Rapid enrichment detection of patulin and alternariol in apple using surface enhanced Raman spectroscopy with coffee-ring effect

Analytical applications of droplet deposition Raman spectroscopy

The droplet deposition methods in Raman spectroscopy have received considerable attention in the field of analytical sensing focusing on effective pre-concentration of the studied analyte (coffee-ring effect or small spots). This review covers different analytical applications of drop-coating deposition Raman scattering (DCDRS) and droplet deposition surface-enhanced Raman scattering (SERS) spectroscopy. Two main advantages of droplet deposition Raman techniques are considered: the drying-induced segregation of the components from the mixtures (such as body fluids) and the sensitivity of detection of various analytically important molecules. Some recent advanced applications, including clinical cancer diagnosis, are discussed and summarized. Finally, the potential and further perspectives of the droplet deposition Raman methods for analytical studies are introduced.

A film-like SERS aptasensor for sensitive detection of patulin based on GO@Au nanosheets

Patulin (PAT) commonly contaminates fruits, posing a significant risk to human health. Therefore, a highly effective and sensitive approach in identifying PAT is warranted. Herein, a SERS aptasensor was constructed based on a two-dimensional film-like structure. GO@Au nanosheets modified with SH-cDNA were employed as capture probes, while core-shell Au@Ag nanoparticles modified with 4-MBA and SH-Apt were utilized as signal probes. Through the interaction between capture probes and signal probes, adjustable hotspots were formed, yielding a significant Raman signal. During sensing, the GO@Au-cDNA competitively attached to Au@AgNPs@MBA-Apt, resulting in an inverse relationship between PAT levels and SERS intensity. The acquired results exhibited linear responses to PAT within the range of 1-70 ng/mL, with a calculated limit of detection of 0.46 ng/mL. In addition, the SERS aptasensor exhibited satisfactory recoveries in apple samples, which aligned closely with HPLC. With high sensitivity and specificity, this method holds significant potential for PAT detection.

Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review

Fruits and vegetables are an important part of our daily diet and contain low-content components that are crucial for our health. Detecting these components accurately is of paramount significance. However, traditional detection methods face challenges such as complex sample processing, slow detection speed, and the need for highly skilled operators. These limitations fail to meet the growing demand for intelligent and rapid detection of low-content components in fruits and vegetables. In recent years, significant progress has been made in intelligent rapid detection technology, particularly in detecting high-content components in fruits and vegetables. However, the accurate detection of low-content components remains a challenge and has gained considerable attention in current research. This review paper aims to explore and analyze several intelligent rapid detection techniques that have been extensively studied for this purpose. These techniques include near-infrared spectroscopy, Raman spectroscopy, laser-induced breakdown spectroscopy, and terahertz spectroscopy, among others. This paper provides detailed reports and analyses of the application of these methods in detecting low-content components. Furthermore, it offers a prospective exploration of their future development in this field. The goal is to contribute to the enhancement and widespread adoption of technology for detecting low-content components in fruits and vegetables. It is expected that this review will serve as a valuable reference for researchers and practitioners in this area.

Advancing Mycotoxin Detection in Food and Feed: Novel Insights from Surface-Enhanced Raman Spectroscopy (SERS)

The implementation of low-cost and rapid technologies for the on-site detection of mycotoxin-contaminated crops is a promising solution to address the growing concerns of the agri-food industry. Recently, there have been significant developments in surface-enhanced Raman spectroscopy (SERS) for the direct detection of mycotoxins in food and feed. This review provides an overview of the most recent advancements in the utilization of SERS through the successful fabrication of novel nanostructured materials. Various bottom-up and top-down approaches have demonstrated their potential in improving sensitivity, while many applications exploit the immobilization of recognition elements and molecular imprinted polymers (MIPs) to enhance specificity and reproducibility in complex matrices. Therefore, the design and fabrication of nanomaterials is of utmost importance and are presented herein. This paper uncovers that limited studies establish detection limits or conduct validation using naturally contaminated samples. One decade on, SERS is still lacking significant progress and there is a disconnect between the technology, the European regulatory limits, and the intended end-user. Ongoing challenges and potential solutions are discussed including nanofabrication, molecular binders, and data analytics. Recommendations to assay design, portability, and substrate stability are made to help improve the potential and feasibility of SERS for future on-site agri-food applications.

Recent developments and applications of surface enhanced Raman scattering spectroscopy in safety detection of fruits and vegetables

This article reviewed the latest research progress of Surface-enhanced Raman Spectroscopy (SERS) in the security detection of fruits and vegetables in recent years, especially in three aspects: pesticide residues, microbial toxin contamination and harmful microorganism infection. The binding mechanism and application potential of SERS detection materials (including universal type and special type) and carrier materials (namely rigid and flexible materials) were discussed. Finally, the application prospect of SERS in fruit and vegetable safety detection was explored, and the problems to be solved and development trends were put forward. The poor stability and reproducibility of SERS substrates make it difficult for practical applications. It is necessary to continuously optimize SERS substrates and develop small and portable Raman spectroscopy analyzers. In the future, SERS technology is expected to play an important role in human health, food safety and economy.

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.

Is chemical analysis suitable for detecting mycotoxins in agricultural commodities and foodstuffs?

The assessment of the risks of mycotoxins to humans through consuming contaminated foods resulted in specific legislation that evaluates the presence, quantities, and type of mycotoxins in agricultural commodities and foodstuffs. Thus, to ensure compliance with legislation, food safety and consumer health, the development of suitable analytical procedures for identifying and quantifying mycotoxins in the free or modified form, in low-concentration and in complex samples is necessary. This review reports the application of the modern chemical methods of analysis employed in mycotoxin detection in agricultural commodities and foodstuffs. It is reported extraction methods with reasonable accuracy and those present characteristics according to guidelines of Green Analytical Chemistry. Recent trends in mycotoxins detection using analytical techniques are presented and discussed, evaluating the robustness, precision, accuracy, sensitivity, and selectivity in the detection of different classes of mycotoxins. Sensitivity coming from modern chromatographic techniques allows the detection of very low concentrations of mycotoxins in complex samples. However, it is essential the development of more green, fast and more suitable accuracy extraction methods for mycotoxins, which agricultural commodities producers could use. Despite the high number of research reporting the use of chemically modified voltammetric sensors, mycotoxins detection still has limitations due to the low selectivity from similar chemical structures of mycotoxins. Furthermore, spectroscopic techniques are rarely employed due to the limited number of reference standards for calibration procedures.

A Method for Capture and Detection of Crop Airborne Disease Spores Based on Microfluidic Chips and Micro Raman Spectroscopy

Airborne crop diseases cause great losses to agricultural production and can affect people's physical health. Timely monitoring of the situation of airborne disease spores and effective prevention and control measures are particularly important. In this study, a two-stage separation and enrichment microfluidic chip with arcuate pretreatment channel was designed for the separation and enrichment of crop disease spores, which was combined with micro Raman for Raman fingerprinting of disease conidia and quasi identification. The chip was mainly composed of arc preprocessing and two separated enriched structures, and the designed chip was numerically simulated using COMSOL multiphysics5.5, with the best enrichment effect at W2/W1 = 1.6 and W4/W3 = 1.1. The spectra were preprocessed with standard normal variables (SNVs) to improve the signal-to-noise ratio, which was baseline corrected using an iterative polynomial fitting method to further improve spectral features. Raman spectra were dimensionally reduced using principal component analysis (PCA) and stability competitive adaptive weighting (SCARS), support vector machine (SVM) and back-propagation artificial neural network (BPANN) were employed to identify fungal spore species, and the best discrimination effect was achieved using the SCARS-SVM model with 94.31% discrimination accuracy. Thus, the microfluidic-chip- and micro-Raman-based methods for spore capture and identification of crop diseases have the potential to be precise, convenient, and low-cost methods for fungal spore detection.

Surface Plasmon Resonance (SPR) biosensor for detection of mycotoxins: A review

Mycotoxin is one of the most important natural pollutants, which poses a global threat to food safety. However, the pollution of mold in food production is inevitable. The detection technology of mycotoxins in food production is an important means to prevent the damage of mycotoxins, so rapid detection and screening to avoid pollution diffusion is essential. The focus of this review is to update the literature on the detection of mycotoxins by surface plasmon resonance (SPR) technology, rather than just traditional chromatographic methods. As a relatively novel and simple analytical method, SPR has been proved to be fast, sensitive and label-free, and has been widely used in real-time qualitative and quantitative analysis of various pollutants. This paper aims to give a broad overview of the sensors for detection and analysis of several common mycotoxins.

Rapid Detection of Clenbuterol Residues in Pork Using Enhanced Raman Spectroscopy

Clenbuterol (CB) is a synthetic β-receptor agonist which can be used to improve carcass leanness in swine, but its residues in pork also pose health risks. In this report, surface-enhanced Raman scattering (SERS) technology was used to achieve rapid detection and identification of clenbuterol hydrochloride (CB) residues. First, the effects of several different organic solvents on the extraction efficiency were compared, and it was found that clenbuterol in pork had a better enhancement effect using ethyl acetate as an extraction agent. Then, SERS signals of clenbuterol in different solvents were compared, and it was found that clenbuterol had a better enhancement effect in an aqueous solution. Therefore, water was chosen as the solvent for clenbuterol detection. Next, enhancement effect was compared using different concentration of sodium chloride solution as the aggregating compound. Finally, pork samples with different clenbuterol content (1, 3, 5, 7, 9, and 10 µg/g) were prepared for quantitative analysis. The SERS spectra of samples were collected with 0.5 mol/L of NaCl solution as aggregating compound and gold colloid as an enhanced substrate. Multiple scattering correction (MSC) and automatic Whittaker filter (AWF) were used for preprocessing, and the fluorescence background contained in the original Raman spectra was removed. A unary linear regression model was established between SERS intensity at 1472 cm^-1 and clenbuterol content in pork samples. The model had a better linear relationship with a correlation coefficient R² of 0.99 and a root mean square error of 0.263 µg/g. This method can be used for rapid screening of pork containing clenbuterol in the market.

Application of Nanomaterials for Coping with Mycotoxin Contamination in Food Safety: From Detection to Control

Mycotoxins, which are toxic secondary metabolites produced by fungi, are harmful to humans. Mycotoxin-induced contamination has drawn attention worldwide. Consequently, the development of reliable and sensitive detection methods and high-efficiency control strategies for mycotoxins is important to safeguard food industry safety and public health. With the rapid development of nanotechnology, many novel nanomaterials that provide tremendous opportunities for greatly improving the detection and control performance of mycotoxins because of their unique properties have emerged. This review comprehensively summarizes recent trends in the application of nanomaterials for detecting mycotoxins (fluorescence, colorimetric, surface-enhanced Raman scattering, electrochemical, and point-of-care testing) and controlling mycotoxins (inhibition of fungal growth, mycotoxin absorption, and degradation). These detection methods possess the advantages of high sensitivity and selectivity, operational simplicity, and rapidity. With research attention on the control of mycotoxins and the gradual excavation of the properties of nanomaterials, nanomaterials are also employed for the inhibition of fungal growth, mycotoxin absorption, and mycotoxin degradation, and impressive controlling effects are obtained. This review is expected to provide the readers insight into this state-of-the-art area and a reference to design nanomaterials-based schemes for the detection and control of mycotoxins.

Raman enhancement effect of different silver nanoparticles on salbutamol

Salbutamol is a β-adrenergic receptor agonist compound which has been abused as an animal growth promoter to improve carcass lean meat percentage. At present, the detection of salbutamol by SERS mostly uses gold colloid as substrate, which is expensive and has a high detection limit. In this report, Raman enhancement signal of salbutamol was compared with concentrated gold and silver colloids. The results show that the concentrated silver colloid prepared by reducing silver nitrate with hydroxylamine hydrochloride had superior performance. Three silver colloids with different particle sizes were synthesized by the same reducing agent and used as substrates for spectra acquisition of salbutamol to explore the enhancement performance of different silver nanoparticles sizes on salbutamol. The results showed that silver nanoparticles with larger particle sizes were more conducive to the adsorption of salbutamol. Finally, under the optimal conditions (Silver colloid A as enhanced substrate, 0.2 mol/L NaOH aqueous solution as aggregating compound), a better linear relationship between the concentration of salbutamol (ranged from 0.2 to 1 mg/L) and SERS intensity. The linear equation between SERS intensity and salbutamol concentration was C = 0.0023∙I-0.079 (mg/L) with a good linearity (R² =0.994) and lower root mean square error (RMSE_c = 0.022 mg/L), where C (mg/L) was the concentration of salbutamol solution and I was the SERS intensity of salbutamol solution. Validation set correlation coefficient was 0.988 and prediction root mean square error was 0.029 mg/L. This method provides a new idea for further reducing the detection limit of salbutamol. This study is helpful to further develop a simple and low-cost SERS detection method of salbutamol based on silver colloid.

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Raman enhancement signal of salbutamol was compared with concentrated gold and silver colloids.

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The effect of silver nanoparticles sizes on the enhancement effect are in particular broached.

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The methods can realize salbutamol at trace concentrations detection.

Applications of surface-enhanced Raman spectroscopy in environmental detection

As the human population grows, the anthropogenic impacts from various agricultural and industrial processes produce unwanted contaminants in the environment. The accurate, sensitive and rapid detection of such contaminants is vital for human health and safety. Surface-enhanced Raman spectroscopy (SERS) is a valuable analytical tool with wide applications in environmental contaminant monitoring. The aim of this review is to summarize recent advancements within SERS research as it applies to environmental detection, with a focus on research published or accessible from January 2021 through December 2021 including early-access publications. Our goal is to provide a wide breadth of information that can be used to provide background knowledge of the field, as well as inform and encourage further development of SERS techniques in protecting environmental quality and safety. Specifically, we highlight the characteristics of effective SERS nanosubstrates, and explore methods for the SERS detection of inorganic, organic, and biological contaminants including heavy metals, pharmaceuticals, plastic particles, synthetic dyes, pesticides, viruses, bacteria and mycotoxins. We also discuss the current limitations of SERS technologies in environmental detection and propose several avenues for future investigation. We encourage researchers to fill in the identified gaps so that SERS can be implemented in a real-world environment more effectively and efficiently, ultimately providing reliable and timely data to help and make science-based strategies and policies to protect environmental safety and public health.