CN108613942A - The method of on-line quick detection chicken hardness - Google Patents
The method of on-line quick detection chicken hardness Download PDFInfo
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- CN108613942A CN108613942A CN201810224847.6A CN201810224847A CN108613942A CN 108613942 A CN108613942 A CN 108613942A CN 201810224847 A CN201810224847 A CN 201810224847A CN 108613942 A CN108613942 A CN 108613942A
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- 241000287828 Gallus gallus Species 0.000 title claims abstract description 19
- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000001514 detection method Methods 0.000 title claims abstract description 12
- 235000013330 chicken meat Nutrition 0.000 claims abstract description 23
- 230000003595 spectral effect Effects 0.000 claims abstract description 13
- 238000001228 spectrum Methods 0.000 claims abstract description 11
- 238000012937 correction Methods 0.000 claims abstract description 10
- 238000000605 extraction Methods 0.000 claims abstract description 10
- 238000004611 spectroscopical analysis Methods 0.000 claims abstract description 8
- 210000000481 breast Anatomy 0.000 claims description 9
- 238000002310 reflectometry Methods 0.000 claims description 7
- 239000000284 extract Substances 0.000 abstract description 4
- 235000013372 meat Nutrition 0.000 abstract description 4
- 238000004364 calculation method Methods 0.000 abstract description 3
- 238000007405 data analysis Methods 0.000 abstract description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 3
- 239000003153 chemical reaction reagent Substances 0.000 abstract 1
- 239000012467 final product Substances 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 3
- HVYWMOMLDIMFJA-DPAQBDIFSA-N cholesterol Chemical compound C1C=C2C[C@@H](O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H]([C@H](C)CCCC(C)C)[C@@]1(C)CC2 HVYWMOMLDIMFJA-DPAQBDIFSA-N 0.000 description 2
- 238000002790 cross-validation Methods 0.000 description 2
- 230000007812 deficiency Effects 0.000 description 2
- 235000013305 food Nutrition 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 1
- 235000012000 cholesterol Nutrition 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000005755 formation reaction Methods 0.000 description 1
- 235000004213 low-fat Nutrition 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 210000003205 muscle Anatomy 0.000 description 1
- 230000000050 nutritive effect Effects 0.000 description 1
- 235000019629 palatability Nutrition 0.000 description 1
- 235000013594 poultry meat Nutrition 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
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- 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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Abstract
The invention discloses the method for on-line quick detection chicken hardness, the high spectrum image of acquisition correction collection chicken meat sample pre-processes the spectrogram of acquisition and carries out the identification of target area and the extraction of spectrogram average spectral data again;The spectroscopic data of extraction is substituted into formula to obtain the final product.In order to reject a large amount of redundancy when the present invention extracts 15 most optimum wavelengths out of 486 all bands, extract useful information, to reduce the calculation amount of data analysis, to improve the precision of partial least square model, to realize the demand of the large-scale online production of meat enterprise.Compared with prior art, the invention has the advantages that:The present invention is not required to pre-process sample, only carries out non-contacting spectral scan and no destructiveness to sample;The present invention does not use any chemical reagents, i.e., green and cost-effective;The present invention is easily operated and saves the time, can realize the extensive on-line checking of chicken hardness.
Description
Technical field
The present invention relates to and Food Quality and Safety detection field, and in particular to the side of on-line quick detection chicken hardness
Method.
Background technology
Chicken is as the main food poultry meat in China, the characteristics of because of its high protein, low fat, low in calories, low cholesterol,
And favored deeply by consumers in general.With the improvement of people's living standards, consumer more fills during buying chicken
Its heavy external appearance characteristic, the edible qualitative characteristics such as palatability and nutritive value, wherein can reflect that the hardness of chicken texture characteristic refers to
Mark, then play conclusive effect to the purchase of consumer.And the institutional framework of chicken meat toughness and muscle, chemical composition have
Close contact, because it determines the quality of chicken eating mouth feel.
Texture instrument is relied primarily on for the measurement of chicken hardness at present to be detected it, but Texture instrument have to sample it is broken
Bad property and higher to the size requirements of sample, it is main to use matter there is also human factor is big and the disadvantages such as detection efficiency is low
Structure instrument much can not meet the large-scale online detection requirements of meat enterprise to the method that chicken hardness is detected.With economy
Fast development, at present meat enterprise be badly in need of a kind of quick nondestructive technology to realize the on-line real time monitoring of chicken meat quality.
Invention content
In order to solve the deficiencies in the prior art, the present invention provides the methods of on-line quick detection chicken hardness.
The technical scheme is that:The method of on-line quick detection chicken hardness, the height of acquisition correction collection chicken meat sample
Spectrum picture pre-processes the spectrogram of acquisition the identification and spectrogram average spectral data for carrying out target area again
Extraction;The spectroscopic data of extraction is substituted into following formula up to Yhardness=-77.060+1870X902.198nm-1210X912.081nm+
460.313X915.375nm-1360X928.551nm+504.77X949.957nm+583.747X958.189nm-565.739X1010.859nm+
557.25X1066.801nm-451.631X1130.956nm+555.352X1162.211nm-383.19X1257.634nm+429.022X1443.761nm-
383.584X1564.287nm-266.841X1648.682nm-603.601X1698.415nm, wherein YhardnessFor the hardness of Fresh Grade Breast sample
Value, X902.198nm、X912.081nm、X915.375nm、X928.551nm、X949.957nm、X958.189nm、X1010.859nm、X1066.801nm、X1130.956nm、
X1162.211nm、X1257.634nm、X1443.761nm、X1564.287nm、X 1648.682nm、X1698.415nm, respectively wavelength 902.198nm,
912.081nm、915.375nm、928.551nm、949.957nm、958.189nm、1010.859nm、1066.801nm、
1130.956nm、1162.211nm、1257.634nm、1443.761nm、1564.287nm、1648.682nm、1698.415nm
The spectral reflectance values at place;Above formula related coefficient is R=0.951, root-mean-square error RMSE=10.961.
Further improvement of the present invention includes:
I.e. black and white plate correction is pre-processed to the spectrogram of acquisition to carry out according to following formula:
Wherein N is the image after correction, and R is original spectrum image;D is blackboard image, and reflectivity 0%, W is blank
Image, reflectivity 99.9%.
The present invention provides a kind of high-resolution, it is quick, lossless, without being pre-processed to sample the advantages that EO-1 hyperion
Imaging technique detects the hardness in chicken, with make up the prior art deficiency, to realizing that chicken hardness exists on a large scale
Line detects.
In order to reject a large amount of redundancy when the present invention extracts 13 most optimum wavelengths out of 486 all bands, extraction has
Information, to reduce the calculation amount of data analysis, to improve the precision of partial least square model, to realize that meat enterprise is big
The demand of the online production of scale.Compared with prior art, the invention has the advantages that:The present invention is not to destroying sample
In the case of product, need to only the hardness number that non-contacting spectral scan can be obtained sample be carried out to sample;It is reduced during experiment
The accidental error caused by manual operation;The extensive on-line checking of Fresh Grade Breast hardness may be implemented in the present invention.
Description of the drawings
Fig. 1 is the spectral signature figure of 111 calibration set samples.
Fig. 2 is extraction of the regression coefficient method to calibration set sample most optimum wavelengths.
Fig. 3 is the correlation between Fresh Grade Breast Hardness Prediction value and measured value.
Specific implementation mode
It elaborates to the present invention with reference to embodiment.
Embodiment
A kind of method and step of quick nondestructive on-line checking chicken hardness of the present embodiment is as follows:
(1) the monoblock fresh grade breast of purchase is divided into the small sample of 3cm*3cm*1cm in laboratory, obtains 111 altogether
A small sample is known as calibration set, then is divided into 7 parts, puts the disposable plastic box with lid into respectively, finally puts
It is refrigerated in 4 DEG C of refrigerator, at 0,1,2,3,4,5,6 day, each portion that takes out was tested;
(2) before the test, 30min opens Hyperspectral imager preheating in advance, while chicken sample also shifts to an earlier date 30min
It takes out out of refrigerator and is dried the moisture on its surface with blotting paper after its recovery to room temperature, the state of imaging system is adjusted to most
Good i.e. spectrum picture picking rate is 6.54mm/s, when the time for exposure is 4.65ms, then carries out the guarantor of blackboard and whiteboard images
It deposits, finally carries out the acquisition of sample image;
(3) detection of the Texture instrument to its hardness to be used immediately to the sample for acquiring spectrum picture, and records hardness number,
The hardness number of i.e. 111 samples is according to being ranked sequentially from small to large, data statistics such as table 1:
The hardness reference value of 1 111 calibration set samples of table
(4) it is carried out black and white board correction according to following formula to obtaining spectrum picture;
Wherein N is the image after correction, and R is original spectrum image;D is blackboard image, and reflectivity 0%, W is blank
Image, reflectivity 99.9%.
After being corrected to primary light spectrogram, the area-of-interest in image is identified first and spectroscopic data is carried
It takes, the spectroscopic data of extraction is spectral reflectance values, that is, spectral signature such as Fig. 1 of the 111 calibration set samples obtained:
(5) come between the spectroscopic data of associated steps (4) and the hardness number of step (3) using Partial Least Squares (PLSR)
Quantitative relationship, to obtain the PLSR models in all band (486 wavelength);Using coefficient R, root-mean-square error RMSE with
And the related coefficient and root-mean-square error of cross validation collection evaluate the precision and stability of institute's established model, when R more connects
It is bordering on 1 and RMSE to get over hour, then the precision and stability of model is better.As a result such as table 2:
The PLSR prediction models of calibration set in 2 all band of table
The coefficient R of the PLSR models of calibration set is up to 0.969 as can be drawn from Table 2, and root-mean-square error is
9.730, the wherein model coefficient R of cross validation collection close to calibration set, and the root-mean-square error of the two also very close to,
Therefore the PLSR model accuracies of calibration set are high and stablize.
(6) 486 wavelength are shared to the spectroscopic data under all band (900-1700nm) of institute's established model in step (5),
Breath amount so cannot meet the needs of online production well greatly, wherein certainly existing a large amount of redundancy, pass through recurrence
Y-factor method Y removes irrelevant information, useful information is extracted, to reduce the calculation amount of data analysis, to improve offset minimum binary mould
The precision of type.As a result such as Fig. 2.
15 most optimum wavelengths are extracted out of all band using regression coefficient method as can be drawn from Figure 2, respectively
902.198nm、912.081nm、915.375nm、928.551nm、949.957nm、958.189nm、1010.859nm、
1066.801nm、1130.956nm、1162.211nm、1257.634nm、1443.761nm、1564.287nm、1648.682nm、
1698.415nm。
(7) hardness number of the most optimum wavelengths and step (3) obtained again come associated steps (6) using Partial Least Squares
Between quantitative relationship, offset minimum binary (PLSR) prediction model after being optimized, result such as table 3:
The calibration set PLSR prediction models of 3 most optimum wavelengths of table
It can be obtained from table, the related coefficient and root-mean-square error of the PLSR models established using 15 most optimum wavelengths and friendship
Fork verification collection very close to, and it is minimum with the related coefficient of all-wave segment model and root-mean-square error gap, thus use 15 it is optimal
The precision for the PLSR models that wavelength is established is very high and stablizes, therefore the PLSR models established using most optimum wavelengths are quite managed
Think.
(8) the PLSR model formations of the most optimum wavelengths obtained are as follows:
Yhardness=-77.060+1870X902.198nm-1210X912.081nm+460.313X915.375nm-1360X928.551nm+
504.77X949.957nm+583.747X958.189nm-565.739X1010.859nm+557.25X1066.801nm-451.631X1130.956nm+
555.352X1162.211nm-383.19X1257.634nm+429.022X1443.761nm-383.584X1564.287nm-266.841X1648.682nm-
603.601X1698.415nm, wherein YhardnessFor the hardness number of Fresh Grade Breast sample, X902.198nm、X912.081nm、X915.375nm、
X928.551nm、X949.957nm、X958.189nm、X1010.859nm、X1066.801nm、X1130.956nm、X1162.211nm、X1257.634nm、X1443.761nm、
X1564.287nm、X 1648.682nm、X1698.415nm, respectively wavelength 902.198nm, 912.081nm, 915.375nm,
928.551nm、949.957nm、958.189nm、1010.859nm、1066.801nm、1130.956nm、1162.211nm、
Spectral reflectance values at 1257.634nm, 1443.761nm, 1564.287nm, 1648.682nm, 1698.415nm.
(9) it tests
1. acquiring the high spectrum image of 36 Fresh Grade Breast samples to be measured, wave-length coverage 900-1700nm;
2. the spectrum picture to acquisition carries out black and white board correction, and extracts the spectroscopic data of area-of-interest, what is obtained is
The spectral reflectivity of sample to be tested;
3. the spectral reflectance values of extraction are brought into the PLSR models for the most optimum wavelengths that step (8) is obtained, finally
The Hardness Prediction value of 36 Fresh Grade Breast to be measured can be obtained;
4. the predicted value of Fresh Grade Breast hardness is carried out linear fit with using the value measured by Texture instrument, related coefficient is high
It is related fine between actual value and predicted value up to 0.951, show the prediction of the partial least square model of built most optimum wavelengths
Ability is preferable, as a result such as Fig. 3.
The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (2)
1. the method for on-line quick detection chicken hardness, which is characterized in that the high spectrum image of acquisition correction collection chicken meat sample, it is right
The spectrogram of acquisition is pre-processed the extraction of the identification and spectrogram average spectral data that carry out target area again;It will extraction
Spectroscopic data substitute into following formula up to Yhardness=-77.060+1870X902.198nm-1210X912.081nm+460.313X915.375nm-
1360X928.551nm+504.77X949.957nm+583.747X958.189nm-565.739X1010.859nm+557.25X1066.801nm-
451.631X1130.956nm+555.352X1162.211nm-383.19X1257.634nm+429.022X1443.761nm-383.584X1564.287nm-
266.841X1648.682nm-603.601X1698.415nm, wherein YhardnessFor the hardness number of Fresh Grade Breast sample, X902.198nm、
X912.081nm、X915.375nm、X928.551nm、X949.957nm、X958.189nm、X1010.859nm、X1066.801nm、X1130.956nm、X1162.211nm、
X1257.634nm、X1443.761nm、X1564.287nm、X1648.682nm、X1698.415nm, respectively wavelength 902.198nm, 912.081nm,
915.375nm、928.551nm、949.957nm、958.189nm、1010.859nm、1066.801nm、1130.956nm、
Spectral reflectance at 1162.211nm, 1257.634nm, 1443.761nm, 1564.287nm, 1648.682nm, 1698.415nm
Rate value;Above formula related coefficient is R=0.951, root-mean-square error RMSE=10.961.
2. the method for on-line quick detection chicken hardness according to claim 1, which is characterized in that the spectrogram of acquisition
I.e. black and white plate correction is pre-processed to carry out according to following formula:
Wherein N is the image after correction, and R is original spectrum image;D is blackboard image, and reflectivity 0%, W is blank figure
Picture, reflectivity 99.9%.
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