CN107655838B - Method for detecting device by relaxation spectrum - Google Patents
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- CN107655838B CN107655838B CN201710732281.3A CN201710732281A CN107655838B CN 107655838 B CN107655838 B CN 107655838B CN 201710732281 A CN201710732281 A CN 201710732281A CN 107655838 B CN107655838 B CN 107655838B
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- 238000001228 spectrum Methods 0.000 title claims abstract description 33
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- 229910052736 halogen Inorganic materials 0.000 claims abstract description 25
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- 239000013307 optical fiber Substances 0.000 claims abstract description 25
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- 239000011159 matrix material Substances 0.000 claims description 15
- 238000003384 imaging method Methods 0.000 claims description 12
- 238000005259 measurement Methods 0.000 claims description 10
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- 239000000126 substance Substances 0.000 claims description 5
- 238000010219 correlation analysis Methods 0.000 claims description 3
- 239000004744 fabric Substances 0.000 claims description 3
- 230000001678 irradiating effect Effects 0.000 claims description 3
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- 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
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- 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
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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- 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
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Abstract
The invention discloses a relaxation spectrum detection method, 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 plurality of acquisition ends for fixing the optical fiber probe, 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 precision.
Description
Technical Field
The invention relates to the technical field of spectrum detection, in particular to a method of a 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 method of a 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 method for detecting a relaxation spectrum 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 plurality of acquisition ends for fixing the optical fiber probes, the optical fiber probes are respectively connected with the visible/near-infrared spectrometer and the halogen lamp through double-branched optical fibers, 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; the sample collecting ends are distributed on the inner wall of the sample pool above the food sample in an annular mode at equal intervals, and the optical fiber probe is fixed on one collecting end;
(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 a light intensity reduction rate and a 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 maximum light intensity to the minimum 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) correlation analysis;
(1-6) optical property imaging;
the optical fiber probe is switched to the other acquisition end, and the step (1-3) is returned; when all the collection ends are used, the step (1-7) is carried out;
(1-7) 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:
selecting the wavelengths of M characteristic peaks in the spectral curve as characteristic wavelength points, and calculating the light intensity of each characteristic wavelength point in the current time period and the light intensity change value of each characteristic wavelength point 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:
preferably, the step (1-5) comprises the steps of:
(3-1) taking each line of data of the spectral intensity variation matrix as a detection signal x (t), and calculating the maximum value x of each line of datamaxAnd the minimum value xmin；
(3-2) Using the formula f1(t)＝(x(t)-xmin)/(xmax-xmin) Calculating a discrete signal f1(t)；
(3-3) Using the formula
Discrete signal f1(t) fitting to a periodic function f (t); s (t) ═ s1，s2，s3，......，sn) S (t) is obtained by discretizing a periodic function f (t);
(3-4) is provided with
Wherein the content of the first and second substances,
is a two-threshold value for the threshold value,
n (t) is white Gaussian noise;
(3-5) establishing a standard matrix Sta (t) of the detection signals of the detected sample,
(3-6) Using the formulaCalculating a variance var (y (t)) of y (t) and a variance var (sta (t)) of detection signal standard data sta (t);is the average value of y (t),
is the mean value of sta (t);
(3-7) Using the formula
Calculating covariance cov (y (t), sta (t)) of y (t) and sta (t);
(3-8) Using the formula
Calculating a correlation coefficient MC;
(3-9) obtaining a correlation coefficient matrix:
preferably, the step (1-6) comprises the steps of:
(4-1) selecting all MCs in the correlation coefficient matrixi1，j1Maximum value of (1) MCmaxAnd minimum value MCmin，1≤i1≤M，1≤j1≤N-1；
(4-2) Using the formula
(4-3) 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 in a certain color, mapped between green and yellow, the image being 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; combining the colors 40 and 48 in the image to form a 16-step color segment, resulting in an optical property image for each measurement location;
(4-4) setting R optical characteristicsThe chroma of each pixel point of the sexual image is respectively IM1, IM2, … and IMR, and the angle formed by the fiber probe and the horizontal plane is respectively sigma when each image is measured1，σ2，……，σR；
Calculating the chroma of each pixel point of the fused image by using a formula
And calculating a fused image IM.
Preferably, the step (1-7) comprises the steps of:
the fused image IM is a printed four-color mode and comprises 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; jointing the light yellow area and the light green area of the fused image IM to form a yellow-green continuous area as a yellow-green connecting part;
if the green pixel points with the Y value being more than or equal to 70 in the fused image IM account for less than 12% 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 70 in the fused image IM account for more than 12% and less than 35% 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 70 in the fused image IM account for more than 35 percent 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 method of a relaxation spectroscopy detection apparatus comprising a computer 1, a visible/near infrared spectrometer 2, a sample tray provided in a sample cell that is opaque to light, a halogen lamp 4, a light source controller 5 and a fiber probe 6 electrically connected to the halogen lamp; 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:
Setting the light intensity reduction 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;
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 410, selecting wavelengths of M characteristic peaks in the spectral curve shown in fig. 2 as characteristic wavelength points, and calculating to obtain light intensity of each characteristic wavelength point in the current time period and light intensity change values of the previous time period 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:
step 420, setting the rate of change v of the spectral intensityijComprises the following steps:
selecting the midpoint of the measurement wavelength band of the spectral curve as shown in FIG. 2 as a reference point, and summing the sum v in the spectral curveijAnd setting the included angle between the connecting line and the positive direction of the transverse axis as theta.
Step 510, using each line of data of the spectrum intensity variation matrix as a detection signal x (t), calculating a maximum value x of each line of datamaxAnd the minimum value xmin；
Step 520, using the formula f1(t)＝(x(t)-xmin)/(xmax-xmin) Calculating a discrete signal f1(t)；
Step 530, using the formula
Discrete signal f1(t) fitting to a periodic function f (t); s (t) ═ s1，s2，s3，......，sn) S (t) is obtained by discretizing a periodic function f (t);
step 540, setWherein the content of the first and second substances,
is a two-threshold value for the threshold value,
n (t) is white Gaussian noise;
step 550, establishing a standard matrix Sta (t) of the detected signal of the detected sample,
step 560, using the formula
Calculating a variance var (y (t)) of y (t) and a variance var (sta (t)) of detection signal standard data sta (t);
is the average value of y (t),
is the mean value of sta (t);
step 570, using the formulaCalculating covariance cov (y (t), sta (t)) of y (t) and sta (t);
step 580, using the formula
Calculating a correlation coefficient MC;
step 590, obtain a correlation coefficient matrix:
Step 610, select all MCs in the correlation coefficient matrixi1，j1Maximum value of (1) MCmaxAnd minimum value MCmin，1≤i1≤M，1≤j1≤N-1；
Step 620, using the formula
Step 630, 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 a certain color between green and yellow to obtain an optical characteristic image of each measurement position;
step 640, setting the chromaticity of each pixel point of the R optical characteristic images as IM1, IM2, … and IMR respectively, and setting the angle formed by the optical fiber probe and the horizontal plane as sigma respectively when each image is measured1，σ2，……，σR；
Calculating the chroma of each pixel point of the fused image by using a formula
And calculating a fused image IM.
The fused image IM is a printed four-color mode and comprises 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 fused image according to the chromaticity diagram as in fig. 4 to form a 16-step color segment;
and green pixel points with the Y value being more than or equal to 70 in the fused image IM account for less than 12% of the total pixel points, and the computer judges the good quality of the sample.
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 (6)
1. A method of a relaxation spectrum detection device is characterized in that the relaxation spectrum detection device comprises a computer (1), a visible/near infrared spectrometer (2), a sample tray arranged in a lightproof 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 plurality of acquisition ends (3) for fixing the optical fiber probe, the optical fiber probe is respectively connected with the visible/near infrared spectrometer and the halogen lamp through a double-branch 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; the sample collecting ends are distributed on the inner wall of the sample pool above the food sample in an annular mode at equal intervals, and the optical fiber probe is fixed on one collecting end;
(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 a light intensity reduction rate and a 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 maximum light intensity to the minimum 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;
selecting the wavelengths of M characteristic peaks in the spectral curve as characteristic wavelength points, and calculating the light intensity of each characteristic wavelength point in the current time period and the light intensity change value of each characteristic wavelength point 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:
(1-5) correlation analysis;
(1-6) optical property imaging;
the optical fiber probe is switched to the other acquisition end, and the step (1-3) is returned; when all the collection ends are used, the step (1-7) is carried out;
(1-7) judging the quality of the sample.
2. The method for detecting a relaxation spectrum according to claim 1, wherein the step (1-5) comprises the steps of:
(2-1) taking each line of data of the spectral intensity variation matrix as a detection signal x (t), and calculating the maximum value x of each line of datamaxAnd the minimum value xmin；
(2-2) Using the formula f1(t)＝(x(t)-xmin)/(xmax-xmin) Calculating a discrete signal f1(t)；
(2-3) Using the formula
Discrete signal f1(t) fitting to a periodic function f (t); s (t) ═ s1，s2，s3，......，sn) S (t) is obtained by discretizing a periodic function f (t);
(2-4) is provided with
Wherein the content of the first and second substances,
is a two-threshold value for the threshold value,
n (t) is white Gaussian noise;
(2-5) establishing a standard matrix Sta (t) of the detection signals of the detected sample,
(2-6) Using the formula
Calculating a variance var (y (t)) of y (t) and a variance var (sta (t)) of detection signal standard data sta (t);is the average value of y (t),
is the mean value of sta (t);
(2-7) Using the formula
Calculating covariance cov (y (t), sta (t)) of y (t) and sta (t);
(2-8) Using the formula
Calculating a correlation coefficient MC;
(2-9) obtaining a correlation coefficient matrix:
3. the method for detecting a relaxation spectrum according to claim 2, wherein the step (1-6) comprises the steps of:
(3-1) selecting all MCs in the correlation coefficient matrixi1，j1Maximum value of (1) MCmaxAnd minimum value MCmin，1≤i1≤M，1≤j1≤N-1；
(3-2) Using the formula
Calculate each MCi1，j1First imaging factor fl of1And a second imaging factor fl2；
(3-3) 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 a certain color between green and yellow to obtain an optical characteristic image of each measurement position;
(3-4) setting the chromaticity of each pixel point of the R optical characteristic images as IM1, IM2, … and IMR respectively, and setting the angle formed by the optical fiber probe and the horizontal plane of each image as sigma respectively when measuring1，σ2，……，σR；
Calculating the chroma of each pixel point of the fused image by using a formula
And calculating a fused image IM.
4. The method for detecting a relaxation spectrum according to claim 3, wherein the step (1-7) comprises the steps of:
the fused image IM is a printed four-color mode and comprises 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; jointing the light yellow area and the light green area of the fused image IM to form a yellow-green continuous area as a yellow-green connecting part;
if the percentage of the green pixel points with the Y value being more than or equal to 70 in the fused image IM in the total pixel points is less than 12%, the computer judges that the quality of the sample is good;
if the percentage of the green pixel points with the Y value being more than or equal to 70 in the fused image IM in the total pixel points is more than or equal to 12 percent and the percentage of the green pixel points with the Y value being more than or equal to 70 in the fused image IM in the total pixel points is less than 35 percent, the computer judges that the quality of the sample is qualified;
if the percentage of the green pixel points with the Y value being more than or equal to 70 in the fused image IM in the total pixel points is more than or equal to 35 percent, the computer judges that the sample has poor quality and is not edible.
5. The method for detecting a relaxation spectrum according to claim 1, wherein M is 6, and the 6 wavelength characteristic points are 607.67nm, 664.55nm, 730.94nm, 546.04nm, 799.11nm and 890.47nm wavelength points, respectively.
6. The method for detecting a relaxation spectrum according to claim 1, 2, 3, 4 or 5, wherein,
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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