Food component detection method, device and system and storage medium
By using nanosilver-silica nuclear pore structure as the SERS substrate, the problem of poor stability of SERS substrate in the prior art is solved, the accuracy and speed of food ingredient detection are improved, and the accuracy of detection is improved through quantitative correction models. .
Patent Information
- Application Number
- CN202310684576.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-06-09
- Publication Date
- 2024-12-10
AI Technical Summary
In the existing surface-enhanced Raman spectroscopy technology, the stability and reproducibility of the SERS substrate are poor, resulting in the accuracy of food ingredient detection being affected by experimental conditions, reducing the accuracy of the detection.
Using nano-silver-silica nuclear pore structure as the SERS substrate, the detection Raman spectrum of the food to be tested after contacting it is determined to determine the matching target preset Raman spectrum, thereby determining the ingredients of the food to be tested and based on linearity. The partial least squares regression algorithm establishes a quantitative correction model to determine the correspondence between the peak intensity of the Raman spectrum and the concentration.
On the basis of ensuring the stability of nanosilver, the Raman signal is enhanced, the accuracy and analysis speed of food ingredient detection are improved, and the food ingredient detection can be quickly qualitatively and quantitatively detected.
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Figure CN119104534A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of food component detection, and in particular, to a food component detection method, device, system and storage medium. Background Art
[0002] Surface-enhanced Raman Spectroscopy (SERS) refers to the spectral phenomenon in which the molecules to be tested are adsorbed on the rough surface of nanometal materials, thereby enhancing the Raman signal of the substance to be tested. This technology has higher sensitivity than ordinary Raman spectroscopy technology and can achieve ultra-sensitive detection of trace substances. This technology is not affected by the morphology of the sample to be tested. It is simple to operate, fast, non-destructive and easy to carry. It is considered to be the most promising food safety analysis technology.
[0003] Raman spectroscopy plays an active role in food ingredient detection. It is mainly used for the content and structure analysis of the main ingredients of food. It can not only qualitatively analyze the changes in the chemical structure and chemical bonds of the ingredients contained in the tested substance, but also quantitatively detect the content of certain ingredients in food. At present, there are relatively mature technologies that can detect the content of carbohydrates, proteins, lipids and other ingredients in food. In the field of food safety, the amount of food additives and the presence of harmful ingredients have always been the focus of food safety testing. The content in food can usually be measured using portable surface-enhanced Raman spectrometers in related technologies.
[0004] The surface enhanced Raman spectroscopy equipment of the related art usually involves a SERS substrate, and there are many methods and materials for preparing the SERS substrate. The SERS substrate prepared according to the method of the related art often has problems of poor stability and reproducibility. The SERS signal obtained by detecting using such a SERS substrate is easily affected by the experimental test conditions, thereby reducing the accuracy of food ingredient detection. Summary of the invention
[0005] The purpose of the present disclosure is to provide a food ingredient detection method, device, system and storage medium, so as to achieve Raman signal enhancement on the basis of ensuring the stability of nanosilver, thereby improving the accuracy of food ingredient detection.
[0006] In order to achieve the above-mentioned object, a first aspect of an embodiment of the present disclosure provides a method for detecting food ingredients, the method comprising:
[0007] Determining the detection Raman spectrum of the food to be tested after contacting with the SERS substrate, wherein the SERS substrate is a nanosilver-silicon dioxide core pore structure;
[0008] Determining a target preset Raman spectrum matching the detected Raman spectrum from the preset Raman spectra;
[0009] The components of the food to be tested are determined according to the target preset Raman spectrum.
[0010] Optionally, after determining the ingredients included in the food to be tested, the method further comprises:
[0011] Determining the peak intensity of the detected Raman spectrum of the component in the detected Raman spectrum;
[0012] Based on the pre-established correspondence between the peak intensity of the Raman spectrum of the component and the concentration, the detection concentration corresponding to the detected Raman spectrum peak intensity is determined.
[0013] Optionally, the corresponding relationship between the Raman spectrum peak intensity and the concentration of the component is established in the following manner:
[0014] Based on pre-prepared reference solutions of the components at different concentrations, measuring the Raman spectrum of the reference solutions after contacting the SERS substrate;
[0015] Determine the Raman spectrum peak intensity in the Raman spectrum corresponding to the reference solution of different concentrations;
[0016] According to the peak intensity of the Raman spectrum and the corresponding concentration, a quantitative correction model is established based on a linear partial least squares regression algorithm, and the quantitative correction model is used to determine the corresponding relationship.
[0017] Optionally, the establishing of a quantitative correction model based on a linear partial least squares regression algorithm according to the peak intensity of the Raman spectrum and the corresponding concentration includes:
[0018] Determine the linear relationship regression equation between the Raman spectrum peak intensity and the concentration based on a linear partial least squares regression algorithm;
[0019] Based on the linear relationship regression equation, the quantitative correction model is established.
[0020] Optionally, the preset Raman spectrum is determined by:
[0021] Based on the pre-prepared pure solution of the component, a standard Raman spectrum of the pure solution after contacting the SERS substrate is measured, and the standard Raman spectrum is used as the preset Raman spectrum.
[0022] Optionally, the step of determining the Raman spectrum of the food to be tested after contacting the SERS substrate comprises:
[0023] The food to be tested that has been in contact with the SERS substrate is irradiated with laser light to collect the generated scattered light, which is used to generate the detection Raman spectrum.
[0024] Optionally, the preset Raman spectrum includes at least standard Raman spectra of sucrose, protein and rhodamine B.
[0025] A second aspect of the embodiments of the present disclosure provides a food component detection device, the food component detection device comprising:
[0026] A Raman spectrum collection module, wherein the Raman spectrum collection module is used to measure the detection Raman spectrum of the food to be tested after contacting with the SERS substrate, wherein the SERS substrate is a nanosilver-silicon dioxide core pore structure;
[0027] A data analysis module is used to determine a target preset Raman spectrum matching the detection Raman spectrum from preset Raman spectra, and to determine the ingredients included in the food to be tested according to the target preset Raman spectrum.
[0028] A third aspect of the embodiments of the present disclosure provides a food component detection system, the food component detection system comprising: a SERS substrate, a Raman optical fiber probe, a CCD detector, a laser transmitter, and a controller;
[0029] Wherein, the SERS substrate is a nanosilver-silicon dioxide core-pore structure, and the controller is used to control the Raman fiber probe, the CCD detector, and the laser emitter to implement the steps of any one of the food ingredient detection methods in the first aspect.
[0030] A fourth aspect of an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the food ingredient detection method described in any one of the first aspects.
[0031] Through the above technical solution, the SERS substrate with nanosilver-silicon dioxide core pore structure is used to detect food ingredients. After the detection Raman spectrum of the food to be tested after contacting the SERS substrate is obtained, the target preset Raman spectrum matching the detection Raman spectrum is determined from the preset Raman spectrum, thereby determining the ingredients included in the food to be tested according to the target preset Raman spectrum. In this way, on the basis of ensuring the stability of nanosilver, Raman signal enhancement is achieved, thereby improving the accuracy of food ingredient detection.
[0032] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:
[0034] Figure 1The figure is a flow chart of a method for detecting food ingredients according to an exemplary embodiment of the present disclosure.
[0035] Figure 2 It is a block diagram of a food ingredient detection device according to an exemplary embodiment of the present disclosure.
[0036] Figure 3 It is a block diagram of a food ingredient detection system according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION
[0037] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.
[0038] The embodiments of the present disclosure provide a method, device, system and storage medium for detecting food ingredients. The method for detecting food ingredients can be used in systems related to Raman spectroscopy for detecting material ingredients, and can be used for quantitative detection of food ingredients. For example, the food ingredient detection system provided in the embodiments of the present disclosure can be applied to products such as food safety detectors or portable Raman spectrometers in the field of food safety detection to perform food adulteration detection and food additive content detection, etc.
[0039] Figure 1 1 is a flow chart of a method for detecting food ingredients according to an exemplary embodiment of the present disclosure. The method for detecting food ingredients may include the following steps:
[0040] Step S101, measuring the Raman spectrum of the food to be tested after contacting with the SERS substrate, the SERS substrate being a nanosilver-silicon dioxide core pore structure.
[0041] The food to be tested may refer to the part of the food to be tested as a sample, that is, the food sample. The contact between the food to be tested and the SERS substrate may refer to the food to be tested being adsorbed on the rough nano-metal surface in the SERS substrate. After the food to be tested is in contact with the SERS substrate, the scattered light generated can be collected by laser irradiation or the like, thereby measuring the detection Raman spectrum of the food to be tested.
[0042] In a possible implementation, nanosilver can be attached to the mesopores of silica after mesoporous silica is prepared, thereby obtaining a SERS substrate with a nanosilver-silicon dioxide core pore structure.
[0043] Compared with ordinary nanosilver substrates, the composite nanosilver-silicon dioxide core pore structure SERS active substrate provided by the embodiment of the present disclosure has higher sensitivity and is more stable, and can achieve ultra-sensitive detection of trace substances. By using the SERS substrate provided by the embodiment of the present disclosure for food component detection, a good signal enhancement effect can be achieved, thereby achieving Raman signal enhancement on the basis of ensuring the stability of nanosilver.
[0044] It is worth noting that, due to the use of SERS substrate, the technical solution provided by the embodiment of the present disclosure does not limit the form of the food to be tested, that is, the food sample can be solid, liquid or other forms. In addition, SERS detection does not require sample pretreatment, and can achieve detection of certain foods without opening the package, meeting the needs of outdoor field detection. No chemical reagents are required during the detection process, so there will be no pollution to the sample and the environment.
[0045] Step S102 : determining a target preset Raman spectrum that matches the detection Raman spectrum from the preset Raman spectra.
[0046] Step S103, determining the ingredients of the food to be tested according to the target preset Raman spectrum.
[0047] The preset Raman spectrum may refer to a standard Raman spectrum of a food component measured in advance, such as a standard Raman spectrum of a protein. By comparing the detected Raman spectrum with the preset Raman spectrum, a target preset Raman spectrum that is partially or completely consistent with the detected Raman spectrum can be determined from the preset Raman spectrum, thereby determining the components included in the food to be tested based on the target preset Raman spectrum. The consistency between the preset Raman spectrum and the detected Raman spectrum may mean that the similarity between the two reaches a preset similarity, which may be determined based on actual conditions, and the present disclosure does not specifically limit this.
[0048] The food component detection method provided in the embodiment of the present disclosure can be executed by a controller in a food component detection system. The controller usually takes a few seconds to a few minutes to perform the detection of the food component detection method. Compared with the conventional chromatography detection which takes a few minutes to a few hours, the technical solution provided in the embodiment of the present disclosure has a fast analysis speed and can quickly perform qualitative analysis on the food to be tested, thereby determining the components included in the food to be tested.
[0049] Through the above technical solution, the SERS substrate with nanosilver-silicon dioxide core pore structure is used to detect food ingredients. After the detection Raman spectrum of the food to be tested after contacting the SERS substrate is obtained, the target preset Raman spectrum matching the detection Raman spectrum is determined from the preset Raman spectrum, thereby determining the ingredients included in the food to be tested according to the target preset Raman spectrum. In this way, on the basis of ensuring the stability of nanosilver, Raman signal enhancement is achieved, thereby improving the accuracy of food ingredient detection.
[0050] Optionally, the above step S101 may include:
[0051] The food to be tested that has been in contact with the SERS substrate is irradiated with a laser to collect the generated scattered light, wherein the scattered light can be used to generate a detection Raman spectrum.
[0052] In a possible implementation, a laser transmitter, a Raman fiber probe, and a CCD (Charge-coupled Device) detector can be equipped in the food component detection system. Thus, the laser transmitter can emit laser light to illuminate the food to be tested after it contacts the SERS substrate, and then the Raman fiber probe and the CCD detector can collect the generated scattered light and generate a detection Raman spectrum based on it.
[0053] In the embodiment of the present disclosure, after the detection Raman spectrum of the food to be tested is measured, the detection Raman spectrum can be compared with the preset Raman spectrum, so as to determine the components included in the food to be tested according to the matched target preset Raman spectrum. The preset Raman spectrum can be determined in the following manner:
[0054] Based on a pure solution of the pre-prepared components, a standard Raman spectrum of the pure solution after contacting with the SERS substrate is measured, wherein the standard Raman spectrum can be used as a preset Raman spectrum.
[0055] Similarly, a laser can be emitted by a laser transmitter to illuminate a pure solution in contact with a SERS substrate, and then the scattered light generated can be collected by a Raman fiber optic probe and a CCD detector to generate a standard Raman spectrum corresponding to the pure solution.
[0056] Optionally, the preset Raman spectrum provided by the embodiment of the present disclosure may at least include standard Raman spectra of sucrose, protein and rhodamine B. Sucrose may refer to carbohydrates.
[0057] For example, a pure sucrose solution is provided, on which basis a standard Raman spectrum of the pure sucrose solution after contacting with the SERS substrate can be measured, and thus the standard Raman spectrum can be used as a preset Raman spectrum for determining sucrose.
[0058] It is not difficult to understand that, according to actual needs, the standard Raman spectra of substances other than sucrose, protein and rhodamine B can also be determined based on the above method, and the standard Raman spectra of other substances can be used as preset Raman spectra, thereby realizing qualitative analysis of other substances included in the food to be tested.
[0059] Optionally, after the above step S103, the technical solution provided by the embodiment of the present disclosure may further include:
[0060] Determine the peak intensity of the detection Raman spectrum of the components in the detection Raman spectrum;
[0061] Based on the pre-established correspondence between the peak intensity of the Raman spectrum of the component and the concentration, the detection concentration corresponding to the detected Raman spectrum peak intensity is determined.
[0062] Among them, the Raman spectrum peak intensity in the Raman spectrum is usually proportional to the concentration of the component. Therefore, the corresponding relationship between the Raman spectrum peak intensity and the concentration of the component can be determined based on the pre-prepared reference solutions of the component at different concentrations. On this basis, the corresponding detection concentration is determined according to the detected Raman spectrum peak intensity, thereby realizing quantitative analysis of the food to be tested.
[0063] In a possible implementation, the corresponding relationship between the Raman spectrum peak intensity and the concentration of the component can be established in the following manner:
[0064] Based on the pre-prepared reference solutions of components at different concentrations, the Raman spectra of the reference solutions after contacting with the SERS substrate are measured;
[0065] Determine the Raman spectrum peak intensity in the Raman spectrum corresponding to the reference solution of different concentrations;
[0066] According to the peak intensity of the Raman spectrum and the corresponding concentration, a quantitative correction model is established based on a linear partial least squares regression algorithm, and the quantitative correction model is used to determine the corresponding relationship.
[0067] Among them, the linear partial least squares regression algorithm (PLS, Partial Least Squares regression) can refer to the relevant technology, and the present disclosure does not make specific limitations on this. On this basis, according to the Raman spectrum peak intensity and the corresponding concentration, the step of establishing a quantitative correction model based on the linear partial least squares regression algorithm can include:
[0068] The linear relationship regression equation between the peak intensity of Raman spectrum and the concentration is determined based on the linear partial least squares regression algorithm;
[0069] A quantitative correction model was established based on the linear relationship regression equation.
[0070] For example, reference solutions of sucrose, protein and rhodamine B of different concentrations can be prepared, and the Raman spectra of these reference solutions after contact with the SERS substrate can be measured respectively, and the Raman spectrum peak intensities in the Raman spectra corresponding to the reference solutions of different components at different concentrations can be clearly determined. On this basis, the linear relationship regression equations between the peak intensity ratios at the Raman shifts of the three components sucrose, protein and rhodamine B and the concentrations can be obtained respectively based on the linear partial least squares regression algorithm. After obtaining the linear relationship regression equations of the three components, they can be imported into the software corresponding to the food component detection method provided in the embodiment of the present disclosure, so as to establish the corresponding quantitative correction model.
[0071] The quantitative calibration model can be used to determine the correspondence between the peak intensity of the Raman spectrum of the component and the concentration. For example, the peak intensity of the Raman spectrum of the determined component can be input into the quantitative calibration model, and the concentration of the component corresponding to the peak intensity of the Raman spectrum output by the quantitative calibration model can be obtained. Alternatively, the concentration of the determined component can be input into the quantitative calibration model, and the peak intensity of the Raman spectrum of the component at the concentration output by the quantitative calibration model can be obtained.
[0072] It is not difficult to understand that, according to actual needs, the corresponding relationship between the Raman spectral peak intensity and concentration of other material components besides sucrose, protein and rhodamine B can also be determined based on the above method, thereby realizing quantitative analysis of other substances included in the food to be tested.
[0073] It is understandable that by using the linear partial least squares regression algorithm in chemometrics to simulate and analyze the content of common carbohydrates, proteins, lipids and food additives in food and predict the content, it is possible to obtain the relationship between different Raman spectral peak intensities and component concentrations, that is, the linear relationship regression equation of the above Raman spectral peak intensity and concentration. On this basis, the linear relationship regression equation obtained by simulation can be imported into computer software in a programmatic manner to generate a corresponding quantitative correction model, so that in actual measurement, the corresponding component concentration or Raman spectral peak intensity can be quickly determined directly based on the quantitative correction model.
[0074] It is worth noting that the quantitative correction model established based on the linear partial least squares regression algorithm usually has a small error. Therefore, the accuracy of quantitative analysis of food ingredients can be further improved on the basis of ensuring the stability of nanosilver to achieve Raman signal enhancement.
[0075] Through the above technical solution, the SERS substrate with nanosilver-silicon dioxide core pore structure is used to detect food ingredients. After the detection Raman spectrum of the food to be tested after contacting the SERS substrate is obtained, the target preset Raman spectrum matching the detection Raman spectrum is determined from the preset Raman spectrum, thereby determining the ingredients included in the food to be tested according to the target preset Raman spectrum. In this way, on the basis of ensuring the stability of nanosilver, Raman signal enhancement is achieved, thereby improving the accuracy of food ingredient detection.
[0076] Based on the same inventive concept, the embodiment of the present disclosure also provides a food component detection device 200. Figure 2 , Figure 2 is a block diagram of a food component detection device 200 according to an exemplary embodiment of the present disclosure. The food component detection device 200 includes:
[0077] A Raman spectrum collection module 201 is used to measure the Raman spectrum of the food to be tested after contacting with the SERS substrate, and the SERS substrate is a nanosilver-silicon dioxide core hole structure;
[0078] The data analysis module 202 is used to determine a target preset Raman spectrum that matches the detection Raman spectrum from the preset Raman spectrum, and to determine the components included in the food to be tested according to the target preset Raman spectrum.
[0079] Through the above technical solution, the SERS substrate with nanosilver-silicon dioxide core pore structure is used to detect food ingredients. After the detection Raman spectrum of the food to be tested after contacting the SERS substrate is obtained, the target preset Raman spectrum matching the detection Raman spectrum is determined from the preset Raman spectrum, thereby determining the ingredients included in the food to be tested according to the target preset Raman spectrum. In this way, on the basis of ensuring the stability of nanosilver, Raman signal enhancement is achieved, thereby improving the accuracy of food ingredient detection.
[0080] Optionally, the data analysis module 202 is further used for:
[0081] After determining the components included in the food to be tested, determining the peak intensity of the detection Raman spectrum of the components in the detection Raman spectrum;
[0082] Based on the pre-established correspondence between the peak intensity of the Raman spectrum of the component and the concentration, the detection concentration corresponding to the detected Raman spectrum peak intensity is determined.
[0083] Optionally, the food component detection device 200 further includes an establishing module, the establishing module being used to establish a corresponding relationship between the Raman spectrum peak intensity and the concentration of the component, the establishing module being used to:
[0084] Based on the pre-prepared reference solutions of components at different concentrations, the Raman spectra of the reference solutions after contacting with the SERS substrate are measured;
[0085] Determine the Raman spectrum peak intensity in the Raman spectrum corresponding to the reference solution of different concentrations;
[0086] According to the peak intensity of the Raman spectrum and the corresponding concentration, a quantitative correction model was established based on the linear partial least squares regression algorithm, and the quantitative correction model was used to determine the corresponding relationship.
[0087] Optionally, build modules for:
[0088] The linear relationship regression equation between the peak intensity of Raman spectrum and the concentration is determined based on the linear partial least squares regression algorithm;
[0089] A quantitative correction model was established based on the linear relationship regression equation.
[0090] Optionally, the Raman spectrum collection module 201 is further used to determine a preset Raman spectrum. The Raman spectrum collection module 201 is used to:
[0091] Based on the pure solution of the pre-prepared components, a standard Raman spectrum of the pure solution after contacting with the SERS substrate is measured, and the standard Raman spectrum is used as a preset Raman spectrum.
[0092] Optionally, the Raman spectrum collection module 201 is used to:
[0093] The food to be tested is exposed to the SERS substrate by laser irradiation to collect the generated scattered light, which is used to generate a Raman spectrum for detection.
[0094] Optionally, the preset Raman spectra include at least standard Raman spectra of sucrose, protein and rhodamine B.
[0095] Regarding the food component detection device 200 in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.
[0096] Based on the same inventive concept, the embodiment of the present disclosure also provides a food component detection system 300. Figure 3 , Figure 3 FIG. 3 is a block diagram of a food component detection system 300 according to an exemplary embodiment. Figure 3 As shown, the food component detection system 300 may include: a SERS substrate 301, a Raman optical fiber probe 302, a CCD detector 303, a laser transmitter 304 and a controller 305;
[0097] The SERS substrate 301 is a nanosilver-silicon dioxide core-pore structure, and the controller 305 is used to control the Raman fiber probe 302, the CCD detector 303, and the laser emitter 304 to implement any of the steps of the above-mentioned food component detection method.
[0098] The technical solution provided by the embodiment of the present disclosure realizes real-time detection of food ingredients based on surface enhanced Raman spectroscopy technology. The food ingredient detection method used therein can be executed by the controller 305 in the food ingredient detection system 300 .
[0099] The controller 305 can collect the scattered light generated by the food to be tested after the laser irradiates and contacts the SERS substrate by controlling the Raman fiber probe 302, the CCD detector 303, and the laser transmitter 304, so as to form a corresponding detection Raman spectrum, compare the detection Raman spectrum with the preset Raman spectrum, and determine the target preset Raman spectrum. On this basis, the components in the food to be tested are qualitatively analyzed according to the target preset Raman spectrum. And based on the pre-established quantitative calibration model, the components included in the food to be tested are quantitatively analyzed.
[0100] Through the above-mentioned food component detection system 300, the food to be tested can be directly tested, so as to qualitatively analyze the components included in the food to be tested, and quantitatively analyze the concentration or content of the components. This process can be controlled by the controller 305, and the controller 305 compares the detected Raman spectrum of the food to be tested with the preset Raman spectrum to obtain the different components included in the food to be tested, and then based on the pre-established quantitative correction model, the corresponding component concentration or content is determined according to the peak intensity of different components in the detected Raman spectrum. The whole process does not require manual calculation, has high accuracy, and fast detection time.
[0101] Regarding the food component detection system 300 in the above embodiment, the specific manner in which each device or apparatus performs operations has been described in detail in the embodiment of the food component detection method, and will not be elaborated here.
[0102] Furthermore, it is to be understood that the food component detection system 300 may further include one or more of a memory, a multimedia component, an input / output (I / O) interface, and a communication component.
[0103] The controller 305 is used to control the overall operation of the food component detection system 300 to complete all or part of the steps in the above-mentioned food component detection method. The memory is used to store various types of data to support the operation of the food component detection system 300, and these data may include, for example, instructions for any application or method operated on the food component detection system 300, and application-related data, such as preset Raman spectra of components such as sucrose, protein, and rhodamine b, and linear regression equations of peak intensity and concentration related to the aforementioned components in a quantitative calibration model, etc. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory or sent through the communication component. The audio component also includes at least one speaker for outputting audio signals. The I / O interface provides an interface between the controller 305 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, a button, etc. These buttons can be virtual buttons or physical buttons. The communication component is used for wired or wireless communication between the food ingredient detection system 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component may include: Wi-Fi module, Bluetooth module, NFC module, etc.
[0104] In an exemplary embodiment, the food ingredient detection system 300 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned food ingredient detection method.
[0105] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned food component detection method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory including program instructions, and the above-mentioned program instructions can be executed by the controller 305 of the food component detection system 300 to complete the above-mentioned food component detection method.
[0106] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program that can be executed by a programmable device. The computer program has a code portion for executing the above-mentioned food component detection method when executed by the programmable device.
[0107] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.
[0108] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.
[0109] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.
Claims
1. A method for detecting food ingredients, characterized in that: The method comprises: Determining the detection Raman spectrum of the food to be tested after contacting with the SERS substrate, wherein the SERS substrate is a nanosilver-silicon dioxide core pore structure; Determining a target preset Raman spectrum matching the detected Raman spectrum from the preset Raman spectra; The components of the food to be tested are determined according to the target preset Raman spectrum.
2. The food component detection method according to claim 1, characterized in that: After determining the ingredients included in the food to be tested, the method further includes: Determining the peak intensity of the detected Raman spectrum of the component in the detected Raman spectrum; Based on the pre-established correspondence between the peak intensity of the Raman spectrum of the component and the concentration, the detection concentration corresponding to the detected Raman spectrum peak intensity is determined.
3. The food component detection method according to claim 2, characterized in that: The corresponding relationship between the Raman spectrum peak intensity and the concentration of the component is established in the following way: Based on pre-prepared reference solutions of the components at different concentrations, measuring the Raman spectrum of the reference solutions after contacting the SERS substrate; Determine the Raman spectrum peak intensity in the Raman spectrum corresponding to the reference solution of different concentrations; According to the peak intensity of the Raman spectrum and the corresponding concentration, a quantitative correction model is established based on a linear partial least squares regression algorithm, and the quantitative correction model is used to determine the corresponding relationship.
4. The method for detecting food ingredients according to claim 3, characterized in that: The method of establishing a quantitative correction model based on a linear partial least squares regression algorithm according to the peak intensity of the Raman spectrum and the corresponding concentration includes: Determine the linear relationship regression equation between the Raman spectrum peak intensity and the concentration based on a linear partial least squares regression algorithm; Based on the linear relationship regression equation, the quantitative correction model is established.
5. The method for detecting food ingredients according to any one of claims 1 to 4, characterized in that: The preset Raman spectrum is determined by: Based on the pre-prepared pure solution of the component, a standard Raman spectrum of the pure solution after contacting the SERS substrate is measured, and the standard Raman spectrum is used as the preset Raman spectrum.
6. The method for detecting food ingredients according to any one of claims 1 to 4, characterized in that: The method of measuring the Raman spectrum of the food to be tested after contacting with the SERS substrate comprises: The food to be tested that has been in contact with the SERS substrate is irradiated with laser light to collect the generated scattered light, which is used to generate the detection Raman spectrum.
7. The method for detecting food ingredients according to any one of claims 1 to 4, characterized in that: The preset Raman spectrum includes at least standard Raman spectra of sucrose, protein and rhodamine B.
8. A food component detection device, characterized in that: The food component detection device comprises: A Raman spectrum collection module, wherein the Raman spectrum collection module is used to measure the detection Raman spectrum of the food to be tested after contacting with the SERS substrate, wherein the SERS substrate is a nanosilver-silicon dioxide core hole structure; A data analysis module is used to determine a target preset Raman spectrum matching the detection Raman spectrum from preset Raman spectra, and to determine the ingredients included in the food to be tested according to the target preset Raman spectrum.
9. A food ingredient detection system, characterized in that: The food component detection system comprises: a SERS substrate, a Raman optical fiber probe, a CCD detector, a laser emitter and a controller; Wherein, the SERS substrate is a nanosilver-silicon dioxide core pore structure, and the controller is used to control the Raman fiber probe, the CCD detector, and the laser emitter to implement the steps of the food ingredient detection method according to any one of claims 1-7.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the food ingredient detection method described in any one of claims 1 to 7 are implemented.