CN112595694A - Mutton quality database based on near-infrared rapid detection and establishment method thereof - Google Patents
Mutton quality database based on near-infrared rapid detection and establishment method thereof Download PDFInfo
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Abstract
The invention provides a mutton quality database based on near-infrared rapid detection and an establishment method thereof, which relate to the technical field of mutton quality detection and comprise a qualitative identification module, a crude protein determination module, a crude fat determination module, a fatty acid determination module, an amino acid determination module and a grading module. According to the method, a mutton database is built, nutritional indexes such as crude protein, crude fat, amino acid and fatty acid, physical indexes such as meat color, brightness and PH and corresponding spectrum information of mutton are contained, the operation is simple, the cost is low, the purposes of quantitative analysis of nutritional components and quality grading can be achieved through simple spectrum scanning, a reliable detection model is provided, the detection accuracy of the crude protein, the crude fat, the amino acid and the fatty acid of the nutritional indexes is extremely high, the blank of quantitative indexes of mutton grading is filled, and the grading accuracy is over 95%.
Description
Technical Field
The invention relates to the technical field of mutton quality detection, in particular to a mutton quality database based on near infrared rapid detection and an establishment method thereof.
Background
Mutton can resist wind cold and tonify body, has treatment and tonifying effects on general cough due to wind cold, chronic tracheitis, asthmatic due to deficiency cold, deficiency of kidney and impotence, abdominal psychroalgia, asthenia, intolerance of cold, soreness and weakness of waist and knees, emaciation with sallow complexion, deficiency of both qi and blood, deficiency of body after illness or postpartum, and the like, is most suitable for being eaten in winter, is called as winter tonic, and is deeply welcomed by people.
Adopt artifical mode to the detection of mutton quality, can consume the manpower, detection effect is lower, also is difficult to carry out quantization index to mutton and classifies, and the classification degree of accuracy is not good, is difficult to satisfy user's demand.
Disclosure of Invention
The invention aims to provide a mutton quality database based on near-infrared rapid detection and an establishment method thereof, the mutton database is established, the mutton database contains nutritional indexes such as crude protein, crude fat, amino acid and fatty acid, physical indexes such as meat color, brightness and PH and corresponding spectrum information of mutton, the operation is simple, the cost is low, the purposes of quantitative analysis of nutritional components and quality grading can be achieved through simple spectrum scanning, and meanwhile, the mutton quality database has a reliable detection model, has extremely high detection accuracy on the crude protein, the crude fat, the amino acid and the fatty acid of the nutritional indexes, fills up the blank of the quantitative indexes of mutton grading, and the grading accuracy is more than 95%.
In order to achieve the purpose, the invention is realized by the following technical scheme: the utility model provides a mutton quality database based on near-infrared short-term test, includes qualitative identification module, crude protein survey module, crude fat survey module, fatty acid survey module, amino acid survey module and hierarchical module, wherein:
and the positioning identification module qualitatively identifies the mutton according to the spectral clustering analysis result.
The crude protein determination module is used for rapidly determining nutrient components of mutton crude protein.
The crude fat determination module is used for rapidly determining nutrient components of the mutton crude fat.
The fatty acid determination module is used for rapidly determining the nutrient content of the fatty acid in the mutton.
The amino acid determination module is used for rapidly determining the nutrient content of the mutton amino acid.
And the grading module is used for accurately grading mutton by integrating all the nutrition index information.
Preferably, the spectral clustering analysis is performed by using near infrared spectroscopy.
Preferably, each of the nutritional index information includes crude protein information, crude fat information, fatty acid information, and amino acid information.
A method for establishing a mutton quality database based on near-infrared rapid detection comprises the following steps:
s1, sample collection: collecting the spectral data of different sheep varieties.
S2, preliminary sample treatment: dividing the samples into fresh meat samples and frozen meat samples, putting the samples into liquid nitrogen, and finally putting the samples into a refrigerator for storage for later use.
S3, measuring each index: and measuring the index data of moisture, protein, ash, fat, trace elements, pH value, flesh color, cooking loss, drip loss, fatty acid and amino acid of the sample.
S4, data processing and integration: and (3) performing integrated analysis on each data, performing significant difference analysis on indexes of different parts of different mutton varieties, and analyzing the difference of nutritional ingredient compositions of different mutton.
S5, establishing a mathematical model: and forming linear correspondence between various index data of different mutton and near infrared data of the mutton, and establishing two mathematical models of fresh meat and frozen meat.
S6, establishing a database: and establishing a database according to the measured data result.
S7, correcting the database: the difference between the estimated value and the measured value is corrected.
Preferably, according to the operation procedure in S1, the different varieties of sheep include five varieties of albus cashmere goats, ummarol Qin sheep, Dorper sheep, Safex sheep and Daqing mountain goat, and the muscle tissues of the front leg, the back leg, the intercostals, the back spine and the gluteus muscle are covered, the near infrared spectrum data of the fresh meat sample is collected on site by using an analyzer, and the number, age, sex and variety related information of the collected sheep are recorded.
Preferably, according to the operation procedure in S2, the preservation temperature of the refrigerator is-80 ℃.
Preferably, according to the operation step in S6, the method includes the steps of:
s601, the qualitative identification module carries out qualitative identification on the mutton by utilizing spectral clustering analysis.
And S602, the crude protein determination module, the crude fat determination module, the fatty acid determination module and the amino acid determination module are used for respectively and rapidly determining the nutrient components of the mutton crude protein, the crude fat, the fatty acid and the amino acid.
S603, the grading module synthesizes all the nutrition index information to accurately grade the mutton, and a database is established.
Preferably, according to the operation step in S603, the ranking is four levels, i.e., level three, level two, level one, and super.
Preferably, according to the operation steps in S603, when performing the accurate classification, samples with data concentrated and distributed in each group of average values plus or minus twice the standard deviation are removed, and whether significant differences exist in fat content between different groups is calculated, if significant differences exist, the fat content is classified into different grades, and if no significant differences exist, the fat content is classified into the same grade.
Preferably, according to the operation step in S7, the method includes the steps of:
s701, randomly selecting random parts and species of different varieties of sheep, and performing infrared spectrum determination on the selected species.
S702, carrying out current comparison and estimating each component of the meat quality.
S703, carrying out actual measurement on different components of the meat, detecting whether a difference value exists between the estimated value and the actual measured value, and detecting whether a linear relation exists between the difference value.
S704, adjusting the linear relation, and correcting the difference between the estimated value and the measured value.
The invention provides a mutton quality database based on near-infrared rapid detection and an establishment method thereof. The method has the following beneficial effects:
according to the method, a mutton database is built, nutritional indexes such as crude protein, crude fat, amino acid and fatty acid, physical indexes such as meat color, brightness and PH and corresponding spectrum information of mutton are contained, the operation is simple, the cost is low, the purposes of quantitative analysis of nutritional components and quality grading can be achieved through simple spectrum scanning, a reliable detection model is provided, the detection accuracy of the crude protein, the crude fat, the amino acid and the fatty acid of the nutritional indexes is extremely high, the blank of quantitative indexes of mutton grading is filled, and the grading accuracy is over 95%.
Drawings
FIG. 1 is a schematic diagram of a database for rapidly detecting mutton quality based on near infrared according to the present invention;
FIG. 2 is a flow chart of a method for establishing a database for rapidly detecting mutton quality based on near infrared according to the present invention;
FIG. 3 is a schematic diagram of mutton spectrum qualitative judgment based on the establishment method of the near-infrared rapid detection mutton quality database;
FIG. 4 is a graph showing a rapid determination linear relationship of nutrient components based on the establishment method of the near-infrared rapid detection mutton quality database of the present invention;
FIG. 5 is a schematic diagram of the prediction of an omega-3/omega-6 prediction model of the establishing method of the mutton quality database based on near infrared rapid detection of the invention;
fig. 6 is a schematic diagram of a detection result of a nutritional index based on the establishment method of the near-infrared rapid detection mutton quality database.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention; it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work are within the scope of the present invention.
Example 1:
referring to fig. 1 and 3, a database for rapidly detecting mutton quality based on near infrared includes a qualitative identification module, a crude protein determination module, a crude fat determination module, a fatty acid determination module, an amino acid determination module, and a classification module, wherein:
and the positioning identification module qualitatively identifies the mutton according to the spectral clustering analysis result.
The crude protein determination module is used for rapidly determining nutrient components of mutton crude protein.
The crude fat determination module is used for rapidly determining the nutrient content of the mutton crude fat.
The fatty acid determination module is used for rapidly determining the nutrient content of the fatty acid in the mutton.
The amino acid determination module is used for rapidly determining the nutrient content of the mutton amino acid.
The grading module is used for accurately grading mutton by integrating all nutrition index information.
Specifically, the spectral clustering analysis adopts near infrared spectroscopy for analysis.
Specifically, each nutritional index information includes crude protein information, crude fat information, fatty acid information, and amino acid information.
When the database for rapidly detecting mutton quality based on near infrared is used, the positioning identification module realizes identification of mutton, such as qualitative model identification results (shown in figure 3) of 156 pork spectrums, 143 chicken spectrums and 616 mutton spectrums, wherein obvious boundaries can be obviously observed among different species of mutton, pork or chicken, the purposes of quantitative analysis of nutrient components and quality grading can be realized through the crude fat determination module, the fatty acid determination module, the amino acid determination module and the grading module, meanwhile, the database has a reliable detection model, has extremely high detection accuracy on crude protein, crude fat, amino acid and fatty acid of nutrient indexes, fills up the blank of quantitative indexes of mutton grading, and has more than 95% grading accuracy.
Example 2:
on the basis of embodiment 1, please refer to fig. 2-6, a method for establishing a database for rapidly detecting mutton quality based on near infrared includes the following steps:
step one, sample collection: collecting spectral data of different varieties of sheep, wherein the different varieties of sheep comprise five varieties of Albastus cashmere goats, Wuzumu Qin sheep, Dorper sheep, Safok sheep and Daqing mountain goats, and cover muscle tissues of multiple parts of front legs, rear legs, intercostals, back ridges and gluteus muscles, collecting near infrared spectral data of fresh meat samples on site by using an analyzer, and recording the serial numbers, ages, sexes and variety related information of the sheep collected by the samples.
Step two, primary treatment of a sample: dividing the sample into a fresh meat sample and a frozen meat sample, putting the samples into liquid nitrogen, and finally putting the samples into a refrigerator for storage at the temperature of minus 80 ℃ for later use.
Step three, determining each index: and measuring the index data of moisture, protein, ash, fat, trace elements, pH value, flesh color, cooking loss, drip loss, fatty acid and amino acid of the sample.
Step four, data processing and integration: and (3) performing integrated analysis on each data, performing significant difference analysis on indexes of different parts of different mutton varieties, and analyzing the difference of nutritional ingredient compositions of different mutton.
Step five, establishing a mathematical model: and forming linear correspondence between various index data of different mutton and near infrared data of the mutton, and establishing two mathematical models of fresh meat and frozen meat.
A model of constant indexes including crude protein, crude fat, PH value, water binding capacity and The like is established by using TQ analysis software, and trace indexes including fatty acid, amino acid and The like are established by using The Unscrambler software. The database contains the established models: modeling the constant nutrient components of the mutton by using a partial least square method of TQ analysis software, wherein the correlation coefficient of a crude fat content prediction model is 0.937, RMSEC is 0.814, and RMSEP is 1.1; the correlation coefficient of a crude protein content prediction model is 0.914, RMSEC is 0.496, and RMSEP is 0.657; the correlation coefficient of the total amino acid content prediction model is 0.9312, RMSEC is 0.779, and RMSEP is 0.919. The RPD value of the crude fat prediction model verification result is 3.23, and the RPD value of the protein content prediction model verification result is 3.71.
The RPD value of the total amino acid content prediction model validation result was 3.31.
And modeling The fatty acid component by using The Unscrambler software by respectively using a partial least square method, a combined interval partial least square method and support vector regression, and finding that The modeling result of The combined interval partial least square method is The best, wherein The prediction capability is The omega-3/omega-6 prediction model, and The RPD is 3.09.
Step six, establishing a database: according to the measured data result, establishing a database, comprising the following steps:
(601) and the qualitative identification module carries out qualitative identification on the mutton by utilizing spectral clustering analysis.
(602) And the crude protein determination module, the crude fat determination module, the fatty acid determination module and the amino acid determination module are used for respectively and rapidly determining the nutrient components of the mutton crude protein, the crude fat, the fatty acid and the amino acid.
(603) And the grading module integrates all nutrition index information to accurately grade the mutton, a database is established, the mutton is graded into four grades, namely grade three, grade two, grade one and special grade, when the mutton is accurately graded, samples with data concentrated and distributed on average values of all groups plus or minus two times of standard deviation are removed, whether the fat content has significant difference between different groups is calculated, if the fat content has significant difference, the mutton is divided into different grades, and if the fat content has no significant difference, the mutton is classified into the same grade.
Step seven, correcting the database: correcting for differences between the estimated and measured values, comprising the steps of:
(701) randomly selecting random parts of different varieties of sheep and randomly selecting species, and carrying out infrared spectrum measurement on the species.
(702) And performing current comparison to estimate each component of the meat quality.
(703) And actually measuring different components of the meat, detecting whether a difference value exists between the estimated value and the measured value, and detecting whether the difference value has a linear relation.
(704) And adjusting the linear relation to correct the difference between the estimated value and the measured value.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various changes and modifications can be made without departing from the inventive concept of the present invention, and these changes and modifications are all within the scope of the present invention.
Claims (10)
1. The utility model provides a mutton quality database based on near-infrared short-term test which characterized in that, including qualitative identification module, crude protein survey module, crude fat survey module, fatty acid survey module, amino acid survey module and hierarchical module, wherein:
the positioning identification module qualitatively identifies mutton according to the spectral clustering analysis result;
the crude protein determination module is used for rapidly determining nutrient components of mutton crude protein;
the coarse fat determination module is used for rapidly determining the nutrient content of the coarse fat of mutton;
the fatty acid determination module is used for rapidly determining the nutrient content of the fatty acid of the mutton;
the amino acid determination module is used for rapidly determining the amino acid nutrient content of mutton;
and the grading module is used for accurately grading mutton by integrating all the nutrition index information.
2. The database for rapidly detecting mutton quality based on near infrared according to claim 1, wherein the spectral clustering analysis adopts near infrared spectroscopy for analysis.
3. The database for rapidly detecting mutton quality based on near infrared according to claim 1, wherein the nutritional index information comprises crude protein information, crude fat information, fatty acid information and amino acid information.
4. A method for establishing a mutton quality database based on near-infrared rapid detection is characterized by comprising the following steps:
s1, sample collection: collecting spectral data of different varieties of sheep;
s2, preliminary sample treatment: dividing the sample into a fresh meat sample and a frozen meat sample, putting the samples into liquid nitrogen, and finally putting the samples into a refrigerator for storage for later use;
s3, measuring each index: measuring the index data of moisture, protein, ash, fat, trace elements, pH value, flesh color, cooking loss, drip loss, fatty acid and amino acid of the sample;
s4, data processing and integration: performing integrated analysis on each data, performing significant difference analysis on indexes of different parts of different mutton varieties, and analyzing differences of nutritional ingredient compositions of different mutton;
s5, establishing a mathematical model: forming linear correspondence between various index data of different mutton and near infrared data of the mutton, and establishing two mathematical models of fresh meat and frozen meat;
s6, establishing a database: establishing a database according to the result of the measured data;
s7, correcting the database: the difference between the estimated value and the measured value is corrected.
5. The method for establishing the mutton quality database based on the near-infrared rapid detection of claim 4, wherein the different varieties of sheep comprise five varieties of an Albas down goat, an Ezemosan sheep, a Dorper sheep, a Safek sheep and a Daqingshan goat according to the operation steps in S1, the muscle tissues of the front leg, the back leg, the intercostals, the back ridge and the gluteus muscle are covered, the analyzer is used for collecting the near-infrared data of the fresh meat sample on site, and the number, the age, the gender and the variety related information of the sheep collected by the sample are recorded.
6. The method for establishing the database for rapidly detecting mutton quality based on near infrared according to claim 1, wherein the preservation temperature of the refrigerator is-80 ℃ according to the operation step in S2.
7. The method for establishing the database for rapidly detecting mutton quality based on near infrared according to claim 1, wherein the method comprises the following steps according to the operation steps in S6:
s601, the qualitative identification module carries out qualitative identification on the mutton by using spectral clustering analysis;
s602, rapidly determining nutrient components of mutton crude protein, crude fat, fatty acid and amino acid by using a crude protein determination module, a crude fat determination module, a fatty acid determination module and an amino acid determination module respectively;
s603, the grading module synthesizes all the nutrition index information to accurately grade the mutton, and a database is established.
8. The method for establishing the database for rapidly detecting mutton quality based on near infrared according to claim 7, wherein the classification is four grades, namely grade three, grade two, grade one and special grade according to the operation steps in S603.
9. The method for establishing the mutton quality database based on the near-infrared rapid detection according to claim 7, wherein, according to the operation steps in S603, when the accurate classification is performed, samples with data concentrated and distributed on each group of average values plus or minus twice the standard deviation are removed, whether the fat content has significant difference among different groups is calculated, if the significant difference exists, the mutton quality database is classified into different grades, and if the significant difference does not exist, the mutton quality database is classified into the same grade.
10. The method for establishing the database for rapidly detecting mutton quality based on near infrared according to claim 4, wherein the method comprises the following steps according to the operation steps in S7:
s701, randomly selecting random parts and species of different varieties of sheep, and performing infrared spectrum measurement on the selected random parts and species;
s702, carrying out current comparison and estimating each component of the meat quality;
s703, carrying out actual measurement on different components of the meat, detecting whether a difference value exists between the estimated value and the actual measured value, and detecting whether a linear relation exists between the difference value;
s704, adjusting the linear relation, and correcting the difference between the estimated value and the measured value.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114264630A (en) * | 2021-12-31 | 2022-04-01 | 内蒙古自治区农畜产品质量安全中心 | Establishment method and application of database for rapidly detecting mutton volatile basic nitrogen based on near infrared |
| CN114371144A (en) * | 2021-12-31 | 2022-04-19 | 中国农业科学院草原研究所 | Establishment method and application of database for detecting mutton biogenic amine content based on near infrared spectrum imaging technology |
| CN115165796A (en) * | 2022-07-07 | 2022-10-11 | 北部湾大学 | Near-infrared living body nondestructive testing method for pinctada fucata |
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