Component detection system and method for food detergent

Abstract

The invention discloses a component detection system and method for a food detergent, and relates to the technical field of food safety detection.The component detection system comprises a sample collection module used for collecting the food detergent and mixing the food detergent with purified water through a solution mixing cavity to form a mixed solution sample; the sample detection module is used for obtaining detection data from the mixed solution sample by using a spectral analysis detection technology; and the evaluation module is used for extracting the characteristics of the detection data, constructing a food detergent component prediction model, predicting the component concentration and performing risk evaluation. Through sample collection, spectral analysis, data feature extraction and component prediction model construction, accurate detection and risk assessment of food detergent components are realized, through a multi-layer perceptron neural network model technology, the detection precision and sensitivity are improved, comprehensive analysis and risk assessment of various different components are also realized, and the detection accuracy and the detection sensitivity are improved. The comprehensiveness and reliability of the detection technology are enhanced, and the safe use of the food detergent is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of food safety detection, and in particular to a food detergent component detection system and method. Background Art

[0002] Food detergent ingredient detection technology plays an important role in the field of food safety and public health. As society's attention to food safety continues to increase, food detergent ingredient detection technology is also constantly developing and improving. At present, the main detection methods include gas chromatography, liquid chromatography, mass spectrometry, spectral analysis, etc. Gas chromatography and liquid chromatography can efficiently separate and quantify organic matter in detergents, but their operation is complicated and costly. Mass spectrometry has high sensitivity and high resolution, but requires complex sample pretreatment; spectral analysis is non-destructive, fast and efficient, and has shown high accuracy and efficiency in the detection of food detergents.

[0003] Although the above technologies have achieved certain achievements in the detection of food detergent ingredients, there are still some shortcomings. Traditional chromatography and mass spectrometry analysis methods usually require complex sample pretreatment, which is cumbersome and time-consuming. Although spectral analysis technology has certain rapidity and non-destructiveness, its detection accuracy and sensitivity still need to be improved when facing complex food detergent ingredients. Existing detection methods mostly focus on the detection of a single ingredient and lack the ability to conduct comprehensive analysis and risk assessment of multiple components. Existing technologies often use traditional statistical analysis methods and cannot make full use of big data and artificial intelligence technologies for in-depth mining and predictive analysis, thereby limiting the further improvement of detection technology. Summary of the invention

[0004] In view of the problems existing in the above-mentioned existing food detergent component detection system and method, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that the existing technology requires complex sample preprocessing, the operation is cumbersome and time-consuming, and it is difficult to meet the needs of rapid detection. The accuracy and sensitivity of spectral analysis technology in detecting complex components are limited. The existing methods are mainly aimed at the detection of single components and lack comprehensive analysis and risk assessment capabilities. Data processing does not fully utilize artificial intelligence technology, which limits the deep mining and predictive analysis of detection technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a food detergent component detection system, comprising:

[0007] A sample collection module, used for collecting food detergent and mixing the food detergent with purified water using a solution mixing chamber to form a mixed solution sample;

[0008] A sample detection module is used to obtain detection data using a spectrum analysis detection technology on a mixed solution sample;

[0009] The prediction module is used to extract the features of the test data, build a prediction model for food detergent ingredients, predict ingredient concentrations and conduct risk assessment;

[0010] The storage module is used to encrypt the test data and evaluation results during the test process, collect the test data and evaluation results and store them.

[0011] As a preferred solution of the food detergent component detection system of the present invention, wherein: the food detergent collection refers to sampling food detergents from different batches and production lines at the sampling location, mixing all sampled food detergents evenly, weighing the mass of the food detergents using an electronic balance, and recording the mass value;

[0012] The inner wall of the sample tank is coated with an anti-stick coating, and the food detergent is weighed using an electronic balance and poured into the sample tank, and the sample tank cover is closed.

[0013] As a preferred solution of the food detergent component detection system described in the present invention, the method of using a solution mixing chamber to mix the food detergent and purified water to form a mixed solution sample refers to connecting a sample tank to the solution mixing chamber, starting the solenoid valve at the bottom of the sample tank, inputting the required food detergent flow parameters into the solenoid valve controller, opening the water inlet valve to allow purified water to flow into the solution mixing chamber, mixing the food detergent and purified water in a ratio of 1:10, starting the rotary agitator in the solution mixing chamber, fully mixing the food detergent and purified water by high-speed rotation, recording the stirring time, and obtaining a mixed solution sample.

[0014] As a preferred solution of the food detergent component detection system of the present invention, wherein: the component detection of the mixed solution sample using spectral analysis technology refers to flowing the mixed solution sample into the detection pool from the liquid outlet, starting the near-infrared spectrometer, and setting the scanning parameters, including the wavelength range and the scanning speed;

[0015] Use optical fiber components to connect the light source and detection cell of the near infrared spectrometer;

[0016] Turn on the light source of the near-infrared spectrometer and record the spectral signal without the mixed solution sample to obtain the background spectral signal I b , then place a standard white plate in the light path, pass the near-infrared light through the standard white plate, record the reference photoelectric signal intensity I0, place the mixed solution sample to be tested in the light path, pass the near-infrared light through the mixed solution sample, and record the spectral signal I of the mixed solution sample;

[0017] The spectrum detector detects and records the spectrum signal of the transmitted light, converts the spectrum signal into an electrical signal, and the data acquisition card converts the electrical signal into a digital signal, which is then transmitted to a computer through a data line for analysis;

[0018] Background correction was performed on I and I0 respectively, and the obtained correction signals were standardized respectively, and the correction signals were converted into the transmission photoelectric signal intensity I of the mixed solution. s and the reference photoelectric signal intensity I 0，s , calculate the absorbance at each wavelength, the formula is:

[0019]

[0020] Where A is the absorbance, I s is the intensity of the photoelectric signal transmitted by the mixed solution sample, I 0，s is the reference photoelectric signal intensity;

[0021] The absorbance A is used as the Y-axis data and the wavelength is used as the X-axis data to generate a spectrum. The spectrum analysis software plots the absorbance at each wavelength on the spectrum to form a near-infrared spectrum.

[0022] In the spectrum, according to the relative baseline and relative height standards, the wavelength with high absorbance is identified as the absorption peak, and the wavelength point and corresponding absorbance of each absorption peak are recorded as the characteristic peak to construct the spectrum data set;

[0023] Import the calculated absorbance data into the chemometric analysis software, calculate the covariance matrix of the absorbance data, perform eigenvalue decomposition on the covariance matrix, obtain eigenvalues ​​and eigenvectors, sort the eigenvalues ​​from large to small, select the eigenvector corresponding to the largest eigenvalue as the principal component, project the absorbance data into the principal component space, reduce the dimension and remove noise;

[0024] The characteristic peak position and intensity of each component are identified and recorded in the principal component space, and compared with the existing spectral standard database to determine the composition of each component. The concentration of each component in the mixed solution sample is calculated using the absorbance of the characteristic peak through the Beer-Lambert law.

[0025] As a preferred solution of the food detergent component detection system of the present invention, wherein: the feature extraction of the detection data refers to calculating the mean and standard deviation of the signal intensity according to the wavelength points in each spectral data set, and performing standardization processing on the signal intensity at each wavelength point to obtain standardized data;

[0026] Perform wavelet decomposition on the standardized data, remove noise from the high-frequency part of the decomposition, perform wavelet reconstruction, and obtain the denoised data;

[0027] According to the characteristic peak position and intensity of each component, construct the characteristic vector F i , each eigenvector is represented by (λ ik , A ik );

[0028] Design the eigenvector function and transform the eigenvector F i Convert to peak feature vector format f(F i ), the formula is:

[0029]

[0030] Among them, λ ik is the position of the characteristic peak of the i-th component of the k-th mixed solution sample, A ik is the intensity of the characteristic peak of the i-th component of the k-th mixed solution sample, a k , b and c are unknown parameters, and nonlinear regression method is used to determine a by minimizing the error between the predicted concentration and the actual concentration. k , b and c are specific values, and n is the number of characteristic peaks;

[0031] By using the peak eigenvector f(F i ) generating a spectral analysis data set;

[0032] The converted peak feature vector f(F i ) forms the joint peak eigenvector matrix V:

[0033]

[0034] Among them, f(F i ) k is the peak feature vector of the kth mixed solution sample;

[0035] Calculate the Pearson correlation coefficient r between each eigenvector in the joint peak eigenvector matrix V ij , the formula is:

[0036]

[0037] Among them, r ij is the linear correlation coefficient between the i-th component and the j-th component, λ ik is the characteristic peak position of the i-th component in the k-th mixed solution sample, A jk is the characteristic peak intensity of the jth component in the kth mixed solution sample, is the mean of the characteristic peak position of the i-th component, is the mean characteristic peak intensity of the jth component, and m is the number of mixed solution samples;

[0038] According to the correlation coefficient rij , calculate the adaptive weight factor, the formula is:

[0039]

[0040] Among them, ω i is the adaptive weight factor of the i-th component, and α is the adjustment parameter;

[0041] For each peak eigenvector f(F i ) multiplied by the weight factor ω i , get the weighted peak eigenvector matrix V`;

[0042] The peak eigenvector matrix is ​​optimized by using the adaptive weight factor, and the weighted peak eigenvector matrix V' is analyzed. i The size of the threshold T ω , retain weight factors greater than the threshold T ω The peak feature vector of the weight factor is less than the threshold T. ω The peak eigenvector of .

[0043] As a preferred solution of the food detergent component detection system of the present invention, wherein: the construction of the food detergent component prediction model to predict the component concentration includes:

[0044] A food detergent ingredient evaluation model was constructed using a multi-layer perceptron neural network model, including an input layer, a hidden layer, and an output layer;

[0045] Set the input layer to the peak feature vector f(F i ), the output layer is the predicted concentration value of each component

[0046] The spectral analysis data set is divided into a training set and a test set, the training set data is input into the model, and the model parameters are initialized;

[0047] The defined objective function is used as the loss function for model training. The objective function is designed as the weighted sum of the component concentration prediction error and the detection error. The formula is:

[0048]

[0049] Among them, L is the loss value of the objective function, Q i is the actual concentration value of the i-th component, is the predicted concentration value of the i-th component, λ ik is the characteristic peak position of the i-th component in the k-th mixed solution sample, is the predicted characteristic peak position of the i-th component in the k-th mixed solution sample, β is the weight adjustment parameter, m is the number of mixed solution samples, and n is the number of characteristic peaks;

[0050] Use the Adam optimization algorithm to optimize model parameters, iteratively update model parameters, minimize the loss function, set the learning rate and batch size, and record the loss value of each iteration until the loss function converges;

[0051] The mean square error and coefficient of determination were calculated to evaluate the predictive performance of the model;

[0052] Tune the model's hyperparameters, including learning rate, number of hidden layer neurons, and batch size, and use Bayesian optimization to find the best hyperparameter combination;

[0053] Save the trained model;

[0054] Food detergent samples were collected from the sampling site, analyzed and detected using spectral technology and preprocessed to extract a new feature vector f(F i ) Input the food detergent component prediction model to obtain the predicted concentration value of each component in the mixed solution sample

[0055] As a preferred solution of the food detergent component detection system of the present invention, wherein: the risk assessment refers to setting the concentration threshold T of each component according to the safety standard of the food detergent component. i , using adaptive weight factors and predicted concentration values ​​of each component Calculate the comprehensive risk assessment value R:

[0056]

[0057] Among them, R i is the comprehensive risk assessment value of the i-th component of the mixed solution sample, ω i is the adaptive weight factor of the ith component, T i is the safety threshold of the ith component, is the predicted concentration value of the i-th component, and p is the number of components in the mixed solution;

[0058] Set the risk threshold R th , compare the comprehensive risk assessment value R i and risk threshold R th Predict risk levels and provide warnings;

[0059] If R i ≤R th , then the risk is within an acceptable range and the system operates normally;

[0060] If R i >Rth , the risk exceeds the acceptable range, triggering an alarm and prompting the user to take action.

[0061] As a preferred solution of the food detergent ingredient detection system described in the present invention, wherein: the detection data and evaluation results in the test process are encrypted, and the storage and collection of the detection data and evaluation results refers to using an AES algorithm to encrypt the detection data and evaluation results collected in the test process, and the encrypted data is transmitted to the database through a secure transmission protocol, a table structure is created in the database, all encryption and storage processes are logged and backed up regularly, and access permissions are set.

[0062] Another object of the present invention is a method for detecting ingredients of food detergents, which comprises:

[0063] collecting food detergent and mixing the food detergent with purified water using a solution mixing chamber to form a mixed solution sample;

[0064] Using spectral analysis detection technology to obtain detection data for mixed solution samples;

[0065] Extract the features of the test data, build a food detergent ingredient prediction model, predict ingredient concentrations and conduct risk assessment;

[0066] The test data and evaluation results during the test process are encrypted and stored.

[0067] The beneficial effects of the present invention are as follows: the present invention realizes accurate detection and risk assessment of food detergent ingredients through sample collection, spectral analysis, data feature extraction and construction of ingredient prediction models. Through the multi-layer perceptron neural network model technology, not only the detection accuracy and sensitivity are improved, but also the comprehensive analysis and risk assessment of various different ingredients are realized, the comprehensiveness and reliability of the detection technology are enhanced, and the safe use of food detergents is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0069] Figure 1 The figure is a schematic diagram of the structure of a food detergent component detection system.

[0070] Figure 2 The present invention is a schematic diagram of a process for detecting ingredients of food detergents. DETAILED DESCRIPTION

[0071] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0072] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0073] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0074] Example 1

[0075] Reference Figure 1 , which is the first embodiment of the present invention, and which provides a food detergent component detection system, a food detergent component detection system comprising:

[0076] S1, a sample collection module, used for collecting food detergent and using a solution mixing chamber to mix the food detergent with purified water to form a mixed solution sample;

[0077] Specifically, collecting food detergents means sampling food detergents from different batches and production lines at the sampling site, mixing all sampled food detergents evenly, weighing the mass of the food detergents using an electronic balance, and recording the mass value;

[0078] The inner wall of the sample tank is coated with an anti-stick coating, and the food detergent is weighed using an electronic balance and poured into the sample tank, and the sample tank cover is closed.

[0079] Food detergent samples are collected from different batches and production lines at the sampling site and mixed evenly to ensure the representativeness and uniformity of the samples. The mass of the food detergent is accurately weighed using an electronic balance to ensure the accuracy of the test data. An anti-stick coating is applied to the inner wall of the sample trough to avoid sample residue, ensure complete sample transfer and reduce errors, thereby improving the reliability and repeatability of the test results.

[0080] Furthermore, using a solution mixing chamber to mix the food detergent and purified water to form a mixed solution sample means connecting a sample tank to the solution mixing chamber, starting a solenoid valve at the bottom of the sample tank, inputting a desired food detergent flow parameter into a solenoid valve controller, opening a water inlet valve to allow purified water to flow into the solution mixing chamber, mixing the food detergent and purified water in a ratio of 1:10, starting a rotary agitator in the solution mixing chamber, fully mixing the food detergent and purified water by high-speed rotation, recording the agitation time, and obtaining a mixed solution sample;

[0081] Calculate the mass value of the food detergent and the mass value of the mixed solution to obtain the concentration of the food detergent in the mixed solution;

[0082] According to the experimental requirements, the standard value M of the mixing uniformity between food detergent and purified water is set. b , use the following formula to get the actual mixing uniformity M to evaluate the mixing uniformity:

[0083]

[0084] Where M is the actual mixing uniformity, C y is the concentration of the yth sample, is the average concentration of the samples, n is the number of samples;

[0085] The calculated actual mixing uniformity M is compared with the standard value M b Compare, if M≤M b , then the actual mixing uniformity meets the requirements and the solution is mixed uniformly. If M>M b , then the actual mixing uniformity does not meet the requirements and needs to be re-mixed according to the adjusted mixing parameters. The mixing parameters include mixing time and mixer speed.

[0086] Food detergent and purified water are mixed in a ratio of 1:10 through a solution mixing chamber, and the flow parameters are controlled by a solenoid valve and a rotary agitator is used for high-speed mixing. The concentration and uniformity of the mixed solution are accurately recorded and calculated to ensure the uniformity and stability of the mixed solution. If the uniformity does not meet the standard, it can be optimized by adjusting the stirring time and the speed of the agitator, which significantly improves the accuracy and reliability of the detection.

[0087] S2, a sample detection module, used to obtain detection data using spectral analysis detection technology on the mixed solution sample;

[0088] Specifically, using spectral analysis technology to detect the components of the mixed solution sample means that the mixed solution sample flows into the detection pool from the liquid outlet, and a near-infrared spectrometer is started. The near-infrared spectrometer is used to detect and analyze the chemical components and molecular structures in the sample, and the scanning parameters are set, including the wavelength range and the scanning speed;

[0089] Use a fiber coupler to connect one end of the fiber optic cable to the light source of the near-infrared spectrometer, and the other end to the input end of the fiber optic probe;

[0090] Fix the output end of the optical fiber probe at the other end of the detection cell, and adjust the position and distance of the optical fiber probe so that the light from the near-infrared spectrometer passes vertically through the mixed solution;

[0091] Turn on the light source of the near-infrared spectrometer, do not place the mixed solution on the detection path, start the spectrum analysis software, record the spectrum data without the mixed solution, and obtain the background spectrum signal I b Then place the standard white board in the light path, and the optical fiber cable transmits the near-infrared light through the standard white board to the spectrum detector, and records the reference photoelectric signal intensity I 0，s ;

[0092] The mixed solution to be tested is placed in the optical path. The optical fiber cable transmits near-infrared light through the mixed solution. The optical fiber probe receives the transmitted light and transmits it to the spectrum detector through the optical fiber cable to record the spectrum signal I of the mixed solution. s ;

[0093] The spectrum detector detects and records the spectrum signal of the transmitted light and converts the spectrum signal into an electrical signal;

[0094] Data acquisition card is a hardware device used to collect, convert and transmit analog and digital signals from various sensors, instruments or equipment. The data acquisition card converts electrical signals into digital signals and transmits them to the computer through data lines for analysis;

[0095] Remove the background spectrum signal I for I and I0 respectively b Perform background correction, standardize the obtained correction signals, and convert the correction signals into the mixed solution transmission photoelectric signal intensity I s and the reference photoelectric signal intensity I 0，s , calculate the absorbance at each wavelength, the formula is:

[0096]

[0097] Where A is the absorbance, I s is the intensity of the photoelectric signal transmitted by the mixed solution sample, I 0，s is the reference photoelectric signal intensity;

[0098] Wavelength refers to the distance a wave travels in one cycle. It is used to describe light waves, sound waves and other forms of fluctuations. The relationship between absorbance and wavelength is reflected by spectral analysis technology. The absorbance A is used as the Y-axis data and the wavelength is used as the X-axis data to generate a spectrum. The spectrum analysis software plots the absorbance at each wavelength on the spectrum to form a near-infrared spectrum.

[0099] In the spectrum, according to the relative baseline and relative height standards, the wavelength with high absorbance is identified as the absorption peak, and the wavelength point and corresponding absorbance of each absorption peak are recorded as the characteristic peak to construct the spectrum data set;

[0100] Import the calculated absorbance data into the chemometric analysis software, calculate the covariance matrix of the absorbance data, perform eigenvalue decomposition on the covariance matrix, obtain eigenvalues ​​and eigenvectors, sort the eigenvalues ​​from large to small, select the eigenvector corresponding to the largest eigenvalue as the principal component, project the absorbance data into the principal component space, reduce the dimension and remove noise;

[0101] The characteristic peak position and intensity of each component are identified and recorded in the principal component space, and compared with the existing spectral standard database to determine the composition of each component. The concentration of each component in the mixed solution sample is calculated using the absorbance of the characteristic peak through the Beer-Lambert law.

[0102] By using spectral analysis technology to detect the components of mixed solution samples, the operating steps of the spectrometer are set up and optimized in detail, from the background spectral signal, the standard white plate reference signal to the actual detection signal of the mixed solution, the accuracy and reliability of the data are ensured. Through the combination of fiber optic coupling technology and near-infrared spectrometer, efficient, non-destructive and precise component detection is achieved, and chemometric analysis software is used for data processing and feature extraction. Finally, the concentration of each component is accurately calculated through the Beer-Lambert law. This method improves the sensitivity and accuracy of detection, while simplifying the detection process, and has the beneficial effects of being fast, accurate and stable.

[0103] S3, prediction module, used to extract the features of the test data, build a food detergent ingredient prediction model, predict ingredient concentration and risk assessment;

[0104] Specifically, extracting the features of the detection data means calculating the mean and standard deviation of the signal intensity according to the wavelength points in each spectral data set, and performing standardization on the signal intensity at each wavelength point to obtain standardized data;

[0105] Perform wavelet decomposition on the standardized data, remove noise from the high-frequency part of the decomposition, perform wavelet reconstruction, and obtain the denoised data;

[0106] According to the characteristic peak position and intensity of each component, construct the characteristic vector F i , each eigenvector is represented by (λ ik , A ik );

[0107] Design the eigenvector function and transform the eigenvector F i Convert to peak feature vector format f(Fi ), the formula is:

[0108]

[0109] Among them, λ ik is the position of the characteristic peak of the i-th component of the k-th mixed solution sample, A ik is the intensity of the characteristic peak of the i-th component of the k-th mixed solution sample, a k , b and c are unknown parameters, and nonlinear regression method is used to determine a by minimizing the error between the predicted concentration and the actual concentration. k , b and c are specific values, and n is the number of characteristic peaks;

[0110] By using the peak eigenvector f(F i ) generating a spectral analysis data set;

[0111] The converted peak feature vector f(F i ) forms the joint peak eigenvector matrix V:

[0112]

[0113] Among them, f(F i ) k is the peak feature vector of the kth mixed solution sample;

[0114] Calculate the Pearson correlation coefficient r between each eigenvector in the joint peak eigenvector matrix V ij , the formula is:

[0115]

[0116] Among them, r ij is the linear correlation coefficient between the i-th component and the j-th component, λ ik is the characteristic peak position of the i-th component in the k-th mixed solution sample, A jk is the characteristic peak intensity of the jth component in the kth mixed solution sample, is the mean of the characteristic peak position of the i-th component, is the mean characteristic peak intensity of the jth component, and m is the number of mixed solution samples;

[0117] According to the correlation coefficient r ij , calculate the adaptive weight factor, the formula is:

[0118]

[0119] Among them, ω i is the adaptive weight factor of the i-th component, and α is the adjustment parameter;

[0120] For each peak eigenvector f(F i ) multiplied by the weight factor ω i , get the weighted peak eigenvector matrix V`;

[0121] The peak eigenvector matrix is ​​optimized by using the adaptive weight factor, and the weighted peak eigenvector matrix V' is analyzed. i The size of the threshold T ω , retain weight factors greater than the threshold T ω The peak feature vector of the weight factor is less than the threshold T. ω The peak eigenvector of .

[0122] By performing detailed feature extraction and processing on the detection data, including calculating the mean and standard deviation of the signal intensity at each wavelength point and performing standardization processing, the fluctuation of the data under different detection conditions can be effectively eliminated, and standardized data can be obtained. Through wavelet decomposition and denoising, the influence of high-frequency noise can be significantly reduced, thereby obtaining a clearer and more accurate signal, further identifying the characteristic peak position and intensity of each component, constructing a feature vector set so that each feature pair can effectively represent the key information of the component, designing a feature vector function, and converting the feature vector into an available input mode, it is helpful to form a representative joint feature vector matrix, calculating the Pearson correlation coefficient between each feature vector in the joint feature vector matrix, and quantitatively analyzing the correlation between each feature, thereby identifying which features have important contributions to model prediction, and calculating the adaptive weight factor based on the correlation coefficient, and further optimizing the feature vector matrix. Through this process, important features can be retained, redundant features can be eliminated, and the quality of the data and the prediction accuracy of the model can be improved.

[0123] Furthermore, a food detergent ingredient prediction model is constructed to predict ingredient concentrations including:

[0124] A food detergent ingredient evaluation model was constructed using a multi-layer perceptron neural network model, including an input layer, a hidden layer, and an output layer;

[0125] Set the input layer to the peak feature vector f(F i ), the output layer is the predicted concentration value of each component

[0126] The spectral analysis data set is divided into a training set and a test set, the training set data is input into the model, and the model parameters are initialized;

[0127] The defined objective function is used as the loss function for model training. The objective function is designed as the weighted sum of the component concentration prediction error and the detection error. The formula is:

[0128]

[0129] Among them, L is the loss value of the objective function, Q i is the actual concentration value of the i-th component, is the predicted concentration value of the i-th component, λ ik is the characteristic peak position of the i-th component in the k-th mixed solution sample, is the predicted characteristic peak position of the i-th component in the k-th mixed solution sample, β is the weight adjustment parameter, m is the number of mixed solution samples, and n is the number of characteristic peaks;

[0130] Use the Adam optimization algorithm to optimize model parameters, iteratively update model parameters, minimize the loss function, set the learning rate and batch size, and record the loss value of each iteration until the loss function converges;

[0131] The mean square error and coefficient of determination were calculated to evaluate the predictive performance of the model;

[0132] Tune the model's hyperparameters, including learning rate, number of hidden layer neurons, and batch size, and use Bayesian optimization to find the best hyperparameter combination;

[0133] Save the trained model;

[0134] Food detergent samples were collected from the sampling site, analyzed and detected using spectral technology and preprocessed to extract a new feature vector f(F i ) Input the food detergent component prediction model to obtain the predicted concentration value of each component in the mixed solution sample

[0135] By using a multi-layer perceptron neural network model to build a food detergent ingredient evaluation model, it is possible to achieve high-precision prediction of ingredient concentrations. By setting the input layer as the feature vector and the output layer as the predicted concentration value of each ingredient, the key features in the spectral analysis data are effectively input into the model. The data set is divided into a training set and a test set, and the model parameters are initialized so that the model has good generalization ability during the training process. The weighted sum of the ingredient concentration prediction error and the detection error is used as the loss function, and the Adam optimization algorithm is used to optimize the model parameters. The model parameters are iteratively updated to minimize the loss function, thereby improving the prediction accuracy of the model. A reasonable learning rate and batch size are set, and the loss value of each iteration is recorded until the loss function converges to ensure that the model converges stably during the training process. The test set data is input into the model, the predicted ingredient concentration value is calculated, and the prediction performance of the model is evaluated by the mean square error and the determination coefficient to ensure the accuracy and reliability of the model. The hyperparameters of the model are tuned, including the learning rate, the number of hidden layer neurons, and the batch size. The Bayesian optimization method is used to find the best hyperparameter combination to further improve the model performance.

[0136] In addition, risk assessment means setting a concentration threshold T for each ingredient based on the safety standards of food detergent ingredients. i , using adaptive weight factors and predicted concentration values ​​of each component Calculate the comprehensive risk assessment value R:

[0137]

[0138] Among them, R i is the comprehensive risk assessment value of the i-th component of the mixed solution sample, ω i is the adaptive weight factor of the ith component, T i is the safety threshold of the ith component, is the predicted concentration value of the i-th component, and p is the number of components in the mixed solution;

[0139] Set the risk threshold R th , compare the comprehensive risk assessment value R i and risk threshold R th Predict risk levels and provide warnings;

[0140] If R i ≤R th , then the risk is within an acceptable range and the system operates normally;

[0141] If R i >R th , the risk exceeds the acceptable range, triggering an alarm and prompting the user to take action.

[0142] By using the food detergent ingredient prediction model, the concentration value of each ingredient in the food detergent can be accurately predicted. According to the safety standards of the food detergent ingredients, the concentration threshold of each ingredient is set, and the comprehensive risk assessment value is calculated using the adaptive weight factor and the predicted concentration. This not only improves the detection accuracy and efficiency, but also can monitor the safety of food detergents in real time. By setting the risk threshold, the system can compare the comprehensive risk assessment and the risk threshold to predict the risk level and give prompts. When the comprehensive risk assessment value is less than or equal to the risk threshold, it means that the risk is within an acceptable range and the system operates normally. When the comprehensive risk assessment value is greater than the risk threshold, it means that the risk exceeds the acceptable range. The system will trigger an alarm and prompt the user to take corresponding measures. This risk assessment mechanism not only ensures the safety of food detergents, but also can provide timely warnings to prevent potential hazards.

[0143] S4, a storage module, used to encrypt the test data and evaluation results during the test process, collect the test data and evaluation results and store them;

[0144] Specifically, the test data and evaluation results collected during the test are encrypted. Storing and collecting the test data and evaluation results means using the AES algorithm to encrypt the test data and evaluation results collected during the test. The AES algorithm is used to protect the security of electronic data. The encrypted data is transmitted to the database via a secure transmission protocol. The secure transmission protocol is a protocol used to protect data from being eavesdropped, tampered with, and forged during transmission. A table structure is created in the database, all encryption and storage processes are logged and backed up regularly, and access permissions are set.

[0145] By using the AES algorithm to encrypt the test data and evaluation results collected during the test process, the encrypted data is transmitted to the database through a secure transmission protocol and a table structure is created in the database, which can significantly improve the security and integrity of the data, ensure the confidentiality of the test data and evaluation results during transmission and storage, and prevent the data from being intercepted or tampered with during transmission. By recording logs of all encryption and storage processes and performing regular data backup, the traceability and reliability of the data are guaranteed to prevent data loss. Setting access permissions further ensures that only authorized personnel can access and decrypt the data, thereby effectively protecting the privacy and security of the data.

[0146] Example 2

[0147] Reference Figure 2 , which is the second embodiment of the present invention, is different from the previous embodiment and provides a method for detecting ingredients of food detergents, which includes:

[0148] collecting food detergent and mixing the food detergent with purified water using a solution mixing chamber to form a mixed solution sample;

[0149] Using spectral analysis detection technology to obtain detection data for mixed solution samples;

[0150] Extract the features of the test data, build a food detergent ingredient prediction model, predict ingredient concentrations and conduct risk assessment;

[0151] The test data and evaluation results during the test process are encrypted and stored.

[0152] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0153] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0154] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0155] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0156] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A food detergent component detection system, characterized in that: include, A sample collection module, used for collecting food detergent and mixing the food detergent with purified water using a solution mixing chamber to form a mixed solution sample; A sample detection module is used to obtain detection data using a spectrum analysis detection technology on a mixed solution sample; The prediction module is used to extract the features of the test data, build a prediction model for food detergent ingredients, predict ingredient concentrations and conduct risk assessment; The storage module is used to encrypt the test data and evaluation results during the test process, and store and collect the test data and evaluation results.

2. A food detergent component detection system as claimed in claim 1, characterized in that: The collecting of food detergents refers to sampling food detergents from different batches and production lines at the sampling site, mixing all sampled food detergents evenly, weighing the mass of the food detergents using an electronic balance, and recording the mass value; The inner wall of the sample tank is coated with an anti-stick coating, and the food detergent is weighed using an electronic balance and poured into the sample tank, and the sample tank cover is closed.

3. A food detergent component detection system as claimed in claim 2, characterized in that: The method of using a solution mixing chamber to mix a food detergent with purified water to form a mixed solution sample refers to connecting a sample tank to the solution mixing chamber, starting a solenoid valve at the bottom of the sample tank, inputting a required food detergent flow parameter into a solenoid valve controller, opening a water inlet valve to allow purified water to flow into the solution mixing chamber, mixing the food detergent and purified water in a ratio of 1:10, starting a rotary agitator in the solution mixing chamber, fully mixing the food detergent and purified water by high-speed rotation, recording the stirring time, and obtaining a mixed solution sample.

4. A food detergent component detection system as claimed in claim 3, characterized in that: The use of spectral analysis technology to detect the components of the mixed solution sample refers to flowing the mixed solution sample into the detection pool from the liquid outlet, starting the near-infrared spectrometer, and setting scanning parameters, including wavelength range and scanning speed; Use optical fiber components to connect the light source and detection cell of the near infrared spectrometer; Turn on the light source of the near-infrared spectrometer and record the spectral signal without the mixed solution sample to obtain the background spectral signal I b , then place a standard white plate in the light path, pass the near-infrared light through the standard white plate, record the reference photoelectric signal intensity I0, place the mixed solution sample to be tested in the light path, pass the near-infrared light through the mixed solution sample, and record the spectral signal I of the mixed solution sample; The spectrum detector detects and records the spectrum signal of the transmitted light, converts the spectrum signal into an electrical signal, and the data acquisition card converts the electrical signal into a digital signal, which is then transmitted to a computer through a data line for analysis; Background correction was performed on I and I0 respectively, and the obtained correction signals were standardized respectively, and the correction signals were converted into the transmission photoelectric signal intensity I of the mixed solution. s and the reference photoelectric signal intensity I 0，s , calculate the absorbance at each wavelength, the formula is: Where A is the absorbance, I s is the intensity of the photoelectric signal transmitted by the mixed solution sample, I 0，s is the reference photoelectric signal intensity; The absorbance A is used as the Y-axis data and the wavelength is used as the X-axis data to generate a spectrum. The spectrum analysis software plots the absorbance at each wavelength on the spectrum to form a near-infrared spectrum. In the spectrum, according to the relative baseline and relative height standards, the wavelength with high absorbance is identified as the absorption peak, and the wavelength point and corresponding absorbance of each absorption peak are recorded as the characteristic peak to construct the spectrum data set; Import the calculated absorbance data into the chemometric analysis software, calculate the covariance matrix of the absorbance data, perform eigenvalue decomposition on the covariance matrix, obtain eigenvalues ​​and eigenvectors, sort the eigenvalues ​​from large to small, select the eigenvector corresponding to the largest eigenvalue as the principal component, project the absorbance data into the principal component space, reduce the dimension and remove noise; The characteristic peak position and intensity of each component are identified and recorded in the principal component space, and compared with the existing spectral standard database to determine the composition of each component. The concentration of each component in the mixed solution sample is calculated using the absorbance of the characteristic peak through the Beer-Lambert law.

5. A food detergent component detection system as claimed in claim 4, characterized in that: The feature extraction of the detection data refers to calculating the mean and standard deviation of the signal intensity according to the wavelength points in each spectral data set, and performing standardization processing on the signal intensity at each wavelength point to obtain standardized data; Perform wavelet decomposition on the standardized data, remove noise from the high-frequency part of the decomposition, perform wavelet reconstruction, and obtain the denoised data; According to the characteristic peak position and intensity of each component, construct the characteristic vector F i , each eigenvector is represented by (λ ik , A ik ); Design the eigenvector function and transform the eigenvector F i Convert to peak feature vector format f(F i ), the formula is: Among them, λ ik is the position of the characteristic peak of the i-th component of the k-th mixed solution sample, A ik is the intensity of the characteristic peak of the i-th component of the k-th mixed solution sample, a k , b and c are unknown parameters, and nonlinear regression method is used to determine a by minimizing the error between the predicted concentration and the actual concentration. k , b and c are specific values, and n is the number of characteristic peaks; By using the peak eigenvector f(F i ) generating a spectral analysis data set; The converted peak feature vector f(F i ) forms the joint peak eigenvector matrix V: Among them, f(F i ) k is the peak feature vector of the kth mixed solution sample; Calculate the Pearson correlation coefficient r between each eigenvector in the joint peak eigenvector matrix V ij , the formula is: Among them, r ij is the linear correlation coefficient between the i-th component and the j-th component, λ ik is the characteristic peak position of the i-th component in the k-th mixed solution sample, A jk is the characteristic peak intensity of the jth component in the kth mixed solution sample, is the mean of the characteristic peak position of the i-th component, is the mean characteristic peak intensity of the jth component, and m is the number of mixed solution samples; According to the correlation coefficient r ij , calculate the adaptive weight factor, the formula is: Among them, ω i is the adaptive weight factor of the i-th component, and α is the adjustment parameter; For each peak eigenvector f(F i ) multiplied by the weight factor ω i , get the weighted peak eigenvector matrix V`; The peak eigenvector matrix is ​​optimized by using the adaptive weight factor, and the weighted peak eigenvector matrix V' is analyzed. i The size of the threshold T ω , retain weight factors greater than the threshold T ω The peak feature vector of the weight factor is less than the threshold T. ω The peak eigenvector of .

6. A food detergent component detection system as claimed in claim 5, characterized in that: The method of constructing a food detergent ingredient prediction model to predict ingredient concentrations comprises: A food detergent ingredient evaluation model was constructed using a multi-layer perceptron neural network model, including an input layer, a hidden layer, and an output layer; Set the input layer to the peak feature vector f(F i ), the output layer is the predicted concentration value of each component The spectral analysis data set is divided into a training set and a test set, the training set data is input into the model, and the model parameters are initialized; The defined objective function is used as the loss function for model training. The objective function is designed as the weighted sum of the component concentration prediction error and the detection error. The formula is: Among them, L is the loss value of the objective function, Q i is the actual concentration value of the i-th component, is the predicted concentration value of the i-th component, λ ik is the characteristic peak position of the i-th component in the k-th mixed solution sample, is the predicted characteristic peak position of the i-th component in the k-th mixed solution sample, β is the weight adjustment parameter, m is the number of mixed solution samples, and n is the number of characteristic peaks; Use the Adam optimization algorithm to optimize model parameters, iteratively update model parameters, minimize the loss function, set the learning rate and batch size, and record the loss value of each iteration until the loss function converges; The mean square error and coefficient of determination were calculated to evaluate the predictive performance of the model; Tune the model's hyperparameters, including learning rate, number of hidden layer neurons, and batch size, and use Bayesian optimization to find the best hyperparameter combination; Save the trained model; Food detergent samples were collected from the sampling site, analyzed and detected using spectral technology and preprocessed to extract a new feature vector f(F i ) Input the food detergent component prediction model to obtain the predicted concentration value of each component in the mixed solution sample 7. A food detergent component detection system as claimed in claim 6, characterized in that: The risk assessment refers to setting the concentration threshold T of each ingredient according to the safety standards of food detergent ingredients. i , using adaptive weight factors and predicted concentration values ​​of each component Calculate the comprehensive risk assessment value R: Among them, R i is the comprehensive risk assessment value of the i-th component of the mixed solution sample, ω i is the adaptive weight factor of the ith component, T i is the safety threshold of the ith component, is the predicted concentration value of the i-th component, and p is the number of components in the mixed solution; Set the risk threshold R th , compare the comprehensive risk assessment value R i and risk threshold R th Predict risk levels and provide warnings; If R i ≤R th , then the risk is within an acceptable range and the system operates normally; If R i >R th , the risk exceeds the acceptable range, triggering an alarm and prompting the user to take action.

8. A food detergent component detection system as claimed in claim 7, characterized in that: The encrypting of the detection data and evaluation results during the test process and storing the collected detection data and evaluation results refers to using the AES algorithm to encrypt the detection data and evaluation results collected during the test process, transmitting the encrypted data to the database through a secure transmission protocol, creating a table structure in the database, logging all encryption and storage processes and backing them up regularly, and setting access permissions.

9. A method for detecting ingredients of a food detergent according to any one of claims 1 to 8, characterized in that: include, collecting food detergent and mixing the food detergent with purified water using a solution mixing chamber to form a mixed solution sample; Using spectral analysis detection technology to obtain detection data for mixed solution samples; Extract the features of the test data, build a food detergent ingredient prediction model, predict ingredient concentrations and conduct risk assessment; The test data and evaluation results during the test process are encrypted and stored.

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