CN103412004B - Method for detecting storage time of citrus sinensis - Google Patents

Method for detecting storage time of citrus sinensis Download PDF

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Publication number
CN103412004B
CN103412004B CN201310369873.5A CN201310369873A CN103412004B CN 103412004 B CN103412004 B CN 103412004B CN 201310369873 A CN201310369873 A CN 201310369873A CN 103412004 B CN103412004 B CN 103412004B
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sweet orange
time
signal
storage time
gas
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CN103412004A (en
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惠国华
郑海霞
王敏敏
陈静
周于人
李晨迪
姜燕
沈凤
王绿野
尹芳缘
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Zhejiang Gongshang University
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Zhejiang Gongshang University
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Abstract

The invention discloses a method for detecting the storage time of citrus sinensis. According to the method, response signals of citrus sinensis samples with different storage time are measured by using a gas sensor and combining an acoustic surface wave; a first prediction model and a second prediction model of citrus sinensis storage time are established according to gas sensor array response data and acoustic surface wave detection frequency; a citrus sinensis storage time comprehensive prediction model is established. The method has the advantages of quickness, no loss and high accuracy; storage and picking time of the citrus sinensis can be arranged reasonably according to storage time; deterioration of the citrus sinensis in transportation and storage processes can be effectively avoided.

Description

The detection method in a kind of sweet orange storage time
Technical field
The present invention relates to fruit freshness detection field, especially relate to a kind of for the storage time that is quick, Non-Destructive Testing sweet orange, and detect the detection method in degree of accuracy high sweet orange storage time.
Background technology
Sweet orange (Citrus Sinensis) is the fruit of Rutaceae citrus plant orange tree, is also called yellow fruit, and fruit is circular to Long Circle, orange-yellow, and oil vacuole is protruding, and pericarp is not easily peeled off.Without bitter taste.Newel enriches, and juice taste is sweet and fragrant, and containing a large amount of sugar and a certain amount of citric acid and abundant vitamin C, nutritive value is higher.
Because the skin of sweet orange is thinner, be subject in storage and transport process environmental factor change impact and produce quality decline even rotten.
Though artificial sense evaluation method can distinguish the slight change of sweet orange in storage process, the result of the method causes repeatability and referential all poor because evaluating the factor impacts such as personnel's individual difference, health status.
Though physical and chemical inspection method can react the freshness situation of fruit sample, but experimental implementation is loaded down with trivial details, required time of a specified duration, be difficult to the needs meeting Fast nondestructive evaluation.
The instrumental methods such as vapor-phase chromatography (GC) and Gas chromatographyMass spectrometry (GC-MS), though the volatilization gas of the different storage life sweet orange sample of Accurate Analysis grading information can be become, but this detection method testing cost is expensive, sense cycle is long, and gained odour component is all the product of sample after being separated.In addition, instrumental analysis has larger dependence to the skilled operation degree of operating personnel usually.
Therefore, in the urgent need to a kind of can the novel detection technique in judgement orange storage time of quick nondestructive.
Chinese patent mandate publication number: CN103163217A, authorize publication date on June 19th, 2013, disclose a kind of SAW (Surface Acoustic Wave) resonator Series detectors system and detection method thereof, described detection system comprises Resonance detector device, direct supply, digital counter, digital counter is connected with computing machine by RS232 communication interface, Resonance detector device is arranged in shielded box, and this Resonance detector utensil has two probes, for placing manaca to be detected between two probes; Described detection system is used to comprise the following steps the method that storage life of Annona squamosa L detects: step one: to place manaca to be measured between two probes of detection system; Step 2: measure manaca to be measured, is gathered the load frequency of Resonance detector device by digital counter; Step 3: the load frequency data being read digital counter collection by computing machine, by calculating cherimoya storage time.Weak point is, this invention has the deficiency of the poor accuracy of detection.
Summary of the invention
The present invention is the deficiency in order to overcome the poor accuracy that detection method of the prior art detects, and provides a kind of for the storage time that is quick, Non-Destructive Testing sweet orange, and detects the detection method in degree of accuracy high sweet orange storage time.
To achieve these goals, the present invention is by the following technical solutions:
The detection method in sweet orange storage time, comprises the steps:
(1-1) select n the sweet orange sample just plucked, sweet orange sample be placed in congealer and store, sweet orange sample is detected:
(1-1-1) setting detection time is m 1, the sequence number in storage time is Time 1, Time 1initial value be 1; Use acoustic surface wave detection device to detect the frequency response of sweet orange, and obtain sweet orange storage time first predictor formula:
Described gas-detecting device comprises air chamber, be located at sensor array in air chamber and analog to digital converter; Described air chamber is provided with sampling probe and cleaning probe, and sampling probe is provided with sampling air pump, and cleaning probe is provided with cleaning air pump; Sensor array is electrically connected with analog to digital converter, analog to digital converter, sampling air pump and cleaning air pump is equipped with the data-interface for computing machine electrical connection; Described sensor array comprises several gas sensors;
Step a, optional 1 sweet orange sample in n sweet orange sample, and sweet orange sample is put into shielded box, two of SAW (Surface Acoustic Wave) resonator electrodes are contacted with the opposite flank of sweet orange sample respectively;
Step b, acoustic surface wave detection device work is after 40 to 60 minutes, and counter gathers the frequency response curve of oscillation circuit, and frequency response curve gathers 80 to 110 frequency values, and each frequency values is formed frequency signal Input (t);
The first stochastic resonance system model is preset with in computing machine wherein, ξ (t) is white Gaussian noise, and A, a and b are all constants, f 0be frequency modulating signal, D is noise intensity, and t is the Brownian movement Particles Moving time, and x is the coordinate of Particles Moving;
Frequency signal Input (t) is inputted in the first stochastic resonance system model, the first stochastic resonance system model is resonated;
Computing machine utilizes formula SNR = 2 [ lim Δω → 0 ∫ Ω - Δω Ω + Δω S ( ω ) dω ] / S N ( Ω ) Calculate output signal-to-noise ratio SNR; 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;
Computing machine draws the output signal-to-noise ratio curve in the first stochastic resonance system model, obtains two peak values of signal to noise ratio (S/N ratio) curve, using the absolute value of the difference of two peak values as signal to noise ratio (S/N ratio) eigenwert F; And signal to noise ratio (S/N ratio) eigenwert F is stored in a computer;
Step c, works as Time 1< m 1, make Time 1value increase by 1, puts back to detected sweet orange sample in congealer and stores, and after 24 hours, repeats step a and step b;
Obtain m 1individual and Time 1the F be associated, by F and Time iform point (F, Time i), according to m 1individual point (F, Time 1) carry out linear fit, obtain sweet orange storage time first predictor formula: Time 1=f (F);
Sweet orange storage time first predictor formula is relevant with the sweet orange storage time, and first predictor formula in different storage time is different;
(1-1-2) setting detection time is m 2, the sequence number in storage time is Time 2, Time 2initial value be 1; Use gas-detecting device to detect the smell of sweet orange volatilization, and obtain sweet orange storage time second predictor formula:
Described gas-detecting device comprises air chamber, be located at sensor array in air chamber and analog to digital converter; Described air chamber is provided with sampling probe and cleaning probe, and sampling probe is provided with sampling air pump, and cleaning probe is provided with cleaning air pump; Sensor array is electrically connected with analog to digital converter, analog to digital converter, sampling air pump and cleaning air pump is equipped with the data-interface for computing machine electrical connection; Described sensor array comprises several gas sensors;
Steps d, computing machine controls cleaning gas pump work, and pure air passes in air chamber and cleans each gas sensor, when the response of each gas sensor is stablized to baseline, closes cleaning air pump;
Step e, in n sweet orange sample, optional 1 sweet orange sample, inserted in sample bottle by sweet orange sample, by sample bottle sealing and standing 30 to 40 minutes, sampling probe and air pressure balancer are injected in sample bottle, computing machine controls the escaping gas that sampling probe collected specimens produces simultaneously; While collection escaping gas, the air through activated carbon filtration imports in sample bottle by air pressure balancer, realizes air pressure balance;
Step f, escaping gas and each sensor contacts, each sensor produces analog response signal respectively; Each analog response signal is converted to digital response signal by analog to digital converter respectively, and computing machine is averaged digital response signal, obtains sensor array digital response signal I (t);
The second accidental resonance model is preset with in computing machine wherein, ξ (t) is white Gaussian noise, and t is the Brownian movement Particles Moving time, and x is the coordinate of Particles Moving, f 0be frequency modulating signal, a, b and A are constant;
Step g, inputs I (t) in the second accidental resonance model, makes the second stochastic resonance system model produce accidental resonance;
Computing machine utilizes formula SNR = 2 [ lim &Delta;&omega; &RightArrow; 0 &Integral; &Omega; - &Delta;&omega; &Omega; + &Delta;&omega; S ( &omega; ) d&omega; ] / S N ( &Omega; ) Calculate output signal-to-noise ratio SNR; 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 h, computing machine draws the output signal-to-noise ratio curve of the second stochastic resonance system model, and the signal to noise ratio (S/N ratio) maximal value choosing signal to noise ratio (S/N ratio) curve in signal to noise ratio (S/N ratio) curve is eigenwert SNR max, and by SNR maxbe stored in computing machine;
Step I, works as Time 2< m 2, make Time 2value increase by 1, puts back to detected sweet orange sample in congealer and stores, and after 24 hours, repeats steps d to step h;
Obtain m 2individual and Time 2the SNR be associated max, by SNR maxand Time 2form point (SNR max, Time 2), according to m 2individual point (SNR max, Time 2) carry out Nonlinear Boltzmann matching, obtain sweet orange storage time second predictor formula Time 2=f (SNR max);
Sweet orange storage time second predictor formula is relevant with the sweet orange storage time, and second predictor formula in different storage time is different;
(1-2) sweet orange storage time Comprehensive Model is set up: Time (F, SNR max)=P 1× f (F)+P 2× f (SNR max), P 1and P 2for undetermined coefficient; By two groups of sweet orange storage times, the signal to noise ratio (S/N ratio) eigenwert F corresponding with the described storage time and the SNR corresponding with the described storage time maxsubstitute into sweet orange storage time Comprehensive Model, thus obtain P 1and P 2;
(1-3) detect and the sweet orange sample W to be detected under described sweet orange sample same stored condition, obtain the signal to noise ratio (S/N ratio) maximal value SNR of sweet orange sample W maxwith signal to noise ratio (S/N ratio) eigenwert F; By SNR maxsubstitute in sweet orange storage time Comprehensive Model with signal to noise ratio (S/N ratio) eigenwert F, calculate the storage time Time of sweet orange sample W prediction.
Surface acoustic wave (SAW) technology is born in the seventies in last century, and the method is that manufactured size is little, the checkout equipment of high sensitivity, high integration provides the foundation.At present, existing many detection application reports based on SAW (Surface Acoustic Wave) device.Also be widely used at food and field of biological detection, as the detection, bacterial growth condition monitoring, pancreatic lipases detection, bio-medical analysis etc. of bacterium in milk.For surface acoustic wave sensing detection technology, the chemical/biological that the focus studied for a long period of time concentrates on SAW (Surface Acoustic Wave) resonator surface is modified, in testing process if there is specific reaction (as antigen antagonist, the specific effect processes such as receptor for ligand), when now surface acoustic wave is by resonator piezoelectric substrate, there is respective change in ripple information (velocity of wave etc.), thus realizes measured object kind/concentration information sign.
Gas sensor detection technique is a kind of smell fingerprint detection method, the process of the olfactory system of simulation people.Take gas as analytic target, caught and detect the aromatic substance of ad-hoc location by the olfactory system of simulating people in real time, thus obtain characteristic signal, therefore this smell finger print detection device is called Electronic Nose visually.Due to the function of its uniqueness, be widely applied in food, cosmetics, petrochemical complex, wrappage, environmental monitoring, the field such as clinical, chemical, receive the concern that each side is increasing.
Accidental resonance technology is shown up prominently in detection data feature values field of extracting at present.This theory is proposed in 1981 by Italian physicist Benzi, in order to explain the phenomenon that the earth meteorological glacial epoch in time immemorial and cycle warm climate phase alternately occur.Accidental resonance has three elements: nonlinear system, weak signal and noise source.Consider from signal transacting angle, accidental resonance is in non-linear signal transmission process, by regulating intensity or other parameter of system of noise, system being exported and reaches optimum value, in fact also can think the collaborative state of input signal, nonlinear system, noise.
Generally, under the excitation of excitation noise, system produces accidental resonance, and now output signal is greater than input signal, thus serves the effect that signal amplifies.Meanwhile, the noise energy in partial detection signals is transformed in signal and goes by accidental resonance, thus effectively inhibits the noisiness in detection signal.Therefore, stochastic resonance system is equivalent to improve the effect improving output signal-noise ratio, and signal, excitation noise and bistable system can regard an efficient signal processor as.On above technical foundation, stochastic resonance system output signal-to-noise ratio analytical technology can react the essential characteristic information of sample preferably.
Grasp noise in comprising in frequency signal Input (t) that surface acoustic wave detects, analyzed by accidental resonance, grasp noise in erasure signal, make testing result more stable, error is less, and accuracy is higher.
After accidental resonance is analyzed, between the sweet orange sample in different storage time, discrimination is better, and detection model linear fit precision is higher.
The reflection of accidental resonance output signal-to-noise ratio characteristic information be the essential information of sample, this characteristic information does not change with the restriction of detection method or multiplicity, only relevant with the character of sample, is conducive to the demarcation of properties of samples, improves accuracy of detection.
Accidental resonance analytical approach favorable reproducibility, repeat 100 times and calculate, the resultant error ratio of output is no more than 0.1%.And the frequency signal error rate that simple surface acoustic wave detects exceeds several times than the error ratio after accidental resonance Analysis signal-to-noise ratio (SNR).
The present invention adopts gas sensor and measures the response signal of different storage time sweet orange sample in conjunction with surface acoustic wave, detect frequency according to gas sensor array response data and surface acoustic wave and build first, second forecast model of sweet orange storage time, and set up sweet orange storage time Comprehensive Model, and the storage time of sweet orange storage time Comprehensive Model to sweet orange sample W is utilized to detect.
What surface acoustic wave detected reflection is fruit internal structural information, and gas detect reflection is the odiferous information of fruit outside, and this combination can situation of change in accurate characterization sweet orange storage process.The method is conducive to instructing such fruit timely collecting, the decline that the fruits nutrition as far as possible avoiding harvest in advance to cause is worth, and also can reducing gathers evening causes the corrupt loss brought.
Further, method of the present invention has fast, can't harm, advantage that accuracy is good, can carry out reasonable arrangement according to the storage time to the storage of sweet orange, plucking time, effectively prevents the putrid and deteriorated of in transport and storage process sweet orange.
As preferably, the centre frequency of described SAW (Surface Acoustic Wave) resonator is 433.92MHz.
As preferably, described n is 6 to 15.
As preferably, described m 1and m 2be 10 to 180.
As preferably, described gas sensor is 8, be respectively the first sensor for detecting sulfide gas, for detecting the second sensor of inflammable gas, for detecting the 3rd sensor of Ammonia gas, for detecting the four-sensor of ethanol class gas, for detecting the 5th sensor of hydrocarbon component gas, for detecting the 6th sensor of alkanes gas, for detecting the 7th sensor of propane and butane, for detecting the 8th sensor of nitride gas.
As preferably, described SAW (Surface Acoustic Wave) resonator comprises piezoelectric substrate, interdigital transducer, 2 reflecting gratings and is located at 4 gain grid of reflecting grating both sides; Described electrode is electrically connected with the link be located on interdigital transducer.
Arranging of gain grid can prevent surface acoustic wave from overflowing from the blank position of reflecting grating, and surface acoustic wave can be made to be reflected back interdigital transducer through reflecting grating, strengthens the intensity of surface acoustic wave.The width of gain grid is identical with reflecting grating, is 9 μm with the distance of reflecting grating.
As preferably, in described steps d, pure air passes in air chamber each gas sensor cleaning 40 to 50 minutes.
As preferably, in described steps d, pure air cleans sensor with the flow velocity of 860mL/min to 1100mL/min.
Therefore, the present invention has following beneficial effect: (1) fast, harmless, accuracy is good; (2) sweet orange putrid and deteriorated in transport and storage process is effectively prevented.
Accompanying drawing explanation
Fig. 1 is a kind of process flow diagram of embodiments of the invention;
Fig. 2 is a kind of theory diagram of acoustic surface wave detection device of the present invention;
Fig. 3 is a kind of theory diagram of gas-detecting device of the present invention;
Fig. 4 is a kind of structural representation of SAW (Surface Acoustic Wave) resonator of the present invention;
Fig. 5 is the output signal-to-noise ratio curve in the first stochastic resonance system model of the present invention;
Fig. 6 is the matched curve of signal to noise ratio (S/N ratio) eigenwert F of the present invention;
Fig. 7 is signal to noise ratio (S/N ratio) eigenwert SNR of the present invention maxmatched curve.
In figure: counter 1, shielded box 2, oscillator 3, SAW (Surface Acoustic Wave) resonator 4, piezoelectric substrate 5, interdigital transducer 6, reflecting grating 7, gain grid 8, computing machine 9, air chamber 10, sensor array 11, analog to digital converter 12, sampling probe 13, cleaning probe 14, sampling air pump 15, cleaning air pump 16, sweet orange 17, link 18, wireless launcher 19.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention will be further described.
Embodiment is as shown in Figure 1 the detection method in a kind of sweet orange storage time, comprises the steps:
Step 100, the sweet orange sample of the firm harvesting selecting 15 sizes identical, be placed in congealer by sweet orange sample and store, the refrigerated storage temperature of congealer is 4 DEG C, detects as follows sweet orange sample:
Step 101, setting detection time is m 1=11, the sequence number in storage time is Time 1, Time 1initial value be 1; Use acoustic surface wave detection device to detect the frequency response of sweet orange, and obtain sweet orange storage time first predictor formula:
As shown in Figure 2, acoustic surface wave detection device comprises counter 1, shielded box 2, is located at the oscillator 3 in shielded box and SAW (Surface Acoustic Wave) resonator 4; Oscillator and SAW (Surface Acoustic Wave) resonator form oscillation circuit, and counter is electrically connected with oscillation circuit, and counter is provided with for the wireless launcher 19 with computing machine 9 wireless connections, and SAW (Surface Acoustic Wave) resonator is provided with two electrodes;
As shown in Figure 4, SAW (Surface Acoustic Wave) resonator comprises piezoelectric substrate 5, interdigital transducer 6,2 reflecting gratings 7 and is located at 4 gain grid 8 of reflecting grating both sides; Electrode is electrically connected with the link 18 be located on interdigital transducer.
Step a, optional 1 sweet orange sample in 15 sweet orange samples, and sweet orange sample is put into shielded box, two of SAW (Surface Acoustic Wave) resonator electrodes are contacted with the opposite flank of sweet orange 17 sample respectively;
Step b, acoustic surface wave detection device work is after 40 minutes, and counter gathers the frequency response curve of oscillation circuit, and frequency response curve gathers 100 frequency values be spacedly distributed, and each frequency values is formed frequency signal Input (t);
The first stochastic resonance system model is preset with in computing machine wherein, ξ (t) is white Gaussian noise, and A, a and b are all constants, f 0be frequency modulating signal, D is noise intensity, and t is the Brownian movement Particles Moving time, and x is the coordinate of Particles Moving;
Frequency signal Input (t) is inputted in the first stochastic resonance system model, the first stochastic resonance system model is resonated;
Computing machine utilizes formula SNR = 2 [ lim &Delta;&omega; &RightArrow; 0 &Integral; &Omega; - &Delta;&omega; &Omega; + &Delta;&omega; S ( &omega; ) d&omega; ] / S N ( &Omega; ) Calculate output signal-to-noise ratio SNR; 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;
Computing machine draws the output signal-to-noise ratio curve in the first stochastic resonance system model, obtains two peak values of signal to noise ratio (S/N ratio) curve, using the absolute value of the difference of two peak values as signal to noise ratio (S/N ratio) eigenwert F; And signal to noise ratio (S/N ratio) eigenwert F is stored in a computer;
Such as, in output signal-to-noise ratio curve as shown in Figure 5, two peak values are respectively-70dB and-94dB, then signal to noise ratio (S/N ratio) eigenwert F=|-94+70|=24dB.
Step c, works as Time 1< 11, makes Time 1value increase by 1, puts back to detected sweet orange sample in congealer and stores, and after 24 hours, repeats step a and step b;
Obtain 11 and Time 1the F be associated, by F and Time 1form point (F, Time 1), according to m 1individual point (F, Time 1) carry out nonlinear fitting, obtain the signal to noise ratio (S/N ratio) eigenwert matched curve shown in Fig. 6 F = 4.00024 + 3.70916 &times; e Time 1 / 3.89102 , And obtain sweet orange storage time first predictor formula: Time 1 = 3.89102 ln F - 4.00024 3.70916 ;
Step 102, setting detection time is m 2=11, the sequence number in storage time is Time 2, Time 2initial value be 1; Use gas-detecting device to detect the smell of sweet orange volatilization, and obtain sweet orange storage time second predictor formula:
As shown in Figure 3, gas-detecting device comprises air chamber 10, is located at sensor array 11 in air chamber and analog to digital converter 12; Air chamber is provided with sampling probe 13 and cleaning probe 14, and sampling probe is provided with sampling air pump 15, and cleaning probe is provided with cleaning air pump 16; Sensor array is electrically connected with analog to digital converter, analog to digital converter, sampling air pump and cleaning air pump is equipped with the data-interface for computing machine electrical connection; Sensor array comprises 8 gas sensors.
8 gas sensors are respectively the first sensor for detecting sulfide gas, for detecting the second sensor of inflammable gas, for detecting the 3rd sensor of Ammonia gas, for detecting the four-sensor of ethanol class gas, for detecting the 5th sensor of hydrocarbon component gas, for detecting the 6th sensor of alkanes gas, for detecting the 7th sensor of propane and butane, for detecting the 8th sensor of nitride gas.
Steps d, computing machine controls cleaning gas pump work, and pure air passes in air chamber and carries out cleaning 40 minutes to each gas sensor, when the response of each gas sensor is stablized to baseline, closes cleaning air pump;
Step e, in 15 sweet orange samples, optional 1 sweet orange sample, inserted in sample bottle by sweet orange sample, by sample bottle sealing and standing 30 minutes, sampling probe and air pressure balancer are injected in sample bottle, computing machine controls the escaping gas that sampling probe collected specimens produces simultaneously; While collection escaping gas, the air through activated carbon filtration imports in sample bottle by air pressure balancer, realizes air pressure balance;
Step f, escaping gas and each sensor contacts, each sensor produces analog response signal respectively; Each analog response signal is converted to digital response signal by analog to digital converter respectively, and computing machine is averaged digital response signal, obtains sensor array digital response signal I (t);
The second accidental resonance model is preset with in computing machine wherein, ξ (t) is white Gaussian noise, and t is the Brownian movement Particles Moving time, and x is the coordinate of Particles Moving, f 0be signal frequency, a, b and A are constant;
Step g, inputs I (t) in the second accidental resonance model, makes the second stochastic resonance system model produce accidental resonance;
Computing machine utilizes formula SNR = 2 [ lim &Delta;&omega; &RightArrow; 0 &Integral; &Omega; - &Delta;&omega; &Omega; + &Delta;&omega; S ( &omega; ) d&omega; ] / S N ( &Omega; ) Calculate output signal-to-noise ratio SNR; 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 h, computing machine draws the output signal-to-noise ratio curve of the second stochastic resonance system model, and the signal to noise ratio (S/N ratio) maximal value choosing signal to noise ratio (S/N ratio) curve in signal to noise ratio (S/N ratio) curve is eigenwert SNR max, and by SNR maxbe stored in computing machine;
Step I, works as Time 2< 11, makes Time 2value increase by 1, puts back to detected sweet orange sample in congealer and stores, and after 24 hours, repeats steps d to step h;
Obtain 11 and Time 2the SNR be associated max, by SNR maxand Time 2form point (SNR max, Time 2), according to m 2individual point (SNR max, Time 2) carry out Nonlinear Boltzmann matching, obtain matched curve as shown in Figure 7 SNR max = 1.43759 ( 1 + e Time 2 - 11.53884 1.58287 ) - 72.91711 , And obtain sweet orange storage time second predictor formula Time 2 = 1.58287 ln ( 1.43759 SNR max + 72.91711 - 1 ) + 11.53884 ;
Step 200, set up sweet orange storage time Comprehensive Model: Time ( F , SNR max ) = P 1 &times; 3.89102 ln F - 4.00024 3.70916 + P 2 &times; { 1.58287 ln ( 1.43759 SNR max + 72.91711 - 1 ) + 11.53884 } , P 1and P 2for undetermined coefficient;
By Time=1, Time 1signal to noise ratio (S/N ratio) eigenwert F=10.2 when=1, Time 2sNR when=1 max=-72.17; And Time=2, Time 1signal to noise ratio (S/N ratio) eigenwert F=11 when=2, Time 2sNR when=2 max=-72.34 substitute into sweet orange storage time Comprehensive Model, thus obtain P 1=-5, P 2=4.467;
Thus obtain sweet orange storage time Comprehensive Model:
Step 300, utilizes step a and b and steps d to detect the sweet orange sample W stored under 4 DEG C of environment to h, obtains signal to noise ratio (S/N ratio) eigenwert F=20 and the signal to noise ratio (S/N ratio) maximal value SNR of sweet orange sample W max=-72.86; By SNR maxsubstitute in sweet orange storage time Comprehensive Model with signal to noise ratio (S/N ratio) eigenwert F, calculate the storage time Time of sweet orange sample W prediction=6 days.
20 sweet orange samples that the storage time using gas-detecting device and acoustic surface wave detection device to detect to store under 4 DEG C of environment is known, obtain 20 signal to noise ratio (S/N ratio) eigenwert F and 20 signal to noise ratio (S/N ratio) maximal value SNR max, substitute in formula sweet orange storage time Comprehensive Model and obtain 20 storage time predicted value Time prediction, utilize formula computational prediction error respectively, and the mean value of computational prediction error, obtaining predicated error mean value is 0.93683, shows to utilize gas-detecting device and acoustic surface wave detection device can predict the sweet orange storage time more accurately.
The present invention can Fast nondestructive evaluation sweet orange fruit, and what surface acoustic wave detected reflection is fruit internal structural information, and gas detect reflection is the odiferous information of fruit outside, and this combination can situation of change in accurate characterization sweet orange storage process.The method is conducive to instructing such fruit timely collecting, the decline that the fruits nutrition as far as possible avoiding harvest in advance to cause is worth, and also can reducing gathers evening causes the corrupt loss brought.
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 (8)

1. the detection method in sweet orange storage time, is characterized in that, comprises the steps:
(1-1) select n the sweet orange sample just plucked, sweet orange sample be placed in congealer and store, sweet orange sample is detected:
(1-1-1) setting detects number of days is m 1, the sequence number in storage time is Time r, Time 1initial value be 1; Use acoustic surface wave detection device to detect the frequency response of sweet orange, and obtain sweet orange storage time first predictor formula:
Described acoustic surface wave detection device comprises counter (1), shielded box (2), is located at the oscillator (3) in shielded box and SAW (Surface Acoustic Wave) resonator (4); Oscillator and SAW (Surface Acoustic Wave) resonator form oscillation circuit, and counter is electrically connected with oscillation circuit, and counter is provided with for the wireless launcher (19) with computing machine (9) wireless connections, and SAW (Surface Acoustic Wave) resonator is provided with two electrodes;
Step a, optional 1 sweet orange sample in n sweet orange sample, and sweet orange sample is put into shielded box, two of SAW (Surface Acoustic Wave) resonator electrodes are contacted with the opposite flank of sweet orange (17) sample respectively;
Step b, acoustic surface wave detection device work is after 40 to 60 minutes, and counter gathers the frequency response curve of oscillation circuit, and frequency response curve gathers 80 to 110 frequency values, and each frequency values is formed frequency signal Input (t);
The first stochastic resonance system model is preset with in computing machine wherein, ξ (t) is white Gaussian noise, and A, a and b are all constants, f 0be frequency modulating signal, D is noise intensity, and t is the Brownian movement Particles Moving time, and x is the coordinate of Particles Moving;
Frequency signal Input (t) is inputted in the first stochastic resonance system model, the first stochastic resonance system model is resonated;
Computing machine utilizes formula calculate output signal-to-noise ratio SNR; 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;
Computing machine draws the output signal-to-noise ratio curve in the first stochastic resonance system model, obtains two peak values of signal to noise ratio (S/N ratio) curve, using the absolute value of the difference of two peak values as signal to noise ratio (S/N ratio) eigenwert F; And signal to noise ratio (S/N ratio) eigenwert F is stored in a computer;
Step c, works as Time 1< m 1, make Time 1value increase by 1, puts back to detected sweet orange sample in congealer and stores, and after 24 hours, repeats step a and step b;
Obtain m 1individual and Time 1the F be associated, by F and Time 1form point (F, Time 1), according to m 1individual point (F, Time 1) carry out linear fit, obtain sweet orange storage time first predictor formula: Time 1=f (F);
(1-1-2) setting detects number of days is m 2, the sequence number in storage time is Time 2, Time 2initial value be 1; Use gas-detecting device to detect the smell of sweet orange volatilization, and obtain sweet orange storage time second predictor formula:
Described gas-detecting device comprises air chamber (10), be located at sensor array (11) in air chamber and analog to digital converter (12); Described air chamber is provided with sampling probe (13) and cleaning probe (14), and sampling probe is provided with sampling air pump (15), and cleaning probe is provided with cleaning air pump (16); Sensor array is electrically connected with analog to digital converter, analog to digital converter, sampling air pump and cleaning air pump is equipped with the data-interface for computing machine electrical connection; Described sensor array comprises several gas sensors;
Steps d, computing machine controls cleaning gas pump work, and pure air passes in air chamber and cleans each gas sensor, when the response of each gas sensor is stablized to baseline, closes cleaning air pump;
Step e, in n sweet orange sample, optional 1 sweet orange sample, inserted in sample bottle by sweet orange sample, by sample bottle sealing and standing 30 to 40 minutes, sampling probe and air pressure balancer are injected in sample bottle, computing machine controls the escaping gas that sampling probe collected specimens produces simultaneously; While collection escaping gas, the air through activated carbon filtration imports in sample bottle by air pressure balancer, realizes air pressure balance;
Step f, escaping gas and each sensor contacts, each sensor produces analog response signal respectively; Each analog response signal is converted to digital response signal by analog to digital converter respectively, and computing machine is averaged digital response signal, obtains sensor array digital response signal I (t);
The second accidental resonance model is preset with in computing machine wherein, ξ (t) is white Gaussian noise, and t is the Brownian movement Particles Moving time, and x is the coordinate of Particles Moving, f 0be frequency modulating signal, a, b and A are constant;
Step g, inputs I (t) in the second accidental resonance model, makes the second stochastic resonance system model produce accidental resonance;
Computing machine utilizes formula calculate output signal-to-noise ratio SNR; 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 h, computing machine draws the output signal-to-noise ratio curve of the second stochastic resonance system model, and the signal to noise ratio (S/N ratio) maximal value choosing signal to noise ratio (S/N ratio) curve in signal to noise ratio (S/N ratio) curve is eigenwert SNR max, and by SNR maxbe stored in computing machine;
Step I, works as Time 2< m 2, make Time 2value increase by 1, puts back to detected sweet orange sample in congealer and stores, and after 24 hours, repeats steps d to step h;
Obtain m 2individual and Time 2the SNR be associated max, by SNR maxand Time 2form point (SNR max, Time 2), according to m 2individual point (SNR max, Time 2) carry out Nonlinear Boltzmann matching, obtain sweet orange storage time second predictor formula Time 2=f (SNR max);
(1-2) sweet orange storage time Comprehensive Model is set up: Time (F, SNR max)=P 1× f (F)+P 2× f (SNR max), P 1and P 2for undetermined coefficient; By two groups of sweet orange storage times, the signal to noise ratio (S/N ratio) eigenwert F corresponding with the described storage time and the SNR corresponding with the described storage time maxsubstitute into sweet orange storage time Comprehensive Model, thus obtain P 1and P 2;
(1-3) detect and the sweet orange sample W to be detected under described sweet orange sample same stored condition, obtain the signal to noise ratio (S/N ratio) maximal value SNR of sweet orange sample W maxwith signal to noise ratio (S/N ratio) eigenwert F; By SNR maxsubstitute in sweet orange storage time Comprehensive Model with signal to noise ratio (S/N ratio) eigenwert F, calculate the storage time Time of sweet orange sample W prediction.
2. the detection method in a kind of sweet orange storage time according to claim 1, is characterized in that, the centre frequency of described SAW (Surface Acoustic Wave) resonator is 433.92MHz.
3. the detection method in a kind of sweet orange storage time according to claim 1, is characterized in that, described n is 6 to 15.
4. the detection method in a kind of sweet orange storage time according to claim 1, is characterized in that, described m 1and m 2be 10 to 180.
5. the detection method in a kind of sweet orange storage time according to claim 1, it is characterized in that, described gas sensor is 8, be respectively the first sensor for detecting sulfide gas, for detecting the second sensor of inflammable gas, for detecting the 3rd sensor of Ammonia gas, for detecting the four-sensor of ethanol class gas, for detecting the 5th sensor of hydrocarbon component gas, for detecting the 6th sensor of alkanes gas, for detecting the 7th sensor of propane and butane, for detecting the 8th sensor of nitride gas.
6. the detection method in a kind of sweet orange storage time according to claim 1, it is characterized in that, described SAW (Surface Acoustic Wave) resonator comprises piezoelectric substrate (5), interdigital transducer (6), 2 reflecting gratings (7) and is located at 4 gain grid (8) of reflecting grating both sides; Described electrode is electrically connected with the link be located on interdigital transducer (18).
7. the detection method in a kind of sweet orange storage time according to claim 1 or 2 or 3 or 4 or 5 or 6, is characterized in that, in described steps d, pure air passes in air chamber each gas sensor cleaning 40 to 50 minutes.
8. the detection method in a kind of sweet orange storage time according to claim 1 or 2 or 3 or 4 or 5 or 6, it is characterized in that, in described steps d, pure air cleans sensor with the flow velocity of 860mL/min to 1100mL/min.
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