A Novel Terahertz Metamaterial Microfluidic Sensing Chip for Ultra-Sensitive Detection.

A terahertz metamaterial microfluidic sensing chip for ultrasensitive detection is proposed to investigate the response of substances to terahertz radiation in liquid environments and enhance the molecular fingerprinting of trace substances. The structure consists of a cover layer, a metal microstructure, a microfluidic channel, a metal reflective layer, and a buffer layer from top to bottom, respectively. The simulation results show that there are three obvious resonance absorption peaks in the range of 1.5-3.0 THz and the absorption intensities are all above 90%. Among them, the absorption intensity at M1 = 1.971 THz is 99.99%, which is close to the perfect absorption, and its refractive index sensitivity and Q-factor are 859 GHz/RIU and 23, respectively, showing excellent sensing characteristics. In addition, impedance matching and equivalent circuit theory are introduced in this paper to further analyze the physical mechanism of the sensor. Finally, we perform numerical simulations using refractive index data of normal and cancer cells, and the results show that the sensor can distinguish different types of cells well. The chip can reduce the sample pretreatment time as well as enhance the interaction between terahertz waves and matter, which can be used for early disease screening and food quality and safety detection in the future.

THz spectrum processing method based on optimal wavelet selection.

Terahertz spectrum is easily interfered by system noise and water-vapor absorption. In order to obtain high quality spectrum and better prediction accuracy in qualitative and quantitative analysis model, different wavelet basis functions and levels of decompositions are employed to perform denoising processing. In this study, the terahertz spectra of wheat samples are denoised using wavelet transform. The compound evaluation indicators (T) are used for systematically analyzing the quality effect of wavelet transform in terahertz spectrum preprocessing. By comparing the optimal denoising effects of different wavelet families, the wavelets of coiflets and symlets are more suitable for terahertz spectrum denoising processing than the wavelets of fejer-korovkin and daubechies, and the performance of symlets 8 wavelet basis function with 4-level decomposition is the optimum. The results show that the proposed method can select the optimal wavelet basis function and decomposition level of wavelet denoising processing in the field of terahertz spectrum analysis.

A Blockchain-Based Traceability Model for Grain and Oil Food Supply Chain.

The structure of the grain-and-oil-food-supply chain has the characteristics of complexity, cross-regionality, a long cycle, and numerous participants, making it difficult to maintain the safety of supply. In recent years, some phenomena have emerged in the field of grain procurement and sale, such as topping the new with the old, rotating grains, the pressure of grades and prices, and counterfeit oil food, which have seriously threatened grain-and-oil-food security. Blockchain technology has the advantage of decentralization and non-tampering Therefore, this study analyzes the characteristics of traceability data in the grain-and-oil-food-supply chain, and presents a blockchain-based traceability model for the grain-and-oil-food-supply chain. Firstly, a new method combining blockchain and machine learning is proposed to enhance the authenticity and reliability of blockchain source data by constructing anomalous data-processing models. In addition, a lightweight blockchain-storage method and a data-recovery mechanism are proposed to reduce the pressure on supply-chain-data storage and improve fault tolerance. The results indicate that the average query delay of public data is 0.42 s, the average query delay of private data is 0.88 s, and the average data-recovery delay is 1.2 s. Finally, a blockchain-based grain-and-oil-food-supply-chain traceability system is designed and built using Hyperledger Fabric. Compared with the existing grain-and-oil-food-supply chain, the model constructed achieves multi-source heterogeneous data uploading, lightweight storage, data recovery, and traceability in the supply chain, which are of great significance for ensuring the safety of grain-and-oil food in China.

Identification of heavy metal pollutants in wheat by THz spectroscopy and deep support vector machine.

This paper proposes to detect heavy metal pollutants in wheat using terahertz spectroscopy and deep support vector machine (DSVM). Five heavy metal pollutants, arsenic, lead, mercury, chromium, and cadmium, were considered for detection in wheat samples. THz spectral data were pre-processed by wavelet denoising. DSVM was introduced to further enhance the accuracy of the SVM classification model. According to the relationship between the accuracy and the training time with the number of hidden layers ranging from 1 to 4, the model performs the best when the hidden layer network has three layers. Besides, using the back-propagation algorithm to optimize the entire DSVM network. Compared with Deep neural network (DNN) and SVM models, the comprehensive evaluation index of the proposed model optimized by DSVM has the highest accuracy of 91.3 %. It realized the exploration enhanced the classification accuracy of the heavy metal pollutants in wheat.

G-RRDB: An Effective THz Image-Denoising Model for Moldy Wheat.

In order to solve the problem of large image noise and unremarkable features caused by factors such as fluctuations in the power of a light source during the terahertz image acquisition of wheat, this paper proposes a THz image-denoising model called G-RRDB. Firstly, a module called Ghost-LKA is proposed by combining a large kernel convolutional attention mechanism module with a Ghost convolutional structure, which improves the characteristics of the network to acquire a global sensory field. Secondly, by integrating a spatial attention mechanism with channel attention, an attention module called DAB is proposed to enhance the network's attention to important features. Thirdly, the Ghost-LKA module and DAB module are combined with the baseline model, thus proposing the dense residual denoising network G-RRDB. Compared with traditional denoising networks, both the PSNR and SSIM are improved. The prediction accuracy of G-RRDB is verified through the classification of the VGG16 network, achieving a rate of 92.8%, which represents an improvement of 1.7% and 0.2% compared to the denoised images obtained from the baseline model and the combined baseline model with the DAB module, respectively. The experimental results demonstrate that G-RRDB, a THz image-denoising model based on dense residual structure for moldy wheat, exhibits excellent denoising performance.

Recent Advances in THz Detection of Water.

The frequency range of terahertz waves (THz waves) is between 0.1 and 10 THz and they have properties such as low energy, penetration, transients, and spectral fingerprints, which are especially sensitive to water. Terahertz, as a frontier technology, have great potential in interpreting the structure of water molecules and detecting biological water conditions, and the use of terahertz technology for water detection is currently frontier research, which is of great significance. Firstly, this paper introduces the theory of terahertz technology and summarizes the current terahertz systems used for water detection. Secondly, an overview of theoretical approaches, such as the relaxation model and effective medium theory related to water detection, the relationship between water molecular networks and terahertz spectra, and the research progress of the terahertz detection of water content and water distribution visualization, are elaborated. Finally, the challenge and outlook of applications related to the terahertz wave detection of water are discussed. The purpose of this paper is to explore the research domains on water and its related applications using terahertz technology, as well as provide a reference for innovative applications of terahertz technology in moisture detection.

A Dual-Band High-Sensitivity THz Metamaterial Sensor Based on Split Metal Stacking Ring.

Terahertz (THz)-detection technology has been proven to be an effective and rapid non-destructive detection approach in biomedicine, quality control, and safety inspection, among other applications. However, the sensitivity of such a detection method is limited due to the insufficient power of the terahertz source and the low content, or ambiguous characteristics, of the analytes to be measured. Metamaterial (MM) is an artificial structure in which periodic sub-wavelength units are arranged in a regular manner, resulting in extraordinary characteristics beyond those possessed by natural materials. It is an effective method to improve the ability of terahertz spectroscopy detection by utilizing the metamaterial as a sensor. In this paper, a dual-band, high-sensitivity THz MM sensor based on the split metal stacking ring resonator (SMSRR) is proposed. The appliance exhibited two resonances at 0.97 and 2.88 THz in the range of 0.1 to 3 THz, realizing multi-point matching between the resonance frequency and the characteristic frequency of the analytes, which was able to improve the reliability and detection sensitivity of the system. The proposed sensor has good sensing performance at both resonant frequencies and can achieve highest sensitivities of 304 GHz/RIU and 912 GHz/RIU with an appropriate thickness of the analyte. Meanwhile, the advantage of multi-point matching of the proposed sensor has been validated by distinguishing four edible oils based on their different refractive indices and demonstrating that the characteristics obtained in different resonant frequency bands are consistent. This work serves as a foundation for future research on band extension and multi-point feature matching in terahertz detection, potentially paving the way for the development of high-sensitivity THz MM sensors.

Adaptive compressed sensing algorithm for terahertz spectral image reconstruction based on residual learning.

Terahertz time-domain spectroscopy (THz-TDS) is widely applied in the field of rapid nondestructive detection of grain owing to its low photon energy and high penetrating power. Nevertheless, terahertz imaging systems suffer from the problems of long image acquisition time and massive data processing. To mitigate these issues, this work presents an adaptive compressed sensing reconstruction algorithm for terahertz spectral images based on residual learning (ATResCS). The algorithm compresses the number of data samples, reducing the amount of data required for imaging and improving the imaging speed. Further, ATResCS reduces the time complexity by employing a convolutional neural network. The algorithm is validated by acquiring terahertz spectral image data via a THz-TDS system. ATResCS outperforms conventional algorithms regarding peak signal-to-noise ratio (PSNR) and structural similarity, significantly reducing the reconstruction time and, thus, enabling real-time reconstruction. Specifically, at low sampling rates (0.1), ATResCS retains key spectral image information. The average PSNR is 0.96 - 1.015 dB higher than that of DR2-Net, reducing the average reconstruction time by 0.1 - 0.2 s. Experiments demonstrate that ATResCS has better reconfiguration capability and lower algorithm complexity, enabling high-quality and fast reconstruction of terahertz spectral images.

Strong Generalized Speech Emotion Recognition Based on Effective Data Augmentation.

The absence of labeled samples limits the development of speech emotion recognition (SER). Data augmentation is an effective way to address sample sparsity. However, there is a lack of research on data augmentation algorithms in the field of SER. In this paper, the effectiveness of classical acoustic data augmentation methods in SER is analyzed, based on which a strong generalized speech emotion recognition model based on effective data augmentation is proposed. The model uses a multi-channel feature extractor consisting of multiple sub-networks to extract emotional representations. Different kinds of augmented data that can effectively improve SER performance are fed into the sub-networks, and the emotional representations are obtained by the weighted fusion of the output feature maps of each sub-network. And in order to make the model robust to unseen speakers, we employ adversarial training to generalize emotion representations. A discriminator is used to estimate the Wasserstein distance between the feature distributions of different speakers and to force the feature extractor to learn the speaker-invariant emotional representations by adversarial training. The simulation experimental results on the IEMOCAP corpus show that the performance of the proposed method is 2-9% ahead of the related SER algorithm, which proves the effectiveness of the proposed method.

Quantitative analysis of wheat maltose by combined terahertz spectroscopy and imaging based on Boosting ensemble learning.

To improve the prediction accuracy of existing data modeling that is based on either spectral data or image data alone, we herein propose a method for the quantitative analysis of wheat maltose contents based on the fusion of terahertz spectroscopy and terahertz imaging, which allows features and balance fusion information to be extracted from the data, and fusion modeling of the feature information to be conducted. Moreover, a Boosting-based, novel multivariate data fusion method and a Boosting iteration termination index based on the structural risk minimization theory are proposed to achieve automatic optimization of the basic model parameters of least squares support vector machines (LS-SVMs). The best results were obtained with data fusion combining spectroscopy and image feature data, with classification performances better than those obtained on single analytical sources, thereby indicating that the multivariate data fusion method proposed is an effective method for the quantitative detection of maltose content in wheat. Furthermore, four unknown maltose concentration wheat samples are analyzed quantitatively using proposed model.

THz Spectroscopic Investigation of Wheat-Quality by Using Multi-Source Data Fusion.

In order to improve the detection accuracy for the quality of wheat, a recognition method for wheat quality using the terahertz (THz) spectrum and multi-source information fusion technology is proposed. Through a combination of the absorption and the refractive index spectra of samples of normal, germinated, moldy, and worm-eaten wheat, support vector machine (SVM) and Dempster-Shafer (DS) evidence theory with different kernel functions were used to establish a classification fusion model for the multiple optical indexes of wheat. The results showed that the recognition rate of the fusion model for wheat samples can be as high as 96%. Furthermore, this approach was compared to the regression model based on single-spectrum analysis. The results indicate that the average recognition rates of fusion models for wheat can reach 90%, and the recognition rate of the SVM radial basis function (SVM-RBF) fusion model can reach 97.5%. The preliminary results indicated that THz-TDS combined with DS evidence theory analysis was suitable for the determination of the wheat quality with better detection accuracy.

Wavelength Selection Method Based on Differential Evolution for Precise Quantitative Analysis Using Terahertz Time-Domain Spectroscopy.

Quantitative analysis of component mixtures is an important application of terahertz time-domain spectroscopy (THz-TDS) and has attracted broad interest in recent research. Although the accuracy of quantitative analysis using THz-TDS is affected by a host of factors, wavelength selection from the sample's THz absorption spectrum is the most crucial component. The raw spectrum consists of signals from the sample and scattering and other random disturbances that can critically influence the quantitative accuracy. For precise quantitative analysis using THz-TDS, the signal from the sample needs to be retained while the scattering and other noise sources are eliminated. In this paper, a novel wavelength selection method based on differential evolution (DE) is investigated. By performing quantitative experiments on a series of binary amino acid mixtures using THz-TDS, we demonstrate the efficacy of the DE-based wavelength selection method, which yields an error rate below 5%.

Quantitative determination of aflatoxin B1 concentration in acetonitrile by chemometric methods using terahertz spectroscopy.

Aflatoxins contaminate and colonize agricultural products, such as grain, and thereby potentially cause human liver carcinoma. Detection via conventional methods has proven to be time-consuming and complex. In this paper, the terahertz (THz) spectra of aflatoxin B1 in acetonitrile solutions with concentration ranges of 1-50µg/ml and 1-50µg/l are obtained and analyzed for the frequency range of 0.4-1.6THz. Linear and nonlinear regression models are constructed to relate the absorption spectra and the concentrations of 160 samples using the partial least squares (PLS), principal component regression (PCR), support vector machine (SVM), and PCA-SVM methods. Our results indicate that PLS and PCR models are more accurate for the concentration range of 1-50µg/ml, whereas SVM and PCA-SVM are more accurate for the concentration range of 1-50µg/l. Furthermore, ten unknown concentration samples extracted from mildewed maize are analyzed quantitatively using these methods.

Early detection of germinated wheat grains using terahertz image and chemometrics.

In this paper, we propose a feasible tool that uses a terahertz (THz) imaging system for identifying wheat grains at different stages of germination. The THz spectra of the main changed components of wheat grains, maltose and starch, which were obtained by THz time spectroscopy, were distinctly different. Used for original data compression and feature extraction, principal component analysis (PCA) revealed the changes that occurred in the inner chemical structure during germination. Two thresholds, one indicating the start of the release of α-amylase and the second when it reaches the steady state, were obtained through the first five score images. Thus, the first five PCs were input for the partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and back-propagation neural network (BPNN) models, which were used to classify seven different germination times between 0 and 48 h, with a prediction accuracy of 92.85%, 93.57%, and 90.71%, respectively. The experimental results indicated that the combination of THz imaging technology and chemometrics could be a new effective way to discriminate wheat grains at the early germination stage of approximately 6 h.

Characterization of wheat varieties using terahertz time-domain spectroscopy.

Terahertz (THz) spectroscopy and multivariate data analysis were explored to discriminate eight wheat varieties. The absorption spectra were measured using THz time-domain spectroscopy from 0.2 to 2.0 THz. Using partial least squares (PLS), a regression model for discriminating wheat varieties was developed. The coefficient of correlation in cross validation (R) and root-mean-square error of cross validation (RMSECV) were 0.985 and 1.162, respectively. In addition, interval PLS was applied to optimize the models by selecting the most appropriate regions in the spectra, improving the prediction accuracy (R = 0.992 and RMSECV = 0.967). Results demonstrate that THz spectroscopy combined with multivariate analysis can provide rapid, nondestructive discrimination of wheat varieties.

[Application of THz technology to nondestructive detection of agricultural product quality].

With recent development of THz sources and detector, applications of THz radiation to nondestructive testing and quality control have expanded in many fields, such as agriculture, safety inspection and quality control, medicine, biochemistry, communication etc. Compared with other detection technique, being a new kind of technique, THz radiation has low energy, good perspectivity, and high signal-to-noise ratio, and thus can obtain physical, chemical and biological information. This paper first introduces the basic concept of THz radiation and the major properties, then gives an extensive review of recent research progress in detection of the quality of agricultural products via THz technique, analyzes the existing shortcomings of THz detection and discusses the outlook of potential application, finally proposes the new application of THz technique to detection of quality of stored grain.

Identification of wheat quality using THz spectrum.

The terahertz (THz) spectra in the range of 0.2-1.6 THz (6.6-52.8 cm-1) of wheat grains with various degrees of deterioration (normal, worm-eaten, moldy, and sprouting wheat grains) were investigated by terahertz time domain spectroscopy. Principal component analysis (PCA) was employed to extract feature data according to the cumulative contribution rates; the top four principal components were selected, and then a support vector machine (SVM) method was applied. Several selection kernels (linear, polynomial, and radial basis functions) were applied to identify the four types of wheat grain. The results showed that the materials were identified with an accuracy of nearly 95%. Furthermore, this approach was compared with others (principal component regression, partial least squares regression, and back-propagation neural networks). The comparisons showed that PCA-SVM outperformed the others and also indicated that the proposed method of THz technology combined with PCA-SVM is efficient and feasible for identifying wheat of different qualities.