Research Status in the Use of Surface-Enhanced Raman Scattering (SERS) to Detect Pesticide Residues in Foods and Plant-Derived Chinese Herbal Medicines.

Surface-enhanced Raman scattering (SERS) technology has unique advantages in the rapid detection of pesticides in plant-derived foods, leading to reduced detection limits and increased accuracy. Plant-derived Chinese herbal medicines have similar sources to plant-derived foods; however, due to the rough surfaces and complex compositions of herbal medicines, the detection of pesticide residues in this context continues to rely heavily on traditional methods, which are time consuming and laborious and are unable to meet market demands for portability. The application of flexible nanomaterials and SERS technology in this realm would allow rapid and accurate detection in a portable format. Therefore, in this review, we summarize the underlying principles and characteristics of SERS technology, with particular focus on applications of SERS for the analysis of pesticide residues in agricultural products. This paper summarizes recent research progress in the field from three main directions: sample pretreatment, SERS substrates, and data processing. The prospects and limitations of SERS technology are also discussed, in order to provide theoretical support for rapid detection of pesticide residues in Chinese herbal medicines.

A comprehensive strategy for prediction and quality evaluation of standardized planting herbs based on plant metabolomics coupled with extreme learning machine: Astragali Radix as an example.

INTRODUCTION: Standardizing the planting process is an effective way to control the quality stability of herbal resources, which are susceptible to external environmental factors (e.g., moisture, soil, etc.). However, how to scientifically and comprehensively assess the effects of standardized planting on plant quality and quickly test unknown samples has not been addressed. OBJECTIVE: The aim of this study was to determine and compare the metabolite levels of herbs before and after standardized planting, to quickly distinguish their sources, and to evaluate their quality, using the typical herb Astragali Radix (AR) as an example. METHODS: In this study, an efficient strategy using liquid chromatography-mass spectrometry (LC-MS) based on plant metabolomics combined with extreme learning machine (ELM) has been developed to efficiently distinguish and predict AR after standardized planting. Moreover, a comprehensive multi-index scoring method has been developed for the comprehensive evaluation of the quality of AR. RESULTS: The results confirmed that AR after standardized planting was significantly differentiated, with a relatively stable content of 43 differential metabolites, mainly including flavonoids. An ELM model was established based on LC-MS data, and the accuracy in predicting unknown samples could reach more than 90%. As expected, higher total scores were obtained for AR after standardized planting, indicating much better quality. CONCLUSION: A dual system for evaluating the impact of standardized planting on the quality of plant resources has been established, which will significantly contribute to innovation in the quality evaluation of medicinal herbs and support the selection of optimal planting conditions.

High-Precision Automatic Identification of Fentanyl-Related Drugs by Terahertz Spectroscopy with Molecular Dynamics Simulation and Spectral Similarity Mapping.

Fentanyl is a potent opioid analgesic with high bioavailability. It is the leading cause of drug addiction and overdose death. To better control the abuse of fentanyl and its derivatives, it is crucial to develop rapid and sensitive detection methods. However, fentanyl-related substrates undergo similar molecular structures resulting in similar properties, which are difficult to be identified by conventional spectroscopic methods. In this work, a method for the automatic identification of 8 fentanyl-related substances with similar spectral characteristics was developed using terahertz (THz) spectroscopy coupled with density functional theory (DFT) and spectral similarity mapping (SSM). To characterize the THz fingerprints of these fentanyl-related samples more accurately, the method of baseline estimation and denoising with sparsity was performed before revealing the unique molecular dynamics of each substance by DFT. The SSM method was proposed to identify these fentanyl analogs based on weighted spectral cosine-cross similarity and fingerprint discrete Fréchet distance, generating a matching list by stepwise searching the entire spectral database. The top matched list returned the identification results of the target fentanyl analogs with accuracies of 94.48~99.33%. Results from this work provide algorithms' increased reliability, which serves as an artificial intelligence-based tool for high-precision fentanyl analysis in real-world samples.

Rapid Quantitative Detection of Deltamethrin in Corydalis yanhusuo by SERS Coupled with Multi-Walled Carbon Nanotubes.

With the increase in demand, artificially planting Chinese medicinal materials (CHMs) has also increased, and the ensuing pesticide residue problems have attracted more and more attention. An optimized quick, easy, cheap, effective, rugged and safe (QuEChERS) method with multi-walled carbon nanotubes as dispersive solid-phase extraction sorbents coupled with surface-enhanced Raman spectroscopy (SERS) was first proposed for the detection of deltamethrin in complex matrix Corydalis yanhusuo. Our results demonstrate that using the optimized QuEChERS method could effectively extract the analyte and reduce background interference from Corydalis. Facile synthesized gold nanoparticles with a large diameter of 75 nm had a strong SERS enhancement for deltamethrin determination. The best prediction model was established with partial least squares regression of the SERS spectra ranges of 545~573 cm-1 and 987~1011 cm-1 with a coefficient of determination (R2) of 0.9306, a detection limit of 0.484 mg/L and a residual predictive deviation of 3.046. In summary, this article provides a new rapid and effective method for the detection of pesticide residues in CHMs.

Rapid Determination of Chlorogenic Acid, Luteoloside and 3,5-O-dicaffeoylquinic Acid in Chrysanthemum Using Near-Infrared Spectroscopy.

The feasibility of near-infrared spectroscopy (NIR) to detect chlorogenic acid, luteoloside and 3,5-O-dicaffeoylquinic acid in Chrysanthemum was investigated. An NIR spectroradiometer was applied for data acquisition. The reference values of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid of the samples were determined by high-performance liquid chromatography (HPLC) and were used for model calibration. The results of six preprocessing methods were compared. To reduce input variables and collinearity problems, three methods for variable selection were compared, including successive projections algorithm (SPA), genetic algorithm-partial least squares regression (GA-PLS), and competitive adaptive reweighted sampling (CARS). The selected variables were employed as the inputs of partial least square (PLS), back propagation-artificial neural networks (BP-ANN), and extreme learning machine (ELM) models. The best performance was achieved by BP-ANN models based on variables selected by CARS for all three chemical constituents. The values of rp2 (correlation coefficient of prediction) were 0.924, 0.927, 0.933, the values of RMSEP were 0.033, 0.018, 0.064 and the values of RPD were 3.667, 3.667, 2.891 for chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid, respectively. The results indicated that NIR spectroscopy combined with variables selection and multivariate calibration methods could be considered as a useful tool for rapid determination of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid in Chrysanthemum.

Sensitive Wavelengths Selection in Identification of Ophiopogon japonicus Based on Near-Infrared Hyperspectral Imaging Technology.

Hyperspectral imaging (HSI) technology has increasingly been applied as an analytical tool in fields of agricultural, food, and Traditional Chinese Medicine over the past few years. The HSI spectrum of a sample is typically achieved by a spectroradiometer at hundreds of wavelengths. In recent years, considerable effort has been made towards identifying wavelengths (variables) that contribute useful information. Wavelengths selection is a critical step in data analysis for Raman, NIRS, or HSI spectroscopy. In this study, the performances of 10 different wavelength selection methods for the discrimination of Ophiopogon japonicus of different origin were compared. The wavelength selection algorithms tested include successive projections algorithm (SPA), loading weights (LW), regression coefficients (RC), uninformative variable elimination (UVE), UVE-SPA, competitive adaptive reweighted sampling (CARS), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), and genetic algorithms (GA-PLS). One linear technique (partial least squares-discriminant analysis) was established for the evaluation of identification. And a nonlinear calibration model, support vector machine (SVM), was also provided for comparison. The results indicate that wavelengths selection methods are tools to identify more concise and effective spectral data and play important roles in the multivariate analysis, which can be used for subsequent modeling analysis.

Ultramicroscopy reveals a layer of multiply folded membranes around the tannin-accumulating vacuole in honeysuckle petal trichomes.

Transmission electron microscopy was used to reveal a layer of multiply folded membranes that closely surrounded the tannin-accumulating vacuole in cells of honeysuckle petal trichomes. A huge amount of tannins were deposited in the peripheral region and the center of the vacuole. The prolific membranes extended to the tannins deposited along the vacuole periphery. It was difficult to distinguish the vacuole membrane, and it seemed as if it was the layer of multiply folded membranes that separated the vacuole lumen from the cytoplasm. In addition, there were also membrane assemblies in the cytoplasm away from the vacuole, which were continuous with the proliferated membranes bordering the vacuole. Therefore, the tannin-accumulating vacuole was in close association with a very large network of proliferated membranes. The occurrence of such a layer of multiply folded membranes around the tannin-accumulating vacuole might be a structural strategy for improvement of the efficiency of vacuolar accumulation of tannins.

Application of visible and near infrared spectroscopy for rapid analysis of chrysin and galangin in Chinese propolis.

A novel method for the rapid determination of chrysin and galangin in Chinese propolis of poplar origin by means of visible and near infrared spectroscopy (Vis-NIR) was developed. Spectral data of 114 Chinese propolis samples were acquired in the 325 to 1,075 nm wavelength range using a Vis-NIR spectroradiometer. The reference values of chrysin and galangin of the samples were determined by high performance liquid chromatography (HPLC). Partial least squares (PLS) models were established using the spectra analyzed by different preprocessing methods. The effective wavelengths were selected by successive projections algorithm (SPA) and employed as the inputs of PLS, back propagation-artificial neural networks (BP-ANN), multiple linear regression (MLR) and least square-support vector machine (LS-SVM) models. The best results were achieved by SPA-BP-ANN models established with the Savitzky-Golay smoothing (SG) preprocessed spectra, where the r and RMSEP were 0.9823 and 1.5239 for galangin determination and 0.9668 and 2.4841 for chrysin determination, respectively. The results show that Vis-NIR demosntrates powerful capability for the rapid determination of chrysin and galangin contents in Chinese propolis.