Aflatoxin b1 detection method based on bic terahertz metamaterial sensor

Abstract

This invention discloses a method for detecting aflatoxin B1 based on a BIC terahertz metamaterial sensor. Based on electromagnetic theory and the time-domain finite-difference method, a continuous-domain bound-state (BIC) terahertz metamaterial sensor was designed. Using this BIC terahertz metamaterial sensor, terahertz enhanced spectra of aflatoxin B1 solutions of different concentrations were acquired. Analysis revealed that the amplitude of the transmission peak of aflatoxin B1 around 1.5 THz gradually decreased with increasing aflatoxin B1 solution concentration, while the frequency of the transmission peak gradually shifted to lower frequencies with increasing aflatoxin B1 solution concentration. PLS and LS-SVM quantitative models for aflatoxin were established. Analysis showed that the LS-SVM model performed better, and the detection limit for aflatoxin B1 was found to be 2.04 × 10⁻⁶. ‑7 μg / ml. This invention enables rapid, non-destructive, and highly sensitive detection of aflatoxin B1 in grains.

Description

Technical Field

[0001] This invention relates to the field of chemical detection technology, and in particular to a method for detecting aflatoxin B1 based on a BIC terahertz metamaterial sensor. Background Technology

[0002] Aflatoxin (AFT) is a metabolite produced by strains of Aspergillus flavus and Parasitic aspergillus, containing a difuran ring and an oxanaphthol structure. There are approximately 20 derivatives, among which B1, B2, G1, and G2 are the most common and toxic. Aflatoxins can contaminate various crops such as cotton, peanuts, corn, and chili peppers, posing a significant threat to human and animal health. Aflatoxin B1 is classified as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC) of the World Health Organization, exhibiting extremely high hepatotoxicity, mutagenicity, and carcinogenicity. Long-term intake may lead to serious health problems such as liver cancer and immunosuppression. Due to its harmfulness, various countries have established strict limits on the content of aflatoxin B1 in food and monitor it through various detection technologies. In China, the maximum allowable content of aflatoxin B1 in vegetable oil is 10 μg / kg, while in Europe it is 2 μg / kg. Aflatoxin must not be detected in infant food. Therefore, it is crucial to establish a highly sensitive, rapid, and non-destructive detection method for aflatoxin B1.

[0003] Currently, common methods for detecting aflatoxin include enzyme-linked immunosorbent assay (ELISA), colloidal gold immunochromatography (CISA), high-performance liquid chromatography (HPLC), liquid chromatography-tandem mass spectrometry (LC-MS / MS), and biosensor methods. These methods suffer from drawbacks such as high cost, long processing time, poor reproducibility, complex and cumbersome experimental procedures, expensive equipment, and potential damage to experimental samples. Therefore, there is an urgent need to establish a highly sensitive, rapid, and non-destructive detection method.

[0004] Terahertz (THz) waves are electromagnetic waves with frequencies ranging from 0.1 to 10 THz, falling between microwaves and infrared light. Due to their low photon energy, strong penetrability, and high spectral resolution, terahertz waves have shown significant potential in biomedicine, chemical detection, and materials analysis. In recent years, researchers have made some progress in using terahertz spectroscopy to detect biomolecules and chemical substances. However, traditional terahertz technology still faces challenges in detecting extremely low concentrations of toxic and harmful substances, mainly because most substances have a weak electromagnetic response to terahertz waves, limiting detection sensitivity. Therefore, how to utilize terahertz technology for the quantitative detection of extremely low concentrations of aflatoxin B1 and improve detection sensitivity is a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of the above, the purpose of this invention is to provide a method for detecting aflatoxin B1 based on terahertz metamaterial sensors and chemometrics, so as to develop a new method based on metamaterials and use the metamaterials to realize the quantitative detection of aflatoxin B1, thus providing a new approach for the quantitative detection of aflatoxin B1.

[0006] This invention provides a method for detecting aflatoxin B1 based on a BIC terahertz metamaterial sensor. The BIC terahertz metamaterial sensor uses a silicon substrate with a gold-plated surface. The BIC terahertz metamaterial sensor is composed of multiple periodic structural units, each of which is a square structure with a width p of 48 μm. Each periodic structural unit has a rectangular non-gold-plated region with a width W of 4.5 μm. The rectangular non-gold-plated region satisfies the condition: I = α * p, where α is an influencing factor and I is the length of the rectangular non-gold-plated region.

[0007] The detection method includes the following steps:

[0008] (1) Prepare multiple aflatoxin B1 standard solutions of different concentrations, and drop the aflatoxin B1 standard solutions onto the BIC terahertz metamaterial sensor respectively, and put them into a drying oven to dry;

[0009] (2) The dried BIC terahertz metamaterial sensor was placed into the terahertz system and measured using the transmission mode to collect the terahertz spectral information of the corresponding sample.

[0010] (3) Use the spectral feature variable extraction algorithm to extract features from terahertz spectral information;

[0011] (4) A quantitative detection model for aflatoxin B1 terahertz transmission spectrum was established based on the extracted features and LS-SVM;

[0012] (5) Obtain the terahertz spectral information of the sample containing aflatoxin B1 to be tested and input it into the quantitative detection model to obtain the content of aflatoxin B1 in the sample to be tested.

[0013] Compared with existing technologies, the aflatoxin B1 detection method based on a BIC terahertz metamaterial sensor provided by this invention designs a continuous-domain bound-state (BIC) terahertz metamaterial sensor based on electromagnetic theory and the finite-difference time-domain method (FDTD). Using this BIC terahertz metamaterial sensor, terahertz enhanced spectra of aflatoxin B1 solutions of different concentrations were acquired. Analysis revealed that the amplitude of the transmission peak of aflatoxin B1 around 1.5 THz gradually decreases with increasing aflatoxin B1 solution concentration, while the frequency of the transmission peak gradually shifts to lower frequencies with increasing aflatoxin B1 solution concentration. PLS and LS-SVM quantitative models for aflatoxin were established. Analysis showed that the LS-SVM model performed better, yielding a detection limit of 2.04 × 10⁻⁶ for aflatoxin B1. -7 μg / ml. This application verifies the feasibility of highly sensitive detection of aflatoxin B1 solution based on BIC terahertz metamaterial sensor combined with THz-TDS technology. This invention uses continuous domain bound state technology to improve the electromagnetic response of aflatoxin B1 to terahertz waves, ultimately improving the detection sensitivity. Using this invention, rapid, non-destructive, and highly sensitive detection of aflatoxin B1 in grains can be achieved.

[0014] Furthermore, the aflatoxin B1 detection method based on a BIC terahertz metamaterial sensor provided by the present invention also has the following technical features:

[0015] The BIC terahertz metamaterial sensor is prepared by the following method:

[0016] The structural parameters of a BIC terahertz metamaterial sensor were designed using the finite-difference time-domain method. A high-Q resonance was achieved by introducing the BIC mechanism into the terahertz metasurface. A silicon substrate was selected, and its surface was cleaned to remove impurities and then oxidized to enhance the adhesion of the photoresist. Subsequently, a chromium layer was deposited on the silicon substrate using thin-film deposition, followed by the deposition of a metal layer to enhance the adhesion between the metal layer and the silicon substrate. Then, a photoresist layer with a thickness of 1-2 μm was coated onto the silicon wafer using a spin-coating process. After coating, pre-baking was performed. Baking at 100℃ for 120 seconds removes the solvent from the photoresist, ensuring it hardens and is ready for exposure. After exposure, the photoresist undergoes a development process, with a development time controlled at 30 seconds, to remove the unexposed areas and leave the pattern. After development, solvent is used for a coating process to remove residual photoresist. Then, reactive ion etching or ion beam etching is performed. After etching, the photoresist mask is removed by ultrasonic cleaning with acetone to ensure no residue remains on the chip surface. Finally, surface oxidation or metal deposition is performed to enhance conductivity and corrosion resistance.

[0017] Specifically, step (1) includes:

[0018] According to the preset mixing scheme, six aflatoxin B1 standard solutions with different concentration gradients were prepared by stepwise dilution using a precision pipette. All samples were shaken thoroughly on a vortex mixer for 3 minutes. Multiple dilution operations were performed during the experiment, and the solutions were stored in volumetric flasks after dilution.

[0019] Specifically, step (2) includes:

[0020] Before the experiment, the system was preheated using an air compressor equipped with an air dryer, and dry air was continuously filled into the optical cavity. The humidity was monitored using a hygrometer to keep it below 10%, and the temperature was kept constant at 25±0.5℃. During the experiment, after each sample was placed in, a 2-minute wait was made for the system to stabilize before starting the measurement. Using a pipette, 20 μl of sample solution was dropped onto the metamaterial sheet in order of increasing sample concentration. After each sample test, the metamaterial sheet was placed in a beaker and shaken to clean it. Before collecting the sample spectra, the metamaterial sheet with the sample was placed in a drying oven for 15 minutes at a temperature of 50℃. Finally, the metamaterial sheet with the sample was placed in the terahertz system and measured using transmission mode. Five points were taken for each sample, and 10 measurements were performed at each point. A total of 300 spectra were obtained from the 6 concentration samples. Each spectrum was the average of 64 repeated detections. All the collected sample terahertz spectra were divided into a calibration set and a prediction set at a ratio of approximately 3:1 using the KS algorithm for subsequent model building.

[0021] In step (5), the established quantitative detection model of aflatoxin B1 terahertz transmission spectrum is evaluated using the prediction set correlation coefficient, prediction set root mean square error, correction correlation coefficient, and correction root mean square error.

[0022] Among them, the spectral feature variable extraction algorithm is any one of the following: competitive adaptive reweighted sampling algorithm, non-information variable elimination algorithm, continuous projection algorithm, variable projection importance analysis method, and iterative information variable retention algorithm.

[0023] Where α takes the value of 0.7. Attached Figure Description

[0024] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0025] Figure 1 This is a schematic diagram of a periodic structural unit in a BIC terahertz metamaterial sensor.

[0026] Figure 2 The transmission spectrum of metamaterial A at different angles is shown.

[0027] Figure 3 The transmission spectrum of metamaterial B at different angles is shown.

[0028] Figure 4 The transmission spectrum of metamaterial C at different angles is shown.

[0029] Figure 5 The transmission spectrum of metamaterial D at different angles is shown.

[0030] Figure 6 The transmission spectrum of the metamaterial E with α = 0.7 was measured at different angles.

[0031] Figure 7 Transmission spectra of metamaterials with different α values ​​at 90°;

[0032] Figure 8 Transmission spectra of aflatoxin B1 solutions of different concentrations on the surface of metamaterials;

[0033] Figure 9 The figure shows the fitting plot of the THz spectrum of aflatoxin B1 solution between the predicted and actual values ​​of the PLS and LS-SVM models. Detailed Implementation

[0034] To make the objectives, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the present invention will be thorough and complete.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0036] Please see Figure 1This invention provides a BIC terahertz metamaterial sensor. In this embodiment, the BIC terahertz metamaterial sensor uses a silicon substrate (refractive index 3.335) with a gold-plated surface. The BIC terahertz metamaterial sensor is composed of multiple periodic structural units. The periodic structural units are square structures with a width p of 48 μm. Each periodic structural unit has a rectangular non-gold-plated region, which refers to a region containing only the silicon substrate and without a gold-plated film. The width W of the rectangular non-gold-plated region is 4.5 μm. The rectangular non-gold-plated region satisfies the condition: I = α * p, where α is an influence factor and I is the length of the rectangular non-gold-plated region.

[0037] Bound states in the continuous domain (BIC) are resonances that occur within the frequency range of the radiation continuum and are completely localized. They possess extremely high quality factors (Q) and exhibit strong interactions between light and matter. This phenomenon is crucial for the development of novel functional devices. Introducing the BIC mechanism into terahertz metasurfaces provides a new approach for customizing high-Q resonances. The project plans to use polarized terahertz light as incident light and introduce a perturbation factor. By adjusting the perturbation factor, the aim is to achieve BIC resonance that enhances the strong interaction between the terahertz spectrum and matter.

[0038] In the fabrication of the BIC terahertz metamaterial sensor, this invention utilizes the finite-difference time-domain (FDTD) method to design the structural parameters of the sensor. Considering that the metamaterial is composed of periodic structural units, the transmission or reflection properties of a single periodic structural unit can represent the spectral properties of the entire metamaterial. Therefore, in the simulation process, simulating a single periodic structural unit of the metamaterial can improve the simulation speed.

[0039] Specifically, a silicon substrate is first selected. The substrate surface is cleaned to remove impurities and then oxidized to enhance the adhesion of the photoresist. Next, a chromium layer is deposited on the silicon substrate using thin-film deposition technology, followed by a metal layer to enhance the adhesion between the metal layer and the silicon substrate. Then, a layer of photoresist with a thickness of 1-2 μm is coated onto the silicon wafer using a spin-coating process. After coating, pre-baking is performed at 100°C for 120 seconds to remove the solvent from the photoresist, ensuring it hardens and is ready for exposure. After exposure, the development process begins, with a development time controlled at 30 seconds, removing the unexposed photoresist and leaving the pattern. After development, solvent is used for descaling to remove residual photoresist. Then, reactive ion etching or ion beam etching is performed. After etching, the photoresist mask is removed by ultrasonic cleaning with acetone to ensure no residue remains on the chip surface. Finally, surface oxidation or metal deposition post-treatment is performed to enhance conductivity and corrosion resistance.

[0040] In this embodiment, α is set to 0.3, 0.4, 0.5, 0.6, and 0.7 respectively, resulting in I values ​​of 14.4, 19.2, 24, 28.8, and 33.2 μm.

[0041] In this embodiment, metamaterials with different α values ​​were tested. Metamaterials with α values ​​of 0.3, 0.4, 0.5, 0.6, and 0.7 were named A, B, C, D, and E, respectively. Terahertz transmission spectra were measured at different angles for each of the metamaterials A, B, C, D, and E. Figure 2 The transmission spectrum of metamaterial A at different angles is shown. Figure 3 The transmission spectrum of metamaterial B at different angles is shown. Figure 4 The transmission spectrum of metamaterial C at different angles is shown. Figure 5 The transmission spectrum of metamaterial D at different angles is shown. Figure 6 The transmission spectrum of metamaterial E with α = 0.7 was measured at different angles. The comparison showed that the BIC metamaterial performed best at angles of 90° and 270°. Figure 7 The images show the transmission spectra of metamaterials with different α values ​​at 90°. From... Figure 7 It can be seen that the metamaterial E with an α of 0.7 has the best effect.

[0042] Another embodiment of the present invention provides a method for detecting aflatoxin B1 based on a BIC terahertz metamaterial sensor, wherein the BIC terahertz metamaterial sensor is the aforementioned BIC terahertz metamaterial sensor, and the detection method includes the following steps (1) to (5):

[0043] (1) Prepare multiple aflatoxin B1 standard solutions of different concentrations, and drop the aflatoxin B1 standard solutions onto the terahertz metamaterial absorber and dry them in a drying oven.

[0044] Aflatoxin B1 standard solution was purchased from Pruibon Biotechnology, with an initial concentration of 25 μg / ml. To meet the requirements for trace detection, the standard solution was serially diluted using ultrapure deionized water (18.2 MΩ·cm, Merck Millipore, USA). The specific operation was as follows: First, according to the preset preparation ratio, six standard solutions with different concentration gradients (the highest concentration being 5.376 × 10⁻⁶) were prepared stepwise using a precision pipette. -7 The concentration was μg / ml, far lower than the solubility of aflatoxin B1 in water (0.012 g / L). All diluted samples were stored in 10 ml volumetric flasks. To ensure solution homogeneity, all samples were vortexed for 3 minutes. Multiple dilutions were performed during the experiment: the initial dilution yielded aflatoxin B1 concentrations ranging from 4.8 × 10⁻⁶ μg / ml.-5 μg / ml~3.84×10 -4 μg / ml; after a second dilution, the concentration range was adjusted to 1.92×10 μg / ml. -6 μg / ml~1.536×10 -5 μg / ml; finally, a third dilution was performed to obtain the ultra-low concentration series of standard solutions shown in Table 1. The entire dilution process strictly followed the operating procedures for trace analysis to ensure the reliability of the experimental data.

[0045] Table 1. Proportions for Aflatoxin B1 Solution

[0046]

[0047] (2) The dried terahertz metamaterial absorber was placed in the terahertz system and measured using the transmission mode to collect the terahertz spectral information of the corresponding sample.

[0048] Terahertz transmission spectroscopy of aflatoxin B1 solution was measured using a TAS7500 terahertz time-domain spectrometer from Advantest, Japan. This system consists of three main parts: a femtosecond laser, a terahertz emitter, and a terahertz detector. The spectral measurement range is 0.1–5.0 THz, and the instrument resolution is 7.6 GHz. To avoid interference from the surrounding environment and to obtain stable sample information, the system was preheated using an air compressor equipped with an air dryer before the experiment. Dry air was continuously filled into the optical cavity, and the humidity was maintained below 10% using a hygrometer. The temperature was kept constant at 25 ± 0.5 °C. During the experiment, a 2-minute waiting period was observed after each sample was placed in the system until it stabilized before measurement began. To reduce the influence of residual concentration on the metamaterial sheet, 20 μl of sample solution was pipetted onto the metamaterial sheet in ascending order of sample concentration using a pipette. After each sample test, the metamaterial sheet was placed in a beaker and shaken to clean it thoroughly. Because water strongly absorbs terahertz waves, to ensure the solution on the metamaterial sheet was thoroughly dried before collecting the sample spectra, the metamaterial sheet containing the sample was placed in a drying oven at 50°C for 15 minutes. Finally, the metamaterial sheet containing the sample was placed in the terahertz system and measured using transmission mode. To reduce the impact of random errors on the experimental results, five points were taken for each sample, and ten measurements were performed at each point. A total of 300 spectra were obtained from the six concentration samples, and each spectrum was the average of 64 repeated measurements. All collected terahertz spectra were divided into a calibration set and a prediction set at approximately a 3:1 ratio using the Kennard-Stone (KS) algorithm for subsequent model building.

[0049] Then, the terahertz spectral parameters were extracted. First, the terahertz time-domain spectra of aflatoxin at different concentrations were collected, and the frequency domain signal was obtained by fast Fourier transform (FFT), which is expressed as formula (1).

[0050]

[0051] Where A(ω) represents the electric field amplitude, The phase difference between the reference signal and the sample signal, E(t), is the terahertz time-domain signal. The refractive index and absorption coefficient of the sample are obtained by formulas (2) and (3).

[0052]

[0053] Where n(ω) is the refractive index, α(ω) is the absorption coefficient, ω is the frequency, k(ω) is the extinction coefficient, ρ(ω) is the amplitude ratio function, d is the sample thickness, and c is the speed of light in vacuum.

[0054] Subsequently, the terahertz transmission spectrum of aflatoxin was extracted by comparing the sample spectrum and the reference spectrum using formula (4).

[0055] T = (A S / A R ) 2 (4)

[0056] Where A S and A R These are the amplitudes of the sample signal and the reference signal, respectively.

[0057] Aflatoxin B1 solution was detected and analyzed using BIC metamaterials combined with THz-TDS technology. Figure 8 The transmission spectrum of aflatoxin B1 solution at a partial concentration on the surface of the metamaterial absorber is shown. Considering the large amount of noise interference in the frequency bands below 1THz and above 3THz, the transmission spectrum in the 1-3THz frequency band was selected to ensure the accuracy of the measurement. The 263 spectral data in this frequency band were used for subsequent modeling. Figure 8 The mid-transmission peaks primarily reflect the transmission characteristics of the metamaterial itself, showing transmission peaks around 1.5 THz. Figure 8The magnified section shows a partial enlarged view of the transmission spectrum of aflatoxin B1 solution at a concentration of 1.5 THz. Although the content of aflatoxin B1 on the surface of the BIC metamaterial is extremely low, after signal enhancement sensing, a clear amplitude and frequency shift response pattern is observed in the transmission peak around 1.5 THz. Specifically, the amplitude of the transmission peak around 1.5 THz gradually decreases with increasing aflatoxin B1 solution concentration, and the frequency of the transmission peak gradually shifts to lower frequencies with increasing aflatoxin B1 solution concentration. This provides a theoretical basis for further analysis of the transmission peak characteristics of the metamaterial and the response relationship between aflatoxin B1 solution concentration.

[0058] (3) Use the spectral feature variable extraction algorithm to extract features from terahertz spectral information.

[0059] The algorithm for extracting spectral feature variables can be any one of the following: competitive adaptive reweighted sampling algorithm, non-information variable elimination algorithm, continuous projection algorithm, variable projection importance analysis method, and iterative information-preserving variable algorithm.

[0060] Competitive Adaptive Reweighted Sampling (CARS) is a feature variable selection method that combines Monte Carlo sampling with adaptive reweighting. This algorithm simulates a "survival of the fittest" competition mechanism, dynamically selecting informational variables with significant spectral characteristics during the iterative process. The core process of the CARS algorithm includes two key stages: first, it gradually reduces the number of candidate variables based on the Exponentially Decreasing Function (EDF); then, it retains important variables with larger absolute regression coefficients through Adaptive Reweighted Sampling (ARS). In each Monte Carlo sampling iteration, the algorithm calculates the regression coefficients of each variable and competitively selects variables based on the absolute value of the coefficients, ultimately obtaining the optimal subset of variables.

[0061] Uninformative Variable Elimination (UVE) is a statistical analysis-based variable selection method that eliminates redundant or uninformative spectral variables by evaluating the contribution of each variable to the model, thereby improving the predictive performance and robustness of the model. Stability is calculated using the mean vector and the standard deviation of the coefficient matrix.

[0062] The Successive Projections Algorithm (SPA) is a forward iterative feature selection method primarily used to address multicollinearity in spectral analysis. This algorithm effectively extracts the most representative spectral features by progressively constructing an optimal subset of variables.

[0063] Variable importance projection (VIP) is a variable selection method based on partial least squares algorithm, primarily used to evaluate the importance of each variable in a dataset for explaining a target variable. This method assesses variable importance by calculating the projected contribution of each variable in principal component analysis or factor analysis.

[0064] Iteratively Retaining Informative Variables (IRIV) is a variable selection method that combines iterative optimization with variable importance assessment. This method progressively optimizes a subset of feature variables by iteratively executing a "retain-remove" bidirectional selection mechanism. Its core idea is to consider both the information contribution and redundancy of variables in each iteration, achieving feature selection by dynamically evaluating the discriminative power of the variables.

[0065] First, the acquired terahertz transmission spectra were divided into a calibration set and a prediction set at a ratio of approximately 3:1 using the KS algorithm. Then, feature extraction was performed using five algorithms: iterative information-preserving variable algorithm, non-information variable elimination method, continuous projection algorithm, variable importance projection method, and competitive adaptive reweighting algorithm. Finally, these features were input into the PLS model. Table 2 shows the validation results of different feature extraction methods on the full-concentration PLS model of the THz spectrum of aflatoxin B1 solution.

[0066] Table 2. Validation results of different feature extraction methods on the THz spectrum PLS model of aflatoxin B1 solution.

[0067]

[0068] The correlation coefficient R of the model prediction after feature extraction using UVE and CARS algorithms P All improved, and the root mean square error of prediction decreased. The PLS model, after CARS variable extraction, showed the highest accuracy, with a prediction correlation coefficient R0. P The RMSEP reached 0.901, and the root mean square error (RMSEP) was 5.68 × 10⁻⁶. -8 .

[0069] The aflatoxin B1 solution content detection model consists of the related coefficient of prediction set (RP), the root mean square error of prediction set (RMSEP), and the related coefficient of correction set (R²). C The model is evaluated using the root mean square error of correction set (RMSEC), where R0... P and R C R is between 0 and 1 P and R C The closer the value is to 1, the higher the accuracy of the model. The closer the values ​​of RMSEP and RMSEC are and the smaller they are, the more stable the model is.

[0070] The limit of detection (LOD) refers to the minimum concentration of a substance that can be detected. The LOD with a confidence interval of 99.86% can be calculated from the slope of the fitting curve between the true and predicted values ​​established using THz transmission spectroscopy and the variance of the prediction error.

[0071]

[0072] Where σ is the standard error of the predicted concentration, m is the slope of the fitted curve, and RMSEP in the model is equal to the value of σ.

[0073] (4) A quantitative detection model for aflatoxin B1 terahertz transmission spectrum was established based on the extracted features and LS-SVM.

[0074] Partial Least Squares Regression (PLSR) is a multivariate statistical modeling method that combines the advantages of multiple linear regression, canonical correlation analysis, and principal component analysis.

[0075] Least Squares Support Vector Machine (LS-SVM) is a machine learning algorithm improved upon the standard Support Vector Machine (SVM). Its core feature is transforming the inequality constraints in SVM into equality constraints, thus simplifying the quadratic programming problem into solving a system of linear equations. Its key parameters are the input vector, the type of kernel function, and its corresponding parameters. Commonly used kernel functions are the linear kernel (Lin-Kernel) and the radial basis function (RBF-kernel).

[0076] Least Squares Support Vector Machine (LS-SVM) is a machine learning method developed based on statistical theory. Building upon the aforementioned research, the KS algorithm was used to divide the samples into a calibration set and a prediction set at approximately a 3:1 ratio. Subsequently, five algorithms—IRIV, UVE, SPA, VIP, and CARS—were used for feature extraction, and these features were then input into the LS-SVM model. Table 3 shows the validation results of different feature extraction methods on the mixed LS-SVM model of the THz spectrum of aflatoxin B1 solution. The LS-SVM model using the RBF basis function showed better accuracy than the Lin basis function, achieving the best performance without feature extraction. The model's prediction correlation coefficient R0 was the highest. P The accuracy reached 0.920, and the root mean square error of prediction (RMSEP) reached 5.30 × 10⁻⁶. -8 At this point, the parameter combination is γ = 4.982107, σ 2 =332.5579.

[0077] Table 3. Validation results of the LS-SVM model for the THz spectra of aflatoxin B1 solution using different feature extraction methods.

[0078]

[0079] Figure 9 The image shows the fitting plots of the THz spectra of aflatoxin B1 solution between the PLS and LS-SVM models and the actual values. The results show that the LS-SVM model generally outperforms the PLS model, with the LS-SVM model based on the RBF kernel function achieving the highest prediction accuracy. The model's prediction correlation coefficient (RP) reaches 0.920, and the root mean square error (RMSEP) reaches 5.30 × 10⁻⁶. -8 The slope of the fitted curve reached 0.7787. Based on the slope of the fitted curve and the root mean square error variance of the prediction, the detection limit was calculated to be 2.04 × 10⁻⁶. -7 μg / ml.

[0080] This invention designs a BIC terahertz metamaterial sensor based on electromagnetic theory. Preliminary experiments determined the metamaterial sheet corresponding to the optimal polarization angle and perturbation factor. Based on this, quantitative detection of aflatoxin B1 was performed, verifying the feasibility of highly sensitive detection of aflatoxin B1 solution using THz-TDS technology based on the metamaterial sensor. Analysis revealed that as the concentration of aflatoxin B1 solution increases, the amplitude of the terahertz transmission peak gradually decreases, and the frequency of the transmission peak gradually shifts to lower frequencies with increasing aflatoxin B1 solution concentration. A quantitative model for aflatoxin B1 solution was established, yielding a detection limit of 2.04 × 10⁻⁶. -7 μg / ml provides a theoretical basis for the highly sensitive detection of aflatoxin B1 solution using a terahertz metamaterial sensor combined with THz-TDS technology.

[0081] (5) Obtain the terahertz spectral information of the sample containing aflatoxin B1 to be tested and input it into the quantitative detection model to obtain the content of aflatoxin B1 in the sample to be tested.

[0082] In summary, the aflatoxin B1 detection method based on a BIC terahertz metamaterial sensor provided by this invention designs a continuous-domain bound-state (BIC) terahertz metamaterial sensor based on electromagnetic theory and the finite-difference time-domain method (FDTD). Using this BIC terahertz metamaterial sensor, terahertz enhanced spectra of aflatoxin B1 solutions of different concentrations were acquired. Analysis revealed that the amplitude of the transmission peak of aflatoxin B1 around 1.5 THz gradually decreases with increasing aflatoxin B1 solution concentration, while the frequency of the transmission peak gradually shifts to lower frequencies with increasing aflatoxin B1 solution concentration. PLS and LS-SVM quantitative models for aflatoxin were established. Analysis showed that the LS-SVM model performed better, yielding a detection limit of 2.04 × 10⁻⁶ for aflatoxin B1. -7 μg / ml. This application verifies the feasibility of highly sensitive detection of aflatoxin B1 solution based on BIC terahertz metamaterial sensor combined with THz-TDS technology. This invention uses continuous domain bound state technology to improve the electromagnetic response of aflatoxin B1 to terahertz waves, ultimately improving the detection sensitivity. Using this invention, rapid, non-destructive, and highly sensitive detection of aflatoxin B1 in grains can be achieved.

[0083] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for detection of aflatoxin B1 based on BIC terahertz metamaterial sensor, characterized in that, The BIC terahertz metamaterial sensor uses a silicon substrate with a gold-plated surface. The BIC terahertz metamaterial sensor consists of multiple periodic structural units, each of which is square and has a width p of 48 μm. Each periodic structural unit has a rectangular non-gold-plated region with a width W of 4.5 μm. The rectangular non-gold-plated region satisfies the condition: I = α * p, where α is an influence factor and I is the length of the rectangular non-gold-plated region. The detection method includes the following steps: (1) Prepare multiple aflatoxin B1 standard solutions of different concentrations, and drop the aflatoxin B1 standard solutions onto the BIC terahertz metamaterial sensor respectively, and put them into a drying oven to dry; (2) The dried BIC terahertz metamaterial sensor was placed into the terahertz system and measured using the transmission mode to collect the terahertz spectral information of the corresponding sample. (3) Use the spectral feature variable extraction algorithm to extract features from terahertz spectral information; (4) A quantitative detection model for aflatoxin B1 terahertz transmission spectrum was established based on the extracted features and LS-SVM; (5) Obtain the terahertz spectral information of the sample containing aflatoxin B1 to be tested and input it into the quantitative detection model to obtain the content of aflatoxin B1 in the sample to be tested.

2. The method for detecting aflatoxin B1 based on a BIC terahertz metamaterial sensor according to claim 1, characterized in that, The BIC terahertz metamaterial sensor was prepared by the following method: The structural parameters of the BIC terahertz metamaterial sensor were designed using the finite-difference time-domain method. By introducing the BIC mechanism into the terahertz metasurface, a high Q-value resonance was achieved. A silicon substrate was selected, and the substrate surface was cleaned to remove impurities and then oxidized to enhance the adhesion of the photoresist. Subsequently, a chromium layer is deposited on the silicon substrate using thin film deposition technology, followed by the deposition of a metal layer to enhance the adhesion between the metal layer and the silicon substrate; Then, a layer of photoresist with a thickness of 1-2 μm is coated onto the silicon wafer using a spin coating process; After coating, pre-baking is performed at 100°C for 120 seconds to remove the solvent from the photoresist, ensuring it hardens and is ready for exposure; After exposure, the development process begins, with the development time controlled at 30 seconds, to remove the photoresist from the unexposed areas and leave the pattern. After development, a solvent is used for photoresist coating to remove residual photoresist. Then, reactive ion etching or ion beam etching is used for etching. After etching, the photoresist mask is removed by ultrasonic cleaning with acetone to ensure that there are no residues on the chip surface. Finally, surface oxidation or metal deposition post-treatment is performed to enhance conductivity and corrosion resistance.

3. The method for detecting aflatoxin B1 based on a BIC terahertz metamaterial sensor according to claim 1, characterized in that, Step (1) specifically includes: According to the preset mixing scheme, six aflatoxin B1 standard solutions with different concentration gradients were prepared by stepwise dilution using a precision pipette. All samples were shaken thoroughly on a vortex mixer for 3 minutes. Multiple dilution operations were performed during the experiment, and the solutions were stored in volumetric flasks after dilution.

4. The method for detecting aflatoxin B1 based on a BIC terahertz metamaterial sensor according to claim 1, characterized in that, Step (2) specifically includes: Before the experiment, the system was preheated using an air compressor equipped with an air dryer, and dry air was continuously filled into the optical cavity. The humidity was monitored using a hygrometer to keep it below 10%, and the temperature was kept constant at 25±0.5℃. During the experiment, after each sample was placed in, a 2-minute wait was made for the system to stabilize before starting the measurement. Using a pipette, 20 μl of sample solution was dropped onto the metamaterial sheet in order of increasing sample concentration. After each sample test, the metamaterial sheet was placed in a beaker and shaken to clean it. Before collecting the sample spectra, the metamaterial sheet with the sample was placed in a drying oven for 15 minutes at a temperature of 50℃. Finally, the metamaterial sheet with the sample was placed in the terahertz system and measured using transmission mode. Five points were taken for each sample, and 10 measurements were performed at each point. A total of 300 spectra were obtained from the 6 concentration samples. Each spectrum was the average of 64 repeated detections. All the collected sample terahertz spectra were divided into a calibration set and a prediction set at a ratio of approximately 3:1 using the KS algorithm for subsequent model building.

5. The method for detecting aflatoxin B1 based on a BIC terahertz metamaterial sensor according to claim 1, characterized in that, In step (5), the established quantitative detection model of carbendazim terahertz transmission spectrum is evaluated using the prediction set correlation coefficient, prediction set root mean square error, correction correlation coefficient, and correction root mean square error.

6. The method for detecting aflatoxin B1 based on a BIC terahertz metamaterial sensor according to claim 1, characterized in that, The spectral feature variable extraction algorithm can be any one of the following: competitive adaptive reweighted sampling algorithm, non-information variable elimination algorithm, continuous projection algorithm, variable projection importance analysis method, and iterative information variable retention algorithm.

7. The method for detecting aflatoxin B1 based on a BIC terahertz metamaterial sensor according to claim 1, characterized in that, The value of α is 0.7.

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