Determination of trace thiophanate-methyl and its metabolite carbendazim with teratogenic risk in red bell pepper (Capsicumannuum L.) by surface-enhanced Raman imaging technique

A highly effective peroxidase-mimic nanozyme of S, N-carbon dot-decorated cerium organic framework-based colorimetric detection of Hg2+ ion and thiophanate methyl

In this study, we proposed a colorimetric probe as S, N-carbon dot-decorated Ce-MOF (S, N-CD@Ce-MOF) for the dual detection of mercury and thiophanate methyl (TM), which are simultaneously present pollutants in the environment and foodstuffs. These pollutants cause serious threats to human health, such as carcinogenicity and neurovirulence. Herein, we synthesized S, N-CD@Ce-MOF using the hydrothermal method and applied it to a "turn-off-on" probe to detect mercury and TM using the colorimetric method in water and food samples. S, N-CD@Ce-MOF shows excellent peroxidase activity by catalyzing the chromogenic substrate of 3,3',5,5'-tetramethylbenzidine (TMB), resulting in deep blue-colored oxidized TMB product (ox TMB) in the presence of H2O2 with a UV absorption wavelength at 654 nm. However, the addition of Hg(II) ions prohibits the oxidation of TMB by an electron transfer effect and easily binds with -S, -N-containing sites on the surface of carbon dots, obstructing the catalytic active sites and decreasing catalytic efficiency with weak UV absorption at 654 nm as a "turn-off". Subsequently, the addition of TM to the above sensing solution as a "turn-on" was triggered by the TM-Hg complex formation and permitted TMB oxidation with a strong absorption peak at 654 nm. Furthermore, this proposed sensor demonstrates a superior linear response to mercury ions and TM in the ranges from 0 to 15 μM and 0 to 14 μM, respectively. The developed colorimetric assay exhibits good sensitivity and selectivity against various possible interferences. Furthermore, we found that the limits of detection for Hg2+ and TM were as low as 0.01 μM and 0.03 μM, respectively. The developed sensor provides various benefits, such as cost-effectiveness, simplicity without a complex detection process, and naked-eye detection. Consequently, our proposed colorimetric technique worked well for the detection of Hg2+ in real water samples and TM in real apple and tomato juice.

RamanFormer: A Transformer-Based Quantification Approach for Raman Mixture Components

Raman spectroscopy is a noninvasive technique to identify materials by their unique molecular vibrational fingerprints. However, distinguishing and quantifying components in mixtures present challenges due to overlapping spectra, especially when components share similar features. This study presents "RamanFormer", a transformer-based model designed to enhance the analysis of Raman spectroscopy data. By effectively managing sequential data and integrating self-attention mechanisms, RamanFormer identifies and quantifies components in chemical mixtures with high precision, achieving a mean absolute error of 1.4% and a root mean squared error of 1.6%, significantly outperforming traditional methods such as least squares, MLP, VGG11, and ResNet50. Tested extensively on binary and ternary mixtures under varying conditions, including noise levels with a signal-to-noise ratio of up to 10 dB, RamanFormer proves to be a robust tool, improving the reliability of material identification and broadening the application of Raman spectroscopy in fields, such as material science, forensics, and biomedical diagnostics.

Smartphone-assisted carbon dots fluorescent sensing platform for visual detection of Thiophanate-methyl in fruits and vegetables

Trimesic acid and o-phenylenediamine (OPD) were employed as precursors to synthesize yellow-green fluorescent carbon dots (Y-G-CDs) by solvothermal synthesis for the sensitive detection of Thiophanate-methyl (TM) in real agricultural products. The Y-G-CDs probe could specifically recognize the TM primarily through π-π stacking. Moreover, the fluorescence quenching of the probe was ultimately dominated by the PET effect, based on the interaction between the abundant carboxyl groups on the surface of the Y-G-CDs and the amino group of TM. A strong linear relationship between the fluorescence quenching of the probe and TM concentration in the range of 0-10 µmol/L was observed and the limit of detection (LOD) was calculated to be 50.7 nmol/L. Compared to the interference pesticides, the Y-G-CDs probe demonstrated exceptional selectivity toward TM, with satisfactory recoveries of 96.3 % - 104.2 % in spiked food samples. The Y-G-CDs probe enables simple pretreatment, cost-effective, and on-site detection of TM in fruits and vegetables with visual detection of the TM employing a smartphone-assisted sensing platform.

Discrimination of Genetic Biomarkers of Disease through Machine-Learning-Based Hypothesis Testing of Direct SERS Spectra of DNA and RNA

Cancer is globally a leading cause of death that would benefit from diagnostic approaches detecting it in its early stages. However, despite much research and investment, cancer early diagnosis is still underdeveloped. Owing to its high sensitivity, surface-enhanced Raman spectroscopy (SERS)-based detection of biomarkers has attracted growing interest in this area. Oligonucleotides are an important type of genetic biomarkers as their alterations can be linked to the disease prior to symptom onset. We propose a machine-learning (ML)-enabled framework to analyze complex direct SERS spectra of short, single-stranded DNA and RNA targets to identify relevant mutations occurring in genetic biomarkers, which are key disease indicators. First, by employing ad hoc-synthesized colloidal silver nanoparticles as SERS substrates, we analyze single-base mutations in ssDNA and RNA sequences using a direct SERS-sensing approach. Then, an ML-based hypothesis test is proposed to identify these changes and differentiate the mutated sequences from the corresponding native ones. Rooted in "functional data analysis," this ML approach fully leverages the rich information and dependencies within SERS spectral data for improved modeling and detection capability. Tested on a large set of DNA and RNA SERS data, including from miR-21 (a known cancer miRNA biomarker), our approach is shown to accurately differentiate SERS spectra obtained from different oligonucleotides, outperforming various data-driven methods across several performance metrics, including accuracy, sensitivity, specificity, and F1-scores. Hence, this work represents a step forward in the development of the combined use of SERS and ML as effective methods for disease diagnosis with real applicability in the clinic.

A Eu-MOF-Based Fluorescent Sensing Probe for the Detection of Tryptophan and Cu2+ in Aqueous Solutions

Abnormal tryptophan (Trp) metabolism can be used as an important indicator of chronic hepatitis, paranoia, Parkinson's disease and other diseases. Deficiency or excessive accumulation of Cu2+ can cause diseases such as Wilson's disease and Alzheimer's disease. Eu-based metal-organic framework (Eu-MOF) was successfully prepared for fluorescence sensing of Trp and Cu2+ in an aqueous solution (pH = 7.4). Eu-MOF showed high selectivity and sensitivity for Trp and Cu2+ with detection limits of 0.22 µM and 0.09 µM and Ksv of 6.17 × 103 M- 1 and 2.37 × 104 M- 1 respectively. Trp and Cu2+ had overlapped UV absorption spectra with that of Eu-MOF and competed for the excitation light source. Trp also attenuated the antennae effect of organic ligands on Eu-MOF, thus quenching the red fluorescence of Eu-MOF. This study provides insights into the application of MOFs in bioanalysis and diagnostics.

A simple and efficient alginate hydrogel combined with surface-enhanced Raman spectroscopy for quantitative analysis of sodium nitrite in meat products

Sodium nitrite is a commonly used preservative and color protectant in the food industry. Conventional analytical methods are highly susceptible to food matrix interference, time-consuming and costly. In this study, the ion cross-linking method was employed to prepare alginate hydrogel substrates, and phenosafranin was chosen as a single-molecule probe to analyze sodium nitrite. Our investigation centered on elucidating the effects of alginate and cross-linking ion concentrations on Raman signal characteristics. The optimal Raman response was observed in the precursor solution with 1% sodium alginate and 0.1 mol L-1 cross-linking ions. The relative standard deviations (RSDs) of the feature peaks from the three substrate batches ranged from 1.22% to 16.30%, attesting the robustness and consistency of the substrates. The signal reduction of the substrates after a four-week storage period remained below 10%, indicating that the substrates had good reproducibility and stability. The limits of detection (LODs) for sodium nitrite in extracts from cured meat, luncheon meat, and sliced ham were determined to range from 3.75 mg kg-1 to 8.11 mg kg-1, with low interference from the food matrix. The support vector machine algorithm was utilized to train and predict the data, which proved to be more accurate (98.6%-99.8% recovery) than the traditional linear regression model (81.9%-112.7% recovery) in predicting the spiked samples. The application of hydrogel-based surface-enhanced Raman spectroscopy (SERS) substrates for nitrite detection in food, combined with machine learning for regression prediction in data processing, collectively augmented the potential of SERS technology in the field of food analysis.

Construction of Graphene@Ag-MLF composite structure SERS platform and its differentiating performance for different foodborne bacterial spores

A new surface-enhanced Raman spectroscopy (SERS) biosensor of Graphene@Ag-MLF composite structure has been fabricated by loading AgNPs on graphene films. The response of the biosensor is  based on plasmonic sensing. The results showed that the enhancement factor of three different spores reached 107 based on the Graphene@Ag-MLF substrate. In addition, the SERS performance was stable, with good reproducibility (RSD<3%). Multivariate statistical analysis and chemometrics were used to distinguish different spores. The accumulated variance contribution rate was up to 96.35% for the top three PCs, while HCA results revealed that the spectra were differentiated completely. Based on optimal principal components, chemometrics of KNN and LS-SVM were applied to construct a model for rapid qualitative identification of different spores, of which the prediction set and training set of LS-SVM achieved 100%. Finally, based on the Graphene@Ag-MLF substrate, the LOD of three different spores was lower than 102 CFU/mL. Hence, this novel Graphene@Ag-MLF SERS substrate sensor was rapid, sensitive, and stable in detecting spores, providing strong technical support for the application of SERS technology in food safety.

CuI-p-DPA coordination polymer isomers for "turn-on" fluorescence detection of thiophanate-methyl

Three copper iodide coordination polymer (CuI-p-DPA) isomers were prepared from the fluorescent organic ligand p-DPA and cuprous iodide (CuI) under different solvothermal conditions, which exhibited quenched fluorescence behaviors after forming coordination polymers (CPs). These CuI-p-DPA isomers showed discrepant fluorescence responses to thiophanate-methyl (TM). Among these CuI-p-DPA isomers, α-CuI-p-DPA exhibited the maximum fluorescence enhancement after its incubation with TM in aqueous solution. The fluorescence enhancement mechanism was that TM competed with the ligand p-DPA to coordinate with CuI clusters, and then α-CuI-p-DPA released p-DPA into the solution and induced fluorescence enhancement. The present detection method possesses the advantages of good selectivity, high sensitivity, short response time, and strong anti-interference ability with a linear range of 0.5-100 μM and a detection limit of 0.01 μM. This study not only reveals that the spatial structures of CPs play an important role in the fluorescence response ability, but also provide a new fluorescence signal-on analysis method to rapidly and sensitively determine the pesticide residue for TM.

Nitroaromatic explosives' detection and quantification using an attention-based transformer on surface-enhanced Raman spectroscopy maps

Rapidly and accurately detecting and quantifying the concentrations of nitroaromatic explosives is critical for public health and security. Among existing approaches, explosives' detection with Surface-Enhanced Raman Spectroscopy (SERS) has received considerable attention due to its high sensitivity. Typically, a preprocessed single spectrum that is the average of the entire or a selected subset of a SERS map is used to train various machine learning models for detection and quantification. Designing an appropriate averaging and preprocessing procedure for SERS maps across different concentrations is time-consuming and computationally costly, and the averaging of spectra may lead to the loss of crucial spectral information. We propose an attention-based vision transformer neural network for nitroaromatic explosives' detection and quantification that takes raw SERS maps as the input without any preprocessing. We produce two novel SERS datasets, 2,4-dinitrophenols (DNP) and picric acid (PA), and one benchmark SERS dataset, 4-nitrobenzenethiol (4-NBT), which have repeated measurements down to concentrations of 1 nM to illustrate the detection limit. We experimentally show that our approach outperforms or is on par with the existing methods in terms of detection and concentration prediction accuracy. With the produced attention maps, we can further identify the regions with a higher signal-to-noise ratio in the SERS maps. Based on our findings, the molecule of interest detection and concentration prediction using raw SERS maps is a promising alternative to existing approaches.

Ratiometric fluorescent detection of carbendazim in foods based on metallic nanoclusters self-assembled nanocomplex

Indicator replacement assay combining with fluorescence resonance energy transfer (FRET) effect has attractive performance in sensing small molecules, however, there wasn't application in pesticide molecule sensing reported so far. In this work, we prepared a nanocomplex (NCP), AuAgNCs-CD, through self-assembly of gold nanoclusters (AuNCs), silver nanoclusters (AgNCs) and carboxymethyl-β-cyclodextrin (CM-β-CD) by one-step method. The emission of AuNCs was significantly enhance. It was found that FRET between the AuAgNCs-CD and rhodamine B (RhB) existed after AuAgNCs-CD combined with RhB. And carbendazim (CBZ) could induce anti-FRET effect through competing with RhB and binding to AuAgNCs-CD. Thus, this phenomenon was utilized to develop a ratiometric fluorescent detection of CBA. This method was applied in food sample detection and reliable results were obtained. Due to high sensitivity, rapidness and good selectivity, this ratiometric fluorescent method was expected to hold high application potential in monitoring CBZ in foods.

Surface-enhanced Raman scattering integrating with machine learning for green tea storage time identification

The rapid and accurate identification of complex samples still remains a great challenge, especially for those with similar compositions. In this work, we report an integration strategy consisting of surface-enhanced Raman scattering (SERS) and machine learning to discriminate complex and similar analytes, in this case green tea products with different storage times. Surface-functionalized Ag nanoparticles (NPs) were used as a SERS substrate to reveal the changes in the sensory components of green tea with variable storage time. Principal components analysis (PCA)-based support vector machine (SVM) classification was used to extract the key spectral features and identify green tea with different storage times. The results showed that such an integration strategy achieved high predictive accuracy on time tag discrimination for green tea. The multiclass SVM classifier successfully recognized green tea with different storage times at a prediction accuracy of 95.9%, sensitivity of 96.6%, and specificity of 98.8%. Therefore, this work illustrates that the SERS-based PCA-SVM platform might be a facile and reliable tool for the identification of complex matrices with subtle differentiations.

Quantitative analysis of polycyclic aromatic hydrocarbons (PAHs) in water by surface-enhanced Raman spectroscopy (SERS) combined with Random Forest

Polycyclic aromatic hydrocarbons (PAHs) have strong carcinogenicity, teratogenicity, mutagenicity and other adverse effects on human beings. They are one of the most dangerous pollutants, which have attracted great attention in the past decades. In this work, aiming at the actual problems that water environment is polluted and human health is threatened by PAHs, surface enhanced Raman spectroscopy (SERS) combined with Random Forest (RF) calibration models were used to quantitative analysis of phenanthrene and fluoranthene in water. Firstly, the SERS data was collected after samples mixed with Ag NPs, after 31 PAHs samples were prepared. Secondly, it was discussed how spectral preprocessing integration strategies affect on the prediction performance of the RF calibration models. And then, the effect of mutual information (MI) variable selection method on the performance of RF calibration models was explored. Finally, the RF calibration models were established for phenanthrene and fluoranthene. For the prediction set, a lowest mean relative error (MRE) and a largest determination coefficient (R²) were obtained. For quantitative analysis of phenanthrene, the final prediction performance results show that R²_p is 0.9780, and MRE_p is 0.0369 based on the D1st-WT-RF calibration model. For fluoranthene, WT-D1st-MI-RF is a better calibration model, and corresponding to R²_p and MRE_p are 0.9770 and 0.0694, respectively. Hence, a rapid and accurate quantitative method of PAHs is established for the real-time detection of water environmental pollution, which is intended to provide new ideas and methods for the quantitative analysis of PAHs in water.

Metabolite profiling and identification in living cells by coupling stable isotope tracing and induced electrospray mass spectrometry

Direct observation of metabolites in living cells by mass spectrometry offers a bright future for biological studies but also suffers a severe challenge to untargeted peak assignment to tentative metabolite candidates. In this study, we developed a method combining stable isotope tracing and induced electrospray mass spectrometry for living-cells metabolite measurement and identification. By using ¹³C₆-glucose and ammonium chloride-¹⁵N as the sole carbon and nitrogen sources for cell culture, Escherichia coli synthesized metabolites with ¹⁵N and ¹³C elements. Tracing the number of carbon and nitrogen atoms could offer a complementary dimension for candidate peak searching. As a result, the identification confidence of metabolites achieved a universal improvement based on carbon/nitrogen labelling and filtration.

Thiophanate-methyl induces notochord toxicity by activating the PI3K-mTOR pathway in zebrafish (Danio rerio) embryos

Thiophanate-methyl (TM), a typical pesticide widely used worldwide, was detected in rivers, soil, fruits, and vegetables. Thus, it is urgent to identify the potential harm of TM residual to non-target organisms and its molecular mechanisms. We used zebrafish (Danio rerio) in this study to evaluate TM toxicity. TM exposure induced developmental toxicity, including inhibited hatchability, reduced heart rates, restrained spontaneous locomotion, and decreased body length. Furthermore, we observed obvious toxicity in the notochord and detected increased expression levels of notochord-related genes (shha, col2a, and tbxta) by in situ hybridization in zebrafish larvae. In addition, calcein staining, alkaline phosphatase (ALP) activity analysis, and anatomic analysis indicated that TM induced notochord toxicity. We used rescue experiments to verify whether the PI3K-mTOR pathway involved in the notochord development was the cause of notochord abnormalities. Rapamycin and LY294002 (an inhibitor of PI3K) relieve notochord toxicity caused by TM, including morphological abnormalities. In summary, TM might induce notochord toxicity by activating the PI3K-mTOR pathway in zebrafish.

Copper-Modified Double-Emission Carbon Dots for Rapid Detection of Thiophanate Methyl in Food

The detection of food safety and quality is very significant throughout the food supply. Stable dual-emission copper-modified fluorescent carbon dots (Cu-CDs) were successfully synthesized by a simple and environment-friendly hydrothermal, which was used for the real-time detection of pesticide residues in agricultural products. By optimizing the reaction conditions, Cu-CDs showed two emission peaks, with the highest fluorescence intensities at 375 and 450 nm. The structure, chemical composition and optical properties of Cu-CDs were investigated by XRD, TEM and IR. The results showed that thiophanate methyl (TM) could induce fluorescence quenching of Cu-CDs with no other ligands by the electron transfer through π-π stacking. The synchronous response of the dual-emission sensor enhanced the specificity of TM, which showed remarkable anti-interference capability. The fluorescence quenching degree of Cu-CDs had a good linear relationship with the TM concentration; the low detection limit for a pear was 0.75 μM, and for an apple, 0.78 μM. The recoveries in the fruit samples were 79.70–91.15% and 81.20–93.55%, respectively, and the relative standard deviations (RSDs) were less than 4.23% for the pear and less than 3.78% for the apple. Thus, our results indicate the feasibility and reliability of our methods in detecting pesticide residues in agricultural products.

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.

Surface-enhanced Raman scattering (SERS) from low-cost silver nanoparticle-decorated cicada wing substrates for rapid detection of difenoconazole in potato

Potato is one of the most important food crops worldwide in terms of human consumption. However, potato farmers employ a variety of pesticides to protect crops from harmful insects and illnesses, and difenoconazole is a commonly used one that has severe effects on human health and the environment. Therefore, detecting difenoconazole quickly and correctly is critical. In this work, we fabricated AgNPs/cicada wing substrates using natural cicada segments, decorated with silver nanoparticles for surface-enhanced Raman scattering (SERS) measurements to detect trace amounts of difenoconazole in potatoes. Results indicated that a linear relationship with the coefficient of detection (R²) of 0.987 and the detection limit (LOD) of 0.016 ppm was observed by targeting a distinctive peak at 808 cm^-1 and logarithmic difenoconazole concentrations of 0.1 to 100 ppm. In addition, difenoconazole LODs in potatoes were 63 μg/kg, lower than those specified by the EU (0.1 mg/kg) and Vietnam (4 mg/kg) utilizing this new technique. Therefore, this proposed SERS method could be used to detect difenoconazole in potatoes at trace levels.

Machine learning for rapid quantification of trace analyte molecules using SERS and flexible plasmonic paper substrates

Given the intrinsic nature of low reproducibility and signal blinking in the surface enhanced Raman scattering (SERS) technique, especially while detecting trace/ultra-trace amounts, it remains a major challenge to quantify the analyte under study. Here we present a simple and economically viable, flexible hydrophobic plasmonic filter paper-based SERS substrate for the quantification of two trace analytes [crystal violet (CV) and picric acid (PA)] using machine learning techniques and SERS data. The wettability of the substrate was modified with an easy and low-cost technique of coating it with silicone oil. Gold nanoparticles were synthesized using a femtosecond laser ablation in water technique. The prepared nanoparticles were characterized using UV, TEM, and SEM techniques and subsequently loaded onto filter papers before using them for SERS studies. We have considered the SERS intensities of the analytes at different concentrations with over 900 spectra to train the model. Principal component analysis (PCA) was used to reduce the dimensionality and, hence, the complexity of the model. Furthermore, support vector regression was used to quantify the analyte molecules and we achieved an R² error of 0.9629 for CV and 0.9472 for PA. In conjunction with a portable Raman spectrometer and a computation time of less than <10 s, we believe that this is an affordable and rapid method for quantification of analytes using the SERS technique.

Handheld SERS coupled with QuEChERs for the sensitive analysis of multiple pesticides in basmati rice

Pesticides are a safety issue globally and cause serious concerns for the environment, wildlife and human health. The handheld detection of four pesticide residues widely used in Basmati rice production using surface-enhanced Raman spectroscopy (SERS) is reported. Different SERS substrates were synthesised and their plasmonic and Raman scattering properties evaluated. Using this approach, detection limits for pesticide residues were achieved within the range of 5 ppb-75 ppb, in solvent. Various extraction techniques were assessed to recover pesticide residues from spiked Basmati rice. Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERs) acetate extraction was applied and characteristic spectral data for each pesticide was obtained from the spiked matrix and analysed using handheld-SERS. This approach allowed detection limits within the matrix conditions to be markedly improved, due to the rapid aggregation of nanogold caused by the extraction medium. Thus, detection limits for three out of four pesticides were detectable below the Maximum Residue Limits (MRLs) of 10 ppb in Basmati rice. Furthermore, the multiplexing performance of handheld-SERS was assessed in solvent and matrix conditions. This study highlights the great potential of handheld-SERS for the rapid on-site detection of pesticide residues in rice and other commodities.

Cholesteric Molecular Tweezer Artificial Receptor for Rapid and Highly Selective Detection of Ag+ in Food Samples

Chiral cholesteric molecular tweezer 7a was synthesized, and its recognition properties for Ag⁺, Al³⁺, Ca²⁺ etc., were investigated by UV and fluorescence spectra. The results showed that in ethanol/Tris (1/1, v/v, pH 7.0) buffer solution, the host molecular tweezer 7a had a specific recognition ability for Ag⁺, the detection limit was up to 1 × 10⁻⁶ mol/L, and other metal ions had little effect on Ag⁺ recognition. At the same time, the naked-eye detection of Ag⁺ was realized by the light red color of the complex solution. Furthermore, the mechanism of recognition of Ag⁺ by molecular tweezer 7a was studied by a nuclear magnetic titration test and computer molecular simulation, and a rapid detection method of Ag⁺ using host molecular tweezer 7a was established. Through the determination of Ag⁺ in milk powder, quinoa and other food samples, it was proved that this novel method had a good application prospect for the detection of Ag⁺ in food.

Protein capture and SERS detection on multiwavelength rainbow-trapping width-graded nano-gratings

Surface-enhanced Raman scattering (SERS) substrates with multiwavelength rainbow-trapping properties hold the potential for a one-size-fits-all platform for rapid and multiplexed disease detection. We present the first report on the utilization of rainbow-trapping width-graded nano-gratings, a new class of chirped metamaterials, to detect protein biomarkers. Using cytochrome c (Cc), a charged analyte with inherent difficulty in adsorbing onto sputtered silver films, we investigated methods of binding Cc on the silver nano-grating in order to improve the SERS signal strength at both 532 and 638 nm excitation. Cc was not detectable on the Ag nano-gratings without surface functionalization at 1μM concentration. Upon charge reversal functionalization of the Ag nano-gratings, 1μM Cc was detectable albeit not reliably. By further crosslinking 1μM Cc to the functionalized Ag nano-gratings, the analyte-capture detection scheme greatly improved the SERS signal strength and reliability at both excitation wavelengths and allowed for quantification of their coefficients of variation with values down to 27%.

Determination, dissipation dynamics, terminal residues and dietary risk assessment of thiophanate-methyl and its metabolite carbendazim in cowpeas collected from different locations in China under field conditions

BACKGROUND

Thiophanate-methyl and its metabolite carbendazim are broad-spectrum fungicides used on many crops. The residues of these chemicals could result in potential environmental and human health problems. Therefore, investigations of the dissipation and residue behaviors of thiophanate-methyl and its metabolite carbendazim on cowpeas and associated dietary risk assessments are essential for the safety of agricultural products.

RESULTS

A simple analytical approach using liquid chromatography with tandem mass spectrometry was developed and validated for the determination of thiophanate-methyl and carbendazim concentrations in cowpeas. Good linearity (R²  > 0.998) was obtained, and the recoveries and relative standard deviations were 80.0-104.7% and 1.4-5.2%, respectively. The dissipation rates of thiophanate-methyl, carbendazim and total carbendazim were high (half-lives of 1.61-2.46 days) and varied in the field cowpea samples because of the different weather conditions and planting patterns. Based on the definition of thiophanate-methyl, the terminal residues of total carbendazim in cowpea samples were below the maximum residue limits set by Japan for other legumes. The acute and chronic risk quotients of three analytes were 0.0-27.6% in cowpea samples gathered from all terminal residue treatments, which were below 100%.

CONCLUSION

An optimized approach for detecting thiophanate-methyl and carbendazim in cowpeas was applied for the investigation of field-trial samples. The potential acute and chronic dietary risks of thiophanate-methyl, carbendazim and total carbendazim to the health of Chinese consumers were low. These results could guide the safe and proper use of thiophanate-methyl in cowpeas and offer data for the dietary risk assessment of thiophanate-methyl in cowpeas. © 2021 Society of Chemical Industry.

Onsite/on-field analysis of pesticide and veterinary drug residues by a state-of-art technology: A review

Pesticides and veterinary drugs are generally employed to control pests and insects in crop and livestock farming. However, remaining residues are considered potentially hazardous to human health and the environment. Therefore, regular monitoring is required for assessing and legislation of pesticides and veterinary drugs. Various approaches to determining residues in various agricultural and animal food products have been reported. Most analytical methods involve sample extraction, purification (cleanup), and detection. Traditional sample preparation is time-consuming labor-intensive, expensive, and requires a large amount of toxic organic solvent, along with high probability for the decomposition of a compound before the analysis. Thus, modern sample preparation techniques, such as the quick, easy, cheap, effective, rugged, and safe method, have been widely accepted in the scientific community for its versatile application; however, it still requires a laboratory setup for the extraction and purification processes, which also involves the utilization of a toxic solvent. Therefore, it is crucial to elucidate recent technologies that are simple, portable, green, quick, and cost-effective for onsite and infield residue detections. Several technologies, such as surface-enhanced Raman spectroscopy, quantum dots, biosensing, and miniaturized gas chromatography, are now available. Further, several onsite techniques, such as ion mobility-mass spectrometry, are now being upgraded; some of them, although unable to analyze field sample directly, can analyze a large number of compounds within very short time (such as time-of-flight and Orbitrap mass spectrometry). Thus, to stay updated with scientific advances and analyze organic contaminants effectively and safely, it is necessary to study all of the state-of-art technology.

Rapid and sensitive SERS detection of melamine in milk using Ag nanocube array substrate coupled with multivariate analysis

In this study, a facile Ag nanocube (NC) array substrate was fabricated for rapid SERS detection of melamine in milk. This easily-prepared substrate exhibited high Raman enhancement factor (~1.02 × 105) and good reproducibility with ~10.75% spot-to-spot variation in Raman intensity. Our proposed method can detect melamine as low as 0.01 ppm in standard solutions and 0.5 ppm in real milk samples after a simple one-step solvent extraction. Two multivariate analysis tools including partial least squares and support vector machines (SVM) were explored to develop reliable regression models for quantitative SERS analysis of melamine. By comparison, SVM regression models exhibited better predictive performance, especially in liquid milk, with root mean square error (RMSE) of calibration = 5.5783, coefficient of determination (R2) of calibration = 0.9807, RMSE of prediction = 1.9636, and R2 of prediction = 0.9736. Hence, this study offers a rapid and sensitive detection of adulterant melamine in milk samples.

A review on the application of spectroscopy to the condiments detection: from safety to authenticity

Condiments are the magical ingredients that make the food present a richer taste. In recent years, due to the increasing consciousness of food safety and human health, much progress has been made in developing rapid and nondestructive techniques for the evaluation of food condiments safety, authentication, and traceability. The potential of spectroscopy techniques, such as near-infrared (NIR), mid-infrared (MIR), Raman, fluorescence, inductively coupled plasma (ICP), and hyperspectral imaging techniques, has been widely enhanced by numerous applications in this field because of their advantages over other analytical techniques. Following a brief introduction of condiment and safety basics, this review mainly focuses on recent vibrational and atomic spectral applications for condiment nondestructive analysis and evaluation, including (1) chemical hazards detection; (2) microbiological hazards detection; and (3) authenticity concerns. The review shows current spectroscopies to be effective tools that will play indispensable roles for food condiment evaluation. In addition, online/real-time applications of these techniques promise to be a huge growth field in the near future.

Advanced Application of Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy in Plant Disease Diagnostics: A Review

Plant diseases result in 20-40% of agricultural loss every year worldwide. Timely detection of plant diseases can effectively prevent the development and spread of diseases and ensure the agricultural yield. High-throughput and rapid methods are in great demand. This review investigates the advanced application of Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) in the detection of plant diseases. The determination of bacterial diseases and stress-induced diseases, fungal diseases, viral diseases, pests in beans, and mycotoxins related to plant diseases using RS and SERS are discussed in detail. Then, biomarkers for RS and SERS detection are analyzed with regard to plant disease diagnosis. Finally, the advantages and challenges are further illustrated. Additionally, potential alternatives are proposed for the challenges. The review is expected to provide a reference and guidance for the use of RS and SERS in plant disease diagnostics.

Anchoring Metallic MoS2 Quantum Dots over MWCNTs for Highly Sensitive Detection of Postharvest Fungicide in Traditional Chinese Medicines

Carbendazim, a very common contamination to the traditional Chinese medicines (TCMs), has posed serious threat to the environment and human health. However, sensitive and selective detection of carbendazim (MBC) in the TCMs is a big challenge for their complex chemical constituents. In this work, a 0D/1D nanohybrid was developed by anchoring 1T-phased MoS₂ quantum dots (QDs) over multiwall carbon nanotubes (MWCNTs) via a facile assembly method. High-resolution transmission electron microscopy (HRTEM), Raman spectroscopy, X-ray photoelectron spectroscopy, and thermogravimetric analysis (TGA) together with EIS reveal that the 1T-phased QDs can anchor over MWCNTs via van der Waals forces, and the anchoring improves the nanohybrid surface area and conductivity. Therefore, the electrochemical sensor fabricated based on the MoS₂ QDs@MWCNT nanohybrid shows excellent catalytic activity to MBC oxidation. Under optimized conditions, the sensor presents a linear voltammetry response to MBC concentration from 0.04 to 1.00 μmol·L^-1, a low detection limit of 2.6 × 10^-8 mol·L^-1, as well as high selectivity, good reproducibility, and long-term stability. Moreover, the sensor has been successfully employed to determine MBC in two typical TCMs and the obtained recoveries are in good accordance with the results achieved by HPLC, showing that the constructed sensor plate holds great practical application in MBC analysis with complex matrix.

Detection of Mycotoxins in Food Using Surface-Enhanced Raman Spectroscopy: A Review

Mycotoxins are toxic metabolites produced by fungi that contaminate many important crops worldwide. Humans are commonly exposed to mycotoxins through the consumption of contaminated food products. Mycotoxin contamination is unpredictable and unavoidable; it occurs at any point in the food production system under favorable conditions, and they cannot be destroyed by common heat treatments, because of their high thermal stability. Early and fast detection plays an essential role in this unique challenge to monitor the presence of these compounds in the food chain. Surface-enhanced Raman spectroscopy (SERS) is an advanced spectroscopic technique that integrates Raman spectroscopic molecular fingerprinting and enhanced sensitivity based on nanotechnology to meet the requirement of sensitivity and selectivity, but that can also be performed in a cost-effective and straightforward manner. This Review focuses on the SERS methodologies applied to date for qualitative and quantitative analysis of mycotoxins based on a variety of SERS substrates, as well as our perspectives on current limitations and future trends for applying this technique to mycotoxin analyses.

A novel thioctic acid-carbon dots fluorescence sensor for the detection of Hg2+ and thiophanate methyl via S-Hg affinity

Mercury ions and thiophanate methyl (TM), are common contaminants present in the environment and food products. These contaminants cause neurovirulence and carcinogenicity effect on the human body. Herein, thioctic acid-carbon dots (SCDs) was synthesized and applied in a fluorescent "turn-off-on" probe to detect Hg²⁺ and TM. The presence of other common metal ions and pesticides did not affect the response of the developed sensor. Further investigation revealed that the fluorescent "turn-off-on" model were static, wherein the "turn-off" was induced by an electron transfer effect, while the "turn-on" was caused by the formation of TM-Hg complexes. Under optimal conditions, the fluorescence sensor method exhibited limits of detection as low as 33.3 nmol/L and 7.6 nmol/L for Hg²⁺ and TM, respectively. The developed sensor was designed to detect Hg²⁺ and TM in real tap water, grape juice and Citri Reticulatae Pericarpium (CRP) water samples.

Visualization of the in situ distribution of contents and hydrogen bonding states of cellular level water in apple tissues by confocal Raman microscopy

Raman spectroscopy has been employed for studying the hydrogen bonding states of water molecules for decades, however, Raman imaging data contain thousands of spectra, making it challenging to obtain information on water with different hydrogen bonds. In the current study, a novel method combining confocal Raman microscopy (CRM) imaging with the iterative curve fitting algorithms was developed to determine the distribution of water contents at the cellular level and water states with different hydrogen bonds in apple tissues. Raman imaging data ranging from 2700 to 3800 cm-1 were acquired from whole cells in the apple tissue, which were then decomposed into seven sub-peaks using the fixed-position Gaussian iterative curve fitting (FPGICF) algorithm. The content and hydrogen bonding states of cellular water were calculated as the area sum of the OH stretching vibration and the area ratio of DA-OH over DDAA-OH stretching vibration or the number of hydrogen bonds of each water molecule, respectively. Finally, the area of each sub-peak, the area sum of the OH stretching vibration, and the area ratio of DA-OH over DDAA-OH stretching vibration were used to visualize the distribution of each sub-peak, water contents and water states with different hydrogen bonds, respectively. In addition, it was found that the number of hydrogen bonds of each water molecule could also be considered as a criterion to describe the hydrogen bond states of water in apple tissues. The availability of such information should provide new insights for future study of cellular water in other food materials.

Residue analysis and kinetics modeling of thiophanate-methyl, carbendazim, tebuconazole and pyraclostrobin in apple tree bark using QuEChERS/HPLC-VWD

Winter is the key period for the control of apple diseases, and fungicides are needed to protect the trunk or main branches. Fungicide residue in apple tree bark is an important basis for the action of the pesticide, but there are no reports on analytical methods or dissipation patterns. In this work, thiophanate-methyl, carbendazim, tebuconazole and pyraclostrobin were selected as typical fungicides and a new QuEChERS-HPLC-VWD(QuEChERS extraction followed by high-performance liquid chromatography detection with a variable wavelength detector) analytical method was developed to estimate their residue kinetics in apple tree bark during the winter months. In the pretreatment step, the sorbent for the clean-up of extracts was optimized as 60 mg/ml primary secondary amine and a gradient-elution model followed by a variable wavelength detection was developed for instrumental analysis. Then this method was validated and applied to the analysis of apple tree bark samples with the linearity range of 0.010-50.00 mg/L, quantification limit range of 0.028-0.080 mg/kg and recovery range of 86.1-101.4%. The dissipation kinetics of thiophanate-methyl and pyraclostrobin could be described by the first-order and two-phase kinetics models, respectively. For carbendazim and tebuconazole, two new models were developed to describe their residue kinetics.

SERS detection of sodium thiocyanate and benzoic acid preservatives in liquid milk using cysteamine functionalized core-shelled nanoparticles

A cysteamine functionalized core shelled nanoparticles (Au@Ag-CysNPs) was presented for simultaneous and rapid detection of sodium thiocyanate (STC) and benzoic acid (BA) preservatives in liquid milk using surface-enhanced Raman spectroscopy (SERS) technique. A spectrum covering 350-2350 cm^-1 region was selected to detect STC with concentrations ranging from 0.5 to 10 mg/L and BA with concentrations ranging from 15 to 240 mg/L in milk samples. Characterization of nanoparticles using high-resolution TEM confirmed that the successful synthesis of Au@AgNPs with core (gold) size of 28 nm and shell (silver) thickness of about 5 nm was grafted with 120 μL of 0.1 nM cysteamine hydrochloride. Results showed that Au@Ag-CysNPs could be used to detect STC up to 0.03 mg/L with a limit of quantification (LOQ) of 0.039 mg/L and a coefficient of determination (R²) of 0.9833 in the milk sample. For detecting BA, it could be screened up to 9.8 mg/L with LOQ of 10.2 mg/L and R² of 0.9903. The proposed substrate was also highly sensitive and the employed method involved only minor sample pretreatment steps. It is thus hoped that the new substrate could be used in the screening of prohibited chemicals in complex food matrices in future studies.