CN112033915A - White spirit adulteration detection system based on portable spectrometer - Google Patents
White spirit adulteration detection system based on portable spectrometer Download PDFInfo
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- CN112033915A CN112033915A CN202010911799.5A CN202010911799A CN112033915A CN 112033915 A CN112033915 A CN 112033915A CN 202010911799 A CN202010911799 A CN 202010911799A CN 112033915 A CN112033915 A CN 112033915A
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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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
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- 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
- G06N3/084—Backpropagation, e.g. using gradient descent
Abstract
The invention discloses a white spirit adulteration detection system based on a portable spectrometer, which comprises a spectrum detection module, a client and a server, wherein the client is in interactive connection with the spectrum detection module; the server is provided with an analysis model, the analysis model analyzes the spectral data, and the server acquires an analysis result and transmits the analysis result back to the client. According to the invention, through the cooperation of the spectrum detection module and the client, a user can carry out measurement at any time and any place; through the cooperation of client and server, real-time analysis and feed back the result to the user, collect scanning detection, data analysis, result show in an organic whole, but convenient to carry and real-time detection have simplified the flow of white spirit quality testing.
Description
Technical Field
The invention relates to the field of food safety detection, in particular to a white spirit adulteration detection system based on a portable spectrometer.
Background
In recent years, food safety problems have been frequent, and wine adulteration has received more and more attention as a prominent problem in food safety. Chinese liquor has large consumption, and the liquor plays an important role in liquor markets at home and abroad. The main categories of liquor adulteration are two main categories, one is the blending of water into the original liquor, and the other is the blending of industrial alcohol into the original liquor. The industrial alcohol contains impurities such as methanol, aldehydes, organic acid and the like, wherein the toxicity of the methanol is fatal, the oral administration lethal dose is 1000mg/kg, and the blending of the industrial alcohol in the raw wine not only influences the quality of the white spirit, but also causes harm to drinkers. Therefore, it is very important to increase the detection strength of the white spirit so as to reduce the occurrence of the adulteration of the white spirit.
In the current stage, detection research on liquor adulteration mostly stays in the mode of using a large instrument to obtain accurate high-resolution spectral data, and although the accuracy of the obtained result is higher, the instrument is expensive, too large in size and weight, high in operation and maintenance cost, and strict in requirements on working conditions, and liquor quality detection cannot be carried out anytime and anywhere. In recent years, with the wide application of portable and handheld small spectrometers in various fields, research for applying the small spectrometers to liquor detection is available, but at present, quality detection based on the small spectrometers is often limited to the research of algorithms, a small internet of things system for user interaction is not formed, and users cannot conveniently and quickly detect liquor in real time at any time and any place.
Disclosure of Invention
The invention aims to provide a white spirit adulteration detection system based on a portable spectrometer, which is convenient to carry and can enable a user to conveniently and quickly detect the quality of white spirit in real time at any time and any place.
In order to solve the technical problems, the invention provides a white spirit adulteration detection method based on spectral analysis, the system comprises a spectral detection module, a client and a server, the client is interactively connected with the spectral detection module, the client is interactively connected with the server,
the client sends a detection instruction to the spectrum detection module, the spectrum detection module collects spectrum data of the liquor and transmits the spectrum data to the client, and the client transmits the spectrum data to the server;
the server is deployed with an analysis model, the server inputs the spectral data into the analysis model, the analysis model analyzes the spectral data, and the server acquires an analysis result and transmits the analysis result back to the client.
Further, the spectrum detection module comprises a portable spectrometer and is used for collecting spectrum data of the white spirit.
Further, the spectrum detection module also comprises an encapsulation shell, and the portable spectrometer is encapsulated in the encapsulation shell.
Further, the analysis model analyzes the spectral data, the content of analysis comprises identification of adulteration type and adulteration concentration of the white spirit, the adulteration type classifies the spectral data by adopting a classification algorithm, and the adulteration concentration is fitted to the spectral data by adopting a fitting algorithm.
Further, the classification algorithm is adopted to classify the spectral data, and the adopted classification algorithm is a support vector machine algorithm.
Further, in the fitting of the spectral data by using the fitting algorithm, the fitting algorithm used is a back propagation neural network algorithm.
Further, the analysis model preprocesses the spectral data before analyzing the spectral data, and the specific method is as follows: and processing the spectral data by using a Savitzky-Golay convolution smoothing algorithm, and then processing by using a first-order difference method.
Furthermore, the client is in interactive connection with the spectrum detection module through Bluetooth, and the client is in interactive connection with the server through a mobile network.
Further, the server is a cloud server.
Furthermore, an upper computer module is arranged on the client side and used for sending a detection instruction to the spectrum detection module and providing a visual operation interface for a user.
The invention has the beneficial effects that: according to the invention, through the cooperation of the spectrum detection module and the client, a user can measure at any time and any place, and the operation is convenient and fast; through the cooperation of client and server, can carry out real-time analysis to the spectral data who gathers to give the user with analysis result real-time feedback, collect sample scanning detection, data analysis, result and show in an organic whole, portable spectrum appearance conveniently carries and but real-time detection, has greatly simplified the flow that white spirit quality detected.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.
Drawings
Fig. 1 is a schematic structural diagram of a white spirit adulteration detection system based on a portable spectrometer.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
In the present invention, unless otherwise expressly stated or limited, the term "coupled" is to be construed broadly, e.g., as meaning either a fixed connection or a removable connection, or an integral part; the connection can be mechanical connection, electrical connection or communication; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations. Unless expressly stated or limited otherwise, the first feature "on" or "under" the second feature may be directly contacting the first and second features, or indirectly contacting the first and second features through intervening media. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements does not include a limitation to the listed steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this embodiment, in order to illustrate the process and effect of the present invention, a white spirit adulteration detection system based on a portable spectrometer is described with reference to fig. 1 and collected sample data. Wherein, fig. 1 is a schematic structural diagram of the present invention; the sample is 603 solutions obtained by respectively blending three white spirits of 56 degrees of cowshed mountains (NLS), 52 degrees of Xiaohutuxian (XHTX) and 40 degrees of Jiangbai (JXB) without adulteration, 0-50% water and 0-50% industrial alcohol in an indoor light environment, and 101 solutions obtained by blending 0-50% water in an NLS sample in a strong light environment; the detection of each solution in every bottle is repeated for three times, the average value of the spectrum data of the three times of repeated detection is used as sample data, and the error generated when the spectrometer collects the sample data can be reduced by the average value.
In this embodiment, the system includes a spectrum detection module, a client, and a server.
The spectrum detection module comprises a portable spectrometer, the spectrum detection module also comprises an encapsulation shell used for encapsulating the portable spectrometer, the portable spectrometer is encapsulated in the encapsulation shell, the portable spectrometer is a near-infrared spectrometer, and the encapsulation shell is precisely manufactured through a 3D printing technology. In the embodiment, the near-infrared spectrometer uses a DLP NIRscan Nano module of Ti company, the wavelength range is 900-1700nm, the resolution is 10nm, power is supplied by a USB (500mA, 5V), two scanning modes including USB + PC and Bluetooth communication are included, and spectrum data of different types of liquor samples can be collected; meanwhile, the near-infrared spectrometer is provided with the optical fiber, so that the flexibility of angles during collection can be improved, the near-infrared spectrometer is packaged to 73mm x 70mm x 41mm in size by using a 3D printing technology, and the near-infrared spectrometer is small and exquisite after being packaged and is convenient for a user to carry.
The client is provided with an upper computer module which is used for sending a detection instruction to the spectrum detection module and providing a visual operation interface for a user; and after the server transmits the analysis result back to the client, the upper computer module reads the analysis result and displays the result to the user. The client is a mobile phone App, and the upper computer module can be developed on the mobile phone App of an Android or IOS system. In this embodiment, the App runs on a Nubiamini mobile phone of a 6.0 android system, and includes functions of scan acquisition, analysis, and result display. By using the scanning acquisition function, a user can send an instruction to a near-infrared spectrometer in the spectrum detection module, the near-infrared spectrometer scans a sample and then transmits the acquired sample data to a client, and the client temporarily stores the sample data and displays the reflectivity, the absorptivity and the light intensity information of the spectrum to the user; by using the analysis function, the client uploads the sample data to the server through the network for analysis; by using the result display function, the client can display the analysis result returned by the server to the user on the mobile phone App.
The server is a cloud server, an analysis model is deployed on the server, and in the embodiment, the cloud server adopts an ECS instance server of the Aliyun company. Different sample data collected by different users are analyzed in the analysis model on the cloud server and simultaneously supplemented into the database on the cloud server in real time, so that data sharing is realized, a complete liquor adulteration sample database is conveniently constructed in the later period, and the expandability and the detection accuracy of the system are further improved.
The client is in interactive connection with the spectrum detection module, and the client is in interactive connection with the server; the client sends a detection instruction to the spectrum detection module, the spectrum detection module collects spectrum data of the liquor and transmits the spectrum data to the client, and the client transmits the spectrum data to the server; the server inputs the spectral data into an analysis model, the analysis model analyzes the spectral data through an algorithm, and the server acquires an analysis result and transmits the analysis result back to the client. In this embodiment, the client is interactively connected with the spectrum detection module through bluetooth, and the bluetooth is embedded in the spectrum detection module and can interact with an Android or IOS system. The client controls the spectrum detection module to sample the liquor sample through the Bluetooth, and sample spectrum data collected by the spectrum detection module is transmitted to the client through the Bluetooth and temporarily stored in the mobile phone. The client is interactively connected with the server through a mobile network, and the mobile network can be Wi-Fi or a cellular mobile communication network. The mobile network uses a TCP communication protocol, a TCP client program written in the smart phone is responsible for uploading the spectral data to the cloud server in a wireless transmission mode and receiving a result analyzed by the cloud server; and the TCP server-side program written in the cloud server is responsible for receiving the spectrum data from the client, transmitting the spectrum data into the analysis model for analysis after obtaining the effective spectrum data, and returning the analysis result to the client.
The analysis of the sample spectral data by the analysis model comprises data preprocessing, adulteration type identification and adulteration concentration identification. The adulteration type identification is used for classifying the spectral data by adopting a classification algorithm, and the adulteration concentration identification is used for fitting the spectral data by adopting a fitting algorithm. In this embodiment, a specific method for preprocessing the spectrum data is as follows: and processing the spectral data by using a Savitzky-Golay (SG) convolution smoothing algorithm, and then performing first-order difference method processing for denoising the spectral data. In this embodiment, the classification algorithm used for identifying the adulteration type is a Support Vector machine algorithm (SVM), which specifically includes: directly calling a python support vector machine library for classification processing, and setting a punishment relaxation variable in an SVM algorithm to be 6 after multiple times of debugging; the kernel function selects RBF Gaussian kernel, which is defined as:
wherein gamma is the only hyper-parameter of the Gaussian kernel function, and the size of gamma is set to 0.1 after debugging, so that the classification effect is the best; inputting the collected sample spectrum data into a support vector machine model with set parameters for classification, and performing classification with the existing white spirit in a database on a cloud serverAnd comparing the spectral characteristics of the adulteration type, and analyzing the adulteration type of the sample spectral data. In this embodiment, the fitting algorithm used for identifying the adulteration concentration is a Back Propagation (BP) neural network algorithm, which specifically includes: the number of nodes of the input layer is the number of wavelengths corresponding to sample data acquired by the spectrometer for one time, the number of the nodes is 234, the number of hidden layer layers is 1 after multiple debugging, the number of the nodes is 25, the number of the nodes of the output layer is 1, in order to enable the model to be converged more quickly, an Adam algorithm is selected for optimization, an error function is a root mean square error, and the training times are 200 times; and after the back propagation neural network algorithm is trained, inputting the collected sample spectral data into the trained back propagation neural network algorithm to obtain a fitting value of the adulteration concentration of the sample spectral data.
Sample data of NLS, XHTX and JXB solutions without adulteration, mixed with water and mixed with industrial alcohol in an indoor light environment are analyzed by an analysis model, after the adulteration type is identified, the sample data are classified into three types, after the adulteration type is compared with the spectral characteristics in a database on a cloud server, the sample data are classified as the sample data without adulteration, mixed with water and mixed with the industrial alcohol, and the classification accuracy is 89.22% compared with an actual value. Spectral data of NLS, XHTX and JXB samples identified as water doping are transmitted into a back propagation neural network algorithm which is trained to obtain fitting values of adulteration concentration when the water doping concentration is 0-50%, and correlation coefficients of the fitting values of the NLS, XHTX and JXB test sets and actual values of the liquor of corresponding types in the database measured in every bottle under the condition that the water doping concentration is 0-50% are 0.9696, 0.9795 and 0.9615 respectively; spectral data of NLS, XHTX and JXB samples identified as industrial alcohol doped samples are transmitted into a back propagation neural network algorithm which is trained to obtain fitting values of adulteration concentration when the industrial alcohol doped concentration is 0-50%, and correlation coefficients of the fitting values of the NLS, XHTX and JXB test sets and actual values of every bottle-separated measurement of white spirit of corresponding types in a database under the condition that the industrial alcohol doped concentration is 0-50% are 0.9683, 0.9792 and 0.8371 respectively. The higher the correlation coefficient is, the higher the accuracy of the fitting value obtained by detection is, so that the fitting result of the method is better, the adulteration concentration of the sample is obtained by calculating the fitting value, and the adulteration concentration can be effectively analyzed. The spectral data of the NLS water-blended sample in the strong light environment is transmitted into a back propagation neural network algorithm which is trained to obtain each fitting value of the water-blended concentration when the water-blended concentration is 0-50%, and the correlation coefficient between each fitting value of the test set and the actual value of the bottle separation measurement of the NLS in the database when the water-blended concentration is 0-50% is 0.9348.
The invention has the beneficial effects that: according to the invention, through the cooperation of the spectrum detection module and the client, a user can measure at any time and any place, and the operation is convenient and fast; through the cooperation of client and server, can carry out real-time analysis to the spectral data who gathers to give the user with analysis result real-time feedback, collect sample scanning and gather, data analysis, result and show in an organic whole, portable spectrum appearance conveniently carries and but real-time detection, has greatly simplified the flow that white spirit quality detected.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (10)
1. The utility model provides a white spirit adulterates detecting system based on portable spectrum appearance which characterized in that: the system comprises a spectrum detection module, a client and a server, wherein the client is in interactive connection with the spectrum detection module and the server,
the client sends a detection instruction to the spectrum detection module, the spectrum detection module collects spectrum data of the liquor and transmits the spectrum data to the client, and the client transmits the spectrum data to the server;
the server is provided with an analysis model, the analysis model analyzes the spectral data, and the server acquires an analysis result and transmits the analysis result back to the client.
2. The white spirit adulteration detection system based on the portable spectrometer as recited in claim 1, wherein: the spectrum detection module comprises a portable spectrometer and is used for collecting spectrum data of the white spirit.
3. The white spirit adulteration detection system based on the portable spectrometer as recited in claim 2, characterized in that: the spectrum detection module further comprises an encapsulation shell, and the portable spectrometer is encapsulated in the encapsulation shell.
4. The white spirit adulteration detection system based on the portable spectrometer as recited in claim 1, wherein: the analysis model analyzes the spectral data, the content of analysis comprises identification of adulteration types and adulteration concentrations of the white spirit, the adulteration types are classified into the spectral data by adopting a classification algorithm, and the adulteration concentrations are fitted into the spectral data by adopting a fitting algorithm.
5. The white spirit adulteration detection system based on the portable spectrometer as recited in claim 4, wherein: and classifying the spectral data by adopting a classification algorithm, wherein the classification algorithm is a support vector machine algorithm.
6. The white spirit adulteration detection system based on the portable spectrometer as recited in claim 4, wherein: in the process of fitting the spectral data by adopting the fitting algorithm, the fitting algorithm is a back propagation neural network algorithm.
7. The white spirit adulteration detection system based on the portable spectrometer as recited in claim 1, wherein: the analysis model is used for preprocessing the spectral data before analyzing the spectral data, and the specific method comprises the following steps: and processing the spectral data by using a Savitzky-Golay convolution smoothing algorithm, and then processing by using a first-order difference method.
8. The white spirit adulteration detection system based on the portable spectrometer as recited in claim 1, wherein: the client is in interactive connection with the spectrum detection module through Bluetooth, and the client is in interactive connection with the server through a mobile network.
9. The white spirit adulteration detection system based on the portable spectrometer as recited in claim 1, wherein: the server is a cloud server.
10. The white spirit adulteration detection system based on the portable spectrometer as recited in claim 1, wherein: the client is provided with an upper computer module which is used for sending a detection instruction to the spectrum detection module and providing a visual operation interface for a user.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114018899A (en) * | 2021-11-08 | 2022-02-08 | 哈尔滨工业大学 | Method for identifying wine commodity from outside of transparent packaging bottle |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106679806A (en) * | 2017-01-19 | 2017-05-17 | 深圳大学 | Spectrum detection system and method based on micro spectrometer |
| CN107589092A (en) * | 2017-08-18 | 2018-01-16 | 北京工商大学 | The fast check system of bluetooth portable near infrared spectrum based on mobile network's terminal |
| CN210571027U (en) * | 2019-11-22 | 2020-05-19 | 四川长虹电器股份有限公司 | Spectrum appearance system |
-
2020
- 2020-09-02 CN CN202010911799.5A patent/CN112033915A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106679806A (en) * | 2017-01-19 | 2017-05-17 | 深圳大学 | Spectrum detection system and method based on micro spectrometer |
| CN107589092A (en) * | 2017-08-18 | 2018-01-16 | 北京工商大学 | The fast check system of bluetooth portable near infrared spectrum based on mobile network's terminal |
| CN210571027U (en) * | 2019-11-22 | 2020-05-19 | 四川长虹电器股份有限公司 | Spectrum appearance system |
Non-Patent Citations (1)
| Title |
|---|
| 杨国强 等: "基于近红外透射光谱的汾阳王酒快速鉴别", 《农业机械学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114018899A (en) * | 2021-11-08 | 2022-02-08 | 哈尔滨工业大学 | Method for identifying wine commodity from outside of transparent packaging bottle |
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Application publication date: 20201204 |