CN115773993A - Method for quickly detecting nutrient components of cheese - Google Patents
Method for quickly detecting nutrient components of cheese Download PDFInfo
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Abstract
The application discloses a cheese nutrient content rapid detection method, based on the implementation scheme of the application, the analysis model is selected according to two dimensions of consistency of spectrum trends of cheese to be detected and the analysis model and consistency of linear range, during subsequent detection, a judgment rule for continuous use of the analysis model is provided, and the detection performance and the detection accuracy of the model can be improved. By adopting the method, the technical requirements of professional detection personnel required by the national standard method can be reduced, the detection period is shortened, the detection cost is saved, small-configuration large-batch detection is realized, in addition, a large amount of laboratory wastes are reduced, the environmental pollution is reduced, and the quality control requirement of rapid development of the industry is met.
Description
Technical Field
The application relates to the technical field of cheese production, in particular to a method for quickly detecting nutritional ingredients of cheese.
Background
The near infrared spectrum detection method is characterized in that a relation model between a near infrared absorption spectrum and the content of an object to be detected is established through chemometrics, the content of various components in a target object is rapidly and accurately measured, and the method is widely applied to various industries such as liquid milk, milk powder, medicines, agriculture and the like, but the research on the detection application of a near infrared detection technology in cheese products is not reported yet.
The cheese is mostly solid or semisolid, the detection of the content of the nutrient components is a national standard method, and the national standard method has the following defects:
1. a certain number and skill of professional testers need to be configured;
2. the investment detection process of matching detection equipment, facilities and sites is complex in operation and high in detection cost;
3. the sample cannot be treated in batches, the detection efficiency is low, more chemical reagents are used in experiments, laboratory wastes are generated, and low-carbon environmental protection cannot be realized;
4. the detection process consumes a long time, and the guiding significance of the process quality control of enterprises is delayed.
Disclosure of Invention
The application mainly aims to provide a method for quickly detecting the nutrient content of cheese so as to solve the current problems.
In order to achieve the above object, the present application provides the following techniques:
a method for rapidly detecting the nutrient content of cheese comprises the following steps:
directionally selecting an analysis model for cheese nutrient components, and calibrating the model;
acquiring spectral information of a cheese sample to be detected;
inputting the spectral information into the analysis model, and calculating the content of the nutrient components of the cheese sample to be detected through the analysis model;
and outputting and displaying the content of the nutrient components of the cheese sample to be detected.
As an alternative embodiment of the present application, optionally, the targeted selection of analytical models for cheese nutritional composition comprises:
directionally selecting an analysis model according to the consistency of the spectrum trend of the cheese to be detected and the spectrum trend of the analysis model, wherein the method comprises the following steps:
when the analysis model has a definite applicable type of the product, selecting the analysis model according to the type of the cheese product to be detected;
and when the analysis model does not determine the applicable type of the product, scanning to obtain a spectrogram of the cheese sample, performing spectrogram superposition comparison on the spectrogram and the model spectrogram, and selecting the analysis model according to the consistency of the trend of the spectrogram of the cheese sample and the peak shape.
As an alternative embodiment of the present application, optionally, the targeted selection of analytical models for cheese nutritional composition comprises:
directionally selecting an analytical model based on the conformity of the linear range, comprising:
acquiring the item content of a cheese product to be detected, judging whether the item content of the cheese product to be detected exceeds the linear detection range of the analysis model, and when the item content of the cheese product to be detected exceeds the linear detection range of the analysis model, not meeting the requirement of the linear detection range; otherwise, the result is satisfied.
As an alternative embodiment of the present application, optionally, the targeted selection of analytical models for cheese nutritional composition comprises:
directionally selecting an analysis model according to the consistency of the spectrum trend of the cheese to be detected and the spectrum trend of the analysis model, wherein the method comprises the following steps: when the analysis model has a definite applicable type of the product, selecting the analysis model according to the type of the cheese product to be detected; when the analysis model does not determine the applicable type of the product, scanning to obtain a spectrogram of the cheese sample, performing spectrogram superposition comparison on the spectrogram and the model spectrogram, and selecting the analysis model according to the consistency of the trend of the spectrogram of the cheese sample and the peak shape;
directionally selecting an analytical model based on the conformity of the linear range, comprising: acquiring the item content of a cheese product to be detected, judging whether the item content of the cheese product to be detected exceeds the linear detection range of the analysis model, and when the item content of the cheese product to be detected exceeds the linear detection range of the analysis model, not meeting the requirement of the linear detection range; otherwise, the result is satisfied.
As an optional embodiment of the present application, optionally, the model calibration includes:
collecting cheese samples according to a preset batch;
respectively detecting the cheese sample by using an analytical model quick detection method and a national standard detection method to obtain detection data;
and (3) comparing and analyzing the obtained detection data by using a t-test method:
(1) When t is less than or equal to 2, the intercept of the analysis model does not need to be adjusted;
(2) And when t is larger than 2, taking the difference value of the average value of the detection data obtained by the rapid detection method of the analysis model and the average value of the detection data obtained by the national standard detection method as the intercept adjustment basis of the analysis model, and adjusting the intercept of the analysis model.
As an optional embodiment of the present application, optionally, after the intercept adjustment is performed on the analysis model, the method further includes:
detecting the cheese sample by using an analytical model quick detection method again to obtain secondary detection data;
calculating the deviation between the secondary detection data and the detection data obtained by the national standard detection method, and judging whether the deviation exceeds the precision requirement of the national standard detection method:
if the deviation exceeds the precision requirement, taking the deviation as an intercept adjustment basis of the analysis model to perform secondary intercept adjustment of the analysis model; otherwise, the calibration is finished.
As an optional embodiment of the present application, optionally, the acquiring spectral information of the cheese sample to be tested includes:
presetting a spectrum sampling environment and calibrating;
placing the cheese sample to be detected in the spectrum sampling environment, and performing spectrum scanning detection to obtain the spectrum information of the cheese sample to be detected;
and acquiring and storing the site spectrum meeting the preset value from the spectrum information.
As an optional embodiment of the present application, optionally, the method further includes:
detecting the cheese sample to be detected by using an analytical model quick detection method according to a preset detection batch to obtain three times of detection data;
calculating the deviation between the three detection data and the detection data obtained by the national standard detection method, and judging whether the deviation meets the precision requirement of the national standard detection method:
if yes, continuing application detection by adopting the analysis model;
if not, taking the deviation as an intercept adjustment basis of the analysis model, and performing third-time intercept adjustment of the analysis model.
Compared with the prior art, this application can bring following technological effect:
based on the implementation scheme of the application, the analysis model is selected according to the consistency of the spectrum trend of the cheese to be detected and the spectrum trend of the analysis model and the conformity of the linear range, and during subsequent detection, a judgment rule for continuously using the analysis model is provided, so that the detection performance and the detection accuracy of the model can be improved. By adopting the method, the technical requirements of professional detection personnel required by the national standard method can be reduced, the detection period is shortened, the detection cost is saved, small-configuration large-batch detection is realized, in addition, a large amount of laboratory wastes are reduced, the environmental pollution is reduced, and the quality control requirement of rapid industrial development is met.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
FIG. 1 is a flow chart diagram of a method for rapidly detecting the nutrient content of cheese.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances in order to facilitate the description of the embodiments of the application herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings. These terms are used primarily to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to a particular orientation or to be constructed and operated in a particular orientation.
Moreover, some of the above terms may be used to indicate other meanings besides the orientation or positional relationship, for example, the term "on" may also be used to indicate some kind of attachment or connection relationship in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as the case may be.
In addition, the term "plurality" shall mean two as well as more than two.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
In the application, the construction and specific application scheme of the analysis model can be referred to the invention patent of the applicant filed on the same day and named as the establishment method of the cheese nutrient component rapid detection analysis model.
The analysis model can be configured in a computer for use, and the spectrum acquired by the spectrum is transmitted to the computer for rapid detection of the nutrient content of the cheese to be detected.
Example 1
As shown in fig. 1, a method for rapidly detecting the nutrient content of cheese comprises the following steps:
s1, directionally selecting an analysis model for cheese nutrient components, and calibrating the model;
the analysis model constructs and stores different model types in the library according to different factors, and can be selected according to different dimensions. In this embodiment, the analysis model is preferably selected according to two dimensions of consistency of the spectrum trend of the cheese to be detected and the analysis model and consistency of the linear range.
As an alternative embodiment of the present application, optionally, the targeted selection of analytical models for cheese nutritional composition comprises:
1) Directionally selecting an analysis model according to the consistency of the spectrum trend of the cheese to be detected and the spectrum trend of the analysis model, wherein the method comprises the following steps: when the analysis model has a definite applicable type of the product, selecting the analysis model according to the type of the cheese product to be detected; when the analysis model does not determine the applicable type of the product, scanning to obtain a spectrogram of the cheese sample, performing spectrogram superposition comparison on the spectrogram and the model spectrogram, and selecting the analysis model according to the consistency of the trend of the spectrogram of the cheese sample and the peak shape;
and (3) performing spectrum detection on the cheese to be detected, judging whether the spectrum trend of the cheese is consistent with the spectrum trend of the current analysis model, and if so, adopting the current analysis model light.
2) Directionally selecting an analytical model based on the conformity of the linear range, comprising: acquiring the item content of a cheese product to be detected, judging whether the item content of the cheese product to be detected exceeds the linear detection range of the analysis model, and when the item content of the cheese product to be detected exceeds the linear detection range of the analysis model, not meeting the requirement of the linear detection range; otherwise, the result is satisfied.
The method screens the analysis model according to the item content of the cheese product to be detected, namely the detection batch of the cheese product to be detected. To match the linear range detectable by the analytical model. As long as the detection batch is in the linear range detectable by the analytical model, the corresponding analytical model can be selected for detection.
The following is an explanation of selecting an analysis model from one dimension, which is not described herein again.
As an alternative embodiment of the present application, optionally, the targeted selection of analytical models for cheese nutritional composition comprises:
directionally selecting an analysis model according to the consistency of the spectrum trend of the cheese to be detected and the spectrum trend of the analysis model, wherein the method comprises the following steps:
when the analysis model has a definite applicable type of the product, selecting the analysis model according to the type of the cheese product to be detected;
when the analysis model does not have the applicable type of a specific product, scanning to obtain a spectrogram of the cheese sample, performing spectrogram superposition comparison on the spectrogram and the model spectrogram, and selecting the analysis model according to the consistency of the trend of the spectrogram of the cheese sample and the peak shape.
As an alternative embodiment of the present application, optionally, the targeted selection of analytical models for cheese nutritional composition comprises:
directionally selecting an analytical model based on the conformity of the linear range, comprising:
acquiring the item content of a cheese product to be detected, judging whether the item content of the cheese product to be detected exceeds the linear detection range of the analysis model, and when the item content of the cheese product to be detected exceeds the linear detection range of the analysis model, not meeting the requirement of the linear detection range; otherwise, the result is satisfied.
After model selection, model calibration is required.
As an optional embodiment of the present application, optionally, the model calibration includes:
collecting cheese samples according to a preset batch;
respectively detecting the cheese samples by using an analytical model quick detection method and a national standard detection method to obtain detection data;
and (3) comparing and analyzing the obtained detection data by using a t-test method:
(1) When t is less than or equal to 2, the intercept of the analysis model does not need to be adjusted;
(2) And when t is larger than 2, taking the difference value of the average value of the detection data obtained by the rapid detection method of the analysis model and the average value of the detection data obtained by the national standard detection method as the intercept adjustment basis of the analysis model, and adjusting the intercept of the analysis model.
In this example, the detection data obtained above was comparatively analyzed by the t-test method. At least more than 5 batches of samples are collected for model calibration. The cheese samples are respectively detected by using an analytical model quick detection method (hereinafter, referred to as a quick detection method) and a national standard detection method (hereinafter, referred to as a national standard method) to obtain detection data. And judging trouble and adjusting the screenshot of the model according to the screenshot.
As an optional embodiment of the present application, optionally, after the intercept adjustment is performed on the analysis model, the method further includes:
detecting the cheese sample by using an analytical model quick detection method again to obtain secondary detection data;
calculating the deviation between the secondary detection data and the detection data obtained by the national standard detection method, and judging whether the deviation exceeds the precision requirement of the national standard detection method:
if the deviation exceeds the precision requirement, taking the deviation as an intercept adjustment basis of the analysis model to perform secondary intercept adjustment of the analysis model; otherwise, the calibration is finished.
And after the intercept is adjusted, the sample is detected by using a quick detection method again, and the deviation of the detection result from the national standard method does not exceed the precision requirement of the national standard method of the corresponding project.
When cheese (sample) to be detected is detected, a collection environment can be deployed, a detection mold is installed in the environment and used for placing the cheese, and a near-infrared spectrometer and a computer (with analysis software) are arranged on one side of the detection mold.
S2, acquiring spectral information of a cheese sample to be detected;
as an optional embodiment of the present application, optionally, the acquiring spectral information of the cheese sample to be tested includes:
presetting a spectrum sampling environment and calibrating;
placing the cheese sample to be detected in the spectrum sampling environment, and performing spectrum scanning detection to obtain the spectrum information of the cheese sample to be detected;
and acquiring and storing the site spectrum meeting the preset value from the spectrum information.
The present disclosure provides a detection scheme:
(1) And (3) calibration: respectively using a black standard plate, a white standard plate and a simulation sample standard plate to carry out TAS calibration, wherein automatic diagnosis indexes of software completely pass, namely the equipment is normal, and sample detection can be carried out;
(2) Sample loading: at least three flaky cheese samples are overlapped, and the flat surfaces of the samples are attached to a detection glass window as much as possible; other forms of cheese samples used a mold.
Filling a proper amount of sample into the inner side of the mold, fitting the sample to the glass at the bottom of the cup as much as possible, covering the cover, pressing from top to bottom and screwing tightly, so that the cheese sample is tightly fitted with a glass window at the bottom of the mold without bubbles;
(3) Scanning: scanning the samples by using a near-infrared spectrometer, scanning the samples by a light source from the bottom of the samples through a glass window of the mold, and collecting spectra of 12 sites for each sample;
(4) Measurement: the content of the nutrient components is calculated by an analysis model through the spectrum and is displayed.
S3, inputting the spectral information into the analysis model, and calculating the content of the nutrient components of the cheese sample to be detected through the analysis model;
and S4, outputting and displaying the content of the nutrient components of the cheese sample to be detected.
And finishing detection, wherein in order to judge the sustainability detection performance of the analysis model, the embodiment also provides a national standard comparison mode, and the judgment of the applicability of the analysis model is enough.
As an optional embodiment of the present application, optionally, the method further includes:
detecting the cheese sample to be detected by using an analytical model quick detection method according to a preset detection batch to obtain three times of detection data;
calculating the deviation between the three detection data and the detection data obtained by the national standard detection method, and judging whether the deviation meets the precision requirement of the national standard detection method:
if yes, continuing application detection by adopting the analysis model;
and if not, taking the deviation as an intercept adjusting basis of the analysis model to carry out three times of intercept adjustment of the analysis model.
Carrying out comparison between a quick test method and a national standard method, carrying out national standard comparison on at least 5 batches of cheese samples by each analysis model, and when the national standard comparison is consistent with the deviation of a detection result of the national standard method, continuously applying the analysis models for detection; when the deviation does not conform to the deviation, the intercept adjustment is performed according to the model calibration, which is not described in detail herein.
In this embodiment, the precision requirement of the national standard method corresponding to the project can be found according to the deviation standard of the detection result of the national standard method, and the deviation standard can be defined according to the quality control requirement of the user.
By adopting the rapid detection method for the cheese nutrient components, the technical requirements of professional detection personnel required by the national standard method can be reduced, the detection period is shortened, the detection cost is saved, small-configuration large-batch detection is realized, in addition, the laboratory wastes are greatly reduced, the environmental pollution is reduced, and the quality control requirement of rapid industrial development is met.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (8)
1. A cheese nutrient component rapid detection method is characterized by comprising the following steps:
directionally selecting an analysis model for cheese nutrient components, and calibrating the model;
acquiring spectral information of a cheese sample to be detected;
inputting the spectral information into the analysis model, and calculating the content of the nutrient components of the cheese sample to be detected through the analysis model;
and outputting and displaying the content of the nutrient components of the cheese sample to be detected.
2. The method for rapid detection of cheese nutritional composition according to claim 1, wherein the directed selection of analytical models for cheese nutritional composition comprises:
directionally selecting an analysis model according to the consistency of the spectrum trend of the cheese to be detected and the spectrum trend of the analysis model, wherein the method comprises the following steps:
when the analysis model has a definite applicable type of the product, selecting the analysis model according to the type of the cheese product to be detected;
and when the analysis model does not determine the applicable type of the product, scanning to obtain a spectrogram of the cheese sample, performing spectrogram superposition comparison on the spectrogram and the model spectrogram, and selecting the analysis model according to the consistency of the trend of the spectrogram of the cheese sample and the peak shape.
3. The method for rapid detection of cheese nutritional composition according to claim 1, wherein the directed selection of analytical models for cheese nutritional composition comprises:
directionally selecting an analytical model based on the conformity of the linear range, comprising:
acquiring the item content of a cheese product to be detected, judging whether the item content of the cheese product to be detected exceeds the linear detection range of the analysis model, and when the item content of the cheese product to be detected exceeds the linear detection range of the analysis model, not meeting the requirement of the linear detection range; otherwise, the result is satisfied.
4. The method for rapidly detecting the nutrient content of cheese according to claim 1, wherein the directionally selecting an analytical model for the nutrient content of cheese comprises:
directionally selecting an analysis model according to the consistency of the spectrum trend of the cheese to be detected and the spectrum trend of the analysis model, wherein the method comprises the following steps: when the analysis model has a definite applicable type of the product, selecting the analysis model according to the type of the cheese product to be detected; when the analysis model does not determine the applicable type of the product, scanning to obtain a spectrogram of the cheese sample, performing spectrogram superposition comparison on the spectrogram and the model spectrogram, and selecting the analysis model according to the consistency of the trend of the spectrogram of the cheese sample and the peak shape;
directionally selecting an analytical model based on the conformity of the linear range, comprising: acquiring the item content of a cheese product to be detected, judging whether the item content of the cheese product to be detected exceeds the linear detection range of the analysis model, and when the item content of the cheese product to be detected exceeds the linear detection range of the analysis model, not meeting the requirement of the linear detection range; otherwise, the result is satisfied.
5. The method for rapidly detecting the nutrient content of cheese according to claim 1, wherein the model calibration comprises:
collecting cheese samples according to a preset batch;
respectively detecting the cheese samples by using an analytical model quick detection method and a national standard detection method to obtain detection data;
and (3) comparing and analyzing the obtained detection data by using a t-test method:
(1) When t is less than or equal to 2, the intercept of the analysis model does not need to be adjusted;
(2) And when t is larger than 2, taking the difference value of the average value of the detection data obtained by the rapid detection method of the analysis model and the average value of the detection data obtained by the national standard detection method as the intercept adjustment basis of the analysis model, and adjusting the intercept of the analysis model.
6. The method for rapidly detecting the nutrient content of cheese according to claim 5, further comprising, after the intercept adjustment of the analytical model:
detecting the cheese sample by using an analytical model quick detection method again to obtain secondary detection data;
calculating the deviation between the secondary detection data and the detection data obtained by the national standard detection method, and judging whether the deviation exceeds the precision requirement of the national standard detection method:
if the deviation exceeds the precision requirement, taking the deviation as an intercept adjustment basis of the analysis model to perform secondary intercept adjustment of the analysis model; otherwise, the calibration is finished.
7. The method for rapidly detecting the nutrient content of cheese according to claim 1, wherein the step of acquiring the spectral information of the cheese sample to be detected comprises the following steps:
presetting a spectrum sampling environment and calibrating;
placing the cheese sample to be detected in the spectrum sampling environment, and performing spectrum scanning detection to obtain the spectrum information of the cheese sample to be detected;
and acquiring and storing the site spectrum meeting the preset value from the spectrum information.
8. The method for rapidly detecting the nutrient content of cheese according to claim 1, further comprising:
detecting the cheese sample to be detected by using an analytical model quick detection method according to a preset detection batch to obtain three times of detection data;
calculating the deviation between the three detection data and the detection data obtained by the national standard detection method, and judging whether the deviation meets the precision requirement of the national standard detection method:
if yes, continuing application detection by adopting the analysis model;
and if not, taking the deviation as an intercept adjusting basis of the analysis model to carry out three times of intercept adjustment of the analysis model.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118072850A (en) * | 2024-04-19 | 2024-05-24 | 四川省地质矿产勘查开发局成都综合岩矿测试中心（国土资源部成都矿产资源监督检测中心） | Method and system for mass analysis of geochemical sample in target area |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118072850A (en) * | 2024-04-19 | 2024-05-24 | 四川省地质矿产勘查开发局成都综合岩矿测试中心（国土资源部成都矿产资源监督检测中心） | Method and system for mass analysis of geochemical sample in target area |
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