CN108572150A - A method of atriphos and bacterial population in sausage are detected based on EO-1 hyperion - Google Patents
A method of atriphos and bacterial population in sausage are detected based on EO-1 hyperion Download PDFInfo
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Abstract
The method of atriphos and bacterial population in sausage is detected based on EO-1 hyperion the present invention provides a kind of comprising step：(1) the instant sausage sample for obtaining different storage number of days, is scanned sausage sample using bloom spectrometer, obtains high spectrum image；(2) gained high spectrum image is pre-processed using multiplicative scatter correction, variable standardization and derivative method；(3) regression coefficient method is utilized to choose 10 characteristic wavelengths；(4) multiple linear regression model of sausage ATP and characteristic wave bands is established：(5) reshape2 in R LISP program LISPs carries out data recombination to the model of step (4), then executes lattice and carry out color expression in map to the ATP values calculated according to model.The efficient detection of the distribution situation to the ATP and bacterial population of its inside can be achieved in the present invention, and detection accuracy is up to 98.99%.
Description
Technical field
The invention belongs to quality of agricultural product detection fields, are related to sausage Quality Detection technology, and in particular to one kind is based on height
The method of atriphos and bacterial population in spectral detection sausage.
Background technology
Currently, in the context of detection of food storage phase, mainly use conventional methods, such as soda acid finger-length measurement, enzymatic activity method
Deng, these procedures are complicated, and can to by inspection product generate damage, influence subsequent storage and sale.
In terms of the above-mentioned drawback for improving conventional method, Visual retrieval technology is one of the R＆D direction to attract people's attention.
Application No. is 201010262347.5 Chinese patent disclose it is a kind of based on olfaction visualization detect fish freshness method and
Device, but its device not yet industrialization for utilizing, it is difficult to be promoted and applied.
Although Visual retrieval technology has obtained preliminary application in food quality and storage phase context of detection, for ripe
For food, since its quality is more vulnerable to the pollutant effects such as bacterium, the precision of detection becomes as most important problem.
Sausage is one of common prepared food, is studied in terms of to the Visual retrieval of the storage phase of sausage at present seldom.Three phosphorus
Adenosine monophosphate (ATP) value is to reflect one of the index of prepared food freshness, is influenced by food storage time and bacterial population number, accurately
Detection prepared food in ATP values can react the resting period of food to a certain extent, however not yet work out to prepared food at present
The high-precision Visual retrieval technology of ATP contents in (especially instant sausage), and based on the storage time of this acquisition prepared food
With the bacterial number information in prepared food.
Invention content
In view of the shortcomings of the prior art, one of the objects of the present invention is to provide one kind based in EO-1 hyperion detection sausage three
The non-destructive testing to the atriphos in sausage may be implemented in the method for adenosine phosphate, this method comprising following steps：
(1) the instant sausage sample that storage number of days is 1,3,5 day is obtained, sausage sample is carried out using bloom spectrometer
Scanning takes the image information of sausage sample at different wavelengths, obtains sausage sample in the different high spectrum images for storing number of days；
(2) gained high spectrum image is pre-processed using multiplicative scatter correction, variable standardization and derivative method；
(3) regression coefficient method is utilized to choose 10 characteristic wavelengths：385nm、390nm、395nm、505nm、580nm、
670nm, 745nm, 780nm, 855nm and 955nm；
(4) multiple linear regression model of sausage ATP and characteristic wave bands is established：
YATP=-5.05+2.98 λ385-1.08λ390-1.44λ395-12.86λ505+13.59λ580+5.89λ670-0.25λ745-
6.50λ780-2.78λ855+2.47λ955
Wherein λ 385, λ 390, λ 395, λ 505, λ 580, λ 670, λ 745, λ 780, λ 855, λ 955 are respectively that sausage sample exists
Spectrum at characteristic wavelength 385nm, 390nm, 395nm, 505nm, 580nm, 670nm, 745nm, 780nm, 855nm, 955nm is anti-
Penetrate value；YATPFor sausage sample ATP values；
(6) reshape2 in R LISP program LISPs carries out data recombination to the model of step (4), then executes lattice pairs
Color expression in map is carried out according to the ATP values that model calculates.
In step (1), when taking the instant sausage sample, take the region in sausage section from center of circle different distance as sense
Interest region.
The situation optional as the one of which in the present invention, in step (1), the temperature of storage is 35 DEG C.
In step (5), specific method is：
1) x, y are defined using colnames sentences, and selects characteristic wavelength and is defined as sw, and will be connected between x and sw
It connects, and it is swn to define this new value；
2) DF is utilized<- as.Matrix (df) establishes a matrix between x, y, swn:df<-as.Matrix df[,c("
x","y",swn)]
3)df[,ref][df[,ref]<min]<-NA；Wherein, ref is wavelength variable, is arranged according to the difference of figure
Different threshold, the reflected value under non-characteristic wavelength is assigned a value of 0, as background；
4) model under input feature vector wavelength：Df $ ATP=-5.05+2.98*df $ X385-1.08*df $ X390-1.44*
df$X395-12.86*df$X505+13.59*df$X580+5.89*df$X670-0.25*df$X745-6.50*df$X780-
2.78*df$X855+2.47*df$X955；
5) the middle order for executing reshape2 carrys out recombination data below the library of R programs；
6) the middle execution lattice below the library of R programs carries out color expression in map to calculated ATP values.
Another object of the present invention is to provide a kind of method detecting bacterial population in sausage based on EO-1 hyperion, the party
Method makes following processing on the basis of the above-mentioned method for detecting atriphos in sausage based on EO-1 hyperion：First to difference
The sausage sample of storage number of days is sampled culture, calculates bacteria colony count；Then sausage is detected based on EO-1 hyperion according to above-mentioned
The method of middle atriphos, establish color in the color drawing expressed by its step (5) with it is above-mentioned be calculated thin
Equation between bacterium clump count.
Beneficial effects of the present invention：
The present invention can realize on the basis of not destroying sausage to the distribution situation of the ATP and bacterial population of its inside
Efficient detection, gained testing result is accurate, and accuracy is up to 98.99%.The present invention can by the detection of the ATP values inside sausage
To detect the storage time of sausage.
Description of the drawings
Fig. 1 is detection process figure of the present invention；Wherein, (a) is sausage sample EO-1 hyperion acquisition process, is (b) sausage sample
The extraction of EO-1 hyperion as a result, (c) be to sausage sample EO-1 hyperion pre-processed results, (d) establish pretreatment averaged spectrum and ATP it
Between Partial Least-Squares Regression Model characteristic wavelength is determined by regression coefficient, (e) obtained using multiple linear regression model
The ATP of sample sausage carries out inverting charting using R language in conjunction with multiple linear regression model, (f) establishes atriphos space
Distribution map.
Specific implementation mode
The present invention is specifically described below by embodiment, it is necessary to which indicated herein is that following embodiment is only used
It is further detailed in the present invention, should not be understood as limiting the scope of the invention, which is skilled in technique
Personnel still fall within protection scope of the present invention according to some nonessential modifications and adaptations that foregoing invention content is made.
Embodiment 1
The present embodiment is within the scope of 380~1000nm, using EO-1 hyperion to being placed under 35 degrees Celsius 1,3,5 days in wavelength
Instant sausage carry out non-destructive testing and identify that steps are as follows：
1, the high spectrum image that instant sausage is stored 1,3,5 day respectively at 35 DEG C is obtained；
2, the area-of-interest of sausage sample, light of the acquisition area-of-interest in characteristic wavelength are selected on high spectrum image
Compose reflected value；
3, spectrum is pre-processed using multiplicative scatter correction, variable standardization and derivative method, obtains high s/n ratio, low
The spectroscopic data of background interference；
4, using the regression coefficient method in Partial Least Squares modeling process choose 10 characteristic wavelengths (385nm, 390nm,
395nm, 505nm, 580nm, 670nm, 745nm, 780nm, 855nm and 955nm)；
5, the multiple linear regression model of sausage ATP and characteristic wave bands is established：
Y ATP=-5.05+2.98 λ385-1.08λ390-1.44λ395-12.86λ505+13.59λ580+5.89λ670-0.25
λ745-6.50λ780-2.78λ855+2.47λ955
Wherein λ 385, λ 390, λ 395, λ 505, λ 580, λ 670, λ 745, λ 780, λ 855, λ 955 are respectively that sausage sample exists
Spectrum at characteristic wavelength 385nm, 390nm, 395nm, 505nm, 580nm, 670nm, 745nm, 780nm, 855nm, 955nm is anti-
Penetrate value；YATPFor sausage sample ATP values；
6, by writing image processing program in R language, calibration model application that will be established based on 10 characteristic wavelengths
The index content of each pixel in prognostic chart picture, and the quantized result that displaying is predicted in the form of pcolor, to
Realize the visualization distribution of its content.Image viewing can intuitively reflect sausage under different storage time, atriphos
Dynamic change, help to realize real time monitoring, specific method is：
1) x, y, are defined using colnames sentences inside R programs, and selects characteristic wavelength and is defined as sw, and by X
It is connected between characteristic wavelength (sw), and it is swn to define this new value；
2) DF, is utilized<- as.Matrix (df) establishes x, a matrix between y, swn:df<-as.Matrix df[,c("
x","y",swn)]；
3)、df[,ref][df[,ref]<min]<-NA；Ref is wavelength variable, is arranged according to different graphic different
The step of threshold, this step is to discriminate between the boundary between sample and background.The reflected value of every obtained different wave length
<Min (it is 0.1 that min is defined in this instance, depending on the value of different data), then be considered as noise, be assigned a value of 0, as the back of the body
Scape；
4), input model：Model under characteristic wavelength：Df $ ATP=-5.05+2.98*df $ X385-1.08*df $ X390-
1.44*df$X395-12.86*df$X505+13.59*df$X580+5.89*df$X670-0.25*df$X745-6.50*df$
X780-2.78*df$X855+2.47*df$X955；
5), the middle order for executing reshape2 carrys out recombination data below the library of R programs；
6) the ATP values calculated according to model are utilized color by, the middle execution lattice below the library of R programs
Mode expressed above drawing.
7, culture is sampled to the sausage sample of different storage number of days, calculates bacteria colony count, and establishment step (5) institute
The equation between color and bacteria colony count in the color drawing of expression, in this way can using the testing result of EO-1 hyperion come
Predict the bacteria colony count in sausage sample.
As shown in Figure 1, the accuracy of detection for ATP of the present invention is up to 98.99%." PredictATP in Fig. 1
Value " is the ATP values predicted using detection method, and " MeasuredATP value " is to utilize conventional method reality
The ATP values that border detects.
By repeating the present embodiment process (random selection after only storage time is marked), it is demonstrated experimentally that this
The method of invention can be with the 100% accurate anti-storage time for measuring sausage.
By being sampled to the bacterium in different samples, counted using tablet detection method, then with the present invention's
Predicted detection bacterial population is compared, and the data obtained difference is no more than 3%.
Claims (5)
1. a kind of method detecting atriphos in sausage based on EO-1 hyperion, which is characterized in that the method includes walking as follows
Suddenly：
(1) the instant sausage sample that storage number of days is 1,3,5 day is obtained, sausage sample is swept using bloom spectrometer
It retouches, takes the image information of sausage sample at different wavelengths, obtain sausage sample in the different high spectrum images for storing number of days；
(2) gained high spectrum image is pre-processed using multiplicative scatter correction, variable standardization and derivative method；
(3) regression coefficient method is utilized to choose 10 characteristic wavelengths：385nm、390nm、395nm、505nm、580nm、670nm、
745nm, 780nm, 855nm and 955nm；
(4) multiple linear regression model of sausage ATP and characteristic wave bands is established：
YATP=-5.05+2.98 λ385-1.08λ390-1.44λ395-12.86λ505+13.59λ580+5.89λ670-0.25λ745-6.50
λ780-2.78λ855+2.47λ955
Wherein λ 385, λ 390, λ 395, λ 505, λ 580, λ 670, λ 745, λ 780, λ 855, λ 955 are respectively sausage sample in feature
Spectral reflectance at wavelength 385nm, 390nm, 395nm, 505nm, 580nm, 670nm, 745nm, 780nm, 855nm, 955nm
Value；YATPFor sausage sample ATP values；
(5) reshape2 in R LISP program LISPs carries out data recombination to the model of step (4), then executes lattice to basis
The ATP values that model calculates carry out color expression in map.
2. according to the method described in claim 1, it is characterized in that, in step (1), when taking the instant sausage sample, perfume (or spice) is taken
Region in intestines section from center of circle different distance is as area-of-interest.
3. according to the method described in claim 1, it is characterized in that, in step (1), the temperature of storage is 35 DEG C.
4. according to the method described in claim 1, it is characterized in that, in step (5), specific method is：
1) x, y are defined using colnames sentences, and selects characteristic wavelength and is defined as sw, and will be connected between x and sw, and
And it is swn to define this new value；
2) DF is utilized<- as.Matrix (df) establishes a matrix between x, y, swn:df<-as.Matrix df[,c("x","
y",swn)]
3)df[,ref][df[,ref]<min]<-NA；Wherein, ref is wavelength variable, and difference is arranged according to the difference of figure
Threshold, the reflected value under non-characteristic wavelength is assigned a value of 0, as background；
4) model under input feature vector wavelength：Df $ ATP=-5.05+2.98*df $ X385-1.08*df $ X390-1.44*df $
X395-12.86*df$X505+13.59*df$X580+5.89*df$X670-0.25*df$X745-6.50*df$X780-2.78*
df$X855+2.47*df$X955；
5) the middle order for executing reshape2 carrys out recombination data below the library of R programs；
6) the middle execution lattice below the library of R programs carries out color expression in map to calculated ATP values.
5. utilizing the method for the bacterial content in any one of Claims 1 to 44 the method detection sausage, which is characterized in that right
The sausage sample of difference storage number of days is sampled culture, calculates bacteria colony count, and the color diagram expressed by establishment step (5)
The equation between color and bacteria colony count in face.
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| CN111289516A (en) * | 2020-03-26 | 2020-06-16 | 中国农业大学 | Method and device for detecting amino acid content of plant leaves |
| CN113820300A (en) * | 2021-10-28 | 2021-12-21 | 南京农业大学 | Method for predicting growth of pseudomonas fluorescens in pork |
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