CN114136895A - Soy sauce component detection method and device - Google Patents
Soy sauce component detection method and device Download PDFInfo
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- CN114136895A CN114136895A CN202111375435.0A CN202111375435A CN114136895A CN 114136895 A CN114136895 A CN 114136895A CN 202111375435 A CN202111375435 A CN 202111375435A CN 114136895 A CN114136895 A CN 114136895A
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- 235000013555 soy sauce Nutrition 0.000 title claims abstract description 96
- 238000001514 detection method Methods 0.000 title claims abstract description 23
- 230000003595 spectral effect Effects 0.000 claims abstract description 67
- 238000000034 method Methods 0.000 claims abstract description 18
- 230000001502 supplementing effect Effects 0.000 claims abstract description 10
- 230000000149 penetrating effect Effects 0.000 claims abstract description 5
- 229910052757 nitrogen Inorganic materials 0.000 claims description 17
- 239000013589 supplement Substances 0.000 claims description 7
- 239000011324 bead Substances 0.000 claims description 3
- 239000011049 pearl Substances 0.000 claims description 3
- 239000000470 constituent Substances 0.000 claims 1
- 238000001228 spectrum Methods 0.000 abstract description 31
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 10
- 238000010586 diagram Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 239000007788 liquid Substances 0.000 description 7
- 239000004615 ingredient Substances 0.000 description 6
- 239000002253 acid Substances 0.000 description 5
- 150000001413 amino acids Chemical class 0.000 description 5
- 150000003839 salts Chemical class 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 2
- 230000000739 chaotic effect Effects 0.000 description 2
- JVTAAEKCZFNVCJ-UHFFFAOYSA-N lactic acid Chemical compound CC(O)C(O)=O JVTAAEKCZFNVCJ-UHFFFAOYSA-N 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 229910000530 Gallium indium arsenide Inorganic materials 0.000 description 1
- KXNLCSXBJCPWGL-UHFFFAOYSA-N [Ga].[As].[In] Chemical compound [Ga].[As].[In] KXNLCSXBJCPWGL-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 235000014655 lactic acid Nutrition 0.000 description 1
- 239000004310 lactic acid Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000005416 organic matter Substances 0.000 description 1
- 239000002210 silicon-based material Substances 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
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- 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
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention belongs to the technical field of soy sauce component detection, and particularly relates to a soy sauce component detection method and a soy sauce component detection device, wherein the method comprises the following steps: collecting a soy sauce sample; supplementing light to the soy sauce sample by using light with different wavelengths; collecting the intensities of light with different wavelengths after penetrating through the soy sauce sample to obtain spectral data; and acquiring the components of the soy sauce sample according to the spectral data. The technical scheme who provides of this application utilizes the relation between the spectrum of different wave bands to obtain the relation between the spectrum and the composition of different wave bands, has improved the accuracy and the reliability that soy sauce composition detected.
Description
Technical Field
The invention belongs to the technical field of soy sauce component detection, and particularly relates to a soy sauce component detection method and device.
Background
After the spectrum irradiates the liquid such as soy sauce and the like, due to the organic matter components in the liquid, the spectrum can absorb different wave bands to a certain extent, and finally the spectrum passing through the liquid is analyzed to measure and calculate the indexes of the components in the liquid, such as amino acid nitrogen, salt, total acid and OD.
In the related art, the spectral values are directly treated as one-dimensional mathematical vectors, and then a simple fully-connected neural network (MLP) is used to perform a regression problem. The method is equivalent to directly combining all band information by linear equations and then using a nonlinear function to make the whole network possibly learn a nonlinear relation.
However, the whole model is too simple, and only the spectral values of all different bands are simply subjected to linear combination and then a nonlinear function, which is equivalent to direct and complete combination, and certain relevance possibly exists between different bands neglected. Secondly, the learning capability of the model is limited, and further correlation between each numerical value of the spectrum cannot be learned, so that the influence of the spectrum data on the liquid content index can not be found.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for detecting soy sauce components, so as to solve the problems that the learning capability of the model is limited, further correlation between each value of the spectrum cannot be learned, and the influence of the spectrum data on the liquid content index cannot be found in the prior art.
According to a first aspect of embodiments of the present application, there is provided a soy sauce ingredient detection method, the method including:
collecting a soy sauce sample;
supplementing light to the soy sauce sample by using light with different wavelengths;
collecting the intensities of light with different wavelengths after penetrating through the soy sauce sample to obtain spectral data;
and acquiring the components of the soy sauce sample according to the spectral data.
Furthermore, the light with different wavelengths is emitted by the plurality of OLED lamp beads.
Further, the intensity of the light of different wavelengths after passing through the soy sauce sample is collected with a receiver.
Further, the acquiring the components of the soy sauce sample according to the spectrum data comprises:
copying the spectral data to obtain N groups of spectral data;
disordering the sequence of each group of spectral data by using a Permution Language Model in an XL-Net Model to obtain N-dimensional spectral data after disordering the sequence;
inputting the N x N dimensional spectral data into a preset ResNet18 model to obtain the components of the soy sauce sample;
and the dimensionality of each group of the spectral data is N, and N is a positive integer.
Further, the method further comprises:
and establishing the preset ResNet18 model.
Further, the establishing of the preset ResNet18 model includes:
and training by taking historical N-N dimensional spectral data as an input layer training sample of a ResNet18 model and historical soy sauce sample components as an output layer training sample of a ResNet18 model to obtain the preset ResNet18 model.
According to a second aspect of embodiments of the present application, there is provided a soy sauce ingredient detection device, the device including:
the first collection module is used for collecting a soy sauce sample;
the light supplementing module is used for emitting light with different wavelengths to supplement light for the soy sauce sample;
the second acquisition module is used for acquiring the intensities of the light with different wavelengths after passing through the soy sauce sample to obtain spectral data;
and the component analysis module is used for acquiring components of the soy sauce sample according to the spectral data.
In some embodiments, the component analysis module may, but is not limited to, implement its function using a single chip microcomputer.
Further, the light supplement module includes: a plurality of OLED lamp pearls.
Further, the second acquisition module adopts a receiver.
Further, the component analysis module is specifically configured to:
copying the spectral data to obtain N groups of spectral data;
disordering the sequence of each group of spectral data by using a Permution Language Model in an XL-Net Model to obtain N-dimensional spectral data after disordering the sequence;
inputting the N x N dimensional spectral data into a preset ResNet18 model to obtain the components of the soy sauce sample;
and the dimensionality of each group of the spectral data is N, and N is a positive integer.
Further, the apparatus further comprises:
and the establishing module is used for establishing the preset ResNet18 model.
Further, the establishing module is specifically configured to:
and training by taking historical N-N dimensional spectral data as an input layer training sample of a ResNet18 model and historical soy sauce sample components as an output layer training sample of a ResNet18 model to obtain the preset ResNet18 model.
By adopting the technical scheme, the invention can achieve the following beneficial effects: by collecting soy sauce samples, light with different wavelengths is used for supplementing light to the soy sauce samples, the intensities of the light with different wavelengths penetrating through the soy sauce samples are collected to obtain spectrum data, components of the soy sauce samples are obtained according to the spectrum data, the relations between the spectrums with different wave bands and the relations between the components can be obtained by using the relations between the spectrums with different wave bands, and the accuracy and the reliability of soy sauce component detection are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart illustrating a soy sauce ingredient detection method according to an exemplary embodiment.
Fig. 2 is a schematic structural view illustrating a soy sauce ingredient detecting apparatus according to an exemplary embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
Fig. 1 is a flow chart illustrating a soy sauce ingredient detection method according to an exemplary embodiment, which may be, but is not limited to, being used in a terminal, as shown in fig. 1, including the steps of:
step 101: collecting a soy sauce sample;
step 102: supplementing light to the soy sauce sample by using light with different wavelengths;
step 103: collecting the intensities of light with different wavelengths after penetrating through the soy sauce sample to obtain spectral data;
step 104: and acquiring the components of the soy sauce sample according to the spectral data.
The wavelength of light is not limited in the embodiments of the present invention, and can be set by a person skilled in the art according to experimental data and the like. For example, light of 22 different wavelengths is used to fill a soy sauce sample.
According to the soy sauce component detection method provided by the embodiment of the invention, the soy sauce sample is collected, the light with different wavelengths is used for supplementing light to the soy sauce sample, the intensity of the light with different wavelengths after passing through the soy sauce sample is collected to obtain the spectrum data, the components of the soy sauce sample are obtained according to the spectrum data, the relationship between the spectra with different wave bands and the components can be obtained by using the relationship between the spectra with different wave bands, and the accuracy and the reliability of soy sauce component detection are improved.
Specifically, optionally, light with different wavelengths is emitted by the plurality of OLED lamp beads.
Further optionally, the intensity of the light of different wavelengths after passing through the soy sauce sample is collected with a receiver.
It should be noted that the manner of "collecting the intensities of the light with different wavelengths after passing through the soy sauce sample by using the receiver" referred to in the embodiments of the present invention is well known to those skilled in the art, and therefore, the specific implementation manner thereof will not be described too much. In some alternative embodiments, the receiver may be, but is not limited to, comprised of a photodiode, a silicon material, an indium gallium arsenide material, and the like.
Further optionally, step 104 obtains the ingredients of the soy sauce sample from the spectral data, including:
step 1041: copying the spectral data to obtain N groups of spectral data;
it should be noted that, when the spectral data is copied, the number of copies is equal to the dimensionality of the spectral data;
step 1042: disordering the sequence of each group of spectral data by using a Permution Language Model in an XL-Net Model to obtain N-dimensional spectral data after disordering the sequence;
step 1043: inputting the N x N dimensional spectral data into a preset ResNet18 model to obtain the components of the soy sauce sample;
and the dimensionality of each group of spectral data is N, and N is a positive integer.
It will be appreciated that the convolution modules in the ResNet18 model are well able to extract local features, and then stacked convolution modules can combine the local features and then learn macroscopic more abstract features.
In some embodiments, compositional indicators for a soy sauce sample may include, but are not limited to: amino acid nitrogen, salt, total acid and OD.
It will be appreciated that the one-dimensional to two-dimensional, chaotic order, becomes two-dimensional in order to better fit the convolution model used first, and the chaotic order is to better fit non-adjacent spectra together to learn relationships and features, since different positions of the spectra do not necessarily relate to the system features only to adjacent positions, so that all possible combined features can be learned as far as possible. The convolution stacking can combine the local features to learn the global features, and the original method only simply makes the linear combination of all the spectral values and adds a nonlinear function, so that the local features cannot be learned at all, the effect caused by the local features cannot be further mined, the global features of the surface are simply mined, and the information mining performance is insufficient.
It should be noted that, because the input of the ResNet18 is two-dimensional, a plurality of copies of one-dimensional spectrum data are made, each copy is a recorded disordered arrangement, and the purpose is to enable interaction among all bands as much as possible when convolution is performed in the ResNet18 model, then to progressively progress layer by layer (because of a deep convolutional network), and to further aggregate small-area information step by step to extract macroscopic features, and finally to further learn and mine the mutual relationship of each band spectrum and the combined features thereof, so that the influence of the spectrum data on the liquid content index can be well found.
For example, assuming that a soy sauce sample is irradiated with 22 different wavelengths of light, the intensity of the light collected by the receiver after the light passes through the soy sauce, spectral data are obtained. A set of spectral data contains 22 data, assuming 22 data are 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, respectively. The sequence of each set of spectral data is disturbed by using a Permutation Language Model in an XL-Net Model to obtain new spectral data of 22 x 22, and the new spectral data is input into a preset ResNet18 Model, so that the content of salt, OD, total acid, amino acid nitrogen and the like is obtained.
Further optionally, the method further comprises:
step 100: and establishing a preset ResNet18 model.
Further optionally, step 100 establishes a preset ResNet18 model, including:
and training by taking historical N-N dimensional spectral data as an input layer training sample of a ResNet18 model and historical soy sauce sample components as an output layer training sample of a ResNet18 model to obtain a preset ResNet18 model.
It should be noted that ResNet18 is a relatively classical deep convolutional network, and above all, it can learn more abstract features deeply, especially in the image domain; secondly, it has utilized the residual error module, and the purpose is in order to be under the too deep condition of network, can be fine carry out the gradient transmission, avoid the gradient to disappear for the network study is more stable, and is more easy to converge. Therefore, the depth model ResNet18 can mine the more hierarchical relationships and features even further.
In order to further illustrate that the soy sauce component detection method provided by the embodiment of the invention is more reliable and accurate in soy sauce component detection, specific data are also provided, as shown in table 1:
TABLE 1 comparison of specific data for examples of the invention and the prior art
| Amino acid nitrogen | Total acid (in terms of lactic acid) | Salinity | OD value (650nm) | |
| Examples of the invention | 0.0671 | 0.0761 | 0.6184 | 0.1294 |
| Fully-connected neural network MLP | 0.070 | 0.113 | 0.827 | 0.257 |
According to the embodiments of the invention and the error values of the prior art method on four indexes of amino acid nitrogen, total acid, salt content and OD, it can be obviously seen that the soy sauce component detection method provided by the embodiments of the invention is more accurate.
According to the soy sauce component detection method provided by the embodiment of the invention, soy sauce samples are collected, light with different wavelengths is used for supplementing light to the soy sauce samples, the soy sauce samples supplemented with light are shot to obtain the target picture, the spectrum data in the target picture is extracted, the components of the soy sauce samples are obtained according to the spectrum data, the relationship between the spectrums in different wave bands and the relationship between the components can be obtained by using the relationship between the spectrums in different wave bands, and the accuracy and reliability of soy sauce component detection are improved.
In order to implement the above-described method for detecting soy sauce components, an embodiment of the present invention further provides a soy sauce component detection apparatus, which includes, with reference to fig. 2:
the first collection module is used for collecting a soy sauce sample;
the light supplementing module is used for emitting light with different wavelengths to supplement light for the soy sauce sample;
the second acquisition module is used for acquiring the intensities of the light with different wavelengths after passing through the soy sauce sample to obtain spectral data;
and the component analysis module is used for acquiring components of the soy sauce sample according to the spectral data.
Specifically, optionally, the light supplement module includes: a plurality of OLED lamp pearls.
Further optionally, the second acquisition module employs a receiver.
It will be appreciated that the intensity of the light of different wavelengths after passing through the soy sauce sample is collected by the receiver.
Further optionally, the component analysis module is specifically configured to:
copying the spectral data to obtain N groups of spectral data;
disordering the sequence of each group of spectral data by using a Permution Language Model in an XL-Net Model to obtain N-dimensional spectral data after disordering the sequence;
inputting the N x N dimensional spectral data into a preset ResNet18 model to obtain the components of the soy sauce sample;
and the dimensionality of each group of spectral data is N, and N is a positive integer.
Further optionally, the apparatus further comprises:
and the establishing module is used for establishing a preset ResNet18 model.
Further optionally, the establishing module is specifically configured to:
and training by taking historical N-N dimensional spectral data as an input layer training sample of a ResNet18 model and historical soy sauce sample components as an output layer training sample of a ResNet18 model to obtain a preset ResNet18 model.
According to the soy sauce component detection method provided by the embodiment of the invention, the soy sauce sample is collected through the first collection module, the light supplement module supplements light to the soy sauce sample by using light with different wavelengths, the second collection module collects the intensity of the light with different wavelengths after passing through the soy sauce sample to obtain spectrum data, the component analysis module obtains the components of the soy sauce sample according to the spectrum data, the relationship between the spectra of different wave bands and the relationship between the components can be obtained by using the relationship between the spectra of different wave bands, and the accuracy and the reliability of soy sauce component detection are improved.
It is to be understood that the apparatus embodiments provided above correspond to the method embodiments described above, and corresponding specific contents may be referred to each other, which are not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A soy sauce component detection method, comprising:
collecting a soy sauce sample;
supplementing light to the soy sauce sample by using light with different wavelengths;
collecting the intensities of light with different wavelengths after penetrating through the soy sauce sample to obtain spectral data;
and acquiring the components of the soy sauce sample according to the spectral data.
2. The method of claim 1, wherein the different wavelengths of light are emitted by a plurality of OLED beads.
3. The method of claim 1,
the intensity of the light of different wavelengths after passing through the soy sauce sample is collected with a receiver.
4. The method of claim 1, wherein said obtaining soy sauce sample constituents from said spectral data comprises:
copying the spectral data to obtain N groups of spectral data;
disordering the sequence of each group of spectral data by using a Permution Language Model in an XL-Net Model to obtain N-dimensional spectral data after disordering the sequence;
inputting the N x N dimensional spectral data into a preset ResNet18 model to obtain the components of the soy sauce sample;
and the dimensionality of each group of the spectral data is N, and N is a positive integer.
5. The method of claim 4, further comprising:
and establishing the preset ResNet18 model.
6. The method of claim 5, wherein the establishing the preset ResNet18 model comprises:
and training by taking historical N-N dimensional spectral data as an input layer training sample of a ResNet18 model and historical soy sauce sample components as an output layer training sample of a ResNet18 model to obtain the preset ResNet18 model.
7. A soy sauce component detecting device, comprising:
the first collection module is used for collecting a soy sauce sample;
the light supplementing module is used for emitting light with different wavelengths to supplement light for the soy sauce sample;
the second acquisition module is used for acquiring the intensities of the light with different wavelengths after passing through the soy sauce sample to obtain spectral data;
and the component analysis module is used for acquiring components of the soy sauce sample according to the spectral data.
8. The apparatus of claim 7, wherein the fill light module comprises: a plurality of OLED lamp pearls.
9. The apparatus of claim 7, wherein the second acquisition module employs a receiver.
10. The apparatus of claim 7, wherein the composition analysis module is specifically configured to:
copying the spectral data to obtain N groups of spectral data;
disordering the sequence of each group of spectral data by using a Permution Language Model in an XL-Net Model to obtain N-dimensional spectral data after disordering the sequence;
inputting the N x N dimensional spectral data into a preset ResNet18 model to obtain the components of the soy sauce sample;
and the dimensionality of each group of the spectral data is N, and N is a positive integer.
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