CN104568776A - Beef storage time detection method - Google Patents

Beef storage time detection method Download PDF

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CN104568776A
CN104568776A CN201510002903.8A CN201510002903A CN104568776A CN 104568776 A CN104568776 A CN 104568776A CN 201510002903 A CN201510002903 A CN 201510002903A CN 104568776 A CN104568776 A CN 104568776A
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beef
sample
monostable
signal strength
omega
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CN104568776B (en
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惠国华
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Zhejiang Gongshang University
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Zhejiang Gongshang University
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Abstract

The invention discloses a beef storage time detection method. The beef storage time detection method comprises a visible/near infrared spectrometer, an optical fiber probe, a sample detection tray, an arc guide rail and a cantilever, and comprises the following detection steps: 1 preparing to detect a beef sample; 2 detecting the beef sample by virtue of the visible/near infrared spectrometer; 3 calculating to obtain two main component output signals PC1 and PC2; 4 carrying out main component analysis; 5 building monostable stochastic resonance system output signal strength characteristic peak data tables of beef with different storage time; and 6 taking a to-be-detected beef sample according to the method in the step 1, and repeating the steps 2 to 4, under the premise that PC1 and PC2 main component contribution rates are greater than or equal to 90%, if the absolute value of (P-Px)/Px is not greater than 5%, judging the storage time of the to-be-detected beef sample to be equal to that corresponding to the signal strength characteristic value Px. According to the beef storage time detection method, the beef storage time can be rapidly, simply and accurately detected; and the safety of the beef food is ensured.

Description

Beef holding time detection method
Technical field
The present invention relates to food storage field, the detection method of especially a kind of beef holding time.
Background technology
Beef be consumers in general than preferable food, the safety of beef food, concerns life security and the social stability of common people.People wish a kind of can the method for quick, easy, accurate detection cold fresh beef holding time, to process the beef stored in an orderly manner in time, guarantee the safety of beef food.
Summary of the invention
The object of the invention is to: a kind of beef holding time detection method is provided, the holding time of beef can be detected quick, easy, accurately, to process the beef stored in an orderly manner in time, guarantee the safety of beef food.
For achieving the above object, the present invention can take following technical proposals:
A kind of beef holding time detection method of the present invention, comprise the Vis/NIR instrument for detecting, two bifurcation fiber, fibre-optical probe, halogen light source and sample detection pallet, described Vis/NIR instrument is connected with computing machine, and be connected with fibre-optical probe by described pair of bifurcation fiber, described halogen light source is by light-source controller controls, and be connected with fibre-optical probe by two bifurcation fiber, described sample detection pallet is spheric, an arc guide rail is provided with directly over this sample detection pallet, the center of circle of this arc guide rail overlaps with the centre of sphere of sample detection pallet, arc guide rail is provided with the slide block driven by gear train one, the upper end edge radial direction of one cantilever is fixedly arranged on described slide block, described cantilever is radially provided with chute, fibre-optical probe is slidably installed in the described chute on cantilever by the driving of gear train two, the bottom of sample detection pallet is installed in the upper end of interior rotating shaft, arc guide rail is installed on outer shaft by strut, motor passes through gear train three respectively with interior, outer shaft is in transmission connection,
Detecting step is---
Step one: preparation detects beef sample
Choose beef sample to be detected, be cut into 5-10 millimeter thin slice, put into sample detection pallet;
Step 2: adopt Vis/NIR instrument to detect beef sample
Fibre-optical probe to point to the centre of sphere of sample detection pallet in vertical direction with the angle of 0 degree, 15 degree, 30 degree, under often kind of angle, all adopts following method image data respectively:
Sample detection pallet often rotates 5 degree, suspend 50 seconds, in 50 seconds that suspend, first 25 seconds by light-source controller controls halogen light source by weak crescendo, and by gear train two control fibre-optical probe radially groove draw near near sample detection pallet, latter 25 seconds by light-source controller controls halogen light source by strong diminuendo, and by gear train two control fibre-optical probe radially groove from the close-by examples to those far off away from sample detection pallet, fibre-optical probe gathered data every 5 seconds, utilize the relaxation spectrum characteristic of beef sample, the effect that different light intensity different time group is excited and absorption difference, abundant detection data, to eliminate beef sample because detection side is to difference, texture is different, the measurement difference that musculature difference causes,
Step 3: by the monostable stochastic resonance system of the above-mentioned testing result input computing machine of Vis/NIR instrument, calculate two major component output signal PC1 and PC2;
Step 4: carry out principal component analysis (PCA)
If the contribution rate sum of the first two major component PC1 and PC2 is more than or equal to 90%, monostable accidental resonance output signal can realize the detection of beef holding time; If the contribution rate sum of the first two major component PC1 and PC2 is less than 90%, then re-start detection;
Step 5: the monostable stochastic resonance system output signal strength characteristic peaks tables of data setting up the beef of different holding time
To go bail for respectively the beef sample deposited 1 to 8 day according to the method for step one, repeat step 2 to four, be more than or equal to the prerequisite of 90% at PC1 and PC2 principal component contributor rate under, extract the monostable stochastic resonance system output signal strength characteristic peaks P of each beef sample, set up the monostable stochastic resonance system output signal strength characteristic peaks tables of data of the beef of different holding time;
Step 6: get beef sample to be measured according to the method for step one, repeat step 2 to four, be more than or equal to the prerequisite of 90% at PC1 and PC2 principal component contributor rate under, extract monostable stochastic resonance system output signal strength characteristic peaks P, each characteristic peaks in the monostable stochastic resonance system output signal strength characteristic peaks tables of data of P value and described beef is compared, if for the signal strength characteristics value Px of certain day, had the time that the then tested beef sample holding time is corresponding with signal strength characteristics value Px is identical.
Light tight sample pool cover is provided with, for reducing extraneous light to the interference of measuring outside sample detection pallet and arc guide rail.
The monostable stochastic resonance system that described monostable stochastic resonance system adopts nonlinear Langevin equation to describe:
dx ( t ) dt = - dU ( x ) dx + S ( t ) + n ( t ) - - - ( 1 )
Wherein, it is the first order derivative that system exports x (t); S (t) is input signal, is input in monostable system by Vis/NIR instrument detection signal goes as S (t); N (t) is the exponential type white Gaussian noise of zero-mean, and its autocorrelation function is for the first order derivative of monostable potential function U (x); And
U ( x ) = - ax + b 4 x 4 - - - ( 2 )
In formula, a is systematic parameter, being biased of representative system, the position of influential system steady state point, and b is systematic parameter, gets the real number being greater than zero,
Formula (1) describes the damped motion of Brownian Particles in monostable system, and when without input noise and signal, system only has a steady state point, does not have potential barrier;
System metastable state distribution function can be expressed as:
P st ( x ) = N st B ( x ) exp [ - U ( x ) D ] - - - ( 3 )
Wherein, N stfor normaliztion constant, U (x) is monostable potential function, B (x)=Dx 2+ 2 λ Dx+D.
We define monostable accidental resonance output signal strength approximate treatment expression formula:
T = A 2 / 2.5 D = A 2 2.5 D - - - ( 4 )
Wherein A is input signal amplitude, and D is coloured noise intensity;
Spectral signal is coupled r (t) one-period signal sin (ω t) as total input signal by us, namely
S(t)=lsin(ωt)+mr(t) (5)
And get the lowest common multiple of l and m as signal input range A;
Therefore the monostable accidental resonance output signal strength that formula (4) is expressed can approximate derivation be:
T = 2 DO ( ω ) A 2 I ( ω ) - - - ( 6 )
Wherein I (ω) is input power spectrum, and O (ω) is output power spectrum.Wherein O (ω) and I (ω) is approximately:
O ( ω ) = π D 2 a ( ( 2 D π exp ( - Δω D ) 2 + ω 2 ) - - - ( 7 )
I ( ω ) = D ( 2 D π exp ( - Δω D ) a ( ( 2 D π exp ( - Δω D ) 2 + ω 2 ) - - - ( 8 )
In spectral detection is analyzed, visible/near infrared wave band comprises abundant material information, and content and the composition of spectral information and measured object self are closely related, and therefore Vis/NIR can be applicable to classification of substances judgement.The beneficial effect of the method for the invention is: owing to adopting technique scheme, Vis/NIR instrument is utilized to detect the beef sample of different holding time, the accidental resonance signal to noise ratio (S/N ratio) eigenvalue of curve extracting spectroscopic data carries out principal component analysis (PCA), achieve the differentiation of the beef sample of different holding time, meanwhile, differentiation degree can be judged according to the first two principal component contributor rate sum of principal component analysis (PCA).The kind method of utilization, can detect the holding time of beef quick, easy, accurately, to process the beef stored in an orderly manner in time, guarantees the safety of beef food, described sample detection pallet is spheric, an arc guide rail is provided with directly over this sample detection pallet, the center of circle of this arc guide rail overlaps with the centre of sphere of sample detection pallet, arc guide rail is provided with the slide block driven by gear train one, the upper end edge radial direction of one cantilever is fixedly arranged on described slide block, described cantilever is radially provided with chute, fibre-optical probe is slidably installed in the described chute on cantilever by the driving of gear train two, the bottom of sample detection pallet is installed in the upper end of interior rotating shaft, arc guide rail is installed on outer shaft by strut, motor passes through gear train three respectively with interior, outer shaft is in transmission connection, this structure, slide block can reciprocatingly slide along arc guide rail, fibre-optical probe can slide up and down along chute, motor can make sample detection pallet and/or arc guide rail rotate by rotating mechanism, no matter cantilever, how light probe moves, the light light sent of popping one's head in is all vertical with the reflecting surface of sample detection pallet, the strongest reflected light signal can be obtained.
Accompanying drawing explanation
Fig. 1 is the structural representation of inventive samples detection system;
Fig. 2 is the diffuse reflection spectrum curve synoptic diagram that beef detects sample;
Fig. 3 is the two-dimensional space schematic diagram that major component PC1 and PC2 are formed;
Fig. 4 is spectral signal principal component analysis (PCA) result schematic diagram.
Embodiment
As shown in Figure 1, a kind of beef holding time detection method of the present invention, comprise the Vis/NIR instrument 12 for detecting, two bifurcation fiber 13, fibre-optical probe 6, halogen light source 14 and sample detection pallet 4, described Vis/NIR instrument 12 is connected with computing machine 11, and be connected with fibre-optical probe 6 by described pair of bifurcation fiber 13, described halogen light source 14 is controlled by light source controller 15, and be connected with fibre-optical probe 6 by two bifurcation fiber 13, described sample detection pallet 4 is spheric, an arc guide rail 9 is provided with directly over this sample detection pallet 4, the center of circle of this arc guide rail 9 overlaps with the centre of sphere of sample detection pallet 4, arc guide rail 9 is provided with the slide block 10 driven by gear train one, the upper end edge radial direction of one cantilever 7 is fixedly arranged on described slide block 10, described cantilever 7 is radially provided with chute 8, fibre-optical probe 6 is slidably installed in the described chute 8 on cantilever 7 by the driving of gear train two, the bottom of sample detection pallet 4 is installed in the upper end of interior rotating shaft 31, arc guide rail 9 is installed on outer shaft 32 by strut, motor 1 passes through gear train 32 respectively with interior, outer shaft 31, 32 are in transmission connection, as preferably, light tight sample pool cover is provided with outside sample detection pallet 4 and arc guide rail 9, for reducing extraneous light to the interference of measuring,
The step of the concrete detection method of beef holding time is as follows:
Step one: preparation detects beef sample
Choose beef sample to be detected, be cut into 5-10 millimeter thin slice, put into sample detection pallet 4; In order to improve testing result further, the beef sample chosen, can specify the position of meat sample, as ox thigh, ox belly etc.;
Step 2: adopt Vis/NIR instrument to detect beef sample
Fibre-optical probe 6 to point to the centre of sphere of sample detection pallet 4 in vertical direction with the angle of 0 degree, 15 degree, 30 degree, under often kind of angle, all adopts following method image data respectively:
Sample detection pallet 4 often rotates 5 degree, suspend 50 seconds, in 50 seconds that suspend, within first 25 seconds, control halogen light source 5 by weak crescendo by light source controller 7, and by gear train two control fibre-optical probe 6 radially groove 8 draw near near sample detection pallet 4, within latter 25 seconds, control halogen light source 5 by strong diminuendo by light source controller 7, and by gear train two control fibre-optical probe 6 radially groove 8 from the close-by examples to those far off away from sample detection pallet 4, fibre-optical probe 6 gathered data every 5 seconds, utilize the relaxation spectrum characteristic of beef sample, the effect that different light intensity different time group is excited and absorption difference, abundant detection data, to eliminate beef sample because detection side is to difference, texture is different, the measurement difference that musculature difference causes,
Detect data in order to abundant further, in testing process, fibre-optical probe 6 can increase arbitrarily with the angle of vertical direction, and the anglec of rotation of sample detection pallet 4 and time out can be chosen arbitrarily; Be subjected to displacement relative to sample detection pallet 4 in rotary course to reduce beef sample and affect testing result, can preferably in rotating shaft static, drive arc guide rail to rotate by outer shaft;
Step 3: by the monostable stochastic resonance system of the above-mentioned testing result input computing machine 11 of Vis/NIR instrument 12, calculate two major component output signal PC1 and PC2;
Step 4: carry out principal component analysis (PCA)
If the contribution rate sum of the first two major component PC1 and PC2 is more than or equal to 90%, monostable accidental resonance output signal can realize the detection of beef holding time; If the contribution rate sum of the first two major component PC1 and PC2 is less than 90%, then re-start detection;
Step 5: the monostable stochastic resonance system output signal strength characteristic peaks tables of data setting up the beef of different holding time
To go bail for respectively the beef sample deposited 1 to 8 day according to the method for step one, repeat step 2 to four, be more than or equal to the prerequisite of 90% at PC1 and PC2 principal component contributor rate under, extract the monostable stochastic resonance system output signal strength characteristic peaks P of each beef sample, set up the monostable stochastic resonance system output signal strength characteristic peaks tables of data of the beef of different holding time;
Step 6: get beef sample to be measured according to the method for step one, repeats step 2 to four, at PC1 and
Under PC2 principal component contributor rate is more than or equal to the prerequisite of 90%, extract monostable stochastic resonance system output signal strength characteristic peaks P, each characteristic peaks in the monostable stochastic resonance system output signal strength characteristic peaks tables of data of P value and described beef is compared, if for the signal strength characteristics value Px of certain day, had the time that the then tested beef sample holding time is corresponding with signal strength characteristics value Px is identical.
The monostable stochastic resonance system that described monostable stochastic resonance system adopts nonlinear Langevin equation to describe:
dx ( t ) dt = - dU ( x ) dx + S ( t ) + n ( t ) - - - ( 1 )
Wherein, it is the first order derivative that system exports x (t); S (t) is input signal, is input in monostable system by Vis/NIR instrument 12 detection signal goes as S (t); N (t) is the exponential type white Gaussian noise of zero-mean, and its autocorrelation function is for the first order derivative of monostable potential function U (x); And
U ( x ) = - ax + b 4 x 4 - - - ( 2 )
In formula, a is systematic parameter, being biased of representative system, the position of influential system steady state point, and b is systematic parameter, gets the real number being greater than zero,
Formula (1) describes the damped motion of Brownian Particles in monostable system, and when without input noise and signal, system only has a steady state point, does not have potential barrier;
System metastable state distribution function can be expressed as:
P st ( x ) = N st B ( x ) exp [ - U ( x ) D ] - - - ( 3 )
Wherein, N stfor normaliztion constant, U (x) is monostable potential function, B (x)=Dx 2+ 2 λ Dx+D.
We define monostable accidental resonance output signal strength approximate treatment expression formula:
T = A 2 / 2.5 D = A 2 2.5 D - - - ( 4 )
Wherein A is input signal amplitude, and D is coloured noise intensity;
Spectral signal is coupled r (t) one-period signal sin (ω t) as total input signal by us, namely
S(t)=lsin(ωt)+mr(t) (5)
And get the lowest common multiple of l and m as signal input range A;
Therefore the monostable accidental resonance output signal strength that formula (4) is expressed can approximate derivation be:
T = 2 DO ( ω ) A 2 I ( ω ) - - - ( 6 )
Wherein I (ω) is input power spectrum, and O (ω) is output power spectrum.Wherein O (ω) and I (ω) is approximately:
O ( ω ) = π D 2 a ( ( 2 D π exp ( - Δω D ) 2 + ω 2 ) - - - ( 7 )
I ( ω ) = D ( 2 D π exp ( - Δω D ) a ( ( 2 D π exp ( - Δω D ) 2 + ω 2 ) - - - ( 8 )
Following table is the monostable stochastic resonance system output signal strength characteristic peaks of the beef of different holding time
The spectral curve that diffuses of Fig. 2 beef sample, spectral signal intensity near 625nm is the highest, and characteristic peak has also appearred in this external 472nm, 562nm, 710nm and 441nm place, and spectral detection signal contains and detects information compared with horn of plenty.
The beef spectral detection data choosing the different storage time are input in monostable stochastic resonance system to be analyzed.A feature of accidental resonance is and intrinsic noise signal in non-elimination detection system, and adopts and add external noise modulation echo signal and reach resonance state, strengthens Target Weak Signal and is easy to detect.Fig. 3 is the two-dimensional space schematic diagram that major component PC1 and PC2 are formed, the output signal-to-noise ratio curve of the beef sample spectroscopic data of different holding time is first in rising trend, start to decline after noise intensity 87 place reaches maximum value, the contribution rate of the first two major component is respectively 80.21% and 12.79%, have obvious differentiation between the beef sample in different storage time, therefore monostable accidental resonance output signal can as the foundation of beef sample differentiation.
Fig. 4 is spectral signal principal component analysis (PCA) result schematic diagram.

Claims (3)

1. a beef holding time detection method, comprise the Vis/NIR instrument (12) for detecting, two bifurcation fiber (13), fibre-optical probe (6), halogen light source (14) and sample detection pallet (4), described Vis/NIR instrument (12) is connected with computing machine (11), and be connected with fibre-optical probe (6) by described pair of bifurcation fiber (13), described halogen light source (14) is controlled by light source controller (15), and be connected with fibre-optical probe (6) by two bifurcation fiber (13), it is characterized in that: described sample detection pallet (4) is spheric, an arc guide rail (9) is provided with directly over this sample detection pallet (4), the center of circle of this arc guide rail (9) overlaps with the centre of sphere of sample detection pallet (4), arc guide rail (9) is provided with the slide block (10) driven by gear train one, the upper end edge radial direction of one cantilever (7) is fixedly arranged on described slide block (10), described cantilever (7) is radially provided with chute (8), fibre-optical probe (6) is slidably installed in the described chute (8) on cantilever (7) by the driving of gear train two, the bottom of sample detection pallet (4) is installed in the upper end of interior rotating shaft (31), arc guide rail (9) is installed on outer shaft (32) by strut, motor (1) by gear train three (2) respectively with interior, outer shaft (31, 32) be in transmission connection,
Detecting step is---
Step one: preparation detects beef sample
Choose beef sample to be detected, be cut into 5-10 millimeter thin slice, put into sample detection pallet (4);
Step 2: adopt Vis/NIR instrument to detect beef sample
Fibre-optical probe (6) to point to the centre of sphere of sample detection pallet (4) in vertical direction with the angle of 0 degree, 15 degree, 30 degree, under often kind of angle, all adopts following method image data respectively:
Sample detection pallet (4) often rotates 5 degree, suspend 50 seconds, in 50 seconds that suspend, within first 25 seconds, control halogen light source (5) by weak crescendo by light source controller (7), and by gear train two control fibre-optical probe (6) radially groove (8) draw near near sample detection pallet (4), within latter 25 seconds, control halogen light source (5) by strong diminuendo by light source controller (7), and by gear train two control fibre-optical probe (6) radially groove (8) from the close-by examples to those far off away from sample detection pallet (4), fibre-optical probe (6) gathered data every 5 seconds, utilize the relaxation spectrum characteristic of beef sample, the effect that different light intensity different time group is excited and absorption difference, abundant detection data, to eliminate beef sample because detection side is to difference, texture is different, the measurement difference that musculature difference causes,
Step 3: by the monostable stochastic resonance system of above-mentioned testing result input computing machine (11) of Vis/NIR instrument (12), calculates two major component output signal PC1 and PC2;
Step 4: carry out principal component analysis (PCA)
If the contribution rate sum of the first two major component PC1 and PC2 is more than or equal to 90%, monostable accidental resonance output signal can realize the detection of beef holding time; If the contribution rate sum of the first two major component PC1 and PC2 is less than 90%, then re-start detection;
Step 5: the monostable stochastic resonance system output signal strength characteristic peaks tables of data setting up the beef of different holding time
To go bail for respectively the beef sample deposited 1 to 8 day according to the method for step one, repeat step 2 to four, be more than or equal to the prerequisite of 90% at PC1 and PC2 principal component contributor rate under, extract the monostable stochastic resonance system output signal strength characteristic peaks P of each beef sample, set up the monostable stochastic resonance system output signal strength characteristic peaks tables of data of the beef of different holding time;
Step 6: get beef sample to be measured according to the method for step one, repeats step 2 to four, at PC1 and
Under PC2 principal component contributor rate is more than or equal to the prerequisite of 90%, extract monostable stochastic resonance system output signal strength characteristic peaks P, each characteristic peaks in the monostable stochastic resonance system output signal strength characteristic peaks tables of data of P value and described beef is compared, if for the signal strength characteristics value Px of certain day, had the time that the then tested beef sample holding time is corresponding with signal strength characteristics value Px is identical.
2. beef holding time detection method according to claim 1, is characterized in that: outside sample detection pallet (4) and arc guide rail (9), be provided with light tight sample pool cover, for reducing extraneous light to the interference of measuring.
3. beef holding time detection method according to claim 1, is characterized in that: the monostable stochastic resonance system that described monostable stochastic resonance system adopts nonlinear Langevin equation to describe:
dx ( t ) dt = - dU ( x ) dx + S ( t ) + n ( t ) - - - ( 1 )
Wherein, it is the first order derivative that system exports x (t); S (t) is input signal, is input in monostable system by Vis/NIR instrument (12) detection signal goes as S (t); N (t) is the exponential type white Gaussian noise of zero-mean, and its autocorrelation function is < n ( t ) n ( s ) > = D k exp ( - | t - s | k ) ; dU ( x ) dx For the first order derivative of monostable potential function U (x); And
U ( x ) = - ax + b 4 x 4 - - - ( 2 )
In formula, a is systematic parameter, being biased of representative system, the position of influential system steady state point, and b is systematic parameter, gets the real number being greater than zero,
Formula (1) describes the damped motion of Brownian Particles in monostable system, and when without input noise and signal, system only has a steady state point, does not have potential barrier;
System metastable state distribution function can be expressed as:
P st ( x ) = N st B ( x ) exp [ - U ( x ) D ] - - - ( 3 )
Wherein, N stfor normaliztion constant, U (x) is monostable potential function, B (x)=Dx 2+ 2 λ Dx+D.
We define monostable accidental resonance output signal strength approximate treatment expression formula:
T = A 2 / 2.5 D = A 2 2.5 D - - - ( 4 )
Wherein A is input signal amplitude, and D is coloured noise intensity;
Spectral signal is coupled r (t) one-period signal sin (ω t) as total input signal by us, namely
S(t)=l sin(ωt)+mr(t) (5)
And get the lowest common multiple of l and m as signal input range A;
Therefore the monostable accidental resonance output signal strength that formula (4) is expressed can approximate derivation be:
T = 2 DO ( &omega; ) A 2 I ( &omega; ) - - - ( 6 )
Wherein I (ω) is input power spectrum, and O (ω) is output power spectrum.Wherein O (ω) and I (ω) is approximately:
O ( &omega; ) = &pi; D 2 a ( ( 2 D &pi; exp ( - &Delta;&omega; D ) 2 + &omega; 2 ) - - - ( 7 )
I ( &omega; ) = D ( 2 D &pi; exp ( - &Delta;&omega; D ) a ( ( 2 D &pi; exp ( - &Delta;&omega; D ) 2 + &omega; 2 ) - - - ( 8 )
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CN115055399A (en) * 2022-06-28 2022-09-16 无锡迅杰光远科技有限公司 Sorting system capable of carrying out infrared detection on fruits and vegetables and tray

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