CN113984736B - Method for separating signals of packaged food based on spatial shift Raman spectrum - Google Patents
Method for separating signals of packaged food based on spatial shift Raman spectrum Download PDFInfo
- Publication number
- CN113984736B CN113984736B CN202111293805.6A CN202111293805A CN113984736B CN 113984736 B CN113984736 B CN 113984736B CN 202111293805 A CN202111293805 A CN 202111293805A CN 113984736 B CN113984736 B CN 113984736B
- Authority
- CN
- China
- Prior art keywords
- detected
- food
- spectrum
- raman
- laser source
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/65—Raman scattering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
The invention discloses a method for separating signals of packaged food based on a spatial migration Raman spectrum, which relates to the technical field of the spatial migration Raman spectrum, and is characterized in that Raman spectrums in partial region ranges are intercepted from initial spectrum data as observation data based on information entropies of the Raman spectrums at different migration distances, independent component analysis is carried out on the observation data, a plurality of independent signal components are obtained through separation, the problem of attribution of the independent signal components obtained through the independent component analysis and separation is solved through characteristic spectrum peak clustering identification, and the Raman signals of the food to be detected in the interior are finally determined.
Description
Technical Field
The invention relates to the technical field of spatial shift Raman spectroscopy, in particular to a method for separating signals of packaged food based on spatial shift Raman spectroscopy.
Background
Food packaging is widely used to ensure the physical characteristics and quality of food during transportation, storage and distribution. The traditional food detection method is to sample and destroy food packages to detect the internal quality of the food. The method not only causes a great deal of resource waste, but also has great randomness of detection, so that a quality nondestructive analysis method for the packaged food is urgently needed to appear.
At present, a common nondestructive testing method for the quality of articles mainly utilizes a Raman spectrum, which is an optical technology reflecting the composition and content of substances through photon-molecule interaction, and is widely applied to the fields of food, medicine, biology, materials and the like because of the characteristics of insensitivity to water, rich information, simple sample preparation and the like, but the traditional Raman spectrum and other optical testing methods are only limited to surface detection or samples with clear surfaces (such as glass) and are difficult to be applied to nondestructive analysis of the quality of packaged food.
The spatial shift Raman technology is a special Raman spectrum technology, which is a technology for collecting Raman scattering signals of different distances of an exciting light incidence point and identifying internal signals of a material according to the difference of photon transmission among multiple layers of materials. However, when the spatial shift raman technique is applied to the quality nondestructive analysis of packaged food, how to accurately separate the package signal from the food signal from the acquired spectrum signal, thereby accurately extracting the raman spectrum of the food for quality detection becomes a difficulty in the application of the technique. Previous research on deep layer exploration under a layered sample mainly focuses on selecting an optimal offset distance to reduce surface signal interference and enhance a downhole signal, namely, a spectrum at a non-zero offset position minus a spectrum at a zero offset position under a proper scale factor is adopted to represent a subsurface signal. However, this method for reducing surface interference requires a high level of expertise in the field of operators, and the parameters of offset distance and subtraction ratio are difficult to determine, greatly influenced by human factors, and difficult to ensure accuracy and processing effect.
Disclosure of Invention
The invention provides a method for separating a packaged food signal based on a spatial shift Raman spectrum, aiming at the problems and technical requirements, and the technical scheme of the invention is as follows:
a method for separating a signal of a packaged food based on spatially shifted raman spectroscopy, the method comprising:
emitting laser through a laser source to irradiate the surface of a sample to be detected, and acquiring Raman spectrums of the sample to be detected at a plurality of positions with different offset distances relative to the laser source to obtain initial spectrum data, wherein the sample to be detected comprises food to be detected and external food packages of the food to be detected;
intercepting the Raman spectrum in a partial region range from the initial spectrum data as observation data based on the information entropy of the Raman spectrum at different offset distances;
carrying out independent component analysis on the observation data, and separating to obtain a plurality of independent signal components;
and determining a typical spectrum peak corresponding to the food to be detected based on a spectrum peak identification technology for the initial spectrum data, matching the peak position of the typical spectrum peak with the peak position of the spectrum peak of each independent signal component, and determining the independent signal component corresponding to the food to be detected.
The further technical scheme is that the method for determining the typical spectral peak corresponding to the food to be detected based on the spectral peak identification technology for the initial spectral data comprises the following steps:
Performing spectral peak identification on the initial spectral data by using a spectral peak identification technology, and normalizing an attenuation curve of an identified spectral peak along with the increase of the offset distance relative to the laser source;
clustering the normalized attenuation curve, wherein the number of the clustered types is equal to the number of layers of the samples to be detected;
and determining the category corresponding to the food to be detected according to the numerical characteristics of each category, and taking the spectral peak belonging to the category as a typical spectral peak corresponding to the food to be detected.
The further technical scheme is that the method for determining the category corresponding to the food to be detected according to the numerical characteristic of each category comprises the following steps:
and calculating the average value of the attenuation values of the spectral peaks which belong to the same category and are formed after clustering, and taking the category with the maximum average value of the corresponding attenuation values as the category corresponding to the food to be detected.
The further technical scheme is that the method also comprises the following steps:
adopting self-adaptive iterative weighted punishment least square to fit the correction base line of the independent signal component of the food to be detected;
and subtracting the correction baseline from the independent signal component of the food to be detected to obtain the Raman spectrum without the baseline of the food to be detected.
The further technical scheme is that the method for capturing the Raman spectrum in the partial region range from the initial spectrum data as the observation data based on the information entropy of the Raman spectrum at different offset distances comprises the following steps:
Calculating information entropies of the Raman spectrums at different offset distances, and determining an offset distance i corresponding to an inflection point of the information entropies;
a raman spectrum with an offset distance in the range of 0 to i is cut out from the initial spectral data as observation data.
The further technical scheme is that the method for calculating the information entropy of the Raman spectrum at different offset distances comprises the following steps:
by the formula
Calculating the Raman spectrum x at the offset distance i i Information entropy of (1), wherein
As a Raman spectrum x at an offset distance i i The parameter J ∈ [1, 2., J, the rounded-down value of the J band in (J), n]Representing a Raman spectrum x i The light intensity values in all the bands of (1) are different,
indicating the probability that the band to which these elements correspond appears in all bands.
The further technical scheme is that the offset distance i corresponding to the inflection point of the information entropy is determined by adopting a sliding variance method according to the following formula:
wherein, H (x) i ) As a Raman spectrum x at an offset distance i i The entropy of the information of (a) is,
is the average of all information entropies, g is the size of the sliding window, and d represents the maximum offset distance in the initial spectral data relative to the laser source.
The method further adopts the technical scheme that independent component analysis is carried out on observation data by using a FastICA algorithm, and decorrelation and scaling are carried out on singular vectors and corresponding singular values which are the same as the number of independent signal components to be separated through singular value decomposition to obtain a plurality of independent signal components.
The further technical scheme is that the method obtains initial spectrum data by obtaining Raman spectra of samples to be detected at a plurality of positions with different offset distances relative to a laser source, and comprises the following steps:
carrying out independent repeated collection operation on a sample to be detected for a plurality of times under the same collection condition, and splicing the Raman spectra of the sample to be detected, which are obtained at positions with different offset distances relative to a laser source, to obtain a sample spectrum when each independent repeated collection operation is carried out;
and eliminating abnormal values of the sample spectrum extracted by each independent repeated acquisition operation, then taking the average value for smoothing, and intercepting the data of the region of interest as initial spectrum data according to a predetermined principle.
The detection device comprises a laser source, a plurality of optical fiber probes, optical fibers, a focusing lens, a high-pass filter and a spectrometer; the laser source and the plurality of optical fiber probes are arranged in the horizontal direction at intervals to form a row and are located above a sample to be detected, the vertical distance between the laser source and each optical fiber probe and the sample to be detected is the same, the distance between each optical fiber probe and the laser source is the offset distance of the optical fiber probe relative to the laser source, a plurality of Raman spectra at different offset distances relative to the laser source are collected through each optical fiber probe, and each optical fiber probe is connected to the spectrometer after sequentially passing through the focusing lens and the high-pass filter.
The beneficial technical effects of the invention are as follows:
the method extracts and selects observation data through information entropy extraction of Raman spectra at different offset distances, solves the problem of attribution of independent signal components obtained by analyzing and separating independent components by combining characteristic spectral peak clustering identification, finally determines the Raman signal of internal food to be detected, is less influenced by human factors, has good separation effect, can effectively solve the problem that the traditional method is difficult to separate overlapped signals and weak signals, and plays a certain role in promoting the identification and quantification of trace components of the packaged food.
Furthermore, aiming at the problem of abnormal contribution of signals separated by FastICA, the reconstruction effect of the spectrum is improved by a correction strategy of the spectrum baseline. The method can be adaptive to different packages, food materials and thicknesses, and can effectively separate internal food signals on the premise that laser can penetrate food packages, so that the method can be used as a pre-data processing means for food quality detection and can also assist in artificially observing ingredient identification of layered samples.
Drawings
Fig. 1 is a schematic flow chart of a packaged food signal separation method based on spatially shifted raman spectroscopy disclosed in the present application.
Fig. 2 is a schematic structural diagram of a detection apparatus for emitting laser and acquiring a raman spectrum of a sample to be detected in the present application.
Fig. 3 is a schematic diagram showing the comparison of the separation effect of four different samples to be tested by the method of the present application.
Detailed Description
The following further describes the embodiments of the present invention with reference to the drawings.
The application discloses a method for separating a packaged food signal based on a spatial shift raman spectrum, please refer to a flowchart shown in fig. 1, and the method comprises the following steps:
1. the laser source emits laser to irradiate the surface of a sample to be detected, Raman spectrums of the sample to be detected are obtained at a plurality of positions with different offset distances relative to the laser source to obtain initial spectrum data, and the sample to be detected comprises food 1 to be detected and food packages 2 outside the food 1.
This application passes through detection device emission laser and obtains the raman spectrum of the sample that awaits measuring, please refer to fig. 2, and detection device includes laser source 3, a plurality of fiber probe 4, focusing lens 5, high pass filter 6 and spectrum appearance 7. The laser source 3 and the plurality of optical fiber probes 4 are arranged in a row at intervals in the horizontal direction and are positioned above a sample to be detected, the surface of the sample to be detected is basically flat, and the vertical distances between the laser source 3 and each optical fiber probe 4 and the sample to be detected are the same. The distance between each fiber probe 4 and the laser source 3 is the offset distance of the fiber probe relative to the laser source 3, and as shown in fig. 2, the distances between each fiber probe 4 and the laser source 3 are D1 and D2 … … Dn, respectively. In practice, the distances between the laser source 3 and each two adjacent components of each fiber-optic probe 4 are equal to form a uniform arrangement, or may be unequal. Each optical fiber probe 4 is connected with the focusing lens 5 and the high-pass filter 6 through the optical fiber 8 in sequence and is finally connected to the spectrometer 7.
When the food packaging device works, the laser source 3 emits laser to irradiate the surface of a sample to be measured, and the laser can penetrate through the food packaging 2 to reach the food 1 to be measured in the food packaging device and does not damage the food packaging 2 and the food 1 to be measured. The raman spectrum is collected by each fiber probe 4, i.e. the raman spectrum at the offset distance of the fiber probe 4 with respect to the laser source 3. In practice, a line scan acquisition device or the like may be used to acquire raman spectra at different offset distances.
In order to improve the effectiveness of data, the method and the device perform independent repeated acquisition operation on a sample to be detected for a plurality of times under the same acquisition condition, when each independent repeated acquisition operation is performed, laser is emitted and Raman spectrums at different offset distances are acquired according to the process, the Raman spectrums of the sample to be detected acquired at the positions with different offset distances relative to a laser source are spliced to obtain a sample spectrum, the abscissa of the spliced sample spectrum represents the total offset distance, and the ordinate represents Raman signals of different wave bands. The method comprises the steps of removing abnormal values (including peak valley caused by cosmic noise) from sample spectra extracted by independent repeated acquisition operation, then averaging and smoothing the sample spectra, and intercepting data of an interested region as initial spectral data according to a preset principle, wherein the preset principle is determined according to sample volume, an effective data interval and equipment characteristics and aims to reduce data volume.
2. And intercepting the Raman spectrum of a partial region range from the initial spectrum data as observation data based on the information entropy of the Raman spectrum at different offset distances. Specifically, the method comprises the following steps:
(1) and calculating the information entropy of the Raman spectrum at different offset distances.
By the formula
Calculating the Raman spectrum x at the offset distance i i Information entropy of (1), wherein
As a Raman spectrum x at an offset distance i i The rounding-down value of j band in (1), which is that the raman spectrum signal is a high-resolution approximately continuous signal, must be statistically calculated by dividing the signal into interval integer values through data quantization, and the rounding-down is to reduce the random noise of the raman spectrum. The parameter J belongs to [1, 2.,. J ]]Description of No repeating elements, indicating Raman spectra x i The light intensity values in all the bands of (1) are different,
indicating the probability that the band to which these elements correspond appears in all bands.
(2) And determining an offset distance i corresponding to the inflection point of the information entropy. As the offset distance increases, informationThe entropy value is gradually reduced, and the fluctuation is large, so the inflection point of the information entropy is determined by adopting a sliding variance method, and is expressed as follows:
wherein, H (x) i ) As a Raman spectrum x at an offset distance i i The entropy of the information of (a) is, 
Is the average of all information entropies, g is the size of the sliding window, and d represents the maximum offset distance relative to the laser source in the initial spectral data, such as Dn in fig. 2. Considering the fluctuation of the spatial resolution of the packaged food and the attenuation of the entropy, g is typically set to 100.
(3) And intercepting the Raman spectrum with the offset distance ranging from 0 to i from the initial spectrum data as observation data, thereby further screening the initial spectrum data and keeping the high-order statistical characteristics of the initial spectrum data.
3. And carrying out independent component analysis on the observation data, and separating to obtain a plurality of independent signal components.
The method utilizes a FastICA algorithm to carry out independent component analysis on observation data, and the process of utilizing the FastICA algorithm to carry out independent component analysis determines independent variables by using fixed point iteration to obtain the maximum non-Gaussian function value, and is expressed by constructing the following functions to make a matrix W converge to the maximum non-Gaussian property in the same direction in the iteration process:
the matrix W' is the value obtained by iterative update of the matrix W, X w For the matrix of observation data, g (x) logcosh (x) is a non-quadratic cumulative distribution function near the source signal.
And performing decorrelation and scaling through singular value decomposition to finally obtain a plurality of independent signal components. The number of singular vectors and corresponding singular values which is the same as the number of independent signal components to be separated are selected to reduce the number of parameters to be estimated.
4. And determining a typical spectrum peak corresponding to the food to be detected based on a spectrum peak identification technology for the initial spectrum data, matching the peak position of the typical spectrum peak with the peak position of the spectrum peak of each independent signal component, and determining the independent signal component corresponding to the food to be detected.
Specifically, the method comprises the following steps: and performing spectral peak identification on the initial spectral data by using a spectral peak identification technology, and normalizing the identified spectral peaks along with the attenuation curve of the increase of the offset distance relative to the laser source. And clustering the normalized attenuation curve, wherein the number of the clustered categories is equal to the number of the layers of the samples to be tested, a hierarchical clustering method can be adopted typically, and the number of the clustered categories is 2 if only one layer of food package 2 is arranged outside the food 1 to be tested conventionally.
And determining the category corresponding to the food to be detected according to the numerical characteristic of each category, wherein the internal food 1 to be detected has the spectral characteristic of slow attenuation, so that the attenuation values of the spectral peaks which belong to the same category and are formed after clustering are averaged, and the category with the largest average value of the corresponding attenuation values is taken as the category corresponding to the food to be detected. And matching the spectral peak belonging to the category as a typical spectral peak corresponding to the food to be detected with the peak position of the spectral peak of each independent signal component, and determining the independent signal component of which the category belongs to the food to be detected.
Furthermore, after the independent signal component of the food to be measured is obtained, the phenomena of negative contribution and baseline drift in the independent signal component are considered, so that the method is not directly used for quality nondestructive analysis. But the correction baseline of the independent signal component of the food to be detected is fitted by adopting self-adaptive iterative weighted punishment least square to the independent signal component of the food to be detected, then the correction baseline is subtracted from the independent signal component of the food to be detected, the Raman spectrum without the baseline of the food to be detected is obtained, and then the Raman spectrum is used for the quality nondestructive analysis of the food to be detected. Specifically, firstly, because the variance of random noise is small, a variance threshold is set by using the sliding variance of the window 5, and a fluorescence background and a Raman peak area containing random noise in an independent signal component are determined by considering the fluorescence background and the Raman peak area as noise signals when the variance threshold is smaller than the threshold; and then fitting the selected background baseline by using air partial least squares, wherein the parameters are 10-order polynomials, and the fitting of the corrected baseline is realized.
To illustrate the effectiveness of the methods of the present application, several example tests were performed as follows: the results of the simulated test on white sugar and a simulated packaged sugar composed of food-grade PE sheets outside the white sugar are shown in fig. 3(a), where the mixed spectrum is the raman spectrum of the simulated packaged sugar, the separated spectrum is the raman spectrum of the separated white sugar, the packaged PE is the raman spectrum of the separated PE sheets, and the reference spectrum is the raman spectrum of the white sugar without food packaging. Similarly, the results of the simulated test for white sugar in the package are shown in FIG. 3(b), the results of the simulated test for bagged rice are shown in FIG. 3(c), and the results of the simulated test for butter in the box are shown in FIG. 3 (d). The separation spectrum and the reference spectrum are compared in similarity through methods such as shape-based Spectral Angle Mapping (SAM), amplitude-based Jensen-Shannon divergence (JS) and linear trend-based Pearson Correlation Coefficient (PCC), the similarity between the extracted separation spectrum and the reference spectrum of the food to be detected without food packaging is high, particularly, in the test of 3(a), the similarity is highest and the signal separation effect is optimal due to the fact that the packaging material is pure and has standard thickness, and therefore the effectiveness of the method can be demonstrated, the separation effect of the Raman signal of the packaged food is good, and the separation spectrum can be used for quality nondestructive detection.
What has been described above is only a preferred embodiment of the present application, and the present invention is not limited to the above embodiment. It is to be understood that other modifications and variations directly derivable or suggested by those skilled in the art without departing from the spirit and concept of the present invention are to be considered as included within the scope of the present invention.
Claims (6)
1. A method for signal separation of a packaged food product based on spatially shifted raman spectroscopy, the method comprising:
emitting laser light through a laser source to irradiate the surface of a sample to be detected, and acquiring Raman spectrums of the sample to be detected at a plurality of positions with different offset distances relative to the laser source to obtain initial spectrum data, wherein the sample to be detected comprises food to be detected and food packages outside the food to be detected;
intercepting a Raman spectrum of a partial region range from the initial spectrum data as observation data based on information entropy of the Raman spectrum at different offset distances, wherein the observation data comprises: by the formula
Calculating Raman spectra x at different offset distances i i Information entropy of (1), wherein
As a Raman spectrum x at an offset distance i i The parameter J ∈ [1, 2., J, the rounded-down value of the J band in (J), n]Representing a Raman spectrum x i The light intensity values in all the bands of (1) are different, 
Representing the probability of the wave bands corresponding to the elements appearing in all the wave bands, and determining the offset distance i corresponding to the inflection point of the information entropy by adopting a sliding variance method according to the following formula:
independent component analysis is carried out on the observation data by utilizing a FastICA algorithm, and decorrelation and scaling are carried out on singular vectors and corresponding singular values which are the same as the number of independent signal components to be separated through singular value decomposition to obtain a plurality of independent signal components; wherein, H (x) i ) As a Raman spectrum x at an offset distance i i The entropy of the information of (a) is,
taking the average value of all information entropies, g is the size of a sliding window, and d represents the maximum offset distance relative to the laser source in the initial spectral data; intercepting a Raman spectrum with an offset distance in a range of 0 to i from the initial spectrum data as observation data;
and determining a typical spectrum peak corresponding to the food to be detected based on a spectrum peak identification technology for the initial spectrum data, matching the peak position of the typical spectrum peak with the peak position of the spectrum peak of each independent signal component, and determining the independent signal component corresponding to the food to be detected.
2. The method of claim 1, wherein said determining a typical spectral peak corresponding to a food product to be tested based on a spectral peak identification technique for said initial spectral data comprises:
Performing spectral peak identification on the initial spectral data by using a spectral peak identification technology, and normalizing an attenuation curve of an identified spectral peak along with the increase of an offset distance relative to the laser source;
clustering the normalized attenuation curve, wherein the number of the clustered categories is equal to the number of the layers of the samples to be detected;
and determining the category corresponding to the food to be detected according to the numerical characteristics of each category, and taking the spectral peak belonging to the category as a typical spectral peak corresponding to the food to be detected.
3. The method of claim 2, wherein determining the category corresponding to the food item to be tested based on the numerical characteristic of each category comprises:
and calculating the average value of the attenuation values of the spectral peaks which belong to the same category and are formed after clustering, and taking the category with the maximum average value of the corresponding attenuation values as the category corresponding to the food to be detected.
4. The method of claim 1, further comprising:
adopting self-adaptive iterative weighted punishment least square to fit the correction base line of the independent signal component of the food to be detected;
and subtracting the correction baseline from the independent signal component of the food to be detected to obtain the Raman spectrum without the baseline of the food to be detected.
5. The method of claim 1, wherein the obtaining of the raman spectra of the sample under test at a number of different offset distances relative to the laser source yields initial spectral data comprising:
performing independent repeated acquisition operation on the sample to be detected for a plurality of times under the same acquisition condition, and splicing the Raman spectra of the sample to be detected, which are acquired at positions with different offset distances relative to the laser source, to obtain a sample spectrum when each independent repeated acquisition operation is performed;
and eliminating abnormal values of the sample spectrum extracted by each independent repeated acquisition operation, then taking a mean value for smoothing, and intercepting data of an interested area as the initial spectrum data according to a preset principle.
6. The method of claim 1, wherein the Raman spectrum of the sample to be tested is obtained by emitting laser light by a detection device, the detection device comprises a laser source, a plurality of optical fiber probes, optical fibers, a focusing lens, a high-pass filter and a spectrometer, the laser source and the plurality of optical fiber probes are arranged at intervals in the horizontal direction to form a row and are positioned above the sample to be detected, the vertical distances between the laser source and each optical fiber probe and the sample to be measured are the same, the distance between each optical fiber probe and the laser source is the offset distance of the optical fiber probe relative to the laser source, and collecting a plurality of Raman spectra at different offset distances relative to the laser source through each optical fiber probe, wherein each optical fiber probe is connected to the spectrometer after passing through the focusing lens and the high-pass filter in sequence.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111293805.6A CN113984736B (en) | 2021-11-03 | 2021-11-03 | Method for separating signals of packaged food based on spatial shift Raman spectrum |
| PCT/CN2022/105463 WO2023077859A1 (en) | 2021-11-03 | 2022-07-13 | Packaged-food signal separation method based on spatially offset raman spectroscopy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111293805.6A CN113984736B (en) | 2021-11-03 | 2021-11-03 | Method for separating signals of packaged food based on spatial shift Raman spectrum |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113984736A CN113984736A (en) | 2022-01-28 |
| CN113984736B true CN113984736B (en) | 2022-07-29 |
Family
ID=79746117
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111293805.6A Active CN113984736B (en) | 2021-11-03 | 2021-11-03 | Method for separating signals of packaged food based on spatial shift Raman spectrum |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN113984736B (en) |
| WO (1) | WO2023077859A1 (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113984736B (en) * | 2021-11-03 | 2022-07-29 | 江南大学 | Method for separating signals of packaged food based on spatial shift Raman spectrum |
| CN117007577B (en) * | 2023-10-07 | 2023-12-08 | 生态环境部华南环境科学研究所(生态环境部生态环境应急研究所) | Intelligent detection system for pollutant toxicity |
| CN117434048A (en) * | 2023-11-14 | 2024-01-23 | 江南大学 | Monte Carlo-based nondestructive detection method for realizing Raman light source conversion of chilled fresh food |
| CN118010649A (en) * | 2024-04-09 | 2024-05-10 | 西安大业食品有限公司 | Pollution detection method for food |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108169201A (en) * | 2016-12-08 | 2018-06-15 | 同方威视技术股份有限公司 | For deducting the Raman spectra detection process of packaging interference |
| CN108827934A (en) * | 2018-05-21 | 2018-11-16 | 江南大学 | A kind of band packaged food quality damage-free detection method based on blind source separating Raman scattering image |
| CN110243806A (en) * | 2019-07-30 | 2019-09-17 | 江南大学 | Component of mixture recognition methods under Raman spectrum based on similarity |
| CN110580492A (en) * | 2019-04-30 | 2019-12-17 | 上海铁大电信科技股份有限公司 | Track circuit fault precursor discovery method based on small fluctuation detection |
| CN112001256A (en) * | 2020-07-24 | 2020-11-27 | 东风汽车集团有限公司 | Method and system for removing power frequency interference of mixed signal |
| CN112014376A (en) * | 2020-09-09 | 2020-12-01 | 江南大学 | Shrimp freshness detection method based on spatial migration Raman spectrum |
| WO2021119946A1 (en) * | 2019-12-16 | 2021-06-24 | Mekitec Oy | Food inspection solution |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101559513B (en) * | 2009-05-21 | 2011-11-09 | 山东大学 | Welding track detection and control method of container corrugated plate based on laser ranging |
| CN104749155B (en) * | 2013-12-27 | 2018-01-16 | 同方威视技术股份有限公司 | For detecting the Raman spectra detection process of the sample contained in body |
| CN105300954B (en) * | 2015-09-02 | 2018-04-13 | 中南大学 | A kind of Raman Characterization method of the heterogeneous froth bed mineral of antimony ore flotation |
| CN105334204B (en) * | 2015-10-16 | 2018-03-02 | 东北大学 | One kind is based on Fourier transform-Raman spectroscopy analysis method |
| CN106153658A (en) * | 2016-09-21 | 2016-11-23 | 中国科学院合肥物质科学研究院 | Multielement feature spectral peak recognition methods in a kind of energy-dispersive X-ray fluorescence (EDXRF) spectrum |
| CN108844939B (en) * | 2018-03-14 | 2021-02-12 | 西安电子科技大学 | Raman spectrum detection baseline correction method based on asymmetric weighted least square |
| CN109765212B (en) * | 2019-03-11 | 2021-06-08 | 广西科技大学 | Method for eliminating asynchronous fading fluorescence in Raman spectrum |
| CN112834481B (en) * | 2020-12-31 | 2023-09-05 | 宁波海关技术中心 | Raman spectrum enhancement measurement system and measurement method |
| CN113109318B (en) * | 2021-03-26 | 2022-05-10 | 中国科学院西安光学精密机械研究所 | Raman spectrum quantitative analysis method and system based on spectral peak height direct extraction |
| CN113984736B (en) * | 2021-11-03 | 2022-07-29 | 江南大学 | Method for separating signals of packaged food based on spatial shift Raman spectrum |
-
2021
- 2021-11-03 CN CN202111293805.6A patent/CN113984736B/en active Active
-
2022
- 2022-07-13 WO PCT/CN2022/105463 patent/WO2023077859A1/en unknown
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108169201A (en) * | 2016-12-08 | 2018-06-15 | 同方威视技术股份有限公司 | For deducting the Raman spectra detection process of packaging interference |
| CN108827934A (en) * | 2018-05-21 | 2018-11-16 | 江南大学 | A kind of band packaged food quality damage-free detection method based on blind source separating Raman scattering image |
| CN110580492A (en) * | 2019-04-30 | 2019-12-17 | 上海铁大电信科技股份有限公司 | Track circuit fault precursor discovery method based on small fluctuation detection |
| CN110243806A (en) * | 2019-07-30 | 2019-09-17 | 江南大学 | Component of mixture recognition methods under Raman spectrum based on similarity |
| WO2021119946A1 (en) * | 2019-12-16 | 2021-06-24 | Mekitec Oy | Food inspection solution |
| CN112001256A (en) * | 2020-07-24 | 2020-11-27 | 东风汽车集团有限公司 | Method and system for removing power frequency interference of mixed signal |
| CN112014376A (en) * | 2020-09-09 | 2020-12-01 | 江南大学 | Shrimp freshness detection method based on spatial migration Raman spectrum |
Non-Patent Citations (5)
| Title |
|---|
| 《Blind source separation of coexisting background in Raman spectra》;Ju Yao 等;《Spectrochimica Acta Part A: Molecular and BiomolecularSpectroscopy》;20200501;全文 * |
| 《Nondestructive freshness evaluation of intact prawns (Fenneropenaeus chinensis) using line-scan spatially offset Raman spectroscopy》;Zhenfang Liu 等;《Food Control》;20210304;全文 * |
| 《基于地面光谱水稻重金属胁迫监测光谱特征尺度识别》;黄芝 等;《中国农业科技导报》;20201231;第22卷(第12期);全文 * |
| 《基于拉曼光谱的混合物组分识别方法》;刘财政 等;《激光与光电子学进展》;20190430;第56卷(第8期);全文 * |
| 《基于滑动统计特征的信号非平稳度评价和比较》;何浩祥 等;《振动、测试与诊断》;20180831;第38卷(第4期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2023077859A1 (en) | 2023-05-11 |
| CN113984736A (en) | 2022-01-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113984736B (en) | Method for separating signals of packaged food based on spatial shift Raman spectrum | |
| Abdullah et al. | The applications of computer vision system and tomographic radar imaging for assessing physical properties of food | |
| CN110261405B (en) | Insulator pollution component identification method based on microscopic hyperspectral technology | |
| CN104568778B (en) | A kind of textile component discrimination method based on high light spectrum image-forming | |
| Pan et al. | Eggshell crack detection based on computer vision and acoustic response by means of back-propagation artificial neural network | |
| Nicolaï et al. | Nondestructive evaluation: Detection of external and internal attributes frequently associated with quality and damage | |
| KR20180011762A (en) | Particle analysis method | |
| US20230016600A1 (en) | Method for Identifying Chemical and Structural Variations Through Terahertz Time-Domain Spectroscopy | |
| Dutta et al. | Image processing based classification of grapes after pesticide exposure | |
| CN1831516A (en) | Method for nondistructive discriminating variety and true and false of cigarette using visible light and near-infrared spectrum technology | |
| Tan et al. | The feasibility of early detection and grading of apple bruises using hyperspectral imaging | |
| CN101750024A (en) | Near infrared spectrum detection method for insulation coating layer thickness of silicon steel | |
| CN114026408A (en) | Method for identifying microorganisms | |
| CN104568824B (en) | Shrimps grade of freshness detection method and device based on Vis/NIR | |
| CN110632021A (en) | Spectrum detection method and system based on portable near-infrared spectrometer | |
| CN112098340A (en) | Turquoise identification method based on hyperspectral imaging technology and assembly line process | |
| CN111521624A (en) | Method and device for X-ray inspection of products, in particular food products | |
| Guo et al. | Detection of foreign materials on surface of ginned cotton by hyper-spectral imaging | |
| CN107300562A (en) | A kind of X-ray lossless detection method of measuring relay finished product contact spacing | |
| Liu et al. | Digital image analysis method for rapid measurement of rice degree of milling | |
| Nguyen et al. | A method for non-destructive determination of cocoa bean fermentation levels based on terahertz hyperspectral imaging | |
| CN113030007A (en) | Method for rapidly testing quality stability of tobacco essence based on similarity learning algorithm | |
| KR101683404B1 (en) | Development of detection method for virus-infected (cucumber green mottle mosaic virus) watermelon seed using near-infrared reflectance spectrum and detection apparatus | |
| Shafri et al. | Hyperspectral signal analysis for detecting disease infection in oil palms | |
| CN113092402B (en) | Non-contact substance terahertz characteristic spectrum detection and identification system and method |
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 |