Dynamic surface-enhanced Raman spectroscopy for the detection of acephate residue in rice by using gold nanorods modified with cysteamine and multivariant methods

Rapid identification and quantitative analysis of malachite green in fish via SERS and 1D convolutional neural network

Rapid and quantitative detection of malachite green (MG) in aquaculture products is very important for safety assurance in food supply. Here, we develop a point-of-care testing (POCT) platform that combines a flexible and transparent surface-enhanced Raman scattering (SERS) substrate with deep learning network for achieving rapid and quantitative detection of MG in fish. The flexible and transparent SERS substrate was prepared by depositing silver (Ag) film on the polydimethylsiloxane (PDMS) film using laser molecular beam epitaxy (LMBE) technique. The wrinkled Ag NPs@PDMS film exhibits high SERS activity, excellent reproducibility and good mechanical stability. Additionally, the fast in situ detection of MG residues onfishscales was achieved by using the wrinkled Ag NPs/PDMS film and a portable Raman spectrometer, with a minimum detectable concentration of 10-6 M. Subsequently, a one-dimensional convolutional neural network (1D CNN) model was constructed for rapid quantification of MG concentration. The results demonstrated that the 1D CNN quantitative analysis model possessed superior predictive performance, with a coefficient of determination (R2) of 0.9947 and a mean squared error (MSE) of 0.0104. The proposed POCT platform, integrating a transparent flexible SERS substrate, a portable Raman spectrometer and a 1D CNN model, provides an efficient strategy for rapid identification and quantitative analysis of MG in fish.

Realization of qualitative to semi-quantitative trace detection via SERS-ICA based on internal standard method

Surface-enhanced Raman spectroscopy (SERS) can quickly identify molecular fingerprints and has been widely used in the field of rapid detection. However, the non-uniformity inherent in SERS substrate signals, coupled with the finite nature of the detection object, significantly hampers the advancement of SERS. Nowadays, the existing mature immunochromatographic assay (ICA) method is usually combined with SERS technology to address the defects of SERS detection. Nevertheless, the porous structure of the strip will also affect the signal uniformity during detection. Obviously, a method using SERS-ICA is needed to effectively solve signal fluctuations, improve detection accuracy, and has certain versatility. This paper introduces an internal standard method combining deep learning to predict and process Raman data. Based on the signal fluctuation of single-antigen SERS-ICA test strip, the double-antigen SERS-ICA test strip was constructed. The full spectrum Raman data of double-antigen SERS-ICA test strip was normalized by the sum of two characteristic peaks of internal standard molecules, and then processed by deep learning algorithm. The Relative Standard Deviation (RSD) of Raman data of bisphenol A was compared before and after internal standard normalization of double-antigen SERS-ICA test strip. The RSD processed by this method was increased by 3.8 times. After normalization, the prediction accuracy of Root Mean Square Error (RMSE) is improved by 2.66 times, and the prediction accuracy of R-square (R2) is increased from 0.961 to 0.994. The results showed that RMSE and R2 were used to comprehensively predict the collected data of double-antigen SERS-ICA test strip, which could effectively improve the prediction accuracy. The internal standard algorithm can effectively solve the challenges of uneven hot spots and poor signal reproducibility on the test strip to a certain extent, so as to improve the semi-quantitative accuracy.

Positively Charged Silver and Gold Nanoparticles with Controllable Size Distribution for SERS Detection of Negatively Charged Molecules

Surface-enhanced Raman spectroscopy (SERS) has been demonstrated as an ultrasensitive tool for various molecules. However, for the negatively charged molecules, the widely used SERS substrate [negatively charged Ag and Au nanoparticles (Ag or Au NPs (-)] showed either low sensitivity or poor stability. The best solution is to synthesize positively charged silver or gold nanoparticles [Ag or Au NPs (+)] with high stability and excellent SERS performance, which are currently unavailable. To this end, we revitalized the strategy of "charge reversal and seed growth". By selection of ascorbic acid as the reductant and surfactant, the surface charge of Ag or Au NP (-) seeds is adjusted to a balanced state, where the surface charge is negative enough to satisfy the stabilization of the NPs (-) but does not hinder the subsequent charge reversal. By optimization of the chain length and electric charge of polyamine molecules, the highly stable and size-controllable uniform Ag NPs (+) and Au NPs (+) were seed-growth synthesized with high reproducibility. More importantly, the SERS performance of both Ag NPs (+) and Au NPs (+) achieved the trace detection of negatively charged molecules at the level of 1 μg/L, demonstrating an improved SERS sensitivity of up to 3 orders of magnitude compared to the previously reported sensitivity. Promisingly, the introduction of polyamine-capped Ag NPs (+) and Au NPs (+) as SERS substrates with high stability (1 year shelf life) will significantly broaden the application of SERS.

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.

Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment

Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment.

SERS with Flexible β-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network

The detection of polycyclic aromatic hydrocarbons (PAHs) on fruit and vegetable surfaces is important for protecting human health and ensuring food safety. In this study, a method for the in situ detection and identification of PAH residues on fruit and vegetable surfaces was developed using surface-enhanced Raman spectroscopy (SERS) based on a flexible substrate and lightweight deep learning network. The flexible SERS substrate was fabricated by assembling β-cyclodextrin-modified gold nanoparticles (β-CD@AuNPs) on polytetrafluoroethylene (PTFE) film coated with perfluorinated liquid (β-CD@AuNP/PTFE). The concentrations of benzo(a)pyrene (BaP), naphthalene (Nap), and pyrene (Pyr) residues on fruit and vegetable surfaces could be detected at 0.25, 0.5, and 0.25 μg/cm2, respectively, and all the relative standard deviations (RSD) were less than 10%, indicating that the β-CD@AuNP/PTFE exhibited high sensitivity and stability. The lightweight network was then used to construct a classification model for identifying various PAH residues. ShuffleNet obtained the best results with accuracies of 100%, 96.61%, and 97.63% for the training, validation, and prediction datasets, respectively. The proposed method realised the in situ detection and identification of various PAH residues on fruit and vegetables with simplicity, celerity, and sensitivity, demonstrating great potential for the rapid, nondestructive analysis of surface contaminant residues in the food-safety field.

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

Ultrasensitive detection of acephate based on carbon quantum dot-mediated fluorescence inner filter effects

Acephate is an organophosphorus pesticide (OP) that is widely used to control insects in agricultural fields such as in vegetables and fruits. Toxic OPs can enter human and animal bodies and eventually lead to chronic or acute poisoning. However, traditional enzyme inhibition and colorimetric methods for OPs detection usually require complicated detection procedures and prolonged time and have low detection sensitivity. High-sensitivity monitoring of trace levels of acephate residues is of great significance to food safety and human health. Here, we developed a simple method for ultrasensitive quantitative detection of acephate based on the carbon quantum dot (CQD)-mediated fluorescence inner filter effect (IFE). In this method, the fluorescence from CQDs at 460 nm is quenched by 2,3-diaminophenazine (DAP) and the resulting fluorescence from DAP at 558 nm is through an IFE mechanism between CQDs and DAP, producing ratiometric responses. The ratiometric signal I558/I460 was found to exhibit a linear relationship with the concentration of acephate. The detection limit of this method was 0.052 ppb, which is far lower than the standards for acephate from China and EU in food safety administration. The ratiometric fluorescence sensor was further validated by testing spiked samples of tap water and pear, indicating its great potential for sensitive detection of trace OPs in complex matrixes of real samples.

Raman spectrum classification based on transfer learning by a convolutional neural network: Application to pesticide detection

Pesticide detection is of tremendous importance in agriculture, and Raman spectroscopy/Surface-Enhanced Raman Scattering (SERS) has proven extremely effective as a stand-alone method to detect pesticide residues. Machine learning may be able to automate such detection, but conventional algorithms require a complete database of Raman spectra, which is not feasible. To bypass this problem, the present study describes a transfer learning method that improves the algorithm's accuracy and speed to extract features and classify Raman spectra. The transfer learning model described here was developed through the following steps: (1) the classification model was pre-trained using an open-source Raman spectroscopy database; (2) the feature extraction layer was saved after training; and (3) the training model for the Raman spectroscopy database was re-established while using self-tested pesticides and keeping the feature extraction layer unchanged. Three models were evaluated with or without transfer learning: CNN-1D, Resnet-1D, and Inception-1D, and they have improved the accuracy of spectrum classification by 6%, 2%, and 3%, with reduced training time and increased curve smoothness. These results suggest that transfer learning can improve the feature extraction capability and therefore accuracy of Raman spectroscopy models, expanding the range of Raman-based applications where transfer learning model can be used to identify the spectra of different substances.

Positively-charged plasmonic nanostructures for SERS sensing applications

Surface-enhanced Raman (SERS) spectroscopy has been establishing itself as an ultrasensitive analytical technique with a cross-disciplinary range of applications, which scientific growth is triggered by the continuous improvement in the design of advanced plasmonic materials with enhanced multifunctional abilities and tailorable surface chemistry. In this regard, conventional synthetic procedures yield negatively-charged plasmonic materials which can hamper the adhesion of negatively-charged species. To tackle this issue, metallic surfaces have been modified via diverse procedures with a broad array of surface ligands to impart positive charges. Cationic amines have been preferred because of their ability to retain a positive zeta potential even at alkaline pH as well as due to their wide accessibility in terms of structural features and cost. In this review, we will describe and discuss the different approaches for generating positively-charged plasmonic platforms and their applications in SERS sensing.

Rapid detection of chlorpyrifos residue in rice using surface-enhanced Raman scattering coupled with chemometric algorithm

Due to the continuous development and progress of society and more and more attention to the quality and safety of food, rapid testing of pesticides in food is of great significance. In this paper, surface-enhanced Raman spectroscopy (SERS) and chemometric algorithms were employed collectively to quantify chlorpyrifos (CP) residues in rice samples. The SERS spectra from different concentrations (0.01-1000 μg/mL) of CP were collected using AgNPs-deposited-ZnO nanoflower (NFs)-like nanoparticles (Ag@ZnO NFs) SERS sensor. Four quantitative chemometric models for CP were comparatively studied, and the competitive adaptive reweighted sampling-partial least squares model achieved the best prediction and practical applicability for predicting CP levels with a limit of detection of 0.01 µg/mL. The results of the student's t-test showed no significant difference between this method and high-performance liquid chromatography (HPLC), and good relative standard deviation (RSD) indicated that this method could be used for the detection of CP in rice.

Rapid field trace detection of pesticide residue in food based on surface-enhanced Raman spectroscopy

Surface-enhanced Raman spectroscopy is an alternative detection tool for monitoring food security. However, there is still a lack of a conclusion of SERS detection with respect to pesticides and real sample analysis, and the summary of intelligent algorithms in SERS is also a blank. In this review, a comprehensive report of pesticides detection using SERS technology is given. The SERS detection characteristics of different types of pesticides and the influence of substrate on inspection are discussed and compared by the typical ways of classification. The key points, including the progress in real sample analysis and Raman data processing methods with intelligent algorithm, are highlighted. Lastly, major challenges and future research trends of SERS analysis of pesticide residue are also addressed. SERS has been proven to be a powerful technique for rapid test of residue pesticides in complex food matrices, but there still is a tremendous development space for future research.

Surface-enhanced Raman spectroscopywith gold nanorods modified by sodium citrate and liquid-liquid interface self-extraction for detection of deoxynivalenol in Fusarium head blight-infected wheat kernels coupled with a fully convolution network

Surface-enhanced Raman spectroscopy (SERS) and deep learning network were adopted to develop a detection method for deoxynivalenol (DON) residues in Fusarium head blight (FHB)-infected wheat kernels. First, the liquid-liquid interface self-extraction was conducted for the rapid separation of DON in samples. Then, the gold nanorods modified with sodium citrate (Cit-AuNRs) were prepared as substrate for a gigantic enhancement of SERS signal. Results showed that the spectral characteristic peaks for DON residues of 99.5-0.5 mg/L were discernible with the relative standard deviation of 4.2%, with the limit of detection of 0.11 mg/L. Meanwhile, the fully convolutional network for the spectra of matrix input form was developed and obtained the optimal quantitative performance, with a root-mean-square error of prediction of 4.41 mg/L and coefficient of determination of prediction of 0.9827. Thus, the proposed method provides a simple, sensitive, and intelligent detection for DON in FHB-infected wheat kernels.

Metal Nanoparticles-Enhanced Biosensors: Synthesis, Design and Applications in Fluorescence Enhancement and Surface-enhanced Raman Scattering

Metal nanoparticles (NP) that exhibit localized surface plasmon resonance play an important role in metal-enhanced fluorescence (MEF) and surface-enhanced Raman scattering (SERS). Among the optical biosensors, MEF and SERS stand out to be the most sensitive techniques to detect a wide range of analytes from ions, biomolecules to macromolecules and microorganisms. Particularly, anisotropic metal NPs with strongly enhanced electric field at their sharp corners/edges under a wide range of excitation wavelengths are highly suitable for developing the ultrasensitive plasmon-enhanced biosensors. In this review, we first highlight the reliable methods for the synthesis of anisotropic gold NPs and silver NPs in high yield, as well as their alloys and composites with good control of size and shape. It is followed by the discussion of different sensing mechanisms and recent advances in the MEF and SERS biosensor designs. This includes the review of surface functionalization, bioconjugation and (directed/self) assembly methods as well as the selection/screening of specific biorecognition elements such as aptamers or antibodies for the highly selective bio-detection. The right combinations of metal nanoparticles, biorecognition element and assay design will lead to the successful development of MEF and SERS biosensors targeting different analytes both in-vitro and in-vivo. Finally, the prospects and challenges of metal-enhanced biosensors for future nanomedicine in achieving ultrasensitive and fast medical diagnostics, high-throughput drug discovery as well as effective and reliable theranostic treatment are discussed.