CN117825601A - Method for measuring sulfur dioxide in food - Google Patents
Method for measuring sulfur dioxide in food Download PDFInfo
- Publication number
- CN117825601A CN117825601A CN202410245041.0A CN202410245041A CN117825601A CN 117825601 A CN117825601 A CN 117825601A CN 202410245041 A CN202410245041 A CN 202410245041A CN 117825601 A CN117825601 A CN 117825601A
- Authority
- CN
- China
- Prior art keywords
- data
- data set
- original data
- sulfur dioxide
- representing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 title claims abstract description 152
- 238000000034 method Methods 0.000 title claims abstract description 64
- 235000013305 food Nutrition 0.000 title claims abstract description 55
- 230000004044 response Effects 0.000 claims abstract description 137
- 238000011208 chromatographic data Methods 0.000 claims abstract description 64
- 238000001914 filtration Methods 0.000 claims description 20
- 230000008569 process Effects 0.000 claims description 13
- 238000010606 normalization Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 2
- 230000009466 transformation Effects 0.000 abstract 1
- 239000000126 substance Substances 0.000 description 13
- 239000000523 sample Substances 0.000 description 11
- 238000004364 calculation method Methods 0.000 description 7
- 230000008859 change Effects 0.000 description 5
- 238000001514 detection method Methods 0.000 description 5
- 238000004128 high performance liquid chromatography Methods 0.000 description 5
- 238000010521 absorption reaction Methods 0.000 description 4
- 239000007788 liquid Substances 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000002835 absorbance Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 239000012488 sample solution Substances 0.000 description 2
- 206010019233 Headaches Diseases 0.000 description 1
- 206010028813 Nausea Diseases 0.000 description 1
- 208000006673 asthma Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011109 contamination Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000002778 food additive Substances 0.000 description 1
- 235000013373 food additive Nutrition 0.000 description 1
- 231100000869 headache Toxicity 0.000 description 1
- 230000031700 light absorption Effects 0.000 description 1
- 230000004298 light response Effects 0.000 description 1
- 230000000813 microbial effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008693 nausea Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 238000002798 spectrophotometry method Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 239000013076 target substance Substances 0.000 description 1
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention relates to the technical field of physical analysis, in particular to a method for measuring sulfur dioxide in food, which comprises the following steps: acquiring a chromatographic data set of food to be detected, and marking the chromatographic data set as an original data set; segmenting an original data set to obtain a data response segment in the original data set; obtaining a spectrogram corresponding to each data response section according to Fourier transformation; obtaining the noise influence degree of the original data set according to the spectrogram of the original data set corresponding to each data response section; denoising the original data set according to the noise influence degree to obtain a filtered and denoised chromatographic data set; thereby obtaining the sulfur dioxide content of the food to be detected. According to the method, when the content of sulfur dioxide in the food to be detected is obtained through the chromatographic data of the food to be detected, the influence of noise on the chromatographic data set is removed, and the chromatographic data set which is closer to the actual situation is obtained, so that the content of more accurate sulfur dioxide is obtained.
Description
Technical Field
The invention relates to the technical field of physical analysis, in particular to a method for measuring sulfur dioxide in food.
Background
Sulfur dioxide is a commonly used food additive for maintaining freshness of foods and preventing microbial contamination. However, excessive sulfur dioxide can be harmful to the human body, such as causing symptoms of asthma, headache, nausea, etc. Therefore, it is very important to accurately detect the sulfur dioxide content in food. Currently, commonly used sulfur dioxide detection methods include high performance liquid chromatography, fluorescence, ultraviolet spectrophotometry, and the like. The method has the advantages of high accuracy, good reliability and the like, and provides a new technical means for food safety guarantee. The high performance liquid chromatography has high sensitivity and accurate detection. However, when the high performance liquid chromatography is used for collecting chromatographic data of food, noise exists in the collected chromatographic data due to the performance of the sensor, and the noise can influence the measurement of the content of sulfur dioxide, so that the obtained chromatographic data needs to be subjected to denoising treatment.
In the prior art, the data denoising algorithm is more, but when the chromatographic data of a sample to be detected is acquired through high performance liquid chromatography, the chromatographic data is acquired according to the energy change of different lights. However, the conventional denoising algorithm cannot explain the chromatographic images generated by various types of light, but only performs smooth filtering according to the change of the data, so that the measurement result of sulfur dioxide in the sample to be detected, which is obtained according to the denoised data, may be inaccurate.
Disclosure of Invention
The invention provides a method for measuring sulfur dioxide in food, which aims to solve the existing problems.
The method for measuring sulfur dioxide in food adopts the following technical scheme:
a method for determining sulfur dioxide in a food product, the method comprising the steps of:
acquiring a chromatographic data set, a standard data set and a time interval corresponding to sulfur dioxide of food to be detected, wherein the concentration of sulfur dioxide is reflected by the standard data set; recording the chromatographic data set of the food to be detected as an original data set; each original data in the original data set comprises a time value and a response signal value; segmenting the original data set according to the time value and the response signal value of each original data in the original data set to obtain a data response segment in the original data set;
performing short-time Fourier transform on each data response segment to obtain a spectrogram; each data point in the spectrogram corresponds to one power and one frequency;
according to the spectrogram corresponding to each data response section, the influence degree of different types of light on each data response section is obtained;
obtaining the noise influence degree of the original data set according to the influence degree of different types of light on each data response section, the response signal value of the original data in the original data set and one power corresponding to each data point in the spectrogram;
denoising the original data according to the noise influence degree to obtain a filtered and denoised chromatographic data set; and obtaining the content of the sulfur dioxide in the food to be detected according to the chromatographic data set, the standard data set and the concentration of the sulfur dioxide reflected by the standard data set after filtering and denoising and the time interval corresponding to the sulfur dioxide.
Further, the method segments the original data set according to the time value and the response signal value of each original data in the original data set to obtain a data response segment in the original data set, and comprises the following specific steps:
calculating an original data set by using a first derivative method to obtain a plurality of extreme points in the original data set;
obtaining a left neighborhood and a right neighborhood of each extreme point according to the time values of the adjacent extreme points;
according to the firstEach original data and +.>The difference of the response signal values of the extreme points respectively obtains the +.>Each original data in the left neighborhood and the right neighborhood of the extreme point is +.>The degree of representation of the left and right boundaries of the extreme points;
according to the firstEach original data in the left neighborhood and the right neighborhood of the extreme point is +.>The degree of representation of the left and right boundary of the extreme point, resulting in the +.>The data response segments.
Further, the obtaining the left neighborhood and the right neighborhood of each extreme point according to the time values of the adjacent extreme points comprises the following specific steps:
will beMarked as +.>Left neighborhood of each extreme point; will->Marked as +.>Right neighborhood of each extreme point; wherein->Indicate->Time value of each extreme point,/->Indicate->The time value of the respective extreme point,indicate->Time values of the extreme points.
Further, according to the firstEach original data and +.>The difference of the response signal values of the extreme points respectively obtains the +.>Each original data in the left neighborhood and the right neighborhood of the extreme point is +.>The representation degree of the left boundary and the right boundary of each extreme point comprises the following specific formulas:
in the method, in the process of the invention,representing +.>The left adjacent part of the extreme point is +.>The original data is->The degree of representation of the left border of the extreme points,/->Representing +.>The right neighbor of the extreme point +.>The original data is->The degree of representation of the right border of the extreme points,/->Representing +.>Response signal values of the extreme points +.>Representing +.>The left adjacent part of the extreme point is +.>Response signal values of the individual original data, +.>Representing +.>The right neighbor of the extreme point +.>Response signal values of the individual original data, +.>Representing an absolute function，/>Minimum response signal value representing all raw data in the raw data set,/->An exponential function based on a natural constant is represented.
Further, according to the firstEach original data in the left neighborhood and the right neighborhood of the extreme point is +.>The degree of representation of the left and right boundary of the extreme point, resulting in the +.>The data response section comprises the following specific steps:
selecting the first of the original datasetAll raw data in the left neighborhood of the extreme point are +.>The time value of the original data corresponding to the maximum value in the degree of representation of the left boundary of the extreme points is taken as +.>Time values of left boundaries of the extreme points;
selecting the first of the original datasetAll raw data in the right neighborhood of the extreme point are +.>The time value of the original data corresponding to the maximum value in the expression level of the right boundary of the extreme points is taken as +.>Time values of the right boundary of the extreme points;
according to the firstAll raw data between the time values of the left boundary and the right boundary of the extreme points constitute +.>The data response segments.
Further, according to the spectrogram corresponding to each data response segment, the influence degree of different types of light on each data response segment is obtained, which comprises the following specific steps:
optionally, marking a data response section as a reference section, and marking a spectrogram corresponding to the reference section as a reference spectrogram;
calculating the power of all data points in the reference spectrogram by using a first derivative method to obtain a plurality of extreme points in the reference spectrogram;
clustering all extreme points in the reference spectrogram according to a DBSCAN density clustering algorithm to obtain a plurality of clustering clusters;
and obtaining the influence degree of different types of light on the reference section according to the difference between the powers of the extreme points in the cluster.
Further, according to the difference between the powers of the extreme points in the cluster, the influence degree of different types of light on the reference segment is obtained, and the specific formula is as follows:
in the method, in the process of the invention,indicating the extent of influence of different types of light on the reference segment,/->Representing the number of extreme points in the reference spectrogram,indicate->Average power of extreme points in the clusters, < +.>Representing the minimum value of the average power of the extreme points in all clusters, < >>Representing the number of clusters, +.>Maximum value of average power representing extreme points in all clusters, +.>Representing the variance of the power of all data points in the reference spectrogram, +.>Represents an exponential function based on natural constants, < ->Representing a linear normalization function, ++>Representing an absolute value function.
Further, the specific formula for obtaining the noise influence degree of the original data set according to the influence degree of different types of light on each data response segment, the response signal value of the original data in the original data set, and the power corresponding to each data point in the spectrogram is as follows:
in the method, in the process of the invention,representation ofNoise influence degree of the original dataset, +.>Representing the extent of influence of different types of light on the t-th data response segment, < >>Representing +.>Maximum response signal value in all raw data in the individual data response segment,/for each data segment>Indicate->Maximum power in all data points in the spectrogram corresponding to each data response segment, +.>Indicate->Power variance of all data points in the spectrogram corresponding to each data response segment, +.>Representing the number of data response segments in the original dataset.
Further, the denoising processing is performed on the original data according to the noise influence degree to obtain a filtered and denoised chromatographic data set, which comprises the following specific steps:
the upward rounding value of the product of the preset window side length and the noise influence degree of the original data set is recorded as the size of a filtering window;
and filtering and denoising the original data set by using a median filtering algorithm according to the size of the filtering window to obtain a denoised chromatographic data set.
Further, the content of sulfur dioxide in the food to be detected is obtained according to the filtered and denoised chromatographic data set, the standard data set, the concentration of sulfur dioxide reflected by the standard data set and the time interval corresponding to the sulfur dioxide, and the specific formula is as follows:
in the method, in the process of the invention,indicates the sulfur dioxide concentration of the food to be tested, +.>Represents the concentration of sulfur dioxide reflected by the standard dataset,/->Mean value of response signal values representing data in a time interval corresponding to sulfur dioxide in denoised chromatographic data set, +.>A mean value of response signal values representing data in a time interval corresponding to sulfur dioxide in the standard data set; each data in the standard data set comprises a time value and a response signal value.
The technical scheme of the invention has the beneficial effects that: acquiring a chromatographic data set, a standard data set and a time interval corresponding to sulfur dioxide of food to be detected, wherein the concentration of sulfur dioxide is reflected by the standard data set; recording the chromatographic data set of the food to be detected as an original data set; each original data in the original data set comprises a time value and a response signal value; segmenting the original data set according to the time value and the response signal value of each original data in the original data set to obtain a data response segment in the original data set, wherein the obtained data response segment better represents the influence of noise on the chromatographic data set; performing short-time Fourier transform on each data response segment to obtain a spectrogram; each data point in the spectrogram corresponds to one power and one frequency; according to the spectrogram corresponding to each data response section, the influence degree of different types of light on each data response section is obtained, wherein when the influence degree of different types of light on each data response section is calculated, not only is the original data set used, but also the spectrogram is used, so that the influence degree of a more accurate different type of light on each data response section is obtained; obtaining the noise influence degree of the original data set according to the influence degree of different types of light on each data response section, the response signal value of the original data in the original data set and the power corresponding to each data point in the spectrogram, and obtaining more accurate noise influence degree of the original data set according to the influence degree of different types of light on a plurality of data response sections when the noise influence degree of the original data set is calculated; denoising the original data according to the noise influence degree to obtain a more accurate chromatographic data set, namely a chromatographic data set after filtering denoising; according to the filtered and denoised chromatographic data set, the standard data set, the concentration of the sulfur dioxide reflected by the standard data set and the time interval corresponding to the sulfur dioxide, the filtered and denoised chromatographic data set is used, so that the calculated content of the sulfur dioxide in the food to be detected is more accurate.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart showing the steps of a method for measuring sulfur dioxide in a food product according to the present invention;
FIG. 2 is a high performance liquid chromatogram;
fig. 3 is a spectrum diagram corresponding to a data response segment.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of a method for measuring sulfur dioxide in food according to the invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the method for measuring sulfur dioxide in food provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for measuring sulfur dioxide in food according to an embodiment of the invention is shown, the method includes the following steps:
step S001: acquiring a chromatographic data set, a standard data set and a time interval corresponding to sulfur dioxide of food to be detected, wherein the concentration of sulfur dioxide is reflected by the standard data set; recording the chromatographic data set of the food to be detected as an original data set; each original data in the original data set comprises a time value and a response signal value; and segmenting the original data set according to the time value and the response signal value of each original data in the original data set to obtain a data response segment in the original data set.
The main purpose of this embodiment is to perform denoising treatment on a chromatographic image, so that it is first required to collect a chromatographic data set of a food to be detected and obtain a time interval corresponding to sulfur dioxide, a standard data set for comparing the chromatographic data set of the food to be detected, and a concentration of sulfur dioxide reflected by the standard data set. In this embodiment, the model number of the high performance liquid chromatograph is the wubi K2025, and the high performance liquid chromatograph of other models may be used to collect the chromatographic data set of the food to be detected in other embodiments, which is not limited in this embodiment.
The specific process of collecting the chromatographic data set is as follows: mixing a food sample to be detected with a solvent to obtain a sample solution, injecting the clarified sample solution into a sample injector of a chromatograph to obtain a chromatographic data set of the food to be detected, and marking the chromatographic data set as an original data set to obtain a standard data set of the food sample to be detected and a time interval corresponding to sulfur dioxide in the chromatogram. It should be noted that each chromatographic data in the chromatographic data set contains a time value and a response signal value, that is, each raw data in the raw data set contains a time value and a response signal value, and each data in the standard data set contains a time value and a response signal value.
The high performance liquid chromatogram is shown in fig. 2, wherein A in fig. 2 represents a chromatogram of only sulfur dioxide, namely a reference chromatogram, B represents a chromatogram of the food to be detected, namely a sample chromatogram, C represents a chromatogram of a standard sample of the food to be detected, namely a negative reference chromatogram, no sulfur dioxide corresponds to C, and a represents a sulfur dioxide interval.
When the content of sulfur dioxide in food is measured by high performance liquid chromatography, the principle is that the absorbance of different substances is determined according to the absorption capacity of the different substances to different lights, and then the absorbance of the substances is compared with a standard data set to determine whether the detected sample contains the target substances. However, when the chromatographic data set is collected, the chromatographic data set may be affected by noise, so that the response signal value of part of the chromatographic data in the obtained chromatographic data set changes, and when the chromatographic data set is compared with the standard data set, the data cannot be corresponding, or incorrect correspondence occurs, so that the sample detection is inaccurate. Thus, denoising the acquired chromatographic data set is required.
When the chromatographic data set is subjected to denoising treatment, because the chromatographic data in the chromatographic data set is formed by superposing the data of the absorption capacity of substances to various lights, the absorption capacities of different substances to different lights are different, and therefore, if the chromatographic data set is directly denoised, the superposition effect of the substances to the data of the various light absorption capacities is changed, so that the accuracy of the final chromatographic data set is affected. Therefore, in order to accurately represent the influence of noise on the chromatographic data set, the embodiment segments the original data set to obtain data response segments in the original data set, converts the original data in each data response segment in the original data set from the time domain to the frequency domain through short-time fourier transform, then determines the influence of noise on the original data set when different data responses appear in the original data set according to the distribution of the original data in the frequency domain, and then compares the differences among the different data response segments, thereby obtaining the noise influence degree of the original data set, namely the noise influence degree of the chromatographic data set.
In particular, when performing a short-time fourier transform on an original data set, it is first necessary to determine a data response segment in the original data set, which represents the response of different substances in the original data set to light.
The specific steps for acquiring the data response segment in the original data set are as follows: all extreme points in the original data set are obtained first, and then the data response section in the original data set is obtained according to the distribution of the neighborhood data at the two sides of each extreme point.
And operating the original data set by using a first derivative method to obtain a plurality of extreme points in the original data set.
The first derivative method is a known technique, and the specific method is not described here.
The distribution of the neighborhood data at both sides of each extreme point in the original data set is as follows, according to the first place in the original data setExtreme points are exemplified by +.>The distribution of the neighborhood data on both sides of each extreme point is as follows:
will be the first in the original datasetThe time value range of the left neighborhood of the extreme point is marked as +.>Will be at the firstThe time value range of the right neighborhood of the extreme point is marked as +.>Wherein->Indicate->Time value of each extreme point,/->Indicate->Time value of each extreme point,/->Indicate->Time value of each extreme point,/->Indicate->Extreme points and->Median of the time values of the extreme points, +.>Indicate->Extreme points and->The median of the time values of the extreme points. Here->The neighborhood data of the extreme points are divided into a left neighborhood and a right neighborhood.
Wherein the firstThe left adjacent part of the extreme point is +.>The original data is->The left boundary of the extreme points is represented to a degree of +.>Wherein->The right neighbor of the extreme point +.>The original data is->The right boundary of the extreme points exhibits a degree of +.>，/>The calculation formula of (2) is as follows:
in the method, in the process of the invention,representing +.>The left adjacent part of the extreme point is +.>The original data is->The degree of representation of the left border of the extreme points,/->Representing +.>The right neighbor of the extreme point +.>The original data is->The degree of representation of the right border of the extreme points,/->Representing +.>Response signal values of the extreme points +.>Representing +.>The left adjacent part of the extreme point is +.>Response signal values of the individual original data, +.>Representing +.>Extreme valueRight neighborhood of point +.>Response signal values of the individual original data, +.>Minimum response signal value representing all raw data in the raw data set,/->Represents an exponential function based on natural constants, < ->Indicate->Extreme points and->The left adjacent part of the extreme point is +.>Difference of response signal values of the respective original data, +.>Indicate->Extreme points and->The right neighbor of the extreme point +.>Difference of response signal values of the respective original data, +.>Representing an absolute value function.
For the firstAll of the two adjacent sides of each extreme pointThe original data are all subjected to the above operation to obtain +.>Each original data in the left neighborhood of the extreme point is +.>The set of manifestations of the left boundary of the extreme points: />Obtain->Each original data in the right neighborhood of the extreme point is +.>The set of manifestations of the right boundary of the extreme points: />. Wherein->Is->The left adjacent part of the extreme point is +.>The original data is->The degree of representation of the left border of the extreme points,/->Is->The right neighbor of the extreme point +.>The original data is->The degree of representation of the right boundary of each extreme point.
At the collectionThe time value of the corresponding original data when the maximum expression level is selected is +.>Time values of left boundaries of the extreme points; in the collection->The time value of the corresponding original data when the maximum expression level is selected is +.>Time values of the right boundary of the extreme points; further, the +.th is formed by all the original data between the time values of the left and right boundaries>The data response segment of the extreme point, which is described as +.>The data response segments.
And carrying out the operation on all the extreme points in the original data set to obtain the data response segment of each extreme point, namely obtaining all the data response segments in the original data set.
Step S002: performing short-time Fourier transform on each data response segment to obtain a spectrogram; each data point in the spectrogram corresponds to one power and one frequency; and obtaining the influence degree of different types of light on each data response segment according to the spectrogram corresponding to each data response segment.
Optionally marking a data response section as a reference section, and carrying out Fourier transform on the original data of the reference section through a short-time Fourier transform algorithm to obtain a reference spectrogram corresponding to the reference section; the abscissa of the reference spectrogram is frequency, and the ordinate is power.
Each original data in the reference section corresponds to a data point in the reference spectrogram, and each data point in the reference spectrogram comprises a frequency value and a power value. As shown in fig. 3, fig. 3 shows a spectrogram corresponding to one data response segment.
Because the original data is obtained by overlapping substances which absorb light with different degrees, and the frequencies of different lights are different, the obtained reference spectrogram has data responses with multiple frequencies. When the original data set is affected by noise, the data of different frequencies are affected by noise, so that the influence degree of different types of light on the reference segment is obtained according to the change of signals in the reference spectrogram.
In the reference spectrogram, the power of the data points is calculated by using a first derivative method, so that a plurality of extreme points in the reference spectrogram are obtained.
And then clustering according to the power value of each extreme point in the reference spectrogram, wherein a DBSCAN density clustering algorithm is used for clustering all the extreme points in the reference spectrogram, so as to obtain a plurality of clustering clusters. Since each extreme point corresponds to data at one frequency, different frequencies represent different light, and since it is affected by noise, there may be more frequencies. It should be noted that, the DBSCAN density clustering algorithm is a representative density-based clustering algorithm, and the algorithm is a prior known technology and will not be described herein, wherein the radius and the minimum cluster number are parameters in the DBSCAN density clustering algorithm, the radius in the preset DBSCAN density clustering algorithm is 3, the minimum cluster number is 4, the radius and the minimum cluster number in other embodiments can be set to other values, and the embodiment does not limit the method. According to the obtained distribution of extreme points in the cluster, the influence degree of different types of light on the reference segment is obtained, and the calculation formula is as follows:
in the method, in the process of the invention,indicating the extent of influence of different types of light on the reference segment,/->Representing the number of extreme points in the reference spectrogram,indicate->Average power of extreme points in the clusters, < +.>Representing the minimum value of the average power of the extreme points in all clusters, < >>Representing the number of clusters, +.>Maximum value of average power representing extreme points in all clusters, +.>Representing the variance of the power of all data points in the reference spectrogram, +.>Represents an exponential function based on natural constants, < ->Representing a linear normalization function, ++>Indicate->Difference between average power of extreme points in each cluster and minimum value of average power of extreme points in all clusters,/->Indicate->Difference between average power of extreme points in each cluster and maximum value of average power of extreme points in all clusters, +.>Representing an absolute value function.
Specifically, the more extreme points in the reference spectrogram, the more signals with different frequencies exist in the reference segment, which is caused by that noise signals with different frequencies are superimposed on the original data, and substances corresponding to the reference segment have different absorption degrees to light, so thatThe larger the reference section is, the greater the influence degree of different types of light and noise on the reference section is; />The difference value between the maximum value of the average power of the extreme points and the minimum value of the average power in all the clusters is represented, because in all the clusters, the cluster with the maximum average power of the extreme points corresponds to the frequency of the substance in which the substance corresponds to the light response signal, and the larger the value is, the larger the influence degree of the light on the substance is indicated; />Maximum value of average power representing extreme points in all clusters and +.>The smaller the difference in average power of the extreme points in the clusters, the greater the degree of difference, which is indicative of the degree of influence of noise with respect to the original signal, and therefore here an exponential function based on a natural constant is used to change the monotonicity of the function; />Representing the power variance of all data points in the reference spectrogram, wherein the larger the variance is, the data in the reference spectrogram is describedThe greater the degree of frequency fluctuation of the spot and therefore the greater the degree of influence of different types of light on the reference segment.
To this end, the extent of influence of different types of light on the reference segment is obtained.
And carrying out the operation on all the data response segments in the original data set to obtain the influence degree of different types of light on each data response segment.
Step S003: and obtaining the noise influence degree of the original data set according to the influence degree of different types of light on each data response section, the response signal value of the original data in the original data set and one power corresponding to each data point in the spectrogram.
The degree of influence of different types of light obtained from the above calculation on each data response segment. Since noise is randomly distributed in various positions of the chromatographic data set when data is acquired, the influence degree of noise on the whole original data set can be approximately regarded as the same, and thus the noise is estimated according to the original data change of different data response segments, so that the noise influence degree of the original data set is obtained. The calculation formula is as follows:
in the method, in the process of the invention,representing the noise impact level of the original dataset, +.>Representing the extent of influence of different types of light on the t-th data response segment, < >>Representing +.>Maximum response signal value in all raw data in the individual data response segment,/for each data segment>Indicate->Maximum power in all data points in the spectrogram corresponding to each data response segment, +.>Indicate->Power variance of all data points in the spectrogram corresponding to each data response segment, +.>Representing the number of data response segments in the original dataset.
In particular, the method comprises the steps of,representing different types of light pairs +.>The ratio of the degree of influence of the data response segment to the sum of the degrees of influence of the different types of light on all the data response segments is larger, which indicates that the different types of light are on the +.>The greater the degree of influence of the data response segment, the weight is taken as it is, and then multiplied by +.>The larger the value obtained, the description is +.>The greater the degree to which the individual data response segments are affected by noise, because +.>Represents +.>Primitive number in each data response segmentAccording to the maximum response signal and +.>The ratio of the maximum power of the data points in the spectrogram corresponding to the data response sections, and the point where the data response appears in the original data set represents the response of the substances in the detection sample appearing in the chromatographic data, but if the detection sample is influenced by noise, the ratio is larger, so that the noise degree is larger.
Thus, the noise influence level of the original data set is obtained.
Step S004: denoising the original data according to the noise influence degree to obtain a filtered and denoised chromatographic data set; and obtaining the content of the sulfur dioxide in the food to be detected according to the chromatographic data set, the standard data set and the concentration of the sulfur dioxide reflected by the standard data set after filtering and denoising and the time interval corresponding to the sulfur dioxide.
According to the noise influence degree of the original data set obtained through calculation, filtering and denoising the original data set through a median filtering algorithm, taking the noise influence degree of the original data set as a filtering weight, and further obtaining the size of a filtering window according to the filtering weight, wherein the calculation formula of the size of the filtering window is as follows:，/>representing the size of the filter window +.>For a predetermined window side length, +.>For the noise influence level of the original dataset, +.>Representing a round-up function. Then, the original data set is filtered and denoised according to the obtained filter window to obtain a denoised chromatographic data set。
In this embodiment, the preset window side lengthThis is described by way of example, and other values may be set in other embodiments, and the present example is not limited thereto. And the median filtering algorithm is a known technology, and will not be described in detail herein.
Comparing the obtained denoised chromatographic data set with a standard data set, and combining the concentration of sulfur dioxide reflected by the standard data set to obtain the concentration of sulfur dioxide reflected by the denoised chromatographic data set of sulfur dioxide, namely the concentration of sulfur dioxide of food to be detected, wherein the calculation formula is as follows:
in the method, in the process of the invention,indicates the sulfur dioxide concentration of the food to be tested, +.>Represents the concentration of sulfur dioxide reflected by the standard dataset,/->Mean value of response signal values representing data in a time interval corresponding to sulfur dioxide in denoised chromatographic data set, +.>The average value of response signal values of data in a time interval corresponding to sulfur dioxide in the standard data set; each data in the standard data set comprises a time value and a response signal value.
The present invention has been completed.
In summary, the above embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for measuring sulfur dioxide in a food product, comprising the steps of:
acquiring a chromatographic data set, a standard data set and a time interval corresponding to sulfur dioxide of food to be detected, wherein the concentration of sulfur dioxide is reflected by the standard data set; recording the chromatographic data set of the food to be detected as an original data set; each original data in the original data set comprises a time value and a response signal value; segmenting the original data set according to the time value and the response signal value of each original data in the original data set to obtain a data response segment in the original data set;
performing short-time Fourier transform on each data response segment to obtain a spectrogram; each data point in the spectrogram corresponds to one power and one frequency;
according to the spectrogram corresponding to each data response section, the influence degree of different types of light on each data response section is obtained;
obtaining the noise influence degree of the original data set according to the influence degree of different types of light on each data response section, the response signal value of the original data in the original data set and one power corresponding to each data point in the spectrogram;
denoising the original data according to the noise influence degree to obtain a filtered and denoised chromatographic data set; and obtaining the content of the sulfur dioxide in the food to be detected according to the chromatographic data set, the standard data set and the concentration of the sulfur dioxide reflected by the standard data set after filtering and denoising and the time interval corresponding to the sulfur dioxide.
2. The method for measuring sulfur dioxide in food according to claim 1, wherein the step of segmenting the original data set according to the time value and the response signal value of each original data in the original data set to obtain the data response segment in the original data set comprises the following specific steps:
calculating an original data set by using a first derivative method to obtain a plurality of extreme points in the original data set;
obtaining a left neighborhood and a right neighborhood of each extreme point according to the time values of the adjacent extreme points;
according to the firstEach original data and +.>The difference of the response signal values of the extreme points respectively obtains the +.>Each original data in the left neighborhood and the right neighborhood of the extreme point is +.>The degree of representation of the left and right boundaries of the extreme points;
according to the firstEach original data in the left neighborhood and the right neighborhood of the extreme point is +.>The degree of representation of the left and right boundary of the extreme point, resulting in the +.>The data response segments.
3. The method for measuring sulfur dioxide in food according to claim 2, wherein the steps of obtaining the left neighborhood and the right neighborhood of each extreme point according to the time values of the adjacent extreme points comprise the following specific steps:
will beMarked as +.>Left neighborhood of each extreme point; will->Marked as +.>Right neighborhood of each extreme point; wherein->Indicate->Time value of each extreme point,/->Indicate->Time value of each extreme point,/->Indicate->Time values of the extreme points.
4. The method for measuring sulfur dioxide in food according to claim 2, wherein the method according to the first aspectEach original data and +.>The difference in the response signal values of the extreme points,respectively get->Each original data in the left neighborhood and the right neighborhood of the extreme point is +.>The representation degree of the left boundary and the right boundary of each extreme point comprises the following specific formulas:
in the method, in the process of the invention,representing +.>The left adjacent part of the extreme point is +.>The original data is->The degree of representation of the left border of the extreme points,/->Representing +.>The right neighbor of the extreme point +.>The original data is->The degree of representation of the right border of the extreme points,/->Representing +.>Response signal values of the extreme points +.>Representing +.>The left adjacent part of the extreme point is +.>Response signal values of the individual original data, +.>Representing +.>The right neighbor of the extreme point +.>Response signal values of the individual original data, +.>Representing an absolute function,/->Minimum response signal value representing all raw data in the raw data set,/->An exponential function based on a natural constant is represented.
5. The method for measuring sulfur dioxide in food according to claim 2, wherein the method according to the first aspectEach original data in the left neighborhood and the right neighborhood of the extreme point is +.>The degree of representation of the left and right boundary of the extreme point, resulting in the +.>The data response section comprises the following specific steps:
selecting the first of the original datasetAll raw data in the left neighborhood of the extreme point are +.>The time value of the original data corresponding to the maximum value in the degree of representation of the left boundary of the extreme points is taken as +.>Time values of left boundaries of the extreme points;
selecting the first of the original datasetAll raw data in the right neighborhood of the extreme point are +.>The time value of the original data corresponding to the maximum value in the expression level of the right boundary of the extreme points is taken as +.>Time values of the right boundary of the extreme points;
according to the firstAll raw data between the time values of the left boundary and the right boundary of the extreme points constitute +.>The data response segments.
6. The method for measuring sulfur dioxide in food according to claim 1, wherein the step of obtaining the influence degree of different types of light on each data response segment according to the spectrogram corresponding to each data response segment comprises the following specific steps:
optionally, marking a data response section as a reference section, and marking a spectrogram corresponding to the reference section as a reference spectrogram;
calculating the power of all data points in the reference spectrogram by using a first derivative method to obtain a plurality of extreme points in the reference spectrogram;
clustering all extreme points in the reference spectrogram according to a DBSCAN density clustering algorithm to obtain a plurality of clustering clusters;
and obtaining the influence degree of different types of light on the reference section according to the difference between the powers of the extreme points in the cluster.
7. The method for measuring sulfur dioxide in food according to claim 6, wherein the specific formula is as follows:
in the method, in the process of the invention,indicating the extent of influence of different types of light on the reference segment,/->Representing the number of extreme points in the reference spectrogram, < ->Indicate->Average power of extreme points in the clusters, < +.>Representing the minimum value of the average power of the extreme points in all clusters,representing the number of clusters, +.>Maximum value of average power representing extreme points in all clusters, +.>Representing the variance of the power of all data points in the reference spectrogram, +.>Represents an exponential function based on natural constants, < ->Representing a linear normalization function, ++>Representing an absolute value function.
8. The method for measuring sulfur dioxide in food according to claim 1, wherein the obtaining the noise influence degree of the original data set according to the influence degree of different types of light on each data response section, the response signal value of the original data in the original data set, and the power corresponding to each data point in the spectrogram comprises the following specific formulas:
in the method, in the process of the invention,representing the noise impact level of the original dataset, +.>Representing the extent of influence of different types of light on the t-th data response segment, < >>Representing +.>The maximum response signal value among all the original data within the individual data response segments,indicate->Maximum power in all data points in the spectrogram corresponding to each data response segment, +.>Indicate->Power variance of all data points in the spectrogram corresponding to each data response segment, +.>Representing the number of data response segments in the original dataset.
9. The method for determining sulfur dioxide in food according to claim 1, wherein the denoising processing is performed on the raw data according to the influence degree of noise to obtain a filtered and denoised chromatographic data set, comprising the following specific steps:
the upward rounding value of the product of the preset window side length and the noise influence degree of the original data set is recorded as the size of a filtering window;
and filtering and denoising the original data set by using a median filtering algorithm according to the size of the filtering window to obtain a denoised chromatographic data set.
10. The method for measuring sulfur dioxide in food according to claim 1, wherein the specific formula is as follows:
in the method, in the process of the invention,indicates the sulfur dioxide concentration of the food to be tested, +.>Represents the concentration of sulfur dioxide reflected by the standard dataset,/->Mean value of response signal values representing data in a time interval corresponding to sulfur dioxide in denoised chromatographic data set, +.>Representing data in a time interval corresponding to sulfur dioxide in a standard data setIs a mean value of the response signal values; each data in the standard data set comprises a time value and a response signal value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410245041.0A CN117825601B (en) | 2024-03-05 | 2024-03-05 | Method for measuring sulfur dioxide in food |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410245041.0A CN117825601B (en) | 2024-03-05 | 2024-03-05 | Method for measuring sulfur dioxide in food |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117825601A true CN117825601A (en) | 2024-04-05 |
| CN117825601B CN117825601B (en) | 2024-05-24 |
Family
ID=90508069
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410245041.0A Active CN117825601B (en) | 2024-03-05 | 2024-03-05 | Method for measuring sulfur dioxide in food |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117825601B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030130823A1 (en) * | 2002-01-09 | 2003-07-10 | General Electric Company | Method for enhancement in screening throughput |
| US20050143931A1 (en) * | 2003-12-19 | 2005-06-30 | Icoria, Inc. | System and methods for non-targeted processing of chromatographic data |
| US8867862B1 (en) * | 2012-12-21 | 2014-10-21 | The United States Of America As Represented By The Secretary Of The Navy | Self-optimizing analysis window sizing method |
| US20200046265A1 (en) * | 2018-08-13 | 2020-02-13 | Ctrl-Labs Corporation | Real-time spike detection and identification |
| JP2021018818A (en) * | 2019-07-18 | 2021-02-15 | 浙江大学Ｚｈｅｊｉａｎｇ Ｕｎｉｖｅｒｓｉｔｙ | Propeller cavitation state detection method based on wavelet and principal component analysis |
| CN114740076A (en) * | 2022-03-16 | 2022-07-12 | 上海润达榕嘉生物科技有限公司 | Filtering processing method for triple quadrupole mass spectrometer system signals |
| US20220335276A1 (en) * | 2019-09-13 | 2022-10-20 | Mitsubishi Electric Corporation | Information processing device and electronic apparatus equipped with same |
| KR20220158462A (en) * | 2021-05-24 | 2022-12-01 | 조선대학교산학협력단 | EMG signal-based recognition information extraction system and EMG signal-based recognition information extraction method using the same |
| US20230152286A1 (en) * | 2021-02-25 | 2023-05-18 | Acchrom Tech (Dalian) Technology Co., Ltd | Chromatographic analysis system, chromatogram detection and analysis method and electronic device |
| CN117030654A (en) * | 2023-10-10 | 2023-11-10 | 武汉怡特环保科技有限公司 | Method for measuring concentration of trace-level sulfur dioxide in air |
| CN117405622A (en) * | 2023-12-14 | 2024-01-16 | 广东伊茗药业有限公司 | Intelligent detection method for nitrite content in bird's nest |
-
2024
- 2024-03-05 CN CN202410245041.0A patent/CN117825601B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030130823A1 (en) * | 2002-01-09 | 2003-07-10 | General Electric Company | Method for enhancement in screening throughput |
| US20050143931A1 (en) * | 2003-12-19 | 2005-06-30 | Icoria, Inc. | System and methods for non-targeted processing of chromatographic data |
| US8867862B1 (en) * | 2012-12-21 | 2014-10-21 | The United States Of America As Represented By The Secretary Of The Navy | Self-optimizing analysis window sizing method |
| US20200046265A1 (en) * | 2018-08-13 | 2020-02-13 | Ctrl-Labs Corporation | Real-time spike detection and identification |
| JP2021018818A (en) * | 2019-07-18 | 2021-02-15 | 浙江大学Ｚｈｅｊｉａｎｇ Ｕｎｉｖｅｒｓｉｔｙ | Propeller cavitation state detection method based on wavelet and principal component analysis |
| US20220335276A1 (en) * | 2019-09-13 | 2022-10-20 | Mitsubishi Electric Corporation | Information processing device and electronic apparatus equipped with same |
| US20230152286A1 (en) * | 2021-02-25 | 2023-05-18 | Acchrom Tech (Dalian) Technology Co., Ltd | Chromatographic analysis system, chromatogram detection and analysis method and electronic device |
| KR20220158462A (en) * | 2021-05-24 | 2022-12-01 | 조선대학교산학협력단 | EMG signal-based recognition information extraction system and EMG signal-based recognition information extraction method using the same |
| CN114740076A (en) * | 2022-03-16 | 2022-07-12 | 上海润达榕嘉生物科技有限公司 | Filtering processing method for triple quadrupole mass spectrometer system signals |
| CN117030654A (en) * | 2023-10-10 | 2023-11-10 | 武汉怡特环保科技有限公司 | Method for measuring concentration of trace-level sulfur dioxide in air |
| CN117405622A (en) * | 2023-12-14 | 2024-01-16 | 广东伊茗药业有限公司 | Intelligent detection method for nitrite content in bird's nest |
Non-Patent Citations (6)
| Title |
|---|
| ADITYA CHAKRAVARTY 等: "Visualization of hydraulic fracture using physics-informed clustering to process ultrasonic shear waves", INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES, vol. 137, 31 January 2021 (2021-01-31), pages 1 - 9 * |
| WEIPENG ZHU 等: "Variable-Speed Frequency-Hopping Signal Sorting: Spectrogram Is Sufficient", ELECTRONICS, vol. 12, 3 November 2023 (2023-11-03), pages 1 - 16 * |
| 刘光宇 等: "基于密度聚类与灰度变换的NSST域声呐图像去噪", 湖南大学学报(自然科学版), vol. 49, no. 8, 31 December 2022 (2022-12-31), pages 186 - 195 * |
| 李娜;刘艳滢;: "基于提升小波的图像去噪算法的FPGA设计", 微计算机信息, no. 14, 15 May 2009 (2009-05-15), pages 166 - 167 * |
| 石松 等: "高斯拟合结合小波降噪处理IMS峰信号", 淮北师范大学学报(自然科学版), vol. 38, no. 2, 31 December 2017 (2017-12-31), pages 55 - 59 * |
| 陶胜;: "图像条带噪声的去除方法", 洛阳理工学院学报(自然科学版), no. 03, 25 September 2017 (2017-09-25), pages 74 - 78 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117825601B (en) | 2024-05-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CA1161271A (en) | Method and apparatus for measuring mean cell volume of red blood cells | |
| CN104990892B (en) | The spectrum picture Undamaged determination method for establishing model and seeds idenmtification method of seed | |
| CN103293118A (en) | Hogwash oil identification method based on near infrared reflectance spectroscopy | |
| CN117030654B (en) | Method for measuring concentration of trace-level sulfur dioxide in air | |
| CN110749565A (en) | Method for rapidly identifying storage years of Pu' er tea | |
| CN117664906B (en) | Water body antibiotic pollution detection method based on spectral characteristics | |
| CN113008805A (en) | Radix angelicae decoction piece quality prediction method based on hyperspectral imaging depth analysis | |
| JP3737257B2 (en) | Spectral data processing method and correction method using two-dimensional representation | |
| CN105138834A (en) | Tobacco chemical value quantifying method based on near-infrared spectrum wave number K-means clustering | |
| CN111523587A (en) | Woody plant species spectrum identification method based on machine learning | |
| CN117825601B (en) | Method for measuring sulfur dioxide in food | |
| CN110779875A (en) | Method for detecting moisture content of winter wheat ear based on hyperspectral technology | |
| CN112362609A (en) | Method for identifying oil stain smoke pollution source based on infrared spectrum technology | |
| CN104076003B (en) | Extraction method of mineral spectrum absorption characteristic parameters | |
| CN116136518B (en) | Chromatograph | |
| CN112716447A (en) | Oral cancer classification system based on deep learning of Raman detection spectral data | |
| CN115586159A (en) | Method for evaluating aging association degree of white spirit based on mid-infrared spectrum detection technology | |
| CN115184281A (en) | Method and system for determining concentration of solution components based on two-dimensional spectrum | |
| CN113125376A (en) | Method, device, equipment and medium for identifying age limit of dried orange peel | |
| Hao et al. | Wavelet-based threshold denoising for imaging hyperspectral data | |
| CN117332258B (en) | Near infrared absorption peak identification method, system and medium based on multi-scale Lorentz | |
| CN117783051B (en) | Methane gas leakage detection method based on multi-sensor data fusion | |
| CN113740268B (en) | Photoacoustic time spectrum-based puncture tissue strip grading method | |
| CN110470396B (en) | Processing method for smoothing and denoising spectrum | |
| CN118190835A (en) | Tea quality detection device and method based on spectral imaging |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |