Quantitative determination of Auramine O by terahertz spectroscopy with 2DCOS-PLSR model

Paper-based visualization of auramine O in food and drug samples with carbon dots-incorporated fluorescent microspheres as sensing element

A paper-based assay for visualization of auramine O (AO) was for the first time established by using CFMs as a ratiometric fluorescent probe (RFP). The CFMs were melamine formaldehyde microspheres (MFMs) incorporated with carbon dots (CDs), where the CDs species as sensing units and MFMs as a signal amplification carrier. The proposed RFP can quantitatively measure AO content from 0.0 to 10.0 μM and exhibited an ultralow limit of detection (LOD, 15.7 nM). In particular, obvious luminescence color change of CFMs from blue to green was perceived with naked-eyes and therefore, a solution-based and a paper-based visualization platform were respectively proposed for on-site visual detection of AO with LODs of 1.15 μM and 0.83 μM, separately. Finally, those fluorescence methods were adopted in sensitively quantitative measurement of AO within various food and drug samples, providing new prospects for analysts and technical support in food quality monitoring.

Rapid Indentification of Auramine O Dyeing Adulteration in Dendrobium officinale, Saffron and Curcuma by SERS Raman Spectroscopy Combined with SSA-BP Neural Networks Model

(1) Background: Rapid and accurate determination of the content of the chemical dye Auramine O(AO) in traditional Chinese medicines (TCMs) is critical for controlling the quality of TCMs. (2) Methods: Firstly, various models were developed to detect AO content in Dendrobium officinale (D. officinale). Then, the detection of AO content in Saffron and Curcuma using the D. officinale training set as a calibration model. Finally, Saffron and Curcuma samples were added to the training set of D. officinale to predict the AO content in Saffron and Curcuma using secondary wavelength screening. (3) Results: The results show that the sparrow search algorithm (SSA)-backpropagation (BP) neural network (SSA-BP) model can accurately predict AO content in D. officinale, with Rp2 = 0.962, and RMSEP = 0.080 mg/mL. Some Curcuma samples and Saffron samples were added to the training set and after the secondary feature wavelength screening: The Support Vector Machines (SVM) quantitative model predicted Rp2 fluctuated in the range of 0.780 ± 0.035 for the content of AO in Saffron when 579, 781, 1195, 1363, 1440, 1553 and 1657 cm-1 were selected as characteristic wavelengths; the Partial Least Squares Regression (PLSR) model predicted Rp2 fluctuated in the range of 0.500 ± 0.035 for the content of AO in Curcuma when 579, 811, 1195, 1353, 1440, 1553 and 1635 cm-1 were selected as the characteristic wavelengths. The robustness and generalization performance of the model were improved. (4) Conclusion: In this study, it has been discovered that the combination of surface-enhanced Raman spectroscopy (SERS) and machine learning algorithms can effectively and promptly detect the content of AO in various types of TCMs.

Terahertz wave modulation properties of graphene with different excitation laser power

The terahertz wave modulation properties of graphene were investigated using an external 975 nm continuous wave laser with different power. Upon excitation laser, the transmission and modulation depth was measured using terahertz time-domain spectroscopy. The experimental results showed that the modulation depth of monolayer graphene and 3-layer graphene was 16% and 32% under the 1495 mW excitation power. Further, we analyzed the graphene modulation mechanism based on the Drude model and the thin-film approximation. Both theoretical analysis and calculation results showed that the terahertz wave could be modulated using graphene with different excitation laser power.

A Novel Method for Carbendazim High-Sensitivity Detection Based on the Combination of Metamaterial Sensor and Machine Learning

Benzimidazole fungicide residue in food products poses a risk to consumer health. Due to its localized electric-field enhancement and high-quality factor value, the metamaterial sensor is appropriate for applications regarding food safety detection. However, the previous detection method based on the metamaterial sensor only considered the resonance dip shift. It neglected other information contained in the spectrum. In this study, we proposed a method for highly sensitive detection of benzimidazole fungicide using a combination of a metamaterial sensor and mean shift machine learning method. The unit cell of the metamaterial sensor contained a cut wire and two split-ring resonances. Mean shift, an unsupervised machine learning method, was employed to analyze the THz spectrum. The experiment results show that our proposed method could detect carbendazim concentrations as low as 0.5 mg/L. The detection sensitivity was enhanced 200 times compared to that achieved using the metamaterial sensor only. Our present work demonstrates a potential application of combining a metamaterial sensor and mean shift in benzimidazole fungicide residue detection.

Rapid Identification of Easily-Confused Mineral Traditional Chinese Medicine (TCM) Based on Low-Wavenumber Raman and Terahertz Spectroscopy

With the unique advantages of mineral TCMs gradually emerging in clinical treatment, health care, and precaution, it has played an important role in the international medical market. Commonly, mineral TCMs with similar appearance and different processing methods have different effects, but they are easy to be confused in preparation, storage, transportation, and other links, which affects the use and causes related problems. In this paper, six kinds of easily confused mineral TCMs, including coral skeleton, ophicalcitum, calamine, matrii sulfas exsiccatus, gypsum, and alumen, are rapidly characterized using Raman spectroscopy, which can be distinguished with different Raman peaks at 0–300 cm−1 due to the different lattice structure. The THz spectra of these mineral TCMs show that different mineral TCMs have different THz absorption coefficients at 0.3–2.0 THz. Furthermore, compared with the ineffectiveness of the Raman spectrum for differentiating mineral TCMs prepared with disparate processing methods, the terahertz absorption spectrum plays an active role in making up the limitation of low-wavenumber Raman spectroscopy. The combination of low-wavenumber Raman and THz spectroscopy provides a simple and feasible scheme for the identification of mineral TCMs, which could play an important role in the quality control of mineral TCMs.

Quantitative determination of glycerol concentration in aqueous glycerol solutions by metamaterial-based terahertz spectroscopy

Glycerol is an important quality indicator for foodstuffs. There is an increasing request for one more accurate, reliable, and convenient detection of the glycerol concentrations. Terahertz radiation is highly sensitive to the low-frequency intermolecular interactions between the glycerol and waters. Considering the enhancement property of localized field from the metamaterials, terahertz spectroscopy has been utilized for the determination of glycerol content with metamaterial-based biosensor, where the interaction between the analyte and the terahertz wave can be greatly increased. But the quantitative sensing performance was poor due to the sensitivity limitation of single-mode resonance of metamaterial and the lack of appropriate modeling methods. We propose the optimized structural design with internal coupling and multiple resonances. The induced remarkable changes in the lineshape of different transmitted dip regions imply that our metastructure biosensor is of high sensitivity to the change of surrounding environment on the surface dielectric constant, which has been also verified by coupled Lorentz oscillator theory. Furthermore, the optimal partial least squares regression model with variables of spectral lineshape for the first dip region covering the frequency range of 0.45-0.85 THz was established. It shows more accurate and reliable predictions of glycerol concentrations with residual predictive deviation value of 6.095. Metamaterial-based terahertz spectroscopy combined with statistical modeling with lineshape features can provide one new strategy for quantitative sensing. It has great potential for the improvement of determination of analyte concentrations in the practical applications of food, pharmaceutical or cosmetic area.

Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy

Feature extraction is a key factor to detect pesticides using terahertz spectroscopy. Compared to traditional methods, deep learning is able to obtain better insights into complex data features at high levels of abstraction. However, reports about the application of deep learning in THz spectroscopy are rare. The main limitation of deep learning to analyse terahertz spectroscopy is insufficient learning samples. In this study, we proposed a WGAN-ResNet method, which combines two deep learning networks, the Wasserstein generative adversarial network (WGAN) and the residual neural network (ResNet), to detect carbendazim based on terahertz spectroscopy. The Wasserstein generative adversarial network and pretraining model technology were employed to solve the problem of insufficient learning samples for training the ResNet. The Wasserstein generative adversarial network was used for generating more new learning samples. At the same time, pretraining model technology was applied to reduce the training parameters, in order to avoid residual neural network overfitting. The results demonstrate that our proposed method achieves a 91.4% accuracy rate, which is better than those of support vector machine, k-nearest neighbor, naïve Bayes model and ensemble learning. In summary, our proposed method demonstrates the potential application of deep learning in pesticide residue detection, expanding the application of THz spectroscopy.

Evaluation of high-linearity bone radiation detectors exposed to gamma-rays via FTIR measurements

In radiation physics, the study of new alternative dosimeters is of interest to the growing branch of dosimetric characterization for radiotherapy applications. The goal of this work was to expose bone samples to high doses and evaluate their linearity response to gamma rays. The Fourier Transform Infrared (FTIR) spectrophotometry technique was employed as the evaluation technique, and based on the spectrophotometry absorbance profiles the linearity was assessed based on the following methods: Area Under the Curve (AUC), Wavenumber Method (WM), Partial Component Regression (PCR) and Partial Least-Square Regression (PLSR) methods. The bone samples were irradiated with absorbed doses of 10 Gy up to 500 Gy using a ⁶⁰Co Gamma Cell-220 system. The results showed, for the calibration curves of the system, adequate linearity on all methods. In conclusion, the results indicate a good linear response and therefore an interesting potential radiation detector.

Terahertz spectroscopy technology as an innovative technique for food: Current state-of-the-Art research advances

With the dramatic development of source and detector components, terahertz (THz) spectroscopy technology has recently shown a renaissance in various fields such as medical, material, biosensing and pharmaceutical industry. As a rapid and noninvasive technology, it has been extensively exploited to evaluate food quality and ensure food safety. In this review, the principles and processes of THz spectroscopy are first discussed. The current state-of-the-art applications of THz and imaging technologies focused on foodstuffs are then discussed. The advantages and challenges are also covered. This review offers detailed information for recent efforts dedicated to THz for monitoring the quality and safety of various food commodities and the feasibility of its widespread application. THz technology, as an emerging and unique method, is potentially applied for detecting food processing and maintaining quality and safety.

Rapid in situ analysis of l-histidine and α-lactose in dietary supplements by fingerprint peaks using terahertz frequency-domain spectroscopy

A simple, green and nondestructive method based on terahertz fingerprint peaks has been developed for rapid in situ analysis of l-histidine and α-lactose in dietary supplements. Fingerprint absorption peaks of l-histidine and α-lactose located at 0.77 and 0.53 THz could be directly used for identification and quantitation of these analytes in commercial dietary supplements. Compared with the partial least squares regression model (PLSR), the linear least squares regression (LLSR) method based on peak areas presented better performance, with the linear correlation coefficients of 0.9899 and 0.9910 for l-histidine and α-lactose, respectively. Furthermore, analysis time per sample can be shortened to less than 1 min due to the narrower spectral acquisition region. The accuracies were 94.8-110% and 98.9-110%, comparable to those of ion chromatography for l-histidine and high-performance liquid chromatography for α-lactose. The results presented great potential of the developed method for rapid in situ analysis of nutrients in dietary supplements.

A Comprehensive Review on Food Applications of Terahertz Spectroscopy and Imaging

Food product safety is a public health concern. Most of the food safety analytical and detection methods are expensive, labor intensive, and time consuming. A safe, rapid, reliable, and nondestructive detection method is needed to assure consumers that food products are safe to consume. Terahertz (THz) radiation, which has properties of both microwave and infrared, can penetrate and interact with many commonly used materials. Owing to the technological developments in sources and detectors, THz spectroscopic imaging has transitioned from a laboratory-scale technique into a versatile imaging tool with many practical applications. In recent years, THz imaging has been shown to have great potential as an emerging nondestructive tool for food inspection. THz spectroscopy provides qualitative and quantitative information about food samples. The main applications of THz in food industries include detection of moisture, foreign bodies, inspection, and quality control. Other applications of THz technology in the food industry include detection of harmful compounds, antibiotics, and microorganisms. THz spectroscopy is a great tool for characterization of carbohydrates, amino acids, fatty acids, and vitamins. Despite its potential applications, THz technology has some limitations, such as limited penetration, scattering effect, limited sensitivity, and low limit of detection. THz technology is still expensive, and there is no available THz database library for food compounds. The scanning speed needs to be improved in the future generations of THz systems. Although many technological aspects need to be improved, THz technology has already been established in the food industry as a powerful tool with great detection and quantification ability. This paper reviews various applications of THz spectroscopy and imaging in the food industry.

Fast Determination of Auramine O in Food by Adsorptive Stripping Voltammetry

The electrochemical behaviour of auramine O on the hanging mercury drop electrode has been investigated by cyclic and square wave voltammetry method. Reduction peak of auramine O was irreversible and adsorptive on the hanging mercury drop electrode. The optimal conditions were chosen to be Briton-Robinson buffer pH 9.0, accumulation potential -0.5 V vs. Ag/AgCl/KCl, accumulation time 60 s, pulse amplitude 250 mV·s^-1, and frequency 50 Hz. At the optimum experimental conditions, the peak of the target analyte was sharp and asymmetric. The linearity of the peak current depending on the concentration ranged from 4.0 × 10^-8 to 6.4 × 10^-7 mol L^-1. The limit of detection and limit of quantitation were 2.46 × 10^-8 mol L^-1 and 8.21 × 10^-8 mol L^-1, respectively. The recovery and relative standard deviation were 94.9% and 2.0% (n = 5). The developed method was successfully applied to determine auramine O in chicken samples with an appropriate sample preparation.

Non-destructive prediction of protein content in wheat using NIRS

A steady and accurate model used for quality detection depends on precise data and appropriate analytical methods. In this study, the authors applied partial least square regression (PLSR) to construct a model based on the spectral data measured to predict the protein content in wheat, and proposed a new method, global search method, to select PLSR components. In order to select representative and universal samples for modeling, Monte Carlo cross validation (MCCV) was proposed as a tool to detect outliers, and identified 4 outlier samples. Additionally, improved simulated annealing (ISA) combined with PLSR was employed to select most effective variables from spectral data, the data's dimensionality reduced from 100 to 57, and the standard error of prediction (SEP) decreased from 0.0716 to 0.0565 for prediction set, as well as the correlation coefficients (R²) between the predicted and actual protein content of wheat increased from 0.9989 to 0.9994. In order to reduce the dimensionality of the data further, successive projections algorithm (SPA) was then used, the combination of these two methods was called ISA-SPA. The results indicated that calibration model built using ISA-SPA on 14 effective variables achieved the optimal performance for prediction of protein content in wheat comparing with other developed PLSR models (ISA or SPA) by comprehensively considering the accuracy, robustness, and complexity of models. The coefficient of determination increased to 0.9986 and the SEP decreased to 0.0528, respectively.