air chamber, by gathering gas in air chamber, processing unit is connected with gas production unit, described admission gear, the solenoid valve of giving vent to anger, collecting unit is connected with control module respectively, by control module, control them and carry out work.In the present invention, gas collection unit carries out pre-service to the gas gathering, and gas is circulated and makes gas mixing more even, and according to detecting data, other are diluted, and the data of the gas detection gathering after processing are like this more accurate.This sample is arranged in sample chamber, and carrier gas has air chamber to pass into, and by the first communicating pipe and the second communicating pipe, carrier gas is circulated between air chamber and sample chamber, and the gas that drives sample to give out circulates together.Set out gas port emission gases, for gas collection unit is cleaned to use.Sample to sample gas in gas production unit, output signal is to processing unit, and processing unit carries out analyzing and processing to signal, analyzes the quality of sample.Control module is controlled the work of the executive components such as admission gear, the solenoid valve of giving vent to anger, collecting unit, has made the step of gas collection, gas production.

As a kind of preferred version, described sample chamber comprises drawing and pulling type pedestal, is provided with the cell body of placing sample on pedestal, on the top of pedestal, leaves cavity, and sample chamber outlet and entrance are arranged on the outlet of ，Qie sample chamber, top, sample chamber and are positioned at cell body top position.In this programme, sample chamber adopts drawer type, and this pedestal can pull, and sample is placed on pedestal, facilitates sample is put into sample chamber like this.On pedestal top, leave cavity, for gas communication use, gas is entered by entrance, in cavity, flows, and takes away the gas that sample distributes, and there is any discrepancy again flows out for gas.

As a kind of preferred version, on the second communicating pipe, be also provided with the first air pump, described the first air pump is connected on control module.The first air pump is for driving gas.

As a kind of preferred version, described admission gear comprises on air inlet, air inlet and is provided with the first solenoid valve, and the first solenoid valve is connected with control module.Whether the first solenoid control air inlet break-make, control carrier gas and pass into.Control module is controlled the first solenoid valve action.

As a kind of preferred version, described gas production unit comprises gas production suction pipe and the sensor array consisting of some sensors, each sensor setting is one independently in chamber, described gas production suction pipe one end is connected on air chamber, on gas production suction pipe port, be provided with the 5th solenoid valve, on gas production suction pipe, be provided with the second air pump, the gas production suction pipe other end is connected respectively on the chamber of each sensor, each sensor is connected on processing unit, on the chamber of each sensor, be also connected with detergent line, in detergent line, be provided with the 6th solenoid valve and the 3rd air pump.

Gas production unit is connected on air chamber, in air chamber, gathers gas, and control module, by controlling the 5th solenoid valve, can be controlled gas production unit and start or stop gas production.The gas gathering is entered into respectively in the chamber of each sensor by suction pipe, and sensor detects gas, and detection data are sent to processing unit.Detergent line is used for passing into pure air, has the 3rd air pump that pneumatic pump is as indoor in each sensor cavity, and sensor is cleaned.Described sensor has 8, is respectively sulfide gas sensor, hydrogen gas sensor, ammonia gas sensor, NOx sensor, charcoal hydrogen component gas sensor, ethanol sensor, benzene class sensor and alkanes sensor.

As a kind of preferred version, in described sample chamber, be provided with stirring air-out mechanism, stir air-out mechanism and comprise rotating shaft, rotating shaft is hollow, rotating shaft is communicated with sample chamber import, in rotating shaft, be connected with stirring pipe, the centre position of stirring pipe is provided with shaft seat, stirring pipe is arranged in rotating shaft by shaft seat, form a T-shaped structure, stirring pipe is hollow seal pipe, and stirring pipe is communicated with rotating shaft, in a side of the termination of stirring pipe, be provided with some the first pores, in another termination of stirring pipe and the opposing side of the first pore, be provided with some the second pores.This stirs air-out mechanism not in sample solution, the in the situation that of ventilation, can be rotated, and solution example is stirred, make sample mix even, and carrier gas is discharged by stirring in air-out mechanism, can evenly mix with sample volatilization gas, it is more accurate to make to detect.In this stirring pipe, the first pore of two ends and the second pore respectively in the opposite direction, can automatic traction stirring pipe be rotated around the shaft like this after gas passes into.

A lemon tea beverage Quality Analysis Methods for smart electronics nasus system, comprises the following steps:

Step 1: 15～25 ℃ of experimental situation temperature are set, and humidity is 45%-55%, and the sensor array of gas production unit is cleaned, is passed into pure air in the chamber of each sensor, and operation 8-12min, makes each sensor in original state;

Step 2: gas is carried out to pre-service, lemon tea sample to be measured is got to 20ml, pour in sample chamber, first gas collection unit is cleaned, after cleaning, by air intake opening, pass into carrier gas, the escaping gas that drives sample to produce by the air pump 20-30min that circulates with carrier gas in gas collection unit;

Step 3: by gas production suction pipe collected specimens gas, gas is drained in the chamber of each sensor, by control module, control each sensor the gas in chamber is detected, be 40-60s detection time, and each sensor sends to processing unit by the information detecting;

Step 4: processing unit is processed the response curve that obtains each sensor to information, and at 30 points of each response curve up-sampling, the data that each curve sampling is obtained are as input data I nput(t), utilize non-linear stochastic resonance model to calculate signal to noise ratio snr, this non-linear stochastic resonance model algorithm is as follows:

Stochastic resonance system comprises three factors: bistable system, and descriptive system feature carried out by power-actuated overdamping Brownian movement of cycle particle with one in input signal and external noise source in bistable state potential well,

V (x) is non-linear symmetric potential function, and ξ (t) is white Gaussian noise, and its auto-correlation connection function is: E[ξ (t) ξ (0)]=2D δ (t), a is input signal strength, f ₀be frequency modulating signal, D is noise intensity, and a, b are all real parameters,

$V\left(x\right)=\frac{1}{8}{\mathrm{ax}}^4-\frac{1}{4}{\mathrm{<figure-callout\; id="2"\; label="bx"\; filenames="HDA00003606742000011.png"\; state="\{\{state\}\}">bx</figure-callout>}}^2$

Therefore above formula can change into:

Obtaining signal to noise ratio (S/N ratio) is:

$\mathrm{SNR}=2\left[\underset{\mathrm{\&Delta;\&omega;}\&RightArrow;0}{\lim }\underset{\mathrm{\&Omega;}-\mathrm{\&Delta;\&omega;}}{\overset{\mathrm{\&Omega;}+\mathrm{\&Delta;\&omega;}}{\&Integral;}}S\left(\mathrm{\&omega;}\right)\mathrm{d\&omega;}\right]/S_N\left(\mathrm{\&Omega;}\right)$

S (ω) is signal spectral density, S _n(Ω) be the noise intensity in signal frequency range;

Get this signal to noise ratio (S/N ratio) peak of curve as signal to noise ratio (S/N ratio) eigenwert;

Step 5: bring input variable into a kind of Nonlinear state space model

In formula:

σ is input variable, signal to noise ratio (S/N ratio) eigenwert, ε be intermediate transfer parameter, τ be initial phase, for output variable, κ are that real parameter, η are that real parameter, Γ are the real parameter of correcting,

Then define residual error variable:

for the actual output of system, for Systems Theory output,

Defining classification master pattern again:

$\mathrm{\&Delta;}=\frac{1}{L}\underset{\mathrm{\&psi;}=N-L+1}{\overset{N}{\mathrm{\&Sigma;}}}{e}^T\left(\mathrm{\&psi;}\right)e\left(\mathrm{\&psi;}\right)$

In formula, L is data length, and just Δ is compared with each threshold value Thr in predefined threshold library, if had

can judge that sample is type under this threshold value, obtain this sample quality information, if

need to re-start type judgement.

As a kind of preferred version, each threshold value of described threshold library is for obtaining in advance, its process is: obtain in advance every class sample, then use analytical equipment to detect every class sample, detecting data input accidental resonance model, analyze, obtain signal to noise ratio (S/N ratio) eigenwert, more every class sample is taken multiple measurements, get the mean value of the signal to noise ratio (S/N ratio) eigenwert that such sample repeatedly obtains as the threshold value Thr of such sample of judgement, the threshold value of all kinds of samples has formed threshold library jointly.

As a kind of preferred version, before being calculated to to-noise ratio, sampled data in step 4 is first normalized, processing procedure is: the data that each sensor response curve is sampled are as one group of detection data, by every group of sampled value substitution formula y=log detecting in data ₁₀(x) calculate, x is the sampled value before normalized.

As a kind of preferred version, before being normalized, every group of detection data first carry out dealing of abnormal data, the steps include: every group of sampled value Input(t detecting in data), be designated as W here, meet normal distribution: W～N (μ, σ ²), μ is the average of sampled value W in every group of data, σ is the standard deviation of sampled value W in every group of data, through deriving, has:

P(|W-μ|＞3σ)≤2-2Φ(3)＝0.003

By the average μ of every group of data, standard deviation sigma and each sampled value W substitution formula | W-μ | > 3 σ, will meet formula | W-μ | the sampled value W of > 3 σ removes as abnormal data.

Therefore, advantage of the present invention is: 1. built electric nasus system, by system, sample gas detected, make testing result more comprehensively, also more objective, simultaneously also avoided sample gas to work the mischief to health; 2. the Electronic Nose that adopts polytype sensor to form, each sensor is all located at independently and in chamber, sample is detected, and has avoided a plurality of sensors to coexist one case and has formed phase mutual interference, has improved accuracy of detection, quick, reproducible; 3. while gathering gas, gas circulates, and gas is mixed more even, makes the data of detection more accurate.

Accompanying drawing explanation

Accompanying drawing 1 is a kind of structural representation of gas collection unit in the present invention;

Accompanying drawing 2 is another kind of structural representations of gas collection unit in the present invention;

Accompanying drawing 3 is a kind of structural representations of gas production unit in the present invention;

Accompanying drawing 4 is a kind of framework schematic diagram that control module of the present invention is connected with sensor, air pump;

Accompanying drawing 5 is a kind of structural representations that stir air-out mechanism in the present invention.

1-gas collection unit 2-air chamber 3-sample chamber 4-the first communicating pipe 5-the second communicating pipe 6-gas outlet 7-solenoid valve 8-air inlet 9-filtered air air intake opening 10-inert gas air intake opening 11-gas production suction pipe 12-first solenoid valve 16-the 5th solenoid valve 17-the 6th solenoid valve 18-first air pump 19-second air pump 20-pedestal 21-cell body 22-cavity 23-sensor 24-chamber 25-the 3rd air pump 26-processing unit 27-control module 28-gas production unit 29-rotating shaft 30-shaft seat 31-stirring pipe 32-first pore 33-the second pore of giving vent to anger

Embodiment

Below by embodiment, and by reference to the accompanying drawings, technical scheme of the present invention is described in further detail.

Embodiment:

A kind of lemon tea beverage attributional analysis device based on smart electronics nasus system of the present embodiment, as shown in Figure 1 and Figure 2, includes gas collection unit 1, gas production unit 28, processes the processing unit 26 that detects data and the control module 27 of controlling executable operations.

Gas collection unit 1 comprise air chamber 2, sample chamber 3, the first communicating pipe 4 and second communicating pipes 4 four part, this air chamber and sample chamber all have entrance and exit, be connected to this first communicating pipe between air chamber outlet and sample chamber entrance, be connected to sample chamber the second communicating pipe and export between gentle chamber inlet, this just forms a back-shaped circulation path structure.In this sample chamber, place sample, in the present embodiment, in sample chamber, pour lemon tea drink sample into, this sample chamber entrance is arranged on bottom, in order to prevent that sample from flowing backwards, on the pipeline that Ke Yu sample chamber entrance connects, U-shaped counterflow-preventing pipeline is set or retaining valve is set on pipeline, sample chamber outlet is arranged on top.On the first connecting pipeline, also have gas outlet 6, on venthole, be provided with and control the solenoid valve 7 of giving vent to anger opening and closing, on the first connecting pipeline, be also provided with the first air pump 18 that driving gas flows.On air chamber, be provided with the admission gear that passes into carrier gas, take and pass into atmospheric carrier air and comprise an air inlet 8 as Li，Ze Gai admission gear in the present embodiment, air inlet is communicated with on air chamber, is provided with and controls the first solenoid valve 12 opening and closing on air inlet.

In sample chamber 3, be provided with stirring air-out mechanism, stirring air-out mechanism is submerged in sample, as shown in Figure 6, stir air-out mechanism and comprise rotating shaft 29, rotating shaft is hollow, rotating shaft is communicated with sample chamber import, in rotating shaft, be connected with stirring pipe 31, the centre position of stirring pipe is provided with shaft seat, stirring pipe is arranged in rotating shaft by shaft seat, form a T-shaped structure, stirring pipe is hollow seal pipe, stirring pipe is communicated with rotating shaft, in a side of the termination of stirring pipe, be provided with some the first pores 32, in another termination of stirring pipe and the opposing side of the first pore, be provided with some the second pores 33.When passing into carrier gas, carrier gas has sample chamber entrance to enter rotating shaft, has rotating shaft to enter stirring pipe, then has respectively the first opposing pore of two ends and the second pore to discharge, and stirring pipe is rotated around the shaft.

Gas production unit 28 is connected with gas collection unit 1, the sensor array that this gas production unit includes gas production suction pipe 11 and consists of 8 sensors 23, this gas production suction pipe one end is connected on air chamber 2, and on this end of gas production suction pipe, is provided with the 5th solenoid valve and the second air-breathing air pump 19 of driving of controlling switching.Here 8 sensors are respectively sulfide gas sensor, hydrogen gas sensor, ammonia gas sensor, NOx sensor, charcoal hydrogen component gas sensor, ethanol sensor, benzene class sensor and alkanes sensor, each sensor is separately positioned on one independently in chamber 24, and the other end of this gas production suction pipe is connected to respectively in the separate chamber of each sensor.

On gas production unit, be also provided with wiper mechanism, for sensor is cleaned.This wiper mechanism comprises detergent line, and this detergent line is connected in each sensor separate chamber, is provided with and controls the 6th solenoid valve 17 of switching and the 3rd air pump 25 of driving gas in detergent line.

Processing unit 26 is processed the data that each sensor detects, and as shown in Figure 4, each sensor is all connected on processing unit.

As shown in Figure 5, each sensor of gas production unit is controlled being connected on control module also, and control module is controlled working sensor.In addition, the first solenoid valve, the 5th solenoid valve, the 6th solenoid valve, the first air pump, the second air pump, the 3rd air pump and all controlled being connected on control module of solenoid valve of giving vent to anger, control module is controlled their work, to complete gas production process.

As shown in Figure 2, give the another kind of structure in gas collection unit, gas collection unit adopts drawer-type structure here, this sample chamber comprise one can pull pedestal 20, on pedestal, be provided with the cell body 21 of placing sample, operating personnel can extract pedestal out, push pedestal after putting into sample again.At pedestal and top, sample chamber, leave and as the outlet of the cavity 22，Gai sample chamber of other circulations and entrance, be all arranged on the outlet of Shang，Qie sample chamber, top and be positioned at cell body top position.

The analytical approach of the lemon tea beverage attributional analysis device of the present embodiment based on smart electronics nasus system is as follows: comprises the following steps,

Step 1:

20 ℃ of experimental situation temperature are set, humidity is 50%, sensor array to gas production unit cleans, in the 5th closed electromagnetic valve situation, open exactly the 6th battery valve, by the 3rd air pump, clean air is passed in the chamber of each sensor, operation 10min, cleans each sensor and makes each sensor in original state.

Step 2:

At sampler chamber, put into lemon tea sample 20ml, then gas collection unit is also cleaned, only adopt a kind of carrier gas as air situation under, open the first solenoid valve of air inlet, the solenoid valve of giving vent to anger, so just make gas collection unit form a gas exhaust piping, pass into air, until gas is all discharged to be full of and is passed into carrier gas in original gas collection unit, then close the battery valve of giving vent to anger, make gas collection unit form back-shaped circulation path, the escaping gas that drives sample to produce by the first air pump 20min that circulates in gas collection unit with carrier gas.

Step 3:

Gas production unit starts gas production, now open the 5th solenoid valve of gas production suction pipe, sample gas is sucked to gas production suction pipe and be passed in the separate chamber of each sensor, control module is controlled each working sensor, gas in chamber is detected, be 50s detection time, and each sensor sends to processing unit by the data that detect.

Step 4: each sensor detects the value of meeting with a response, using time and response intensity as coordinate axis, obtain the response curve of each sensor, 8 sensors obtain 8 bar response curves, then equidistantly at 30 points of each response curve up-sampling, obtain 240 each point data as one group of input data I nput(t).

Sampled data is carried out to dealing of abnormal data, and the data that each sensor response curve is sampled detect data, every group of sampled value Input(t detecting in data as one group), be designated as W here, meet normal distribution: W～N (μ, σ ²), μ is the average of sampled value W in every group of data, σ is the standard deviation of sampled value W in every group of data, through deriving, has:

P(|W-μ|＞3σ)≤2-2Φ(3)＝0.003

By the average μ of every group of data, standard deviation sigma and each sampled value W substitution formula | W-μ | > 3 σ, will meet formula | W-μ | the sampled value W of > 3 σ removes as abnormal data.

Sampled data is first normalized, by every group of sampled value substitution formula y=log detecting in data ₁₀(x) calculate, x is the sampled value before normalized.

Step 5: sampled data W substitution non-linear stochastic resonance model is calculated to signal to noise ratio snr, and this non-linear stochastic resonance model algorithm is:

Stochastic resonance system comprises three factors: bistable system, and descriptive system feature carried out by power-actuated overdamping Brownian movement of cycle particle with one in input signal and external noise source in bistable state potential well,

V (x) is non-linear symmetric potential function, and ξ (t) is white Gaussian noise, and its auto-correlation connection function is: E[ξ (t) ξ (0)]=2D δ (t), a is input signal strength, f ₀be frequency modulating signal, D is noise intensity, and a, b are all real parameters,

$V\left(x\right)=\frac{1}{8}{\mathrm{ax}}^4-\frac{1}{4}{\mathrm{<figure-callout\; id="2"\; label="bx"\; filenames="HDA00003606742000011.png"\; state="\{\{state\}\}">bx</figure-callout>}}^2$

Therefore above formula can change into:

Obtaining signal to noise ratio (S/N ratio) is:

$\mathrm{SNR}=2\left[\underset{\mathrm{\&Delta;\&omega;}\&RightArrow;0}{\lim }\underset{\mathrm{\&Omega;}-\mathrm{\&Delta;\&omega;}}{\overset{\mathrm{\&Omega;}+\mathrm{\&Delta;\&omega;}}{\&Integral;}}S\left(\mathrm{\&omega;}\right)\mathrm{d\&omega;}\right]/S_N\left(\mathrm{\&Omega;}\right)$

S (ω) is signal spectral density, S _n(Ω) be the noise intensity in signal frequency range;

Input data I nput(t) be updated to in formulacalculate signal to noise ratio snr, this signal to noise ratio snr is curve, gets this signal to noise ratio (S/N ratio) peak of curve as signal to noise ratio (S/N ratio) eigenwert.

Step 5:

By signal to noise ratio (S/N ratio) eigenwert, be that input variable is brought a kind of Nonlinear state space model into

In formula:

σ is input variable, signal to noise ratio (S/N ratio) eigenwert, ε be intermediate transfer parameter, τ be initial phase,

for output variable, κ are that real parameter, η are that real parameter, Γ are the real parameter of correcting,

Then define residual error variable:

for the actual output of system, for Systems Theory output,

Defining classification master pattern again:

$\mathrm{\&Delta;}=\frac{1}{L}\underset{\mathrm{\&psi;}=N-L+1}{\overset{N}{\mathrm{\&Sigma;}}}{e}^T\left(\mathrm{\&psi;}\right)e\left(\mathrm{\&psi;}\right)$

In formula, L is data length, and just Δ is compared with each threshold value Thr in predefined threshold library, if had can judge that sample is type under this threshold value, obtain this sample quality information, if

need to re-start type judgement.

Wherein above-mentioned each threshold value of the threshold library of mentioning Thr is for obtaining in advance, its process is: obtain in advance every class sample, as first day to the lemon tea sample of eight days, then use analytical equipment to detect every class sample, detecting data input accidental resonance model, analyze, obtain signal to noise ratio (S/N ratio) eigenwert, every class sample is taken multiple measurements, for example the lemon tea sample of first day is carried out 10 times, obtain 10 signal to noise ratio (S/N ratio) eigenwerts, then measurement is got to the mean value of the signal to noise ratio (S/N ratio) eigenwert that such sample repeatedly obtains as the threshold value Thr of such sample of judgement, obtain the threshold value of all kinds of samples, the threshold value of all kinds of samples has formed threshold library jointly.

Specific embodiment described herein is only to the explanation for example of the present invention's spirit.Those skilled in the art can make various modifications or supplement or adopt similar mode to substitute described specific embodiment, but can't depart from spirit of the present invention or surmount the defined scope of appended claims.

Although more used the terms such as gas collection unit, air chamber, sample chamber, the first communicating pipe, the second communicating pipe herein, do not got rid of the possibility of using other term.Use these terms to be only used to describe more easily and explain essence of the present invention; They are construed to any additional restriction is all contrary with spirit of the present invention.

Claims (10)

1. the lemon tea beverage attributional analysis device based on smart electronics nasus system, it is characterized in that: comprise gas collection unit (1), gas production unit (28), processing unit (26) and control module (27), described gas collection unit comprises air chamber (2), sample chamber (3), the first communicating pipe (4) and the second communicating pipe (5), air chamber and sample chamber all have import and outlet, be connected to described the first communicating pipe between air chamber outlet and sample chamber entrance, be connected to described the second communicating pipe between air chamber entrance and sample chamber outlet, make air chamber and sample chamber form the circulation path of a hollow, in described gas compartment, be provided with admission gear, on the second communicating pipe, be provided with gas outlet (6), on gas outlet, be provided with the solenoid valve of giving vent to anger (7), described gas production unit is connected to air chamber, by gathering gas in air chamber, processing unit is connected with gas production unit, described admission gear, the solenoid valve of giving vent to anger, collecting unit is connected with control module respectively, by control module, control them and carry out work.

2. a kind of lemon tea beverage attributional analysis device based on smart electronics nasus system according to claim 1, it is characterized in that described sample chamber (3) comprises drawing and pulling type pedestal (20), on pedestal, be provided with the cell body (21) of placing sample, on the top of pedestal, leave cavity (22), sample chamber outlet and entrance are arranged on the outlet of ，Qie sample chamber, top, sample chamber and are positioned at cell body top position.

3. a kind of lemon tea beverage attributional analysis device based on smart electronics nasus system according to claim 1 and 2, is characterized in that being also provided with the first air pump (18) on the second communicating pipe, and described the first air pump is connected on control module (27).

4. a kind of lemon tea beverage attributional analysis device based on smart electronics nasus system according to claim 3, it is characterized in that described admission gear comprises on air inlet (8), air inlet is provided with the first solenoid valve (12), and the first solenoid valve (12) is connected with control module (27).

5. a kind of lemon tea beverage attributional analysis device based on smart electronics nasus system according to claim 4, it is characterized in that described gas production unit (28) comprises gas production suction pipe (11) and the sensor array consisting of some sensors (23), each sensor setting is one independently in chamber (24), described gas production suction pipe one end is connected on air chamber, on gas production suction pipe port, be provided with the 5th solenoid valve, on gas production suction pipe, be provided with the second air pump (19), the gas production suction pipe other end is connected respectively on the chamber of each sensor, each sensor is connected on processing unit (26), on the chamber of each sensor, be also connected with detergent line, in detergent line, be provided with the 6th solenoid valve (17) and the 3rd air pump (25), described sensor (23) has 8, be respectively sulfide gas sensor, hydrogen gas sensor, ammonia gas sensor, NOx sensor, charcoal hydrogen component gas sensor, ethanol sensor, benzene class sensor and alkanes sensor.

6. a kind of lemon tea beverage attributional analysis device based on smart electronics nasus system according to claim 1, it is characterized in that being provided with stirring air-out mechanism in described sample chamber (3), stir air-out mechanism and comprise rotating shaft (29), rotating shaft is hollow, rotating shaft is communicated with sample chamber import, in rotating shaft, be connected with stirring pipe (31), the centre position of stirring pipe is provided with shaft seat, stirring pipe is arranged in rotating shaft by shaft seat, form a T-shaped structure, stirring pipe is hollow seal pipe, stirring pipe is communicated with rotating shaft, in a side of the termination of stirring pipe, be provided with some the first pores (32), in another termination of stirring pipe and the opposing side of the first pore, be provided with some the second pores (33).

7. the lemon tea beverage Quality Analysis Methods based on smart electronics nasus system, the device that adopts claim 1-6 any one to describe, is characterized in that: comprise the following steps:

Step 1: 15～25 ℃ of experimental situation temperature are set, and humidity is 45%-55%, and the sensor array of gas production unit is cleaned, is passed into pure air in the chamber of each sensor, and operation 8-12min, makes each sensor in original state;

Step 2: gas is carried out to pre-service, lemon tea sample to be measured is got to 20ml, pour in sample chamber, first gas collection unit is cleaned, after cleaning, by air intake opening, pass into carrier gas, the escaping gas that drives sample to produce by the air pump 20-30min that circulates with carrier gas in gas collection unit;

Step 3: by gas production unit collected specimens gas, gas is drained in the chamber of each sensor in gas production unit, by control module, control each sensor the gas in chamber is detected, be 40-60s detection time, and each sensor sends to processing unit by the information detecting;

Step 4: processing unit is processed the response curve that obtains each sensor to information, and at 30 points of each response curve up-sampling, the data that each curve sampling is obtained are as input data I nput(t), substitution non-linear stochastic resonance model calculates signal to noise ratio snr, and this non-linear stochastic resonance model algorithm is as follows:

Stochastic resonance system comprises three factors: bistable system, and descriptive system feature carried out by power-actuated overdamping Brownian movement of cycle particle with one in input signal and external noise source in bistable state potential well,

V (x) is non-linear symmetric potential function, and ξ (t) is white Gaussian noise, and its auto-correlation connection function is: E[ξ (t) ξ (0)]=2D δ (t), a is input signal strength, f ₀be frequency modulating signal, D is noise intensity, and a, b are all real parameters,

$V\left(x\right)=\frac{1}{8}{\mathrm{ax}}^4-\frac{1}{4}{\mathrm{bx}}^2$

Therefore above formula can change into:

Obtaining signal to noise ratio (S/N ratio) is:

$\mathrm{SNR}=2\left[\underset{\mathrm{\&Delta;\&omega;}\&RightArrow;0}{\lim }\underset{\mathrm{\&Omega;}-\mathrm{\&Delta;\&omega;}}{\overset{\mathrm{\&Omega;}+\mathrm{\&Delta;\&omega;}}{\&Integral;}}S\left(\mathrm{\&omega;}\right)\mathrm{d\&omega;}\right]/S_N\left(\mathrm{\&Omega;}\right)$

S (ω) is signal spectral density, S _n(Ω) be the noise intensity in signal frequency range;

Get this signal to noise ratio (S/N ratio) peak of curve as signal to noise ratio (S/N ratio) eigenwert;

Step 5: bring input variable into a kind of Nonlinear state space model

In formula:

σ is input variable, signal to noise ratio (S/N ratio) eigenwert, ε be intermediate transfer parameter, τ be initial phase,

for output variable, κ are that real parameter, η are that real parameter, Γ are the real parameter of correcting,

Then define residual error variable:

for the actual output of system,

for Systems Theory output,

Defining classification master pattern again:

$\mathrm{\&Delta;}=\frac{1}{L}\underset{\mathrm{\&psi;}=N-L+1}{\overset{N}{\mathrm{\&Sigma;}}}{e}^T\left(\mathrm{\&psi;}\right)e\left(\mathrm{\&psi;}\right)$

In formula, L is data length, and just Δ is compared with each threshold value Thr in predefined threshold library, if had

can judge that sample is type under this threshold value, obtain this sample quality information, if need to re-start type judgement.

8. a kind of lemon tea beverage Quality Analysis Methods based on smart electronics nasus system according to claim 7, it is characterized in that each threshold value of described threshold library is for obtaining in advance, its process is: obtain in advance every class sample, then use analytical equipment to detect every class sample, detecting data input accidental resonance model, analyze, obtain signal to noise ratio (S/N ratio) eigenwert, again every class sample is taken multiple measurements, get the mean value of the signal to noise ratio (S/N ratio) eigenwert that such sample repeatedly obtains as the threshold value Thr of such sample of judgement, the threshold value of all kinds of samples has formed threshold library jointly.

9. according to a kind of lemon tea beverage Quality Analysis Methods based on smart electronics nasus system described in claim 7 or 8, before it is characterized in that the sampled data in step 4 is calculated to to-noise ratio, be first normalized, processing procedure is: the data that each sensor response curve is sampled are as one group of detection data, by every group of sampled value substitution formula y=log detecting in data ₁₀(x) calculate, x is the sampled value before normalized.

10. a kind of lemon tea beverage Quality Analysis Methods based on smart electronics nasus system according to claim 9, before it is characterized in that every group of detection data to be normalized, first carry out dealing of abnormal data, the steps include: every group of sampled value Input(t detecting in data), here be designated as W, meet normal distribution: W～N (μ, σ ²), μ is the average of sampled value W in every group of data, σ is the standard deviation of sampled value W in every group of data, through deriving, has:

P(|W-μ|＞3σ)≤2-2Φ(3)＝0.003

By the average μ of every group of data, standard deviation sigma and each sampled value W substitution formula | W-μ | > 3 σ, will meet formula | W-μ | the sampled value W of > 3 σ removes as abnormal data.

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