CN107655839B - Detection method of ascending relaxation spectrum detection device - Google Patents

Detection method of ascending relaxation spectrum detection device Download PDF

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CN107655839B
CN107655839B CN201710734066.7A CN201710734066A CN107655839B CN 107655839 B CN107655839 B CN 107655839B CN 201710734066 A CN201710734066 A CN 201710734066A CN 107655839 B CN107655839 B CN 107655839B
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green
halogen lamp
yellow
ascending
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CN107655839A (en
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郑豪男
周慧敏
邵晨宁
李莎怡
吴思妃
叶文俊
龚志涵
叶振龙
杨鑫
李剑
惠国华
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Zhejiang A&F University ZAFU
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N2021/3185—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry typically monochromatic or band-limited
    • G01N2021/3188—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry typically monochromatic or band-limited band-limited
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/06—Illumination; Optics
    • G01N2201/061—Sources
    • G01N2201/06166—Line selective sources
    • G01N2201/0618—Halogene sources
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/122—Kinetic analysis; determining reaction rate
    • G01N2201/1226—Relaxation methods, e.g. temperature jump, field jump

Abstract

The invention discloses a detection method of an ascending relaxation spectrum detection device, which comprises a computer, a visible/near infrared spectrometer, a sample tray arranged in a lightproof sample cell, a halogen lamp, a light source controller electrically connected with the halogen lamp and an optical fiber probe; the sample cell is provided with a collection end, the optical fiber probe is respectively connected with the visible/near infrared spectrometer and the halogen lamp through a double-branched optical fiber, and the computer is in data connection with the visible/near infrared spectrometer. The invention has the characteristics of high detection efficiency and high detection accuracy.

Description

Detection method of ascending relaxation spectrum detection device
Technical Field
The invention relates to the technical field of spectrum detection, in particular to a detection method of an ascending relaxation spectrum detection device with high detection efficiency and high detection precision.
Background
The current situation is as follows: the visible/near infrared spectrum analysis technology has the advantages of simplicity, convenience, no damage, rapidness, suitability for various state analysis objects and online detection, and has wide application prospect in the food industry.
The main defects of the existing equipment are as follows: (1) the existing spectrum detection technology adopts a static spectrum technology, and only focuses on the characteristics of reflected or projected light parameters after a light beam irradiates a detection sample to be stable. (2) The characteristic functional groups of the internal chemical components of the food sample quality have different absorption effects on the spectrum under the irradiation of the saturation spectrum and the intensity-controlled light spectrum, and the detection precision is low.
Disclosure of Invention
The invention aims to overcome the defect of low detection precision of a spectrum detection method in the prior art, and provides a detection method of an ascending relaxation spectrum detection device with high detection efficiency and high detection precision.
In order to achieve the purpose, the invention adopts the following technical scheme:
a detection method of an ascending relaxation spectrum detection device comprises a computer, a visible/near infrared spectrometer, a sample tray arranged in a lightproof sample cell, a halogen lamp, a light source controller electrically connected with the halogen lamp and an optical fiber probe; the sample cell is provided with a collection end, the optical fiber probe is respectively connected with the visible/near infrared spectrometer and the halogen lamp through a double-branched optical fiber, and the computer is in data connection with the visible/near infrared spectrometer; the method comprises the following steps:
(1-1) placing a food sample on a sample tray, and covering a sample cell with a shading cloth;
(1-2) starting a light source controller, starting a halogen lamp, starting a visible/near-infrared spectrometer and preparing to acquire a detection signal;
(1-3) setting the light intensity rising rate and the measurement period T of the halogen lamp in the light source controller; dividing a measurement period T into N time periods; the halogen lamp gradually changes from the minimum light intensity to the maximum light intensity in each time period;
(1-4) irradiating a food sample by using the halogen lamp, acquiring reflected light of the food by using an optical fiber probe, and analyzing a detected spectral curve by using a visible/near infrared spectrometer;
(1-5) imaging the optical characteristics of the tested sample;
(1-6) judging the quality of the sample.
The invention belongs to the initiative in the technical field of relaxation spectrum detection in China at present.
(1) The application value of the content stated in the invention lies in the expansibility thereof. Through the comprehensive application of the single-frequency light source relaxation spectrum detection technology, the multi-frequency light source relaxation spectrum detection technology and the nonlinear signal analysis technology, the modernization of the existing visible/near infrared spectrum equipment can be realized, so that each traditional visible/near infrared spectrometer has an intelligent judgment and detection function, the aim of accurately detecting the food quality is fulfilled, the technical problem that the traditional visible/near infrared spectrum detection equipment cannot accurately detect the food quality is solved, and the food quality safety detection capability is comprehensively improved.
(2) Characteristic functional groups of internal chemical components of the food sample quality have different absorption effects on spectra under the irradiation of a saturation spectrum and the irradiation of an intensity-controlled spectrum, but at present, the research of analyzing key characteristics of dynamic spectrum change to represent the food quality condition under the irradiation of the intensity-controlled spectrum is not available.
(3) The relaxation is the process of the system returning to the equilibrium state from the non-equilibrium state, when a beam of light is applied to a tested sample with gradually increasing intensity, various functional groups in the sample generate a gradual absorption process of a spectrum with a special sensitive frequency, and the characteristics of the absorbed reflection/projection spectrum are not consistent with those of the traditional spectrum because of the relaxation absorption process of the functional groups.
(4) By utilizing the relaxation spectrum technology, the quality condition of the food can be more accurately determined.
Preferably, the step (1-4) comprises the steps of:
(2-1) selecting the wavelengths of M characteristic peaks in the spectral curve as characteristic wavelength points, and calculating the light intensity change value of each characteristic wavelength point in the current time period and the light intensity change value in the previous time period;
setting a variable i as the serial number of the characteristic wavelength point, wherein i is more than 1 and less than or equal to M;
setting a variable j as the serial number of each time period, wherein j is more than 1 and less than or equal to N;
setting the value of the spectral intensity change measured by the ith characteristic wavelength point in the j time period as hijThe following spectral intensity variation matrix is constructed:
Figure GDA0002298407530000031
(2-2) setting the rate of change v of the spectral intensityijComprises the following steps:
Figure GDA0002298407530000041
Δ t is the time interval of adjacent time segments;
(2-3) converting the spectrum intensity change matrix into a spectrum change rate matrix: :
Figure GDA0002298407530000042
selecting the midpoint of the measurement wavelength band of the spectral curve as a reference point, and summing the midpoint of the spectral curve with vijConnecting the corresponding characteristic wavelength point with the midpoint of the frequency band, and setting the included angle between the connecting line and the positive direction of the transverse axis as theta;
using formulas
Figure GDA0002298407530000043
For vijCorrecting to obtain corrected speed data vij(θ)(ii) a Listing two different vij(θ)Equation of line T between velocity dataijk(ii) a Using the equation of each link TijkCalculating to obtain the coordinates J of the intersection point of each connecting lineijk(ax，ay) P is usually sin (θ).
Preferably, the step (1-5) comprises the steps of: selecting all velocity data vij(θ)Maximum velocity v ofmaxAnd minimum velocity vmin(ii) a Using formulas
Figure GDA0002298407530000044
Calculate each vij(θ)First imaging factor fl of1And a second imaging factorSub fl2(ii) a According to fl2Determining whether it belongs to yellow or green, and determining the color according to fl1Determining the chromaticity of yellow or green, and determining fl1And fl2And imaging the image in a certain color between green and yellow to represent the optical characteristic image of the tested sample.
Preferably, the step (1-6) comprises the steps of:
the optical characteristic image is a printed four-color pattern comprising four standard colors: the C value represents cyan, the M value represents magenta, the Y value represents yellow, and the K value represents black; joining the colors referenced 40 and 48 in the image to form a 16-step color region segment; joining a yellowish region in yellow with a pale green region in green as a connecting portion of yellow and green to form a continuous region in yellow-green;
if the green pixel points with the Y value being more than or equal to 80 in the optical characteristic image account for less than 15% of the total pixel points, the computer judges that the quality of the sample is good;
if the green pixel points with the Y value being more than or equal to 80 in the optical characteristic image account for more than 15% and less than 45% of the total pixel points, the computer judges that the quality of the sample is qualified;
if the green pixel points with the Y value being more than or equal to 80 in the optical characteristic image account for more than 45% of the total pixel points, the computer judges that the sample has poor quality and is not edible.
Preferably, M is 6, and the 6 characteristic wavelength points are 607.67nm, 664.55nm, 730.94nm, 546.04nm, 799.11nm and 890.47nm wavelength points, respectively.
Preferably, the device also comprises a brightness sensor electrically connected with the computer, the brightness sensor is positioned in the sample cell opposite to the optical fiber probe, and the computer controls the light source controller to quickly adjust the diffuse reflection light intensity to be above 100 candela when the detected diffuse reflection signal intensity is lower than 100 candela.
Therefore, the invention has the following beneficial effects: the detection efficiency is high, and the detection precision is high.
Drawings
FIG. 1 is a block diagram of the present invention;
FIG. 2 is a spectral plot of the present invention;
FIG. 3 is a graph of the variation of light intensity at characteristic wavelength points of the present invention;
FIG. 4 is a chromaticity diagram used in the present invention;
fig. 5 is a flow chart of the present invention.
In the figure: the device comprises a computer 1, a visible/near infrared spectrometer 2, a collection end 3, a halogen lamp 4, a light source controller 5 and an optical fiber probe 6.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
The embodiment shown in fig. 1 is a detection method of an ascending relaxation spectrum detection apparatus, which includes a computer 1, a visible/near infrared spectrometer 2, a sample tray disposed in a light-tight sample cell, a halogen lamp 4, a light source controller 5 electrically connected to the halogen lamp, and a fiber probe 6; the sample cell is provided with a collection end, the optical fiber probe is respectively connected with the visible/near infrared spectrometer and the halogen lamp through a double-branched optical fiber, and the computer is in data connection with the visible/near infrared spectrometer; as shown in fig. 5, the method comprises the following steps:
step 100, placing a complete apple on a sample tray, and covering a sample cell with shading cloth;
step 200, turning on a light source controller, turning on a halogen lamp, turning on a visible/near-infrared spectrometer and preparing to acquire a detection signal;
step 300, light intensity control
Setting the light intensity rising rate and the measuring period T of the halogen lamp in the light source controller; dividing a measurement period T into N time periods; the halogen lamp gradually changes from the minimum light intensity to the maximum light intensity in each time period;
the computer controls the light source controller to quickly adjust the intensity of the diffuse reflection light to be above 100 candela under the condition that the intensity of the detected diffuse reflection signal is lower than 100 candela. T is 1s, and N is 5.
Step 400, irradiating the surface of the apple with light emitted by a halogen lamp, acquiring reflected light on the surface of the apple by using an optical fiber probe, and analyzing a detected spectral curve by using a visible/near infrared spectrometer;
step 410, selecting wavelengths of M characteristic peaks in the spectrum curve as characteristic wavelength points, and calculating to obtain light intensity of each characteristic wavelength point in the current time period and a light intensity change value in the previous time period as shown in fig. 3;
setting a variable i as the serial number of the characteristic wavelength point, wherein i is more than 1 and less than or equal to M;
setting a variable j as the serial number of each time period, wherein j is more than 1 and less than or equal to N;
setting the value of the spectral intensity change measured by the ith characteristic wavelength point in the j time period as hijThe following spectral intensity variation matrix is constructed:
Figure GDA0002298407530000071
step 420, setting the rate of change v of the spectral intensityijComprises the following steps:
Figure GDA0002298407530000072
Δ t is the time interval of adjacent time periods, and Δ t is 200 ms;
step 430, converting the array of spectrally measured intensities into an array of spectral rates of change:
Figure GDA0002298407530000081
selecting the midpoint of the measurement wavelength band of the spectral curve as shown in FIG. 3 as a reference point, and summing the sum v in the spectral curveijConnecting the corresponding characteristic wavelength point with the midpoint of the frequency band, and setting the included angle between the connecting line and the positive direction of the transverse axis as theta;
using formulas
Figure GDA0002298407530000082
For vijMaking a correction to obtain a corrected speed numberAccording to vij(θ)(ii) a Listing two different vij(θ)Equation of line T between velocity dataijk(ii) a Using the equation of each link TijkCalculating to obtain the coordinates J of the intersection point of each connecting lineijk(ax，ay) P is usually sin (θ).
Step 500, imaging the optical characteristics of the tested sample;
selecting all velocity data vij(θ)Maximum velocity v ofmaxAnd minimum velocity vmin(ii) a Using formulas
Figure GDA0002298407530000083
Calculate each vij(θ)First imaging factor fl of1And a second imaging factor fl2(ii) a According to fl2Determining whether it belongs to yellow or green, and determining the color according to fl1Determining the chromaticity of yellow or green, and determining fl1And fl2Imaging the color between green and yellow to obtain an optical characteristic image;
and step 600, judging the quality of the sample.
The optical characteristic image is a printed four-color pattern comprising four standard colors: the C value represents cyan, the M value represents magenta, the Y value represents yellow, and the K value represents black; joining the colors 40 and 48 in the image according to the chromaticity diagram shown in fig. 4 to form a 16-step color region segment, joining a yellowish region in yellow with a pale green region in green as a joint of yellow and green to form a continuous region of yellow and green;
and green pixel points with the Y value more than or equal to 80 in the optical characteristic image account for less than 15% of the total pixel points, and the computer judges that the apple quality is good.
M is 6, and 6 characteristic wavelength points are 607.67nm, 664.55nm, 730.94nm, 546.04nm, 799.11nm and 890.47nm wavelength points respectively.
It should be understood that this example is for illustrative purposes only and is not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.

Claims (5)

1. The detection method of the ascending relaxation spectrum detection device is characterized in that the ascending relaxation spectrum detection device comprises a computer (1), a visible/near infrared spectrometer (2), a sample tray arranged in a lighttight sample cell, a halogen lamp (4), a light source controller (5) electrically connected with the halogen lamp and an optical fiber probe (6); the sample cell is provided with a collection end (3), the optical fiber probe is respectively connected with the visible/near infrared spectrometer and the halogen lamp through a double-branched optical fiber, and the computer is in data connection with the visible/near infrared spectrometer; the method comprises the following steps:
(1-1) placing a food sample on a sample tray, and covering a sample cell with a shading cloth;
(1-2) starting a light source controller, starting a halogen lamp, starting a visible/near-infrared spectrometer and preparing to acquire a detection signal;
(1-3) setting the light intensity rising rate and the measurement period T of the halogen lamp in the light source controller; dividing a measurement period T into N time periods; the halogen lamp gradually changes from the minimum light intensity to the maximum light intensity in each time period;
(1-4) irradiating a food sample by using the halogen lamp, acquiring reflected light of the food by using an optical fiber probe, and analyzing a detected spectral curve by using a visible/near infrared spectrometer;
(1-4-1) selecting the wavelengths of M characteristic peaks in the spectral curve as characteristic wavelength points, and calculating the light intensity change value of each characteristic wavelength point in the current time period and the light intensity change value in the previous time period;
setting a variable i as the serial number of the characteristic wavelength point, wherein i is more than 1 and less than or equal to M;
setting a variable j as the serial number of each time period, wherein j is more than 1 and less than or equal to N;
setting the value of the spectral intensity change measured by the ith characteristic wavelength point in the j time period as hijThe following spectral intensity variation matrix is constructed:
Figure FDA0002356733750000021
(1-4-2) setting the rate of change v of the spectral intensityijComprises the following steps:
Figure FDA0002356733750000022
Δ t is the time interval of adjacent time segments;
(1-4-3) converting the spectral intensity change matrix into a spectral change rate matrix:
Figure FDA0002356733750000023
selecting the midpoint of the measurement wavelength band of the spectral curve as a reference point, and summing the midpoint of the spectral curve with vijConnecting the corresponding characteristic wavelength point with the midpoint of the frequency band, and setting the included angle between the connecting line and the positive direction of the transverse axis as theta;
using formulas
Figure FDA0002356733750000024
For vijCorrecting to obtain corrected speed data vij(θ)(ii) a Listing two different vij(θ)Equation of line T between velocity dataijk(ii) a Using the equation of each link TijkCalculating to obtain the coordinates J of the intersection point of each connecting lineijk(ax，ay) P is sin (theta);
(1-5) imaging the optical characteristics of the tested sample;
(1-6) judging the quality of the sample.
2. The method for detecting an ascending relaxation spectroscopy according to claim 1, wherein the step (1-5) comprises the steps of: selecting all velocity data vij(θ)Maximum velocity v ofmaxAnd minimum velocity vmin(ii) a Using formulas
Figure FDA0002356733750000031
Calculate each vij(θ)First imaging factor fl of1And a second imaging factor fl2(ii) a According to fl2Determining whether it belongs to yellow or green, and determining the color according to fl1Determining the chromaticity of yellow or green, and determining fl1And fl2And imaging the image in a certain color between green and yellow to represent the optical characteristic image of the tested sample.
3. The method for detecting an ascending relaxation spectroscopy according to claim 2, wherein the step (1-6) comprises the steps of:
the optical characteristic image is a printed four-color pattern comprising four standard colors: the C value represents cyan, the M value represents magenta, the Y value represents yellow, and the K value represents black; joining the colors referenced 40 and 48 in the image to form a 16-step color region segment; joining a yellowish region in yellow with a pale green region in green as a connecting portion of yellow and green to form a continuous region in yellow-green;
if the green pixel points with the Y value being more than or equal to 80 in the optical characteristic image account for less than 15% of the total pixel points, the computer judges that the quality of the sample is good;
if the green pixel points with the Y value being more than or equal to 80 in the optical characteristic image account for more than 15% and less than 45% of the total pixel points, the computer judges that the quality of the sample is qualified;
if the green pixel points with the Y value being more than or equal to 80 in the optical characteristic image account for more than 45% of the total pixel points, the computer judges that the sample has poor quality and is not edible.
4. The detecting method of an ascending relaxation spectrum detecting device according to claim 1, wherein M is 6, and the 6 characteristic points are respectively wavelength points of 607.67nm, 664.55nm, 730.94nm, 546.04nm, 799.11nm and 890.47 nm.
5. The method for detecting an ascending relaxation spectroscopy detection apparatus according to claim 1, 2, 3 or 4,
the computer controls the light source controller to quickly adjust the intensity of the diffuse reflection light to be above 100 candela under the condition that the intensity of the detected diffuse reflection signal is lower than 100 candela.
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Publication number Priority date Publication date Assignee Title
CN1831515A (en) * 2006-04-03 2006-09-13 浙江大学 Method for nondistructive discriminating crop seed variety using visible light and near-infrared spectrum technology
CN102507459A (en) * 2011-11-23 2012-06-20 中国农业大学 Method and system for quick lossless evaluation on freshness of fresh beef
KR20130141009A (en) * 2012-06-15 2013-12-26 브러커옵틱스코리아 주식회사 Apparatus and method for analyzing a composing solution in the semiconductor and the lcd process in real time by using the spectrometer of multiple channels type
JP2014016247A (en) * 2012-07-09 2014-01-30 Shimadzu Corp Wavelength variable monochromatic light source
CN203849162U (en) * 2014-04-28 2014-09-24 浙江大学 Small-size visible/near-infrared spectrum dynamic on-line collecting device for fruits
CN104174597A (en) * 2014-08-13 2014-12-03 山西农业大学 On-line rapid non-destructive detection and sorting device for slightly damaged fresh dates
CN104568794A (en) * 2015-01-01 2015-04-29 浙江工商大学 Method for detecting storage time of hairtail meat

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1831515A (en) * 2006-04-03 2006-09-13 浙江大学 Method for nondistructive discriminating crop seed variety using visible light and near-infrared spectrum technology
CN102507459A (en) * 2011-11-23 2012-06-20 中国农业大学 Method and system for quick lossless evaluation on freshness of fresh beef
KR20130141009A (en) * 2012-06-15 2013-12-26 브러커옵틱스코리아 주식회사 Apparatus and method for analyzing a composing solution in the semiconductor and the lcd process in real time by using the spectrometer of multiple channels type
JP2014016247A (en) * 2012-07-09 2014-01-30 Shimadzu Corp Wavelength variable monochromatic light source
CN203849162U (en) * 2014-04-28 2014-09-24 浙江大学 Small-size visible/near-infrared spectrum dynamic on-line collecting device for fruits
CN104174597A (en) * 2014-08-13 2014-12-03 山西农业大学 On-line rapid non-destructive detection and sorting device for slightly damaged fresh dates
CN104568794A (en) * 2015-01-01 2015-04-29 浙江工商大学 Method for detecting storage time of hairtail meat

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