CN113945539B - Method and system for predicting quality of near infrared spectrum characteristic wave band based on GWAS (Global positioning System) screening - Google Patents
Method and system for predicting quality of near infrared spectrum characteristic wave band based on GWAS (Global positioning System) screening Download PDFInfo
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Abstract
The invention provides a method and a system for predicting quality based on near infrared spectrum characteristic wave bands after GWAS screening, which apply a special near infrared spectrum characteristic wave band screening strategy, firstly utilize near infrared spectrum acquisition equipment to scan a configured original sample to obtain spectrum data of the sample, measure various meat quality physical and chemical indexes of the sample, then introduce two kinds of data into a corresponding system according to a set mode to conduct GWAS analysis, further draw a matched Manhattan diagram according to an analysis result and a set threshold line, screen out a plurality of matching wave bands which exceed a threshold value and correspond to various meat quality indexes based on information in the diagram, and finally realize meat quality prediction based on the screened wave bands by utilizing a near infrared spectrum technology.
Description
Technical Field
The invention relates to the technical field of quality nondestructive reliability tests and detection monitoring, in particular to a method and a system for predicting quality based on near infrared spectrum characteristic wave bands after GWAS screening, which can be used for nondestructive detection and monitoring of meat products.
Background
In recent years, with the improvement of the living standard of people, the demands of consumers on high-quality meat products with delicious taste and rich nutrition are continuously increased, the market consumption structure is changed, pork is taken as main consumption meat in the market, and more breeding companies are used for inspecting and improving the quality of the pork as a long-term target of pig breeding. There are many indices for evaluating pork quality in general, including fat, moisture, protein, fatty acid content, PH, meat color, etc. The evaluation method of the pork quality indexes in the food and agriculture and animal husbandry industries around the world is not only a conventional physicochemical analysis, but also a nondestructive, rapid and convenient meat product component measurement method based on a visible light/near infrared spectrum technology.
However, in the process of industrialized practical application of the near infrared spectrum analysis technology, the available near infrared spectrum measuring instrument is low in cost, especially the measuring probes matched with the instrument and configured in different wave bands are often expensive, which may be a huge expense for scientific research staff or related practitioners, so that the application of the nondestructive, rapid and convenient meat product component measuring technology of the visible light/near infrared spectrum technology is obviously limited, and the practicability is limited; in addition, the meat quality characteristics are predicted based on full-band configuration by using a visible light/near infrared spectrum instrument, so that the reliability of a prediction result is insufficient due to insufficient pertinence, and the requirements of markets and meat quality culture cannot be met.
The information disclosed in the background section of the invention is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
To solve the above problems, the present invention provides a method for predicting quality of near infrared spectrum characteristic bands based on GWAS screening, in one embodiment, the method includes:
sample configuration, namely configuring original meat samples with set scales based on the comprehensive meat quality characteristics to be tested, performing sample preparation according to a set processing strategy, and numbering each prepared target sample;
a data measurement step, namely scanning each target sample through near infrared spectrum acquisition equipment to obtain near infrared spectrum data, acquiring meat quality character data of each target sample by utilizing a measurement function module attached to the near infrared equipment or a matched physicochemical measurement means, and correlating and recording the two data;
a GWAS analysis step, namely calling a linear mixed model by using GEMMA software to perform GWAS analysis on the two, and obtaining a corresponding analysis result;
a feature map drawing step, drawing a corresponding Manhattan map by matching with a set significance threshold line based on a result file after GWAS analysis;
a feature screening step, namely screening and determining effective spectrum bands corresponding to all meat quality character indexes according to the distribution condition of each meat quality character index characteristic information relative to spectrum band characteristic information in the Manhattan diagram;
and a prediction application step, wherein the meat quality characteristics are predicted based on the effective spectrum bands obtained by screening by utilizing a near infrared spectrum technology.
In an alternative embodiment, in the sample configuration step, the meat quality trait to be tested includes the following indicators: fat, moisture, protein, lean fraction, collagen, salt, ash, saturated fatty acid, energy kJ/100g, energy kcal/100g, sodium salt, PH, L, a, b.
Further, in one embodiment, in the sample configuration step, the process of performing the sample preparation process according to the set processing policy includes:
the visible fat and fascia on the surface of each original sample were removed and each sample was crushed using a crushing device to allow the crushed sample to be evenly packed into a round glass plate.
In one embodiment, in the data determining step, the scanning to obtain near infrared spectrum data further includes:
and removing samples exceeding the Markov distance threshold by using a Markov distance discrimination method, and putting the rest samples as effective samples into a meat quality character data acquisition and subsequent steps.
In a preferred embodiment, in the GWAS analysis step, it includes: and taking meat quality character data as a phenotype file, taking near infrared spectrum data as a genotype file, inputting the genotype file into corresponding GEMMA software or a platform, and calling a linear mixed model to perform GWAS analysis so as to effectively eliminate the influence of multiple collinearity existing between spectrum bands on an analysis result.
Further, in one embodiment, in the GWAS analysis step, a linear hybrid model represented by the following formula is invoked:
Y＝Xb+Zu+ε
wherein Y is a phenotype vector corresponding to a sample, X is a fixed effect matrix generated by covariates, Z is a random effect matrix which aggregates the spectrum information of all samples, b and u respectively represent weight coefficients of the fixed effect and the random effect, and epsilon is a residual error.
Specifically, in one embodiment, in the feature map drawing step, a spectrum band is used as an abscissa, a saliency p value is subjected to-log 10 conversion and then used as an ordinate to draw a scatter diagram, and a saliency threshold line is set by using a Bonferroni correction method.
In an alternative embodiment, in the feature screening step, for each meat quality attribute index, each peak of each index exceeding a threshold in the feature map and 5 bands around each peak are selected, and these bands are used as the matched near infrared feature bands corresponding to the individual meat quality attribute index.
Further, in one embodiment, the method further includes an analysis and verification step, and before the predictive application step, the screened characteristic bands are verified with respect to the full band by using a multiple linear regression model, and the following parameters are calculated: correction set decision coefficient
Cross validation set decision coefficient +.>
The comparison of the correction set Root Mean Square Error (RMSEC) and the cross validation set Root Mean Square Error (RMSECV) measures the predicted results of the filtered characteristic bands relative to the full band.
Based on other aspects of the method described in any one or more of the embodiments above, the present invention further provides a system for predicting quality based on a post-GWAS filtered near infrared spectral feature band, which performs the method described in any one or more of the embodiments above.
Compared with the closest prior art, the invention has the following beneficial effects:
according to the method and the system for predicting the quality based on the near infrared spectrum characteristic wave bands after the GWAS screening, a special near infrared spectrum characteristic wave band screening strategy is applied, firstly, a near infrared spectrum acquisition device is used for scanning a configured original sample, spectral data of the sample are obtained, each meat quality character physicochemical index of the sample is measured, then two kinds of data are imported into a corresponding system according to a set mode for GWAS analysis, a matched Manhattan diagram is drawn according to an analysis result and a set threshold line, a plurality of matching wave bands which are corresponding to each meat quality character index and exceed a threshold value are screened based on information in the diagram, finally, the meat quality is predicted based on the screened wave bands by utilizing a near infrared spectrum technology, on one hand, the input cost of research and development and purchase stages of near infrared equipment in the future is effectively controlled, on the other hand, the data processing flow is simplified, the prediction timeliness is optimized, and meanwhile the accuracy of a prediction result is improved.
Further, in the invention, the correlation result of the spectrum and the sample index is displayed in a visual mode in a Manhattan diagram mode, so that visual feeling is given to researchers or related industry personnel;
in addition, researchers of the invention consider that in the GWAS analysis with genetics as a background, each independent data individual has the characteristic of high dimensionality, and the collinearity exists among the dimensionalities to a great extent, and the collinearity is called linkage disequilibrium among SNP loci in the genetic analysis. The linear mixed model is called in the GWAS analysis step to realize analysis, so that the method not only comprises a fixed effect, but also comprises a random effect, can effectively eliminate the influence of multiple collinearity on an analysis result, and ensures the reliability of the analysis result.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention, without limitation to the invention. In the drawings:
FIG. 1 is a flowchart of a method for predicting quality based on a near infrared spectrum characteristic band after GWAS screening according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method for predicting quality based on a near infrared spectrum characteristic band screened by GWAS according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a configuration sample in a method for predicting quality of a near infrared spectrum characteristic band based on GWAS screening according to another embodiment of the present invention;
FIG. 4 is an example of a near infrared spectrum graph before outliers are removed in a method for predicting quality of near infrared spectrum characteristic bands based on GWAS screening according to an embodiment of the present invention;
FIG. 5 is an example of a near infrared spectrum graph after outliers are removed in a method for predicting quality based on a near infrared spectrum characteristic band after GWAS screening according to an embodiment of the present invention;
FIG. 6 is a Manhattan diagram illustration of a method for predicting quality based on a near infrared spectral signature band after GWAS screening according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a system for predicting quality based on a near infrared spectrum characteristic band after GWAS screening according to still another embodiment of the present invention.
Detailed Description
The following will explain the embodiments of the present invention in detail with reference to the drawings and examples, so that the practitioner of the present invention can fully understand how to apply the technical means to solve the technical problems, achieve the implementation process of the technical effects, and implement the present invention according to the implementation process. It should be noted that, as long as no conflict is formed, each embodiment of the present invention and each feature of each embodiment may be combined with each other, and the formed technical solutions are all within the protection scope of the present invention.
Although a flowchart depicts operations as a sequential process, many of the operations can be performed in parallel, concurrently, or at the same time. The order of the operations may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
The computer device includes a user device and a network device. Wherein the user equipment or client includes, but is not limited to, a computer, a smart phone, a PDA, etc.; network devices include, but are not limited to, a single network server, a server group of multiple network servers, or a cloud based cloud computing consisting of a large number of computers or network servers. The computer device may operate alone to implement the invention, or may access a network and implement the invention through interoperation with other computer devices in the network. The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a VPN network, and the like.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Pork is used as main consumption meat in the market, and more breeding companies are used for inspecting and improving pork quality as a long-term target of pig breeding. There are many indices for evaluating pork quality in general, including fat, moisture, protein, fatty acid content, PH, meat color, etc. The evaluation method of the pork quality indexes in the food and agriculture and animal husbandry industries around the world is not only a conventional physicochemical analysis, but also a nondestructive, rapid and convenient meat product component measurement method based on a visible light/near infrared spectrum technology.
However, in the process of industrialized practical application of the near infrared spectrum analysis technology, the available near infrared spectrum measuring instrument is low in cost, especially the measuring probes matched with the instrument and configured in different wave bands are often expensive, which may be a huge expense for scientific research staff or related practitioners, so that the application of the nondestructive, rapid and convenient meat product component measuring technology of the visible light/near infrared spectrum technology is obviously limited, and the practicability is limited; in addition, the meat quality characteristics are predicted based on full-band configuration by using a visible light/near infrared spectrum instrument, so that the reliability of a prediction result is insufficient due to insufficient pertinence, and the requirements of markets and meat quality culture cannot be met.
Based on the invention, researchers consider that the near infrared band or the band range really needed in the prediction work is screened out in a targeted way and is brought into the research and development and purchasing stages of near infrared equipment, so that the overflow of the hardware equipment in the use process is effectively reduced, the use cost is effectively reduced, and the purpose of promoting the industrialized application of the near infrared detection technology is effectively achieved.
The common screening methods of spectral characteristic bands are roughly divided into two types, wherein the first type is screening based on mathematical statistics, and the first type comprises a continuous projection algorithm (SPA), a correlation coefficient method (CC), a Monte Carlo Method (MC) and the like. The second category is artificial intelligence based feature robot optimization such as Genetic Algorithm (GA), ant Colony Algorithm (ACA), random frog-leaping algorithm (RF), etc. In related reports at home and abroad, the methods have achieved good results in the screening of the characteristic wave bands of the spectrum. However, the methods require users to have a certain degree of mathematical modeling and program compiling basis in specific applications, and set a low threshold for common practitioners, so that popularization and promotion of the methods in the near infrared technology industrialization practical application process are limited. Therefore, the research project group of the invention develops a novel, efficient and convenient near infrared spectrum characteristic wave band screening method based on GWAS (whole genome association analysis) for the research project group, and applies the method to meat quality prediction technology.
Specifically, in order to solve the problems, the invention provides a method and a system for predicting quality of near infrared spectrum characteristic wave bands based on GWAS screening.
The detailed flow of the method of embodiments of the present invention is described in detail below based on the attached drawing figures, where the steps shown in the flowchart of the figures may be performed in a computer system containing, for example, a set of computer executable instructions. Although a logical order of steps is depicted in the flowchart, in some cases the steps shown or described may be performed in a different order than presented.
Example 1
Fig. 1 is a flow chart of a method for predicting quality of near infrared spectrum characteristic bands based on GWAS screening according to an embodiment of the invention, and referring to fig. 1, the method includes the following steps.
Sample configuration, namely configuring original meat samples with set scales based on the comprehensive meat quality characteristics to be tested, performing sample preparation according to a set processing strategy, and numbering each prepared target sample;
a data measurement step, namely scanning each target sample through near infrared spectrum acquisition equipment to obtain near infrared spectrum data, acquiring meat quality character data of each target sample by utilizing a measurement function module attached to the near infrared equipment or a matched physicochemical measurement means, and correlating and recording the two data;
a GWAS analysis step, namely calling a linear mixed model by using GEMMA software to perform GWAS analysis on the two, and obtaining a corresponding analysis result;
a feature map drawing step, drawing a corresponding Manhattan map by matching with a set significance threshold line based on a result file after GWAS analysis;
a feature screening step, namely screening and determining effective spectrum bands corresponding to all meat quality character indexes according to the distribution condition of each meat quality character index characteristic information relative to spectrum band characteristic information in the Manhattan diagram;
and a prediction application step, wherein the meat quality characteristics are predicted based on the effective spectrum bands obtained by screening by utilizing a near infrared spectrum technology.
By adopting the operation logic in the embodiment, aiming at the problems that the surplus of modeling wave bands leads to overflow of hardware equipment performance in the actual production and application process of the current near infrared technology, the industrialized popularization cost is high, and meanwhile, the existing most characteristic wave band screening methods are difficult to realize, have high threshold and the like, the screening strategy of near infrared spectrum characteristic wave bands based on GWAS (whole genome association analysis) is specially applied, and the method is quick, convenient and effective. Based on genetic knowledge background, the method presents the correlation result in a visual mode by establishing the correlation between the spectrum wave band and each physicochemical index of the sample, and carries out modeling verification on the screened characteristic wave band, thereby realizing the elevation of the prediction potential of multiple physicochemical indexes and providing assistance for the near infrared technology on the industrialized application road.
In practical application, the adopted screening strategy of the near infrared spectrum characteristic wave band comprises the following ideas: step (1), measuring and collecting sample data; step (2): GWAS analysis using GEMMA; step (3), drawing a Manhattan diagram by using R software; and (4) extracting a target characteristic wave band according to the information in the Manhattan diagram.
The method comprises the steps of (1) carrying out numbering scanning on a plurality of fresh pork samples through near infrared spectrum acquisition equipment, and simultaneously measuring various meat quality traits of the corresponding samples by using a traditional method or a measuring module attached to the near infrared equipment;
and (2) recording meat quality characteristics and near infrared spectrum data in a text format respectively, wherein the meat quality characteristics and the near infrared spectrum data are used as phenotype files, the phenotype files are input into GEMMA software as genotype files, and a linear mixed model is called to perform GWAS analysis.
And (3) inputting the result file after the GWAS analysis into R software, setting a significance threshold line and drawing a Manhattan diagram.
And (4) observing the Manhattan diagram, and screening the characteristic spectrum band according to the peaks and valleys formed by the scattered points, as shown in fig. 2.
The near infrared band screened by the process is the characteristic band corresponding to the physicochemical components of the sample. The method is an unprecedented spectral band screening method, establishes the association between all near infrared spectral bands and physicochemical components to be predicted, has the advantages of simple flow, strong repeatability, low technical threshold and the like, and simultaneously, the visualization of the association result provides the most intuitive feeling for researchers or related practitioners, and provides effective assistance for the industrialized application of near infrared spectral analysis technology.
Wherein, GWAS (whole genome association analysis) is a genetic statistical analysis technology which is based on association analysis, makes full use of linkage disequilibrium at population level, takes Single Nucleotide Polymorphism Sites (SNPs) in genome as molecular genetic markers, and locates genetic factors affecting complex phenotypic traits. The basic principle is that genetic variation is selected in the whole genome range for genotyping, and the difference of the frequency of each genetic variation between abnormal individuals and a control group is compared, so that the correlation strength between each variation and the target character is analyzed statistically.
The invention mainly combines the genetic related background, adopts the thought and flow of whole genome association analysis (GWAS), establishes the reliable association between the spectrum band and each physicochemical property of the sample, and thus realizes the screening of the characteristic band.
In order to ensure that the characteristic band obtained after screening can fully cover various meat quality property indexes, the meat quality property indexes to be measured need to be fully considered in the process of preparing a sample, specifically, in one embodiment, in the sample configuration step, the meat quality properties to be measured need to be considered include the following indexes: fat, moisture, protein, lean fraction, collagen, salt, ash, saturated fatty acid, energy kJ/100g, energy kcal/100g, sodium salt, PH, L, a, b.
Furthermore, before data are measured, reasonable samples are required to be configured, and in practical application, the original samples of the individual for scaling modeling in the experiment are respectively obtained by slaughtering three pig raising companies in Guangdong, jiangxi and Guangxi. Wherein 90% of the individuals are long white pigs, large white pigs and filial generation thereof, and 10% of the individuals are local pigs, and slaughter is carried out after reaching the uniform market age (average age of 200 days). Acid is discharged from the slaughtered carcasses for 24 hours in a cold storage at 0-4 ℃, then the carcasses are segmented, and the longus dorsi muscle at 5-6 ribs of the left half of the carcasses is sampled and measured.
In a preferred embodiment, in the sample configuration step, the process of performing the sample preparation process according to the set process policy includes:
the visible fat and fascia on the surface of each original sample were removed and each sample was crushed using a crushing device to allow the crushed sample to be evenly packed into a round glass plate.
In practical application, all samples can be thawed at room temperature (20 ℃), sampled according to the method prescribed in China 'GB/T9695.19-2008 meat and meat product sampling method', fat, fascia and the like visible on the surfaces of the samples are removed and numbered, each sample is respectively crushed by a small electric bench type meat grinder (Skyworth-P407, 50HZ,300w,28000 r/min), and the samples are filled into round glass plates (with the diameter of 80mm and the depth of 13 mm) after uniform crushing is ensured, as shown in figure 3. The near infrared spectrum collected by the FoodScan2 spectrum analyzer used in this experiment was 400 to 1099.5 nanometers in wavelength. Near infrared spectral data for each sample was taken from an average of 18 spectral subsets recorded at 18 different points from an automatically rotating plate in the analyzer, stored in log (1/R), R representing reflectance.
Further, foodScan was used according to the official method approved by American society of analytical chemists AOAC (Association of Official Analytical Chemists) TM A2 Meat Analyser (Foss Analytical, denmark) apparatus was used to determine fat, protein, moisture in the longissimus dorsi of pigs in transmission mode15 meat quality traits such as collagen content, and the detailed data are shown in the following table 1);
TABLE 1 meat quality trait determination (15 items total)
Furthermore, in the invention, the researchers consider that random errors possibly exist in the measuring process, and meanwhile, factors such as environment, batch and the like can also influence the subsequent modeling, so that in order to avoid interference of basic data sample errors (or outlier sample information) on analysis results, the collected sample data is corrected by using a Markov distance discrimination method according to the standard described in (NY/T2797-2015).
Thus, in a preferred embodiment, in the data determining step, the scanning to obtain near infrared spectrum data further includes:
and removing samples exceeding the Markov distance threshold by using a Markov distance discrimination method, and putting the rest samples as effective samples into a meat quality character data acquisition and subsequent steps.
In the experiment, the spectrum range acquired by using Foodscan is between 400 and 1099.5nm, the interval is 0.5nm, samples exceeding the Markov distance threshold are removed by using a Markov distance discrimination method, and 1206 samples are finally obtained by screening. Fig. 4 and 5 show near infrared diffuse reflection spectra before and after abnormal individual rejection, respectively.
In practical application, the following Markov distance discrimination principle can be adopted:
wherein MD is i The mahalanobis distance, x, for the ith sample i ＝(x i1 ，...，x ik ) T For the ith sample x i Score in k dimensions, μ= (μ) 1 ，...，μ k ) T For x expectations, S is the covariance matrix of x, MD L As a result of the mahalanobis distance threshold,
mean value of the mahalanobis distance of the sample, SD MD Is the standard deviation of the mahalanobis distance of the sample.
Based on the scheme in the embodiment, error samples exceeding the mahalanobis distance threshold are removed, and the rest samples are put into meat quality character data acquisition operation and subsequent operation, so that not only can the consumption of computing resources be saved, the analysis timeliness be improved, but also the influence of the error samples on the analysis result can be overcome, and the accuracy of the analysis result is improved.
Further calling a linear mixed model by using GEMMA software to perform GWAS analysis on the acquired meat quality character data and near infrared spectrum data, and acquiring a corresponding analysis result; in one embodiment, in the GWAS analysis step, the method includes: and taking the meat quality character data as a phenotype file, taking near infrared spectrum data as a genotype file, inputting the genotype file into corresponding GEMMA software or a platform, and obtaining a corresponding analysis result.
The obtained 15 meat quality traits and near infrared spectrum information are respectively input into a text file, the obtained trait file presents a matrix of n x 15, the spectrum file presents a matrix of p x (3+n), wherein n represents the total number of samples, p represents the total number of spectrum bands, the meat quality trait file does not need to be added with row names and column names, the first column of the spectrum information file is a band serial number (400-1099.5), and the second column and the third column are filled in with the same base type, namely any one of A, T, C, G. And then, taking the meat quality character file as a phenotype file, calling by using a parameter '-p', taking the spectrum information file as a genotype file, and calling by using a parameter '-g'. Meanwhile, as the "-g" call is not a real genotype file, a parameter "-notsnp" needs to be added to ensure the normal running of the program. Finally, parameters "-1mm 1" are called to perform GWAS analysis.
Since the genotype data used in GWAS analysis has a high similarity to near infrared spectrum data. In addition to having a population size and each individual in the population possessing a high degree of dimensionality, there is a large degree of collinearity between the dimensions, known in the genotype data as linkage disequilibrium between SNP sites. Therefore, to eliminate the effect of multiple collinearity on the analysis results, a linear mixture model is called in the GEMMA for GWAS analysis.
The linear mixture model is a variance component model that includes both fixed and random effects. When the parameter can be considered constant, the effect produced is a fixed effect. However, when the parameter is also characterized by a random variable, we refer to the random effect, which is generally expressed in the form of:
Y＝Xb+Zu+ε
wherein Y is a phenotype vector corresponding to the sample; x is a fixed effect matrix (such as covariates of sample type, sampling time, etc.); z is a random effect matrix, which is a correlation matrix aggregating all sample spectral information in this study; b and u represent coefficients of a fixed effect and a random effect, respectively, for weighting between different effects; epsilon is the residual error.
After completion of the GWAS analysis using GEMMA software, a text file with suffix assoc.txt will be output, which contains 11 columns of results. Only the "rs" of column 2 and the "p_wald" information of column 11 are referred to in this item. Importing the text file into R software, taking the information of the 2 nd column 'rs' as an abscissa, taking the information of the 11 th column 'p_wald' as an ordinate to draw a scatter diagram after performing log10 conversion,
further, in an embodiment, in the feature map drawing step, a spectrum band is taken as an abscissa, after the saliency p value is subjected to-log 10 conversion, a scatter diagram is drawn as an ordinate, a saliency threshold line is set by adopting a Bonferroni correction method, for example, 0.05/p or 0.01/p, p represents the total number of spectrum bands, 1400 in the item, and the drawn scatter diagram is a "manhattan diagram".
In a preferred embodiment, in the feature screening step, for each meat quality attribute index, each peak of each index exceeding a threshold in the feature map and a plurality of bands around the peak are selected, and these bands are used as the matched near infrared feature bands corresponding to the individual meat quality attribute index.
In specific application, each peak wavelength of all meat quality character indexes exceeding a threshold value in association analysis and five wave bands on the left and right are selected, and the wave bands are used as near infrared characteristic wave bands of the meat quality character, and specific data are shown in the following table 2.
TABLE 2 number of characteristic peaks and location
Based on the GWAS basic principle, the 15 meat quality traits are subjected to full spectrum band correlation analysis, and the thresholds are set to p=0.05/1400 and p=0.05/1400. Each dot in fig. 6 represents a wavelength, and the orange dot represents a dot above the threshold, indicating that this wavelength contributes to the prediction of meat quality traits. For example, by observing the manhattan diagram, the wavelengths of three indexes of salt (%), PH and L which do not exceed the threshold value show that there is substantially no effective information in the spectral wavelengths from 400 to 1099.5; less ash, a, b index above the threshold wavelength, indicating less effective information in the 400 to 1099.5 spectral wavelengths; the other indexes are rich in effective information in the spectrum wavelength of 400-1099.5, which shows that the method has good prediction potential in the wavelength range, and the matching characteristic wave bands corresponding to the meat quality character indexes are selected based on the same strategy.
Further, in one embodiment, the method further includes an analysis and verification step, and before the predictive application step, the screened characteristic bands are verified with respect to the full band by using a multiple linear regression model, and the following parameters are calculated: the correction set decision coefficients, the cross-validation set decision coefficients, the correction set root mean square error and the cross-validation set root mean square error contrast measure the predicted results of the filtered characteristic bands relative to the full band.
For these characteristic waves obtained by screeningThe long-run multiple linear regression model is used for prediction, and in theory, the problem of multiple collinearity of the spectrum data is greatly improved. The main parameters for measuring the model quality in the study comprise correction set decision coefficients
Cross validation set decision coefficient +.>
Correction set Root Mean Square Error (RMSEC) and cross validation set Root Mean Square Error (RMSECV). A good model should have a high +.>
And->
Coefficients, and lower RMSEC and RMSECV values, and the smaller the difference between RMSEC and RMSECV, the better.
The calculation results are shown in the following table 3, and the results show that in the process of using full-band information to perform multiple linear regression prediction, a serious overfitting phenomenon exists, and the correction result is obviously superior to the cross validation set, which is probably caused by the characteristics of multiple dimensions and strong collinearity of the spectrum data. When the characteristic wave bands screened by the novel research method are predicted again, the prediction results of fat (%), moisture (%), protein (%), lean meat percentage (%), collagen (%), saturated fatty acid (%), energy kJ/100g, energy kcal/100g and sodium salt (%) are improved greatly.
Table 3 contrast of full band and characteristic band prediction effects in multiple linear regression model
At the same time, the prediction results are substantially consistent with the manhattan diagram presentation results as a whole, i.e., the prediction effect is positively correlated with the number of wavelengths exceeding the threshold.
In summary, the method for selecting the characteristic wavelength of the meat quality character by performing the correlation analysis by using the near infrared spectrum in the study is feasible, and the prediction result shows that the method is a very reliable screening method after the modeling prediction analysis is performed on the characteristic wave band screened by using the method.
The method establishes the association between the spectrum wave band and the physicochemical properties of the sample based on the thought and the flow of the whole genome association analysis (GWAS), and proves that the characteristic wave band screened by the method can improve the prediction accuracy of the physicochemical indexes through subsequent modeling cross verification, so that the prediction of the meat quality is further realized based on a feasible near infrared spectrum characteristic wave band screening strategy, the input cost in the research and development stage and the purchasing stage of near infrared equipment during the prediction operation is effectively controlled, and the reliability and the accuracy of the prediction result are improved.
In addition, compared with the traditional prior characteristic wave band screening method, the data processing flow is simple to operate, high in repeatability and low in technical threshold, and meanwhile, the correlation result of the spectrum and the sample index is displayed in a visual mode in a Manhattan diagram mode, so that visual feeling is given to researchers or related industrial personnel, and substantial help is provided for industrial popularization and application of the near infrared spectrum analysis technology.
For the foregoing method embodiments, for simplicity of explanation, the methodologies are shown as a series of acts, but one of ordinary skill in the art will appreciate that the present invention is not limited by the order of acts, as some steps may, in accordance with the present invention, occur in other orders or concurrently. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.
It should be noted that in other embodiments of the present invention, the method may also be used to obtain a new data processing and analysis prediction application method by combining one or some of the above embodiments, so as to implement application optimization of infrared spectroscopy.
It should be noted that, based on the method in any one or more of the foregoing embodiments of the present invention, the present invention further provides a storage medium, where a program code capable of implementing the method in any one or more of the foregoing embodiments is stored, where the code, when executed by an operating system, is capable of implementing the method for predicting quality based on the near infrared spectrum characteristic band after GWAS screening as described above.
Example two
The method is described in detail in the above embodiments of the present disclosure, and the method of the present disclosure may be implemented by using various devices or systems, so, based on other aspects of the method described in any one or more embodiments, the present disclosure further provides a system for predicting quality based on a post-GWAS-filtered near-infrared spectrum characteristic band, where the system is configured to perform the method for predicting quality based on a post-GWAS-filtered near-infrared spectrum characteristic band described in any one or more embodiments. Specific examples are given below for details.
Specifically, fig. 7 is a schematic structural diagram of a system for predicting quality based on a near infrared spectrum characteristic band after GWAS screening according to an embodiment of the present invention, as shown in fig. 7, the system includes:
the sample configuration module is configured to configure original meat samples with set scales based on the comprehensive meat quality characteristics to be tested, perform sample preparation processing according to a set processing strategy, and number each prepared target sample;
the data measurement module is configured to acquire near infrared spectrum data by scanning each target sample through near infrared spectrum acquisition equipment, acquire meat quality character data of each target sample by utilizing a measurement function module attached to the near infrared equipment or matched physicochemical measurement means, and correlate and record the two data;
the GWAS analysis module is configured to call the linear mixed model by using GEMMA software to perform GWAS analysis on the two, and obtain a corresponding analysis result;
the feature map drawing module is configured to draw a corresponding Manhattan map by matching with the set significance threshold line based on a result file after GWAS analysis;
the characteristic screening module is configured to screen and determine effective spectrum bands corresponding to all meat quality character indexes according to the distribution condition of various meat quality character index characterization information relative to spectrum band characterization information in the Manhattan diagram;
and the prediction application module is configured to realize the prediction of meat quality characteristics based on the effective spectrum bands obtained by screening by utilizing a near infrared spectrum technology.
In a preferred embodiment, when the sample configuration module configures the original meat sample with a set size, the meat quality to be tested according to the sample configuration module includes the following indexes: fat, moisture, protein, lean fraction, collagen, salt, ash, saturated fatty acid, energy kJ/100g, energy kcal/100g, sodium salt, PH, L, a, b.
Further, in one embodiment, the sample configuration module performs the sample preparation process according to a set process strategy based on:
the visible fat and fascia on the surface of each original sample were removed and each sample was crushed using a crushing device to allow the crushed sample to be evenly packed into a round glass plate.
In a specific embodiment, the data determination module is further configured to perform the following operations after scanning to obtain near infrared spectrum data:
and removing samples exceeding the Markov distance threshold by using a Markov distance discrimination method, and putting the rest samples as effective samples into a meat quality character data acquisition and subsequent steps.
Further, in one embodiment, the GWAS analysis module is configured to: and taking the meat quality character data as a phenotype file, taking near infrared spectrum data as a genotype file, inputting the genotype file into corresponding GEMMA software or a platform, and calling a linear mixed model to perform GWAS analysis so as to avoid the influence that genotype data used in the GWAS analysis has similarity with near infrared spectrum data.
Specifically, in one embodiment, the GWAS analysis module invokes a linear hybrid model to implement analysis as shown in the following formula:
Y＝Xb+Zu+ε
wherein Y is a phenotype vector corresponding to a sample, X is a fixed effect matrix generated by covariates, Z is a random effect matrix which aggregates the spectrum information of all samples, b and u respectively represent weight coefficients of the fixed effect and the random effect, and epsilon is a residual error.
In an alternative embodiment, the feature map drawing module is specifically configured to: taking a spectrum band as an abscissa, performing-log 10 conversion on a significance p value, then drawing a scatter diagram as an ordinate, and setting a significance threshold line by adopting a Bonferroni correction method.
Further, in one embodiment, the feature screening module is specifically configured to: for each meat quality character index, selecting each peak of each index exceeding a threshold value in the feature map and each wave band of set quantity on the left and right of each peak, and taking the wave bands as matching near infrared feature wave bands corresponding to the single meat quality character index.
Additionally, in a preferred embodiment, the system further includes an analysis and verification module configured to verify the filtered characteristic bands against the full band using a multiple linear regression model prior to the predictive application step by calculating the following parameters: the correction set decision coefficients, the cross-validation set decision coefficients, the correction set root mean square error and the cross-validation set root mean square error contrast measure the predicted results of the filtered characteristic bands relative to the full band.
In the system for predicting quality based on the near infrared spectrum characteristic wave band after the GWAS screening, each module or unit structure can independently operate or operate in a combined mode according to actual analysis and operation requirements so as to achieve corresponding technical effects.
It is to be understood that the disclosed embodiments are not limited to the specific structures, process steps, or materials disclosed herein, but are intended to extend to equivalents of these features as would be understood by one of ordinary skill in the relevant arts. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.
Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrase "one embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment.
Although the embodiments of the present invention are described above, the embodiments are only used for facilitating understanding of the present invention, and are not intended to limit the present invention. Any person skilled in the art can make any modification and variation in form and detail without departing from the spirit and scope of the present disclosure, but the scope of the present disclosure is still subject to the scope of the appended claims.
Claims (10)
1. A method for predicting quality based on a GWAS screened near infrared spectrum characteristic band, the method comprising:
sample configuration, namely configuring original meat samples with set scales based on the comprehensive meat quality characteristics to be tested, performing sample preparation according to a set processing strategy, and numbering each prepared target sample;
a data measurement step, namely scanning each target sample through near infrared spectrum acquisition equipment to obtain near infrared spectrum data, acquiring meat quality character data of each target sample by utilizing a measurement function module attached to the near infrared equipment or a matched physicochemical measurement means, and correlating and recording the two data;
a GWAS analysis step, namely calling a linear mixed model by using GEMMA software to perform GWAS analysis on the two, and obtaining a corresponding analysis result;
a feature map drawing step, drawing a corresponding Manhattan map by matching with a set significance threshold line based on a result file after GWAS analysis;
a feature screening step, namely screening and determining effective spectrum bands corresponding to all meat quality character indexes according to the distribution condition of each meat quality character index characteristic information relative to spectrum band characteristic information in the Manhattan diagram;
and a prediction application step, wherein the meat quality characteristics are predicted based on the effective spectrum bands obtained by screening by utilizing a near infrared spectrum technology.
2. The method according to claim 1, wherein in the sample configuration step, the meat quality trait to be tested includes the following indicators: fat, moisture, protein, lean fraction, collagen, salt, ash, saturated fatty acid, energy kJ/100g, energy kcal/100g, sodium salt, PH, L, a, b.
3. The method of claim 1, wherein in the sample configuration step, the process of performing the sample preparation process according to the set process strategy comprises:
the visible fat and fascia on the surface of each original sample were removed and each sample was crushed using a crushing device to allow the crushed sample to be evenly packed into a round glass plate.
4. The method of claim 1, wherein in the data determining step, scanning to obtain near infrared spectrum data further comprises:
and removing samples exceeding the Markov distance threshold by using a Markov distance discrimination method, and putting the rest samples as effective samples into a meat quality character data acquisition and subsequent steps.
5. The method of claim 1, wherein in the GWAS analysis step, comprising: and taking meat quality character data as a phenotype file, taking near infrared spectrum data as a genotype file, inputting the genotype file into corresponding GEMMA software or a platform, and calling a linear mixed model to perform GWAS analysis so as to effectively eliminate the influence of multiple collinearity existing between spectrum bands on an analysis result.
6. The method of claim 5, wherein in the GWAS analysis step, a linear hybrid model of the formula:
Y＝Xb+Zu+ε
wherein Y is a phenotype vector corresponding to a sample, X is a fixed effect matrix generated by covariates, Z is a random effect matrix which aggregates the spectrum information of all samples, b and u respectively represent weight coefficients of the fixed effect and the random effect, and epsilon is a residual error.
7. The method of claim 1, wherein in the feature map drawing step, a saliency threshold line is set by Bonferroni correction method with a spectral band as an abscissa and a saliency p value after-log 10 conversion as an ordinate.
8. The method according to claim 1, wherein in the feature screening step, for each meat quality index, each peak of each index exceeding a threshold in the feature map and a set number of bands around the peak are selected, and the bands are used as the matched near infrared feature bands corresponding to the individual meat quality index.
9. The method of claim 1, further comprising the step of analyzing and verifying, prior to the predictive applying step, the filtered characteristic bands against the full band using a multiple linear regression model by calculating the following parameters: correction set decision coefficient
Cross validation set decision coefficient +.>
The comparison of the correction set Root Mean Square Error (RMSEC) and the cross validation set Root Mean Square Error (RMSECV) measures the predicted results of the filtered characteristic bands relative to the full band.
10. A system for predicting quality based on a GWAS filtered near infrared spectral characteristic band, wherein the system performs the method of any one of claims 1 to 9.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018010352A1 (en) * | 2016-07-11 | 2018-01-18 | 上海创和亿电子科技发展有限公司 | Qualitative and quantitative combined method for constructing near infrared quantitative model |
| CN107860740A (en) * | 2017-12-08 | 2018-03-30 | 中国农业科学院茶叶研究所 | A kind of evaluation method of the fermentation of black tea quality based on near-infrared spectrum technique |
| CN107917897A (en) * | 2017-12-28 | 2018-04-17 | 福建医科大学 | The method of the special doctor's food multicomponent content of near infrared ray |
| CN109799207A (en) * | 2019-01-15 | 2019-05-24 | 上海交通大学 | The quantitative detecting method of talcum powder is mixed in the root of Dahurain angelica based on near-infrared spectrum analysis |
-
2021
- 2021-10-18 CN CN202111209987.4A patent/CN113945539B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018010352A1 (en) * | 2016-07-11 | 2018-01-18 | 上海创和亿电子科技发展有限公司 | Qualitative and quantitative combined method for constructing near infrared quantitative model |
| CN107860740A (en) * | 2017-12-08 | 2018-03-30 | 中国农业科学院茶叶研究所 | A kind of evaluation method of the fermentation of black tea quality based on near-infrared spectrum technique |
| CN107917897A (en) * | 2017-12-28 | 2018-04-17 | 福建医科大学 | The method of the special doctor's food multicomponent content of near infrared ray |
| CN109799207A (en) * | 2019-01-15 | 2019-05-24 | 上海交通大学 | The quantitative detecting method of talcum powder is mixed in the root of Dahurain angelica based on near-infrared spectrum analysis |
Non-Patent Citations (1)
| Title |
|---|
| 基于反射光谱特征的牛肉嫩度预测模型研究;田潇瑜;唐鸣;白竣文;;食品科技(第09期);全文 * |
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