CN1749734A - Method for establishing relevance model of glass bottled food component and food detecting method - Google Patents
Method for establishing relevance model of glass bottled food component and food detecting method Download PDFInfo
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- CN1749734A CN1749734A CN 200410077817 CN200410077817A CN1749734A CN 1749734 A CN1749734 A CN 1749734A CN 200410077817 CN200410077817 CN 200410077817 CN 200410077817 A CN200410077817 A CN 200410077817A CN 1749734 A CN1749734 A CN 1749734A
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Abstract
The method of establishing relevance model of glass bottled food components includes standard analysis process to measure the components in the sample; acquiring their near IR spectrum in diffuse reflection mode; eliminating the effect of bottle body via mathematic operation on the acquired near IR spectrum; extracting characteristic data information of the components in the spectrum via mathematic operation; and establishing the relevance model between the component content values and the spectral characteristic data. The food detecting method includes acquiring the near IR spectrum of bottled food; eliminating the effect of bottle body on the near IR spectrum; eliminating the scattering; and measuring the component contents based on the said relevance model. The present invention can realize non-destructive detection, and is fast and accurate.
Description
Technical field
The present invention relates to a kind of method for building up of food ingredients correlation model and utilize this correlation model to carry out the method that food ingredients detect, especially a kind of by setting up in glass pack near infrared spectrum and the food the corresponding correlation model between the actual composition and utilizing this model the composition of bottled food to be carried out the method for fast detecting.
Background technology
Along with the extensive concern of people to nutrition kind, content in the food, food production enterprise has also released the behave of dividing product hierarchy according to the product nutrient content accordingly, has so not only satisfied consumer demand, has also won the favor in market simultaneously.
Most of its every nutrition content of processed food is known, but food (as fermented bean curd etc.) for the spontaneous fermentation type, because the mode of production is to adopt encapsulation in advance, again by himself the fermentation variation and food prepared therefrom, food nutrition component content characteristics all inequality when therefore just having occurred dispatching from the factory in each packing, cause manufacturing enterprise to carry out the accurate metering of component content in quick nondestructive ground, also can't divide product hierarchy according to composition.
At present, solution to this problem is that packing is opened in sampling, carry out chemical analysis according to related standards, estimate rule of thumb that again other do not open each component content of product, the accuracy of this method is lower, though and the food of having opened can obtain to analyze accurately data result, can not repack sale again.
Therefore, various component contents have become the people in the industry and have wished one of problem that solves as early as possible in how accurate, quick, the undamaged mensuration packaged food.
Summary of the invention
Technical problem underlying to be solved by this invention is to provide a kind of method and bottled food fast non-destructive detection method of setting up glass pack composition correlation model at the above-mentioned present situation that various compositions in the packaged food are carried out fast measuring of present shortage, this method is by gathering the near infrared spectrum of knowing the bottled food samples of its accurate component content through standard method, set up the corresponding relation model between the various compositions in correlation model between bottled food ingredients and its near infrared spectrum and the bottled food, and can utilize this correlation model and corresponding relation model to provide effective technical means and foundation for the various compositions of the bottled food of fast detecting.
The technical scheme that solves the problems of the technologies described above comprises a kind of method of glass pack composition correlation model and a kind of detection method of bottled food ingredients set up.
The method of setting up glass pack composition correlation model may further comprise the steps:
Step 1: adopt standard method of analysis that the content of each composition in the bottled food samples of above standard is measured respectively, obtain the standard component content value; A described bottled food samples of above standard is that component content is different separately, and its numerical value is the representative sample of distribution shape;
Step 2: adopt near infrared spectrometer to gather their near infrared spectrum respectively to the described representative sample of step 1 by the diffuse reflection mode;
Step 3: the near infrared spectrum of being gathered is eliminated the influence of bottle body near infrared spectrum in the mathematical operation mode;
Step 4: the characteristic information of from the near infrared spectrum that step 2 is gathered, extracting each composition in the described representative sample spectrum again in the mode of mathematical operation;
Step 5: correlation model in the standard component content value of setting up each composition in the above-mentioned representative sample and its spectrum between the characteristic of each corresponding composition, for having dissimilar food in the same bottle, set up the described correlation model of each type of food product respectively.
Standard method of analysis in the step 1 is currently used standard chemical analytical approach.
In above-mentioned steps, the quantity of representative sample can be 10 or 50, also can be more.The quantity that improves representative sample helps to improve the precision of correlation model.Above-mentioned diffuse reflection can be adopted the integrating sphere diffuse reflection of 833-2500nm wavelength coverage, also can adopt the optical fiber diffuse reflection of 1000-2500nm wavelength coverage.
Also can adopt polynary scatter correction method to eliminate representative sample after the step 3 and produce the step that accuracy is influenced because of scattering.
Mathematical operation mode in the step 3 can be first derivation or second order derivative operation mode.
Mathematical operation mode described in the step 4 can be the partial least squares regression mode, also can be polynary progressively linear regression compute mode.
In the technique scheme, the food that in the packing of representing sample, has comprised a plurality of types, as both having comprised the fermented bean curd piece in the fermented bean curd, when also having comprised soup stock, the relational model that step 5 is set up, also comprised and having set up in the bottled food, the corresponding relation model between each composition in the same bottle between the dissimilar food.
The correlation model that employing is set up by technique scheme may further comprise the steps the method that glass pack carries out the composition detection:
Step 1:, adopt near infrared spectrometer to gather the near infrared spectrum of tested bottled food by diffuse reflection;
Step 2: eliminate the influence of bottle near infrared spectrum; Eliminate the diffuse transmission influence of tested bottled food;
Step 3: according to the content of each composition in the described tested bottled food of described correlation model prediction.
As shown from the above technical solution, the present invention utilizes in the near infrared spectrum of 780nm-2500nm, comprise that almost all contain hydrogen group in the organism, as C-H, O-H, the frequency multiplication of vibrating between the intramolecule atom of N-H and C=O etc. and the information of sum of fundamental frequencies, specific former subgroup (or claims group/component, as moisture, albumen, fat etc.) in this wavelength coverage, the characteristic of correspondence absorbing wavelength is arranged, and meet Beer law (Beer ' s Law): promptly be absorbed the atom concentration class that absorbs this wavelength light in the logarithm value of light quantity and the sample and have linear relationship, the former subgroup that can know this special wavelength correspondence by inference by the absorption value of working sample under a certain special wavelength light, the just concentration class of composition, i.e. percentage composition.Thereby the near infrared spectrum of collection food samples, and the near infrared spectrum of being gathered is analyzed in conjunction with the accurate content of known each composition of food samples, can obtain the association relation model of food samples composition and its near infrared spectrum, and to utilize this correlation model be measurable food ingredients with specific near infrared spectrum.
The invention has the beneficial effects as follows, by correlation model between glass pack composition that provides of the present invention and the near infrared spectrum is provided, utilizing this model just to can be implemented in does not destroy, or do not open under the prerequisite of packing vial and detect various compositions in the food fast, accurately, with no damage, promptly can save detection time, improve detection efficiency, can not influence the food quality after the detection again.Utilize method provided by the present invention can also realize the online detection of not damaged of glass pack,, perhaps detect the good technical means that provide for the market of administrative service division for determining to produce the quality grade or the classification of product.
Below, also in conjunction with the accompanying drawings the method for setting up correlation model provided by the present invention is described in further detail by a specific embodiment.
Description of drawings
Fig. 1 is the process flow diagram of setting up correlation model of an embodiment provided by the present invention.
Fig. 2 be embodiment illustrated in fig. 1 in bottled dissimilar food position synoptic diagram.
Fig. 3 is the reflectance spectrum of bottled fermented bean curd average reflection spectrum and glass bottle.
Fig. 4 is the near infrared spectrum that Fig. 2 indicating positions is gathered.
Fig. 5 is with predicting the outcome behind the integrating sphere collection spectrum.
Fig. 6 is with predicting the outcome behind the collecting fiber spectrum.
Fig. 7 is the characteristic absorption peak curve map of related coefficient in each composition near infrared region of bottled fermented bean curd.
Fig. 8 is the correlationship figure of salt content in the middle inhomogeneity food embodiment illustrated in fig. 1.
Fig. 9 is the correlationship figure of middle amino acid nitrogen content embodiment illustrated in fig. 1.
Figure 10 is the graph of a relation of middle soluble protein embodiment illustrated in fig. 1 and amino acid nitrogen content.
Figure 11 is based on the process flow diagram that the correlation model of being set up detects bottled food ingredients.
Embodiment
In the present embodiment, bottled food is fermented bean curd.The flow process of setting up the correlation model of each composition in the bottled fermented bean curd as shown in Figure 1.
1, selects a fermented bean curd kind, extract 30 bottles of bottled fermented bean curd samples of normal glass, adopt standard method of analysis that the content of each composition in the sample is measured respectively, obtain the standard component content value.Obtain the representative sample that its numerical value is the distribution shape.
2, the representative sample to above-mentioned steps adopts near infrared spectrometer to gather their near infrared spectrum respectively.Concrete can adopt the irreflexive method of integrating sphere, in the 833-2500nm wavelength coverage, (both relative positions in vial as shown in Figure 2, wherein A is the fermented bean curd piece, and B is a soup stock to gather the near infrared spectrum of above-mentioned bottled fermented bean curd piece and soup stock respectively with near infrared spectrometer; Both near infrared spectrums as shown in Figure 4, wherein a is the near infrared spectrum of fermented bean curd piece, b is the near infrared spectrum of soup stock; Fermented bean curd piece and soup stock belong to the dissimilar food that comprised in the same bottle, therefore need gather respectively).
3, for improving the accuracy of model, utilize polynary scatter correction (MSC) method that spectroscopic data is carried out pre-service,, simultaneously, eliminate the influence of bottle reflectance spectrum to eliminate the influence of sample scattering.As shown in Figure 3, the reflectance spectrum c of fermented bean curd has four more significant absorption peaks respectively 988,1200,1454 and 1945nm, and the reflectance spectrum d of empty bottle is similar to straight line, therefore can adopt mathematical method, for example single order or second order differentiate can be eliminated the influence of bottle.
5, utilize the method for partial least squares regression (PLS), extract in the near infrared spectrum after the above-mentioned processing gathered correlated characteristic information respectively about each composition.
6, set up the correlationship between each chemical analysis and near infrared spectrum in fermented bean curd piece and the soup stock respectively, promptly set up model, can also set up the relational model between each composition of fermented bean curd piece and soup stock simultaneously.
Be illustrated in figure 5 as and adopt integrating sphere to gather predicting the outcome to moisture, salinity, soluble protein and amino-acid nitrogen institute's established model in the glass bottle fermented bean curd behind the spectrum.Wherein to its coefficient R of moisture E
2Be 0.98, modeling standard deviation (RMSEC) is 0.389; To its related coefficient of soluble protein F is 0.97, and the modeling standard deviation is 0.189; To its related coefficient of amino-acid nitrogen G is 0.97, and the modeling standard deviation is 0.0191; To its related coefficient of salinity H is 0.99, and the modeling standard deviation is 0.120.
7, related according between fermented bean curd piece composition and soup stock composition set up the correlationship between both each compositions respectively, sets up the correlationship model between fermented bean curd piece and soup stock composition;
Be illustrated in figure 8 as the correlationship figure of salinity in fermented bean curd piece and the soup stock, its correlationship is 0.9415, and predictive equation is y=1.0822x+1.6575;
As shown in Figure 9, be the correlationship figure of amino acid nitrogen content in fermented bean curd piece and the soup stock, its correlationship is 0.9568, and predictive equation is y=0.8405x+0.0953;
Be the correlationship figure of amino acid nitrogen content in soluble protein and the fermented bean curd piece in the fermented bean curd piece as shown in figure 10, its correlationship is 0.8097, and predictive equation is y=0.0799x+0.0039.
Utilize the correlationship model of the fermented bean curd piece set up and the soup stock component content in can the bottled fermented bean curd of quick, undamaged online detection, and no matter the near infrared spectrum of gathering is a fermented bean curd piece sample, or the soup stock sample, after can utilizing separately facies relationship model to calculate the content of each composition respectively, the correlationship model that utilizes step 6 to set up is again obtained the content of each composition in the another kind of sample.
The foregoing description can also adopt the irreflexive method of optical fiber, in the 1000-2500nm wavelength coverage, with the near infrared spectrum of fermented bean curd piece and soup stock in the near infrared spectrometer collection glass bottle fermented bean curd.
Predicting the outcome as shown in Figure 6 to moisture, salinity, soluble protein and amino-acid nitrogen institute's established model in the bottled fermented bean curd behind the employing collecting fiber spectrum.To its coefficient R of moisture E1
2Be 0.96, modeling standard deviation (RMSEC) is 0.566; To its related coefficient of soluble protein F1 is 0.98, and the modeling standard deviation is 0.150; To its related coefficient of amino-acid nitrogen G1 is 0.96, and the modeling standard deviation is 0.0219; To its related coefficient of salinity H1 is 0.99, and the modeling standard deviation is 0.109.
In above-mentioned steps 5, also can adopt polynary progressively linear regression method to extract to be gathered near infrared spectrum in about the correlated characteristic information of each composition.As shown in Figure 7, utilize the near infrared characteristic absorption peak of each composition in the bottled fermented bean curd that polynary progressively linear regression method obtains exactly.The characteristic wavelength of moisture curve E2 be 957,1145,1253,1369,1410,1617 and 1786nm (± 5nm); The characteristic wavelength of soluble protein curve F2 be 1179,1280,1652,1828,2276 and 2309nm (± 5nm); The characteristic wavelength of amino-acid nitrogen curve G2 be 1176,1278,1648,1830 and 2298nm (± 5nm); The characteristic wavelength of salt component curve H2 be 992,1227,1443,1594,1716,1793 and 2174nm (± 5nm).
In the foundation of above-mentioned correlation model, build together and found three class models, it is the correlation model of each composition in the fermented bean curd piece, the correlation model of each composition in the soup stock, correlationship model between each composition of fermented bean curd and soup stock (corresponding relation of a kind of composition), therefore, utilize the method for bottled each composition of fermented bean curd of above-mentioned model prediction can be divided into discrete roll off the production line prediction and continuous on-line prediction.
When dispersing prediction, operating personnel can select the position at fermented bean curd piece place and carry out spectra collection, carry out the composition prediction then.When carrying out continuous on-line prediction, its prediction flow process as shown in figure 11.
At first, automatic on-line is gathered spectrum; Then, judging that spectrum is (matching degree of the averaged spectrum by this spectrum and fermented bean curd piece or soup stock is judged) the fermented bean curd piece or soup stock, is the fermented bean curd piece as the spectrum of gathering, and carries out composition according to the correlation model of above-mentioned each composition of fermented bean curd piece and predicts; If spectrum belongs to soup stock, then earlier carry out the prediction of composition according to the correlation model of above-mentioned soup stock, then according to the correlationship model between above-mentioned fermented bean curd piece and soup stock composition, be converted into the content of the corresponding composition in the fermented bean curd piece; Obtain each component content at last in the bottled fermented bean curd of fermented bean curd piece composition.
The present invention not only can be used for bottled food production enterprise and carry out the continuous composition of robotization detect on production line, content according to each Main Ingredients and Appearance in the product is divided product quality, and the rejecting substandard product, but also can be applied to administrative service division the bottled food sold on the market content by its each Main Ingredients and Appearance is carried out product quality quality, qualified and underproof detection.Because the correlation model that utilizes the present invention to set up does not need to open the packing of product in testing process, therefore, can accomplish that fully all bottled food are carried out a hundred per cent to be detected.Make the product quality of enterprise obtain good assurance like this, simultaneously, also make consumer's legitimate rights and interests obtain effective assurance.
It should be noted last that: above embodiment is the unrestricted technical scheme of the present invention in order to explanation only, although the present invention is had been described in detail with reference to the foregoing description, those of ordinary skill in the art is to be understood that: still can make amendment or be equal to replacement the present invention, and not breaking away from any modification or partial replacement of the spirit and scope of the present invention, it all should be encompassed in the middle of the claim scope of the present invention.
Claims (10)
1. method of setting up glass pack composition correlation model is characterized in that may further comprise the steps:
Step 1: adopt standard method of analysis that the content of each composition in the bottled food samples of above standard is measured respectively, obtain the standard component content value; A described bottled food samples of above standard is that component content is different separately, and its numerical value is the representative sample of distribution shape;
Step 2: adopt near infrared spectrometer to gather their near infrared spectrum respectively to the described representative sample of step 1 by the diffuse reflection method;
Step 3: the near infrared spectrum of being gathered is eliminated the influence of bottle body near infrared spectrum in the mathematical operation mode;
Step 4: the characteristic information of from the near infrared spectrum that step 2 is gathered, extracting each composition in the described representative sample spectrum again in the mode of mathematical operation;
Step 5: correlation model in the standard component content value of setting up each composition in the above-mentioned representative sample and its spectrum between the characteristic of each corresponding composition, for having dissimilar food in the same bottle, set up the described correlation model of each type of food product respectively.
2, method according to claim 1 is characterized in that: the quantity of the representative sample in described step 1, step 2, step 4 and the step 5 is 10-100.
3, method according to claim 1 and 2 is characterized in that: also be provided with after the described step 2 and adopt polynary scatter correction method to eliminate described representative sample to produce step to the accuracy influence because of scattering.
4, method according to claim 1 is characterized in that: in the described step 2, the diffuse reflection method is integrating sphere diffuse reflection or optical fiber diffuse reflection.
5, method according to claim 1 is characterized in that: the mathematical operation mode in the described step 2 is first derivation or second order differentiate.
6, method according to claim 1 is characterized in that: the mathematical operation mode in the described step 3 is partial least squares regression or polynary progressively linear regression computing.
7, method according to claim 1 is characterized in that: in the described step 5, also comprise and setting up in the bottled food, the corresponding correlation model between each composition between the dissimilar food in the same bottle.
8, method according to claim 4 is characterized in that: in the described step 2, the irreflexive wavelength coverage of integrating sphere is 833-2500nm; The irreflexive wavelength coverage of optical fiber is 1000-2500nm.
9, a kind of correlation model of being set up based on the arbitrary described method of claim 1-8 carries out the method that composition detects to glass pack, it is characterized in that comprising:
Step 1:, adopt near infrared spectrometer to gather the near infrared spectrum of tested bottled food by diffuse reflection;
Step 2: eliminate the influence of bottle near infrared spectrum; Eliminate the diffuse transmission influence of tested bottled food;
Step 3: according to the content of each composition in the described tested bottled food of described correlation model prediction.
10, detection method according to claim 9 is characterized in that: described step 2 is to utilize the operational method of single order or second order differentiate to eliminate the influence of bottle near infrared spectrum; Described step 3 adopts polynary scatter correction method to eliminate the diffuse transmission influence of tested bottled food.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101832922A (en) * | 2010-05-19 | 2010-09-15 | 中国农业大学 | Method for transferring near infrared model of organic fertilizer product |
| CN106124451A (en) * | 2016-08-31 | 2016-11-16 | 上海创和亿电子科技发展有限公司 | A kind of update the system and method for detection that through packaging bag, product is carried out On-line near infrared analyzer detection |
| CN107835938A (en) * | 2015-07-08 | 2018-03-23 | 皮道练 | Food state measuring device, food state measuring block, include its intelligent apparatus |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002048709A (en) * | 2000-07-31 | 2002-02-15 | Mitsui Mining & Smelting Co Ltd | Internal quality measuring method for vegetable and fruit internal quality evaluating device |
| JP2004530875A (en) * | 2001-04-13 | 2004-10-07 | カーギル、インコーポレイテッド | Agricultural and / or food raw material evaluation methods, applications and products |
| JP2003004631A (en) * | 2001-06-18 | 2003-01-08 | Kawasaki Kiko Co Ltd | Component-measuring apparatus |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101832922A (en) * | 2010-05-19 | 2010-09-15 | 中国农业大学 | Method for transferring near infrared model of organic fertilizer product |
| CN101832922B (en) * | 2010-05-19 | 2012-04-18 | 中国农业大学 | Method for transferring near infrared model of organic fertilizer product |
| CN107835938A (en) * | 2015-07-08 | 2018-03-23 | 皮道练 | Food state measuring device, food state measuring block, include its intelligent apparatus |
| US10788418B2 (en) | 2015-07-08 | 2020-09-29 | Do Yeon PI | Food state measuring device, food state measuring module, and smart device including the same |
| CN106124451A (en) * | 2016-08-31 | 2016-11-16 | 上海创和亿电子科技发展有限公司 | A kind of update the system and method for detection that through packaging bag, product is carried out On-line near infrared analyzer detection |
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