CN107655837B - Detection method of descending relaxation spectrum detection device - Google Patents

Detection method of descending relaxation spectrum detection device Download PDF

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CN107655837B
CN107655837B CN201710732239.1A CN201710732239A CN107655837B CN 107655837 B CN107655837 B CN 107655837B CN 201710732239 A CN201710732239 A CN 201710732239A CN 107655837 B CN107655837 B CN 107655837B
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value
sample
halogen lamp
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CN107655837A (en
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郑豪男
周慧敏
邵晨宁
吴思妃
李莎怡
龚志涵
叶文俊
杨鑫
叶振龙
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 a descending 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 precision.

Description

Detection method of descending relaxation spectrum detection device
Technical Field
The invention relates to the technical field of spectrum detection, in particular to a detection method of a descending 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 a descending 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 a descending 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 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;
(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:
(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 hijForming a spectral intensity variation matrix：
Figure GDA0002233637640000031
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
Figure GDA0002233637640000041
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) Using the formulaCalculating y (t); wherein the content of the first and second substances,is a two-threshold value for the threshold value,
Figure GDA0002233637640000044
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 formula
Figure GDA0002233637640000045
Calculating the variance var (y (t)) of y (t), and detecting the variance var (Sta (t)) of the signal standard data Sta (t);
Figure GDA0002233637640000046
is the average value of y (t),
Figure GDA0002233637640000047
is the mean value of sta (t);
(3-7) Using the formulaCalculating 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:
Figure GDA0002233637640000051
preferably, the step (1-6) comprises the steps of:
(4-1) selecting all MCs in the correlation coefficient matrixi1，jiMaximum value of (1) MCmaxAnd minimum value MCmin，1≤i1≤M，1≤j1≤N-1；
(4-2) Using the formula
Calculate each MCi1，jiFirst imaging factor fl of1And a second imaging factor fl2；
(4-3) according to fl2Determining whether it belongs to yellow or green, and determining the color according to fl1Judging the chromaticity belonging to yellow or green, establishing the corresponding mapping from the characteristic value to the yellow-green area, and mapping fl1And fl2Imaging of a certain color mapped between green and yellow;
the imaged 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; the colors referenced 40 and 48 in the image are combined to form a 16-step color field.
Preferably, the step (1-7) comprises the steps of:
if the green pixel points with the Y value being more than or equal to 75 in the image account for less than 10% 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 75 in the image account for more than 10% and less than 25% 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 75 in the image account for more than 25% 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 a descent-type relaxation spectrum detection apparatus, which includes a computer 1, a visible/near-infrared spectrometer 2, a sample tray provided 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 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 400, the halogen lamp emits light to irradiate a food sample, the optical fiber probe acquires the reflected light of the food, and the visible/near infrared spectrometer analyzes the detected spectral curve shown in fig. 2;
step 410, selecting wavelengths of M characteristic peaks in the spectrum curve as characteristic wavelength points, and calculating a light intensity change value of each characteristic wavelength point in a current time period and a light intensity change value of each characteristic wavelength point in a 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 GDA0002233637640000081
step 500, correlation analysis;
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
Figure GDA0002233637640000082
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, using the formula
Figure GDA0002233637640000083
Calculating y (t); wherein the content of the first and second substances,
Figure GDA0002233637640000091
is a two-threshold value for the threshold value,
Figure GDA0002233637640000092
n (t) is white Gaussian noise;
step 550, establishing a standard matrix Sta (t) of the detection signal of the detected sample;
step 560, using the formula
Figure GDA0002233637640000093
Calculating the variance var (y (t)) of y (t), and detecting the variance var (Sta (t)) of the signal standard data Sta (t);
Figure GDA0002233637640000094
is the average value of y (t),
Figure GDA0002233637640000095
is the mean value of sta (t);
step 570, using the formula
Figure GDA0002233637640000096
Calculating covariance cov (y (t), sta (t)) of y (t) and sta (t);
step 580, using the formula
Figure GDA0002233637640000097
Calculating a correlation coefficient MC;
step 590, obtain a correlation coefficient matrix:
Figure GDA0002233637640000098
step 600, optical property imaging
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
Using formulas
Figure GDA0002233637640000099
Calculate each MCi1，jiFirst imaging factor fl of1And a second imaging factor fl2；
Step 630, according to fl2Determining whether it belongs to yellow or green, and determining the color according to fl1Judging the chromaticity belonging to yellow or green, establishing the corresponding mapping from the characteristic value to the yellow-green area, and mapping fl1And fl2Imaging of a certain color mapped between green and yellow;
the imaged 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; the 16-step color segment is formed by joining the colors referenced 40 and 48 in the image according to the chromaticity diagram shown in fig. 4.
Step 700, making a determination of sample quality.
And the green pixel points with the Y value more than or equal to 75 in the image account for less than 10 percent 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 detection method of a descending relaxation spectrum detection device is characterized in that the descending 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 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:
Figure FDA0002255624300000021
(1-5) correlation analysis;
(1-6) optical property imaging;
(1-7) judging the quality of the sample.
2. The detection method of a falling relaxation spectrum detection device 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
Figure FDA0002255624300000022
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) Using the formulaCalculating y (t); wherein the content of the first and second substances,
Figure FDA0002255624300000024
is a two-threshold value for the threshold value,
Figure FDA0002255624300000025
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
Figure FDA0002255624300000031
Calculating the variance var (y (t)) of y (t), and detecting the variance var (Sta (t)) of the signal standard data Sta (t);
Figure FDA0002255624300000032
is the average value of y (t),
Figure FDA0002255624300000033
is the mean value of sta (t);
(2-7) Using the formula
Figure FDA0002255624300000034
Calculating covariance cov (y (t), sta (t)) of y (t) and sta (t);
(2-8) Using the formula
Figure FDA0002255624300000035
Calculating a correlation coefficient MC;
(2-9) obtaining a correlation coefficient matrix:
Figure FDA0002255624300000036
3. the detection method of a falling relaxation spectrum detection device as claimed in claim 2, wherein the step (1-6) comprises the steps of:
(3-1) selecting all MCs in the correlation coefficient matrixi1，j1Maximum ofValue MCmaxAnd minimum value MCmin，1≤i1≤M，1≤j1≤N-1；
(3-2) Using the formula
Figure FDA0002255624300000037
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 fl1Judging the chromaticity belonging to yellow or green, establishing the corresponding mapping from the characteristic value to the yellow-green area, and mapping fl1And fl2Imaging of a certain color mapped between green and yellow;
the imaged 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; the colors referenced 40 and 48 in the image are combined to form a 16-step color field.
4. The method for detecting a falling relaxation spectrum sensor as claimed in claim 3, wherein the step (1-7) comprises the steps of:
if the percentage of the green pixel points with the Y value being more than or equal to 75 in the image in the total pixel points is less than 10%, the computer judges that the quality of the sample is good;
if the percentage of the green pixel points with the Y value of more than or equal to 75 in the image in the total pixel points is more than or equal to 10 percent and the percentage of the green pixel points with the Y value of more than or equal to 75 in the total pixel points is less than 25 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 75 in the image to the total pixel points is more than or equal to 25 percent, the computer judges that the sample has poor quality and is not edible.
5. The detecting method of a descending relaxation spectrum detecting device as claimed in claim 1, wherein M is 6, and the 6 wavelength characteristic points are respectively 607.67nm, 664.55nm, 730.94nm, 546.04nm, 799.11nm and 890.47nm wavelength points.
6. The detection method of the falling relaxation spectrum detecting device 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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