CN106501211A - The method for building up of near infrared spectrum information evaluation beef quality data model and application - Google Patents
The method for building up of near infrared spectrum information evaluation beef quality data model and application Download PDFInfo
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- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 54
- 238000013499 data model Methods 0.000 title claims abstract description 32
- 238000011156 evaluation Methods 0.000 title claims abstract description 15
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- 238000001228 spectrum Methods 0.000 claims description 18
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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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- 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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Abstract
The present invention relates to meat products technical field of quality detection, in particular to a kind of method for building up of the data model of use near infrared spectrum information systems evaluation beef quality, and application of the method in terms of beef quality is evaluated, the method comprises the steps：1). the detection of beef quality relevant parameter is carried out to which after setting up the big-sample data of beef near infrared spectrum；2). the corresponding relation according to the big-sample data and the beef quality relevant parameter of infrared spectrum sets up near infrared spectrum data model；The beef quality relevant parameter is main chemical compositions index and/or the index of quality；The main chemical compositions index includes one or more in protein content, fat content and moisture；The index of quality includes one or more in shear force value, cooking loss rate, retention ability and aberration L*, a*, b* value.
Description
Technical field
The present invention relates to meat products technical field of quality detection, uses near infrared light spectrum information in particular to a kind of
Evaluate method for building up and the application of beef quality data model.
Background technology
With the quickening and the continuous fusion of Chinese and western cooking culture of modern society's life rhythm, beef is with its unique wind
The processing cooking method (such as pan-fried, roasting etc.) of taste, nutritive peculiarity and uniqueness, is increasingly favored by young consumers.
Raw material is to affect one of beef consumption value principal element, and different parts, the Raw material processing of different qualities are cooked
Beef often have very big difference, particularly in China market, beef source mixes, feedings of modern essence, Grazing grassland, always residual naughty
The different quality feedstocks such as eliminate and requirements at the higher level are proposed to beef quality evaluation and grading and classification etc..Traditional beef quality detection and
Grading and classification lays particular emphasis on trunk evaluation during block trade, it is difficult to suitable for specific beef product, and many with chemical analysis, instrument
Based on the destructive modes such as device detection, not only process is complicated, waste time and energy, and can also cause substantial amounts of contamination of raw material and waste.Cause
This, is to promote Beef Industry development, it is necessary to which the quality characteristic and consumer need for beef is set up and a set of fast and effectively examined
Survey means and stage division, realize the quick detection of beef quality.
Near-infrared (NIR) is a kind of electromagnetic wave between visible ray (VIS) and mid-infrared light (IR), U.S. material inspection
Survey association (ASTM) and be defined as the spectral regions that wavelength is 780～2526nm, be developed since eighties of last century seventies
The modern analytical technique for coming, is all had a wide range of applications in every field at present, and the processing mode of its sample is simple, and
Multiple indexs of meat can be assessed simultaneously, the place of production is currently used primarily in and the discriminating of kind, the evaluation of quality and safety are examined
The aspects such as survey.And near-infrared spectrum technique for operating efficiency is improved, reduces raw material the features such as have quick, accurate, pollution-free
Loss, reduction labour intensity etc. have very great help.Some researchs show that near-infrared spectrum technique refers in the part of meat and meat products
There is good prediction effect in mark analyses and prediction, but based near infrared spectrum information evaluation to beef edible quality and chemistry
The systems approach of ingredient prediction is ripe not enough.
In view of this, the special proposition present invention.
Content of the invention
It is an object of the invention to provide a kind of systems approach based near infrared spectrum information systems evaluation beef quality
To solve the above problems.
In order to realize that the above-mentioned purpose of the present invention, spy are employed the following technical solutions：
A kind of method for building up of the data model of use near infrared spectrum information evaluation beef quality, the data model lead to
Cross following methods acquisition：
1). the detection of beef quality relevant parameter is carried out to which after setting up the big-sample data of beef near infrared spectrum；
2). the corresponding relation according to the big-sample data and the beef quality relevant parameter of infrared spectrum sets up near-infrared
Spectroscopic data model；
The beef quality relevant parameter is main chemical compositions index and/or the index of quality；
The main chemical compositions index includes one or more in protein content, fat content and moisture；
The index of quality include the one kind in shear force value, cooking loss rate, retention ability and aberration L*, a*, b* value or
Multiple.
Preferably, the method for building up of data model as above, when the beef quality relevant parameter is primary chemical
During component target, the beef sample state is meat gruel or cube meat；Preferably meat gruel；
When the beef quality relevant parameter is the index of quality, the beef sample state is cube meat.
Preferably, the method for building up of data model as above, in the process, the near infrared spectrum of acquisition is
Spectrum in the range of 1000～2500nm.
Preferably, the method for building up of data model as above：
When the beef quality relevant parameter is protein content, the near infrared spectrum of acquisition is 1400nm～1600nm；
When the beef quality relevant parameter is fat content, the near infrared spectrum of acquisition is 1200nm～1400nm；
When the beef quality relevant parameter is moisture, the near infrared spectrum of acquisition is 1100nm～1200nm.
Preferably, the method for building up of data model as above, the method for obtaining near infrared spectrum is to use near infrared light
Spectrometer is scanned acquisition, and mean scan number of times is 30 times.
Preferably, the method for building up of data model as above, the beef sample take from ectoloph and cuke bar portion
Position.
Preferably, the method for building up of data model as above, the method for building up of the near infrared spectrum data model
For differentiating PLS.
Preferably, the method for building up of data model as above, the near infrared spectrum is through first derivative process.
Preferably, the method for building up of data model as above：
When the beef quality relevant parameter is protein content, main cause subnumber is 6, and processing method is SNV；
When the beef quality relevant parameter is fat content, main cause subnumber is 10, and processing method is Smooth-G
(7)；
When the beef quality relevant parameter is moisture, main cause subnumber is 10, and processing method is Smooth-G
(3)；
When the beef quality relevant parameter is shearing force, main cause subnumber is 7, and processing method is SNV；
When the beef quality relevant parameter is cooking loss, main cause subnumber is 4, and processing method is Smooth-G (7)；
When the beef quality relevant parameter is L*, main cause subnumber is 7, and processing method is Smooth-G (7)；
When the beef quality relevant parameter is a*, main cause subnumber is 6, and processing method is SNV；When the beef quality
When relevant parameter is b*, main cause subnumber is 6, and processing method is MSC.
A kind of method based near infrared spectrum information evaluation beef quality, including：
The use for obtaining the near infrared spectrum of beef sample to be detected and passing through described in any one of claim 1～9 is closely red
The data model that the method for building up of the data model of external spectrum information evaluation beef quality is set up is to the beef sample to be detected
Quality evaluated.
Compared with prior art, beneficial effects of the present invention are：
The method operation based near infrared spectrum information evaluation beef quality that the present invention is provided is quick, simple, repeated
Good, the fast and accurately Quality Detection of beef source meat can be used successfully to, so as to for relevant manufacturers and enterprise save the time and
Fund cost, it is to avoid unnecessary waste on beef inferior, the judgement for beef quality and beef price provide cost savings,
The reliable foundation of science.
Description of the drawings
In order to be illustrated more clearly that the specific embodiment of the invention or technical scheme of the prior art, below will be to concrete
Needed for embodiment or description of the prior art, accompanying drawing to be used is briefly described, it should be apparent that, in describing below
Accompanying drawing is some embodiments of the present invention, for those of ordinary skill in the art, before creative work is not paid
Put, can be with according to these other accompanying drawings of accompanying drawings acquisition.
Fig. 1 is the near-infrared average light spectrogram of sample in the embodiment of the present invention；
Fig. 2 is put down for the near-infrared of sample under the meat gruel in the embodiment of the present invention after first derivative process, cube meat state
Equal spectrogram.
Specific embodiment
Embodiment of the present invention is described in detail below in conjunction with embodiment, but those skilled in the art will
Understand, the following example is merely to illustrate the present invention, and is not construed as limiting the scope of the present invention.Unreceipted concrete in embodiment
Condition person, the condition that advises according to normal condition or manufacturer are carried out.Agents useful for same or the unreceipted production firm person of instrument, are
The conventional products that commercially available purchase is obtained can be passed through.
Embodiment
Present embodiments provide a kind of method based near infrared spectrum information evaluation beef quality.For realizing with near-infrared
For the beef quality of means, technology predicts that the present invention applies domestic portable near infrared spectrometer, conventional from barbecue beef
Ectoloph and cuke bar are raw material, under plant operation environment, respectively with the near red of cube meat and meat gruel state acquisition raw material beef
Outer data, set up quick, the pollution-free forecast model of barbecue raw material beef quality, probe into near-infrared spectrum technique prediction barbecue
Ability with raw material beef quality index.
1 material and method
1.1 materials and reagent
Ox ectoloph (longissimus dorsi muscle), cuke bar (semitendinosus), choose Beijing Yu Xiang gardens group and Ningxia summer Hua Qing respectively
Simmental Crossbred Progeny 20 and 60 fatten in true meat product company, kill acid discharge 48h at 0-4 DEG C.After the completion of beef acid discharge, in
Remove ectoloph and cuke bar in scene.
It is pure that copper sulphate, potassium sulfate, sulfuric acid, hydrochloric acid etc. are analysis.
1.2 instrument and equipment
SupNIR-1550 portable near infrared spectrometers：Hangzhou optically focused scientific ＆ technical corporation；
CR-400 colour difference meters：Minolta company；
MODEL235 Warner--Bratzkr shear force instruments：G-R Manufacturing companies of Britain；
YYW-2 type strain controlling formulas are without confining pressure instrument：Nanjing Soil Apparatus Factory；
The triumphant formula azotometers of KjeltecTM2300：Foss companies of Denmark；
Soxtec TM2050 Milko-Testers：Foss companies of Denmark；
HHS-21-6 type electric-heated thermostatic water baths：Shanghai Medical Equipment Plant of Bo Xun Industrial Co., Ltd.s；
JH2102 electronic balances：Shanghai Techcomp Jingke Science Instrument Co., Ltd..
1.3 test method
1.3.1 spectra collection
Beef is cut into cube meat for thickness 3cm, size about 3 × 6 × 6cm3Intact sample；Meat gruel be then 2.0 ×
103Homogeneous samples are obtained after 15s is rubbed under conditions of rmp, respectively the near infrared spectrum under collection cube meat and meat gruel state, in black
Application grating dispersion type portable near infrared spectrometer collection spectrum under dark background, wave-length coverage is 1000～2500nm, differentiates
Rate is 1nm, and instrument mean scan number of times is 30 times, and multiple scanning sample takes its averaged spectrum 3 times.
1.3.2 the measure of the index of quality
1.3.2.1 shear force value (Warner Bratzler shear force, WBSF)
Shear force value is determined with reference to NY/T 1180-2006《The measure shearing amylograph of meat tenderness degree》.By the meat that handles well
Sample is syncopated as 3 × 3 × 6cm3Cube meat, weigh, be designated as W1, be placed in retort pouch, boil in 85 DEG C of thermostat water bath
After heart temperature 70 C, take out, be cooled to room temperature, surface moisture dried with blotting paper and weighed, be designated as W2.Then 0～4 DEG C is placed on
Lower storage 12h, drills through meat sample with the conical sampler parallel muscle machine direction of diameter 1.27cm.Hung down with Wo-cloth shear force instrument
Directly sample is sheared in muscle fibre direction, average and obtain the shear force value of the sample.
1.3.2.2 cooking loss rate (Cooking-loss, CL)
Weighing results twice when being determined according to shearing force calculate cooking loss rate, and concrete formula is as follows：
Cooking loss rate %=[(W1-W2)/W1] × 100
1.3.2.3 retention ability (Water-holding Capacity, WHC)
Cube meat is cut 1.00cm thickness along vertically myofibrillar direction, (area of a circle is 5.00cm to diameter 2.523cm2) circle
Shape cedductor, weighs, and records M1, is wrapped up with double gauze, then is clipped in 18 layers of blotting paper, and on Instrument for Pressure, pressurization 35kg keeps
5min, removes gauze and blotting paper, is re-weighed, is designated as M2.It is calculated as follows retention ability：
Retention ability %=[(M1-M2)/M1] × 100
1.3.2.4 aberration L*, a*, b* values
Cube meat is cut out fresh cross section, oxygen and 30min under white background is placed on, with CR-400 colour difference meters in beef cube meat
Different 3 groups of L*, a*, b* values of chromatism of position finding in three, surface, average.
1.3.3 chemical constituents determination
According to GB/T 9695.11-2008《Meat and meat products：Nitrogen analysis》、GB/T 9695.7-2008《Meat and meat
Product：Total lipid content is determined》With GB/T 9695.15-2008《Meat and meat products：Determination of moisture》Sample is determined respectively
Protein content, fat content and moisture.
1.3.4 data analysis
Enter row format conversion to the spectroscopic data for gathering, application The Unscrambler (version 9.8, CAMO) is built
The quantitative forecast of vertical forecast model, quality and chemical composition adopts PLS (PLS).With calibration set and forecast set sample
Measured value and the coefficient R of predicted value2 cAnd R2 p, cross-validation root-mean-square error (RMSECV) and prediction root mean square
Error (RMSEP) is used as evaluation model quality index.
2 results and analysis
2.1 sample quality indicator-specific statistics
By 160 beef samples according to 3:1 ratio sets up calibration set and forecast set, raw meat items chemical composition content
1 is such as shown in Table with index of quality statistics.Quality of the near-infrared in the prediction sample degree of accuracy is except being subject to each index measurement essence
Outside the impact of degree, also affected by the range of variation of the number and reference data of the included information of spectrum.Data set is set up
When, some characteristic values (maximum, minimum of a value etc.) are included in calibration set, it is ensured that scope of the calibration set scope comprising forecast set,
Ensure that the applicability and reliability of institute's established model.
1 beef sample quality indicator-specific statistics table of table
2.2 spectrum analyses and pretreatment
2.2.1 near-infrared averaged spectrum
Under cube meat and meat gruel state, the averaged spectrum of beef sample is shown in Fig. 1.It can be seen that 1150nm,
There is stronger absworption peak in 1450nm and 1930nm, and they are the one-level frequency multiplication of O-H keys and sum of fundamental frequencies, because major part is in beef
Water, content more than 70%, O-H keys absworption peak clearly.And C-H is in first frequency multiplication of 1600～1800nm, 1100～
The first frequency multiplication of second frequency multiplication of 1400nm and N-H in 1400～1600nm, and OH can also in the absworption peak of 2100nm
Recognize from figure, for the changes of contents of the organic matters such as indicator protein matter, fat.Because protein and fat content are to affect to burn
The deciding factor of roast beef quality, so the change of these absworption peaks is even more important to forecasting accuracy.Can from figure
Go out, the near infrared spectrum trend gathered under different sample states is roughly the same, but in the range of 1000～1900nm, meat gruel shape
Spectrogram fluctuation range under state is bigger, and absworption peak also becomes apparent from.
2.2.2 Pretreated spectra
The forecasting accuracy of near infrared spectrum is often disturbed with properties of samples irrelevant factor by some, such as environment temperature
Degree, sample state, the scattering of light and instrument response etc., these factors result in the baseline drift of near infrared spectrum and repeatability
Difference.It is therefore desirable to being analyzed to the near infrared spectrum data for gathering and pre-processing, to eliminate these adverse effects, improve pre-
Survey ability.The present invention take respectively smooth (Smoothing), standard normal (Standard normol variate, SNV),
The differences such as multiplicative scatter correction (Multiplicative signal correction, MSC), derivative processing (Derivative)
Method processes near infrared spectrum, to eliminate various high frequency noises and baseline drift, improves repeatability and signal to noise ratio.
Fig. 2 is the atlas of near infrared spectra after first derivative process.It can be seen that spectrum is through first derivative
After process, linear baseline drift is effectively reduced, bands of a spectrum feature is enhanced.The spectroscopic data of collection is absorbed in the O-H of 1150nm
The N-H absworption peaks at peak, the C-H absworption peaks of 1200-1400nm and 1400～1600nm all become apparent from.Wherein, under complete cube meat
Fluctuate in the range of 1000～1100nm, 1400nm bigger, the absworption peak under meat gruel state at the Near-infrared Spectral Absorption peak of collection
Then more project in the range of 1200～1400nm and 1500～1900nm.
Quantitative forecast of 2.3 barbecues with raw material beef quality index
Under meat gruel and cube meat state, the PLS quantitative analysis results of indices are shown in Table 2, as seen from the table, under meat gruel state
The albumen of foundation, fat, the forecast model of moisture are significantly better than cube meat state, the shear force value of foundation, boiling under cube meat state
The forecast model of the index of quality such as loss late, L*, a*, b* is more preferably.
In the PLS models that sets up under meat gruel state, by screening suitable main cause subnumber and pre-processing through distinct methods
Afterwards, the protein content of sample, fat content, moisture and L* preferably can be predicted the outcome.Wherein, when main cause subnumber
For 6, when processing through SNV, to the prediction effect of protein content preferably, R2 c、R2 p, RMSECV and RMSEP be respectively 0.7992,
0.7499th, 0.0059 and 0.0067；When main cause subnumber is 10, when processing through Smooth-G (7), the prediction effect to fat content
Preferably, R2 c、R2 p, RMSECV and RMSEP be respectively 0.8786,0.7021,0.0046 and 0.0073；When main cause subnumber is 10, warp
During Smooth-G (3) process, the prediction effect to moisture is preferable, R2 c、R2 p, RMSECV and RMSEP be respectively 0.9097,
0.8260th, 0.0040 and 0.0056；When main cause subnumber is 9, when processing through SNV, the prediction effect to L* is preferable, R2 c、R2 p、
RMSECV and RMSEP is respectively 0.8187,0.7161,1.8070 and 2.2847.
In the PLS models that sets up under cube meat state, shearing force, cooking loss rate, L*, a*, b* and protein content are same
Obtain good prediction effect.Wherein, when main cause subnumber is 7, when processing through SNV, the prediction effect to shearing force is preferable, R2 c、
R2 p, RMSECV and RMSEP be respectively 0.8431,0.7137,0.5548 and 0.7571；When main cause subnumber is 4, through Smooth-G
(7), when processing, the prediction effect to cooking loss is preferable, R2 c、R2 p, RMSECV and RMSEP be respectively 0.8069,0.6786,
0.0157 and 0.0205；When main cause subnumber is 7, when processing through Smooth-G (7), the prediction effect to L* is preferable, R2 c、R2 p、
RMSECV and RMSEP is respectively 0.8304,0.7772,1.6564 and 1.9172；When main cause subnumber is 6, when processing through SNV, right
The prediction effect of a* is preferable, R2 c、R2 p, RMSECV and RMSEP be respectively 0.7442,0.6545,1.2011 and 1.4100；Work as master
Factor number is 6, and when processing through MSC, the prediction effect to b* is preferable, R2 c、R2 p, RMSECV and RMSEP be respectively 0.8690,
0.7403rd, 1.2011 and 1.4100；When main cause subnumber is 4, when processing through MSC, the prediction effect to protein content is preferable, R2 c、
R2 p, RMSECV and RMSEP be respectively 0.7363,0.6907,0.0073 and 0.0081.
Each index quantification PLS models in the sample that table 2-1 difference preprocessing procedures are set up
Each index quantification PLS models in the sample that table 2-2 difference preprocessing procedures are set up
3 discuss
Forecast analysis of 3.1 near infrared spectrums to beef source chemical composition
The chemistry such as protein content of the near-infrared spectrum technique in prediction meat and meat products, fat content and moisture into
Have a very wide range of applications on point, a lot of researchs confirm ability of the near infrared technology in prediction main chemical compositions.This
Invention achieves near-infrared under meat gruel state to roasting the Accurate Prediction of raw material beef, calibration set R2Respectively reach with RMSECV
0.7992 and 0.0059 (protein content), 0.8786 and 0.0046 (fat content), 0.9097 and 0.0040 (moisture),
Demonstrate after meat sample is smashed, near-infrared is capable of the chemical composition of Accurate Prediction sample.The present invention is built under complete cube meat state
Vertical model can also realize the prediction to albumen, fat and moisture, R2Respectively reach 0.7363,0.6574 and 0.6890.
By contrasting predicting the outcome for different sample treatment states, in the prediction to chemical composition, the meat that smashes
Modeling result under rotten state can be significantly better than complete cube meat state, muscle fibre in sample when this is mainly gathered with spectral information
Space structure relevant with distribution.When sample is in good working condition, the difference of muscular tissue structure and chemical constituents analysis
The different reflectivity that can cause different light scattering effects, and then affect infrared light, and the sample for homogenizing then upset original
Myoarchitecture, destruction is simultaneously arranged muscle fibre sequentially again, it is to avoid the scattering effect of light.Sample is after smashing and processing simultaneously
The distribution of chemical composition is more uniform, and the near infrared spectrum for being gathered is more representative, so under meat gruel state, near infrared spectrum
Can be more accurate to the prediction of chemical composition.
Forecast analysis of 3.2 near infrared spectrums to the beef source index of quality
Predict the outcome difference of the near-infrared to different indexs, these entrained by this near infrared spectrum for mainly being gathered refer to
The number of mark information is relevant.In the present invention, under cube meat state gather near infrared spectrum to shearing force, cooking loss rate and
The colour indexs such as L*, a*, b* be obtained for relatively satisfactory predict the outcome, this and Forrest, Ripoll and Meullenet etc.
Predict the outcome close.To in shearing force, the prediction of cooking loss rate it is known that near infrared spectrum can not be built with these indexs
Vertical directly contact, but it with musculature in albumen, the chemical composition such as fatty and moisture closely related.Such as, meat into
During ripe, the hydrolysis of lactose reduces can pH, cause the electrostatic attraction between water and albumen, form the structure of a closing,
Prevent the loss of moisture.The increase of intramuscular fat content can then prevent collagenous fibres from forming beam, reduce the toughness of meat.Therefore, lead to
These relations are crossed, near-infrared can be to the preferable prediction effect of these Index Establishments.And when predicting to color, although near-infrared is not
Including visible region, but the color such as brightness and redness also can by meat in myoglobins and intramuscular fat content affected,
And the two materials just correspond to the firsts and seconds frequency multiplication of C-H.By contrast, the quality prediction result that meat gruel state is set up
Bad, this is likely due to homogenize process and produces unfavorable shadow to the index of quality prediction that some could be embodied by complete structure
Ring, such as shearing force, cooking loss rate and color etc. are all related to myofibrillar species, quantity and arrangement in complete meat sample etc., from
It is difficult to obtain enough relevant informations in the infrared spectrum for smashing rear sample.Additionally, the index of quality is all by meat sample integrality
After embodiment, be also to complete in sample good working condition during measure, and sample is rubbed, the referential data of these indexs can occur very big
Change, now collects near infrared light spectrum information and just has very big difference with original index.
Forecast model is set up respectively by the spectral information gathered under cube meat, meat gruel two states, it is also possible to contrast not
Same sample treatment state models the impact of accuracy to near-infrared, i.e., when to chemical component prediction, gather the meat gruel sample that homogenizes
The spectrum of product can obtain more preferable result；To during the prediction of the index of quality, using the spectral information effect of complete cube meat sample
More preferably.Therefore, also should need to select different sample treatments according to difference in practical operation.
The present invention chooses barbecue beef source ectoloph and cuke bar as sample, applies portable near infrared spectrometer
Spectra collection is carried out to the fresh sample under plant operation environment in 1000～2500nm scopes, and determines main chemical compositions
(albumen, fat, moisture) and the index of quality (shearing force, cooking loss, retention ability, L*, a*, b*).Using 160 samples as building
Mould collection, sets up quality prediction model using PLS (PLS), finally gives under meat gruel state, to albumen, fat and
Moisture has preferable prediction effect, R2Respectively 0.7992,0.8786 and 0.9097.Under cube meat state, to shearing force, boiling
Loss and L*, a*, b* have preferable prediction effect, R2Respectively 0.8431,0.8089,0.8304,0.7742 and 0.8690.
In sum, what the present invention was provided can be used successfully to based on the method for near infrared spectrum information evaluation beef quality
The fast and accurately Quality Detection of beef source meat, so as to being that relevant manufacturers and enterprise save time and fund cost, it is to avoid
Unnecessary waste on beef inferior, the judgement for beef quality and beef price provide cost savings, science reliable foundation.
Finally it should be noted that：Various embodiments above only in order to technical scheme to be described, rather than a limitation；To the greatest extent
Pipe has been described in detail to the present invention with reference to foregoing embodiments, but it will be understood by those within the art that：Its
Still the technical scheme described in foregoing embodiments can be modified, or to which part or all technical characteristic
Carry out equivalent；And these modifications or replacement, do not make the essence of appropriate technical solution depart from various embodiments of the present invention skill
The scope of art scheme.
Claims (10)
1. a kind of method for building up of the data model of use near infrared spectrum information systems evaluation beef quality, it is characterised in that
The data model is prepared by the following：
1). the detection of beef quality relevant parameter is carried out to which after setting up the big-sample data of beef near infrared spectrum；
2). the corresponding relation according to the big-sample data and the beef quality relevant parameter of infrared spectrum sets up near infrared spectrum
Data model；
The beef quality relevant parameter is main chemical compositions index and/or the index of quality；
The main chemical compositions index includes one or more in protein content, fat content and moisture；
The index of quality includes one or more in shear force value, cooking loss rate, retention ability and aberration L*, a*, b* value.
2. the method for building up of data model according to claim 1, it is characterised in that when the beef quality relevant parameter
For main chemical compositions index when, the beef sample state is meat gruel or cube meat；Preferably meat gruel；
When the beef quality relevant parameter is the index of quality, the beef sample state is cube meat.
3. the method for building up of data model according to claim 1, it is characterised in that in the process, acquisition near
Infrared spectrum is the spectrum in the range of 1000～2500nm.
4. the method for building up of data model according to claim 3, it is characterised in that
When the beef quality relevant parameter is protein content, the near infrared spectrum of acquisition is 1400nm～1600nm；
When the beef quality relevant parameter is fat content, the near infrared spectrum of acquisition is 1200nm～1400nm；
When the beef quality relevant parameter is moisture, the near infrared spectrum of acquisition is 1100nm～1200nm.
5. the method for building up of data model according to claim 4, it is characterised in that the method for obtaining near infrared spectrum is
Acquisition is scanned near infrared spectrometer, mean scan number of times is 30 times.
6. the method for building up of data model according to claim 1, it is characterised in that the beef sample take from ectoloph and
Cuke bar position.
7. the method for building up of the data model according to any one of claim 1～6, it is characterised in that the near infrared light
The method for building up of modal data model is differentiation PLS.
8. the method for building up of data model according to claim 7, it is characterised in that the near infrared spectrum is through single order
Derivative processing.
9. the method for building up of data model according to claim 8, it is characterised in that
When the beef quality relevant parameter is protein content, main cause subnumber is 6, and processing method is SNV；
When the beef quality relevant parameter is fat content, main cause subnumber is 10, and processing method is Smooth-G (7)；
When the beef quality relevant parameter is moisture, main cause subnumber is 10, and processing method is Smooth-G (3)；
When the beef quality relevant parameter is shearing force, main cause subnumber is 7, and processing method is SNV；
When the beef quality relevant parameter is cooking loss, main cause subnumber is 4, and processing method is Smooth-G (7)；
When the beef quality relevant parameter is L*, main cause subnumber is 7, and processing method is Smooth-G (7)；
When the beef quality relevant parameter is a*, main cause subnumber is 6, and processing method is SNV；When beef quality correlation
When parameter is b*, main cause subnumber is 6, and processing method is MSC.
10. a kind of method based near infrared spectrum information evaluation beef quality, it is characterised in that include：
Obtain the near infrared spectrum of beef sample to be detected and by the use near infrared light described in any one of claim 1～9
Spectrum information evaluates product of the data model of the method for building up foundation of the data model of beef quality to the beef sample to be detected
Matter is evaluated.
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