CN112557328B - Pork quality nondestructive testing device and method - Google Patents
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- 235000015277 pork Nutrition 0.000 title claims abstract description 107
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000009659 non-destructive testing Methods 0.000 title claims abstract description 16
- 239000000523 sample Substances 0.000 claims abstract description 98
- 238000001228 spectrum Methods 0.000 claims abstract description 53
- 229910052736 halogen Inorganic materials 0.000 claims abstract description 39
- 229910052721 tungsten Inorganic materials 0.000 claims abstract description 39
- 239000010937 tungsten Substances 0.000 claims abstract description 39
- 230000003287 optical effect Effects 0.000 claims abstract description 36
- 238000001514 detection method Methods 0.000 claims abstract description 35
- 150000002367 halogens Chemical class 0.000 claims abstract description 33
- WFKWXMTUELFFGS-UHFFFAOYSA-N tungsten Chemical compound [W] WFKWXMTUELFFGS-UHFFFAOYSA-N 0.000 claims abstract description 33
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 12
- 238000012937 correction Methods 0.000 claims description 12
- 229920006395 saturated elastomer Polymers 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 6
- 229910052757 nitrogen Inorganic materials 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 6
- 230000003595 spectral effect Effects 0.000 claims description 6
- -1 tungsten halogen Chemical class 0.000 claims description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 6
- 238000012935 Averaging Methods 0.000 claims description 3
- 238000010561 standard procedure Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 abstract description 9
- 230000005611 electricity Effects 0.000 abstract description 4
- 238000010521 absorption reaction Methods 0.000 description 2
- 238000000862 absorption spectrum Methods 0.000 description 2
- 125000003636 chemical group Chemical group 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 230000031700 light absorption Effects 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 238000000985 reflectance spectrum Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
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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
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
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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
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3554—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for determining moisture content
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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
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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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
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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Abstract
The invention discloses a pork quality nondestructive testing device and method. The device includes computer, spectrum appearance and detection mechanism, detection mechanism includes the black case, be equipped with optical probe, testing platform in the black case, can drive riser and two halogen tungsten lamps that testing platform goes up and down, testing platform is last to be equipped with the district of placing that is used for placing the pork sample that awaits measuring, and two halogen tungsten lamp symmetries set up upper left side, the upper right side at testing platform, optical probe is located and places the district directly over, optical probe is connected with the input electricity of spectrum appearance, the computer is connected with the output of spectrum appearance, halogen tungsten lamp electricity respectively. The invention can rapidly and nondestructively detect the pork quality, has simple operation and improves the working efficiency.
Description
Technical Field
The invention relates to the technical field of pork detection, in particular to a pork quality nondestructive detection device and method.
Background
The pork is rich in nutrients such as protein and has high edible value. However, since slaughtered pork enters the circulation stage, pork stored for different storage periods has great differences in quality, taste and the like, and is directly related to food safety and consumer health.
The traditional pork quality inspection methods comprise physicochemical inspection, a weighing method, a drying method and the like, and the methods have the problems of complex operation, large influence of the methods on precision, repeatability and the like, and cannot carry out on-site rapid detection.
Disclosure of Invention
In order to solve the technical problems, the invention provides a pork quality nondestructive testing device and a pork quality nondestructive testing method, which can rapidly and nondestructively test the pork quality.
In order to solve the problems, the invention adopts the following technical scheme:
the invention discloses a pork quality nondestructive testing device, which comprises a computer, a spectrometer and a detection mechanism, wherein the detection mechanism comprises a black box, an optical probe, a detection platform, a lifter capable of driving the detection platform to ascend and descend and two halogen tungsten lamps are arranged in the black box, a placing area for placing a pork sample to be tested is arranged on the detection platform, the two halogen tungsten lamps are symmetrically arranged at the upper left and upper right of the detection platform, the optical probe is positioned right above the placing area, the optical probe is electrically connected with the input end of the spectrometer, and the computer is respectively electrically connected with the output end of the spectrometer and the halogen tungsten lamps.
In the scheme, the pork sample to be detected is pork with the thickness of 6cm multiplied by 5.5cm multiplied by 2.5cm, and is balanced for 20min at the room temperature of 25 ℃ before detection. During detection, a pork sample to be detected is placed in the placement area, and the height of the detection platform is adjusted through the lifter so that the distance between the surface of the pork sample to be detected and the optical probe is 1.2 cm;
the computer controls the light emitted by the halogen tungsten lamp to irradiate the pork sample to be detected, the intensity of the light emitted by the halogen tungsten lamp is gradually increased from small to large according to a set rate, the optical probe samples diffuse reflection light data of the pork sample to be detected once at intervals of T and sends the diffuse reflection light data to the spectrometer, the spectrometer draws a diffuse reflection light spectrum curve in a rectangular coordinate system with diffuse reflection light wavelength as a horizontal coordinate and diffuse reflection light intensity as a vertical coordinate and sends the diffuse reflection light spectrum curve to the computer, the spectrum range of the diffuse reflection light spectrum curve is 400-1000 nm, the optical probe samples the diffuse reflection light data of the pork sample to be detected n +1 times, and the spectrometer draws corresponding n +1 diffuse reflection light spectrum curves;
the computer calculates corresponding characteristic values spk according to the n +1 diffuse reflection spectrum curves;
substituting the characteristic value spk into the pork quality prediction model
And calculating the value of the quality prediction factor qr, if the quality prediction factor qr is more than or equal to a set value A, judging that the pork sample to be detected is not fresh, and if the quality prediction factor qr is less than the set value A, judging that the pork sample to be detected is fresh, wherein a, b, c and f are constants.
The detection method does not damage the pork sample to be detected, has high detection speed and simple operation, and improves the working efficiency.
Preferably, the spectrometer is a visible/near infrared spectrometer with a spectral resolution of 1 nm.
Preferably, the tungsten halogen lamp is a 12W tungsten halogen lamp.
Preferably, the optical probe is further provided with a distance measuring sensor for detecting the distance between the optical probe and the pork sample to be detected, and the computer is electrically connected with the distance measuring sensor and the lifter respectively. The distance measuring sensor detects the distance between the optical probe and the pork sample to be measured, and the computer controls the lifter to lift and lower so that the distance between the optical probe and the pork sample to be measured is adjusted to a set value.
The invention relates to a pork quality nondestructive testing method, which is used for the pork quality nondestructive testing device and comprises the following steps:
s1: the method comprises the steps that light emitted by a halogen tungsten lamp irradiates a pork sample to be detected, the intensity of the light emitted by the halogen tungsten lamp is gradually increased from small to large according to a set rate, an optical probe samples diffuse reflection light data of the pork sample to be detected once at intervals of T and sends the diffuse reflection light data to a spectrometer, the spectrometer draws a diffuse reflection spectrum curve in a rectangular coordinate system with the wavelength of the diffuse reflection light as a horizontal coordinate and the intensity of the diffuse reflection light as a vertical coordinate and sends the diffuse reflection spectrum curve to a computer, the spectrum range of the diffuse reflection spectrum curve is 400-1000 nm, the optical probe samples the diffuse reflection light data of the pork sample to be detected n +1 times, and the spectrometer draws corresponding n +1 diffuse reflection spectrum curves;
s2: the computer calculates corresponding characteristic values spk according to the n +1 diffuse reflection spectrum curves;
s3: substituting the characteristic value spk into the pork quality prediction model
And calculating the value of the quality prediction factor qr, if the quality prediction factor qr is more than or equal to a set value A, judging that the pork sample to be detected is not fresh, and if the quality prediction factor qr is less than the set value A, judging that the pork sample to be detected is fresh, wherein a, b, c and f are constants.
In the scheme, the computer accurately controls the current of the halogen tungsten lamp, so that the current of the halogen tungsten lamp is increased from small to large according to a certain speed, and the brightness of the halogen tungsten lamp is gradually increased from weak to strong according to a certain speed. In the process of increasing the illumination intensity of the halogen tungsten lamp, different chemical groups in pork tissues have different absorption capacities for light with different frequencies due to different chemical properties, and diffuse reflection absorption spectrums detected by an optical probe are different due to different light absorption emitted by the halogen tungsten lamp, so that a pork quality prediction model is established by utilizing different spectrum data.
Preferably, the method for calculating the corresponding characteristic value spk by the computer according to the n +1 diffuse reflection spectrum curves is as follows:
m1: taking the wavelength value corresponding to the peak point in the diffuse reflection spectrum curve as a wavelength characteristic point, acquiring m wavelength characteristic points in the diffuse reflection spectrum curve, calculating a characteristic data group corresponding to each wavelength characteristic point, and calculating a characteristic data group corresponding to the w-th wavelength characteristic point, wherein w is more than or equal to 1 and less than or equal to m:
obtaining the characteristic area corresponding to the w-th wavelength characteristic point in each diffuse reflection spectrum curve, calculating the characteristic area change rate of two adjacent sampling times, and obtaining n characteristic area change rates
Wherein Δ SnwThe nth characteristic area change rate, h, of the w-th wavelength characteristic pointnwThe longitudinal coordinate value of a peak point corresponding to the w-th wavelength characteristic point of the diffuse reflection spectrum curve obtained by the nth sampling is shown, and delta lambda is the width of the peak point;
performing energy correction on the n characteristic area change rates to obtain n corresponding correction values Delta SE1w、ΔSE2w…ΔSEnw，ΔSEnw＝ΔSnw×EwWherein, Δ SEnwAn nth correction amount for the w-th wavelength characteristic point, EwIs the energy distribution percentage of the w wavelength characteristic point;
n correction amounts Δ SE1w、ΔSE2w…ΔSEnwThe characteristic data group corresponding to the w-th wavelength characteristic point is obtained;
m2: constructing a feature matrix D according to the feature data groups corresponding to the m wavelength feature points,
m3: performing quadratic spline interpolation on each line of data of the characteristic matrix D to obtain n curves x (t) corresponding to each line of data, performing the same data processing on the n curves x (t) to obtain n saturated resonance characteristic factors F, wherein the data processing on each curve x (t) comprises the following steps:
substituting x (t) into the nonlinear directional saturated resonance model:
function of saturation potential
The detection signal loading component coe (t) ═ tanh (ω t + ψ) + x (t),
wherein t is an interpolation variable, B is a real parameter, P is a real parameter, stis (t) is colored noise with uneven power spectral density function, mu is a real parameter, omega is a real parameter, psi is a real parameter,
adjusting the value of t, and enabling the nonlinear directional saturated resonance model to be t-trAnd (3) point location resonance, calculating a saturation resonance characteristic factor F:
wherein max | K (x (t)) | is the maximum value of | K (x (t)) |;
m4: averaging the n saturated resonance characteristic factors F to obtain an average value which is the value of the characteristic value spk.
Preferably, the values of a, b, c and f in the pork quality prediction model are calculated by the following method:
n1: taking W pork samples with different freshness, wherein W is more than or equal to 3, and respectively detecting each pork sample by adopting the method of the steps S1-S2 to obtain a characteristic value spk corresponding to each pork sample;
n2: detecting pH value, water content WR and volatile basic nitrogen TVBN of each pork sample by adopting a national standard method;
n3: and substituting the characteristic value spk, the pH value, the water content WR and the volatile basic nitrogen TVBN corresponding to each pork sample into a formula spk ═ a multiplied by pH + b multiplied by WR + c multiplied by TVBN + f to obtain W equations, and solving the equation set to calculate the values of a, b, c and f.
The invention has the beneficial effects that: can be fast nondestructive detect out pork quality, easy operation has improved work efficiency.
Drawings
FIG. 1 is a schematic structural view of an embodiment;
FIG. 2 is a flow chart of an embodiment;
FIG. 3 is a schematic representation of a diffuse reflectance spectrum curve;
fig. 4 is a graph of qr value change for different storage times for pork samples.
In the figure: 1. the device comprises a computer, 2, a spectrometer, 3, a black box, 4, an optical probe, 5, a detection platform, 6, a lifter, 7, a halogen tungsten lamp, 8, a distance measuring sensor, 9 and a pork sample to be detected.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b): the pork quality nondestructive testing device of this embodiment, as shown in fig. 1, including computer 1, spectrum appearance 2 and detection mechanism, detection mechanism includes black case 3, be equipped with optical probe 4 in the black case 3, testing platform 5, can drive riser 6 and two halogen tungsten lamp 7 that testing platform 5 goes up and down, be equipped with the district of placing that is used for placing the pork sample 9 that awaits measuring on testing platform 5, two halogen tungsten lamp 7 symmetries set up the upper left side at testing platform 5, the upper right side, optical probe 4 is located directly over the district of placing, still be equipped with the range finding sensor 8 that is used for detecting the distance between optical probe 4 and the pork sample 9 that awaits measuring on optical probe 4, optical probe 4 is connected with the input electricity of spectrum appearance 2, computer 1 respectively with the output of spectrum appearance 2, halogen tungsten lamp 7, range finding sensor 8, riser 6 electricity is connected.
The spectrometer 2 is a visible/near infrared spectrometer with a spectral resolution of 1 nm. The tungsten halogen lamp 7 is a 12W tungsten halogen lamp.
In the scheme, the pork sample to be detected is pork with the thickness of 6cm multiplied by 5.5cm multiplied by 2.5cm, and is balanced for 20min at the room temperature of 25 ℃ before detection. During detection, the pork sample to be detected is placed in the placement area, the distance between the optical probe and the pork sample to be detected is detected by the distance measuring sensor, and the elevator is controlled by the computer to lift so that the distance between the optical probe and the pork sample to be detected is adjusted to 1.2 cm;
the computer controls the light emitted by the halogen tungsten lamp to irradiate the pork sample to be detected, the intensity of the light emitted by the halogen tungsten lamp is gradually increased from small to large according to a set rate, the optical probe samples diffuse reflection light data of the pork sample to be detected once at intervals of T and sends the diffuse reflection light data to the spectrometer, the spectrometer draws a diffuse reflection light spectrum curve in a rectangular coordinate system with diffuse reflection light wavelength as a horizontal coordinate and diffuse reflection light intensity as a vertical coordinate and sends the diffuse reflection light spectrum curve to the computer, the spectrum range of the diffuse reflection light spectrum curve is 400-1000 nm, the optical probe samples the diffuse reflection light data of the pork sample to be detected n +1 times, and the spectrometer draws corresponding n +1 diffuse reflection light spectrum curves;
the computer calculates corresponding characteristic values spk according to the n +1 diffuse reflection spectrum curves;
substituting the characteristic value spk into the pork quality prediction model
And calculating the value of the quality prediction factor qr, if the quality prediction factor qr is more than or equal to a set value A, judging that the pork sample to be detected is not fresh, and if the quality prediction factor qr is less than the set value A, judging that the pork sample to be detected is fresh, wherein a, b, c and f are constants.
The detection method does not damage the pork sample to be detected, has high detection speed and simple operation, and improves the working efficiency.
As shown in fig. 2, the nondestructive testing method for pork quality in this embodiment is used for the nondestructive testing apparatus for pork quality, and includes the following steps:
s1: the method comprises the steps that light emitted by a halogen tungsten lamp irradiates a pork sample to be detected, the intensity of the light emitted by the halogen tungsten lamp is gradually increased from small to large according to a set rate, an optical probe samples diffuse reflection light data of the pork sample to be detected once at intervals of T and sends the diffuse reflection light data to a spectrometer, the spectrometer draws a diffuse reflection spectrum curve in a rectangular coordinate system with the wavelength of the diffuse reflection light as a horizontal coordinate and the intensity of the diffuse reflection light as a vertical coordinate and sends the diffuse reflection spectrum curve to a computer, the spectrum range of the diffuse reflection spectrum curve is 400-1000 nm, the optical probe samples the diffuse reflection light data of the pork sample to be detected n +1 times, and the spectrometer draws corresponding n +1 diffuse reflection spectrum curves;
s2: the computer calculates corresponding characteristic values spk according to the n +1 diffuse reflection spectrum curves;
s3: substituting the characteristic value spk into the pork quality prediction model
Calculating the value of the quality prediction factor qr, and judging if the quality prediction factor qr is more than or equal to a set value AAnd (3) keeping the to-be-detected pork sample not fresh, and if the quality prediction factor qr is less than a set value A, judging that the to-be-detected pork sample is fresh, wherein a, b, c and f are constants.
The method for calculating the corresponding characteristic value spk by the computer according to the n +1 diffuse reflection spectrum curves is as follows:
m1: taking the wavelength value corresponding to the peak point in the diffuse reflection spectrum curve as a wavelength characteristic point, acquiring m wavelength characteristic points in the diffuse reflection spectrum curve, calculating a characteristic data group corresponding to each wavelength characteristic point, and calculating a characteristic data group corresponding to the w-th wavelength characteristic point, wherein w is more than or equal to 1 and less than or equal to m:
obtaining the characteristic area corresponding to the w-th wavelength characteristic point in each diffuse reflection spectrum curve, calculating the characteristic area change rate of two adjacent sampling times, and obtaining n characteristic area change rates
Wherein Δ SnwN-th characteristic area change rate, h, of w-th wavelength characteristic pointnwA longitudinal coordinate value of a peak point corresponding to the w-th wavelength characteristic point of the diffuse reflection spectrum curve obtained by the nth sampling is obtained, and delta lambda is the width of the peak point;
performing energy correction on the n characteristic area change rates to obtain n corresponding correction values Delta SE1w、ΔSE2w…ΔSEnw，ΔSEnw＝ΔSnw×EwWherein, Δ SEnwAn nth correction amount for the w-th wavelength characteristic point, EwIs the energy distribution percentage of the w wavelength characteristic point;
n correction amounts Δ SE1w、ΔSE2w…ΔSEnwThe characteristic data group corresponding to the w-th wavelength characteristic point is obtained;
m2: constructing a feature matrix D according to the feature data groups corresponding to the m wavelength feature points,
m3: performing quadratic spline interpolation on each line of data of the characteristic matrix D to obtain n curves x (t) corresponding to each line of data, performing the same data processing on the n curves x (t) to obtain n saturated resonance characteristic factors F, wherein the data processing on each curve x (t) comprises the following steps:
substituting x (t) into the nonlinear directional saturated resonance model:
function of saturation potential
The detection signal loading component coe (t) ═ tanh (ω t + ψ) + x (t),
wherein t is an interpolation variable, B is a real parameter, P is a real parameter, stis (t) is a colored noise with uneven power spectral density function, mu is a real parameter, omega is a real parameter, psi is a real parameter,
adjusting the value of t, and enabling the nonlinear directional saturated resonance model to be t-trAnd (3) point location resonance, calculating a saturation resonance characteristic factor F:
wherein max | K (x (t)) | is the maximum value of | K (x (t)) |;
m4: averaging the n saturated resonance characteristic factors F to obtain an average value which is the value of the characteristic value spk.
In the scheme, the computer accurately controls the current of the halogen tungsten lamp, so that the current of the halogen tungsten lamp is increased from small to large according to a certain speed, and the brightness of the halogen tungsten lamp is gradually increased from weak to strong according to a certain speed. In the process of increasing the illumination intensity of the halogen tungsten lamp, different chemical groups in pork tissues have different absorption capacities for light with different frequencies due to different chemical properties, and diffuse reflection absorption spectrums detected by an optical probe are different due to different light absorption emitted by the halogen tungsten lamp, so that a pork quality prediction model is established by utilizing different spectrum data.
As shown in fig. 3, the ordinate of the peak point corresponding to a certain wavelength characteristic point is h, Δ λ is the width of the peak point, and the characteristic area corresponding to the wavelength characteristic point is h × Δ λ.
The values of a, b, c and f in the pork quality prediction model are calculated by the following method:
n1: taking W pork samples with different freshness, wherein W is more than or equal to 3, and respectively detecting each pork sample by adopting the method of the steps S1-S2 to obtain a characteristic value spk corresponding to each pork sample;
n2: detecting pH value, water content WR and volatile basic nitrogen TVBN of each pork sample by adopting a national standard method;
n3: substituting the characteristic value spk, the pH value, the water content WR and the volatile basic nitrogen TVBN corresponding to each pork sample into a formula spk which is a multiplied by pH + b multiplied by WR + c multiplied by TVBN + f to obtain W equations, and solving the equation set to calculate the values of a, b, c and f.
The set point a was 6.5, and the set point a was obtained by:
and (3) taking a fresh pork sample, detecting the qr value of the pork sample at set time intervals, detecting whether the pork sample is fresh by adopting a traditional detection method, and determining the detected qr value corresponding to the time point when the pork sample is detected to be stale for the first time by adopting the traditional detection method as a set value A.
For example: the qr value change curve of a certain pork sample at different storage times is shown in fig. 4, and it can be seen from fig. 4 that the qr value of the pork sample increases with the increase of the storage time. The pork samples became stale on day 6 of storage, and as can be seen in fig. 4, the qr value at day 6 was greater than 6.5.
Claims (4)
1. A pork quality nondestructive testing method is used for a pork quality nondestructive testing device, the pork quality nondestructive testing device comprises a computer (1), a spectrometer (2) and a detection mechanism, the detection mechanism comprises a black box (3), an optical probe (4), a detection platform (5), a lifter (6) capable of driving the detection platform (5) to ascend and descend and two halogen tungsten lamps (7) are arranged in the black box (3), a placing area for placing a pork sample (9) to be tested is arranged on the detection platform (5), the two halogen tungsten lamps (7) are symmetrically arranged at the upper left and upper right of the detection platform (5), the optical probe (4) is positioned right above the placing area, the optical probe (4) is electrically connected with the input end of the spectrometer (2), the computer (1) is electrically connected with the output end of the spectrometer (2) and the halogen tungsten lamps (7) respectively, the method is characterized by comprising the following steps:
s1: the method comprises the steps that light emitted by a halogen tungsten lamp irradiates a pork sample to be detected, the intensity of the light emitted by the halogen tungsten lamp is gradually increased from small to large according to a set rate, an optical probe samples diffuse reflection light data of the pork sample to be detected once at intervals of T and sends the diffuse reflection light data to a spectrometer, the spectrometer draws a diffuse reflection spectrum curve in a rectangular coordinate system with the wavelength of the diffuse reflection light as a horizontal coordinate and the intensity of the diffuse reflection light as a vertical coordinate and sends the diffuse reflection spectrum curve to a computer, the spectrum range of the diffuse reflection spectrum curve is 400-1000 nm, the optical probe samples the diffuse reflection light data of the pork sample to be detected n +1 times, and the spectrometer draws corresponding n +1 diffuse reflection spectrum curves;
s2: the computer calculates corresponding characteristic values spk according to the n +1 diffuse reflection spectrum curves;
s3: substituting the characteristic value spk into the pork quality prediction model
Calculating the value of the quality prediction factor qr, if the quality prediction factor qr is more than or equal to a set value A, judging that the pork sample to be detected is not fresh, and if the quality prediction factor qr is less than the set value A, judging that the pork sample to be detected is fresh, wherein a, b, c and f are constants;
the method for calculating the corresponding characteristic value spk by the computer according to the n +1 diffuse reflection spectrum curves is as follows:
m1: taking the wavelength value corresponding to the peak point in the diffuse reflection spectrum curve as a wavelength characteristic point, acquiring m wavelength characteristic points in the diffuse reflection spectrum curve, calculating a characteristic data group corresponding to each wavelength characteristic point, and calculating a characteristic data group corresponding to the w-th wavelength characteristic point, wherein w is more than or equal to 1 and less than or equal to m:
obtaining the characteristic area corresponding to the w-th wavelength characteristic point in each diffuse reflection spectrum curve, calculating the characteristic area change rate of two adjacent sampling times, and obtaining n characteristic area change rates delta S1w、ΔS2w…ΔSnw，
Wherein Δ SnwThe nth characteristic area change rate, h, of the w-th wavelength characteristic pointnwThe longitudinal coordinate value of a peak point corresponding to the w-th wavelength characteristic point of the diffuse reflection spectrum curve obtained by the nth sampling is shown, and delta lambda is the width of the peak point;
performing energy correction on the n characteristic area change rates to obtain n corresponding correction values Delta SE1w、ΔSE2w…ΔSEnw，ΔSEnw＝ΔSnw×EwWherein, Δ SEnwAn nth correction amount for the w-th wavelength characteristic point, EwIs the energy distribution percentage of the w wavelength characteristic point;
n correction amounts Δ SE1w、ΔSE2w…ΔSEnwThe characteristic data group corresponding to the w-th wavelength characteristic point is obtained;
m2: constructing a feature matrix D according to the feature data groups corresponding to the m wavelength feature points,
m3: performing quadratic spline interpolation on each line of data of the characteristic matrix D to obtain n curves x (t) corresponding to each line of data, performing the same data processing on the n curves x (t) to obtain n saturated resonance characteristic factors F, wherein the data processing on each curve x (t) comprises the following steps:
substituting x (t) into the nonlinear directional saturated resonance model:
function of saturation potential
The detection signal loading component coe (t) ═ tanh (ω t + ψ) + x (t),
wherein t is an interpolation variable, B is a real parameter, P is a real parameter, stis (t) is colored noise with uneven power spectral density function, mu is a real parameter, omega is a real parameter, psi is a real parameter,
adjusting the value of t, and enabling the nonlinear directional saturated resonance model to be t-trAnd (3) point location resonance, calculating a saturation resonance characteristic factor F:
wherein max | K (x (t)) | is the maximum value of | K (x (t)) |;
m4: averaging n saturated resonance characteristic factors F to obtain an average value which is the value of the characteristic value spk;
the values of a, b, c and f in the pork quality prediction model are calculated by the following method:
n1: taking W pork samples with different freshness, wherein W is more than or equal to 3, and respectively detecting each pork sample by adopting the method of the steps S1-S2 to obtain a characteristic value spk corresponding to each pork sample;
n2: detecting pH value, water content WR and volatile basic nitrogen TVBN of each pork sample by adopting a national standard method;
n3: substituting the characteristic value spk, the pH value, the water content WR and the volatile basic nitrogen TVBN corresponding to each pork sample into a formula spk which is a multiplied by pH + b multiplied by WR + c multiplied by TVBN + f to obtain W equations, and solving the equation set to calculate the values of a, b, c and f.
2. The nondestructive testing method for pork quality according to claim 1, wherein the spectrometer (2) is a visible/near infrared spectrometer with a spectral resolution of 1 nm.
3. The nondestructive testing method for pork quality according to claim 1, wherein the tungsten halogen lamp (7) is a 12W tungsten halogen lamp.
4. The nondestructive detection method for pork quality according to claim 1, wherein a distance measuring sensor (8) for measuring the distance between the optical probe (4) and the pork sample (9) to be detected is further disposed on the optical probe (4), and the computer (1) is electrically connected to the distance measuring sensor (8) and the lifter (6), respectively.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201885969U (en) * | 2010-09-19 | 2011-06-29 | 北京工商大学 | Meat quality detector |
| CN102564964A (en) * | 2011-12-29 | 2012-07-11 | 南京林业大学 | Spectral image-based meat quality visual non-contact detection method |
| CN103344577A (en) * | 2013-07-12 | 2013-10-09 | 中国农业大学 | Non-destructive detection method for freshness of livestock meat based on multispectral imaging technology |
| JP2015057596A (en) * | 2013-08-16 | 2015-03-26 | 独立行政法人農業・食品産業技術総合研究機構 | Method for detecting pork in food |
| CN111175248A (en) * | 2020-01-22 | 2020-05-19 | 中国农业科学院农产品加工研究所 | Intelligent meat quality online detection method and detection system |
-
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- 2020-12-23 CN CN202011542904.9A patent/CN112557328B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201885969U (en) * | 2010-09-19 | 2011-06-29 | 北京工商大学 | Meat quality detector |
| CN102564964A (en) * | 2011-12-29 | 2012-07-11 | 南京林业大学 | Spectral image-based meat quality visual non-contact detection method |
| CN103344577A (en) * | 2013-07-12 | 2013-10-09 | 中国农业大学 | Non-destructive detection method for freshness of livestock meat based on multispectral imaging technology |
| JP2015057596A (en) * | 2013-08-16 | 2015-03-26 | 独立行政法人農業・食品産業技術総合研究機構 | Method for detecting pork in food |
| CN111175248A (en) * | 2020-01-22 | 2020-05-19 | 中国农业科学院农产品加工研究所 | Intelligent meat quality online detection method and detection system |
Non-Patent Citations (2)
| Title |
|---|
| Predicting pork quality using Vis/NIR spectroscopy;Juliana Monteiro Balage et al;《Meat Science》;20150509;第37-43页 * |
| 猪肉质性状评定方法及其研究进展;王圣楠等;《猪肉质性状评定方法及其研究进展》;20200131;第148-151页 * |
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