Au@Ag nanostructure based SERS substrate for simultaneous determination of pesticides residue in tea via solid phase extraction coupled multivariate calibration

A Sensitive SERS Sensor Combined with Intelligent Variable Selection Models for Detecting Chlorpyrifos Residue in Tea

Chlorpyrifos is one of the most widely used broad-spectrum insecticides in agriculture. Given its potential toxicity and residue in food (e.g., tea), establishing a rapid and reliable method for the determination of chlorpyrifos residue is crucial. In this study, a strategy combining surface-enhanced Raman spectroscopy (SERS) and intelligent variable selection models for detecting chlorpyrifos residue in tea was established. First, gold nanostars were fabricated as a SERS sensor for measuring the SERS spectra. Second, the raw SERS spectra were preprocessed to facilitate the quantitative analysis. Third, a partial least squares model and four outstanding intelligent variable selection models, Monte Carlo-based uninformative variable elimination, competitive adaptive reweighted sampling, iteratively retaining informative variables, and variable iterative space shrinkage approach, were developed for detecting chlorpyrifos residue in a comparative study. The repeatability and reproducibility tests demonstrated the excellent stability of the proposed strategy. Furthermore, the sensitivity of the proposed strategy was assessed by estimating limit of detection values of the various models. Finally, two-tailed paired t-tests confirmed that the accuracy of the proposed strategy was equivalent to that of gas chromatography-mass spectrometry. Hence, the proposed method provides a promising strategy for detecting chlorpyrifos residue in tea.

Ag@Au core-shell nanoparticle-based surface-enhanced Raman scattering coupled with chemometrics for rapid determination of chloramphenicol residue in fish

The alarming increase in drug-resistant bacteria in fish resulting from the misuse of antibiotics poses a significant threat to ecosystems and human health. Therefore, the development of a reliable approach for detecting antibiotic residues in fish is crucial. In this study, a rapid and simple method for detecting chloramphenicol (CAP) residue in tilapia was developed using surface-enhanced Raman scattering (SERS) combined with chemometric algorithms. Silver and gold core-shell nanoparticles (Ag@Au CSNPs) were used as SERS nanosensors to achieve strong signal amplification with an enhancement factor of 2.67 × 106. The results demonstrated that the variable combination population analysis-partial least square (VCPA-PLS) model combined with the standard normal variable transformation pretreatment method exhibited the best predictive performance with a detection limit of 1 × 10-5 µg/mL. Thus, an SERS technique was established based on Ag@Au CSNPs combined with VCPA-PLS to rapidly detect CAP in tilapia.

Breaking boundaries: Artificial intelligence for pesticide detection and eco-friendly degradation

Pesticides are extensively used agrochemicals across the world to control pest populations. However, irrational application of pesticides leads to contamination of various components of the environment, like air, soil, water, and vegetation, all of which build up significant levels of pesticide residues. Further, these environmental contaminants fuel objectionable human toxicity and impose a greater risk to the ecosystem. Therefore, search of methodologies having potential to detect and degrade pesticides in different environmental media is currently receiving profound global attention. Beyond the conventional approaches, Artificial Intelligence (AI) coupled with machine learning and artificial neural networks are rapidly growing branches of science that enable quick data analysis and precise detection of pesticides in various environmental components. Interestingly, nanoparticle (NP)-mediated detection and degradation of pesticides could be linked to AI algorithms to achieve superior performance. NP-based sensors stand out for their operational simplicity as well as their high sensitivity and low detection limits when compared to conventional, time-consuming spectrophotometric assays. NPs coated with fluorophores or conjugated with antibody or enzyme-anchored sensors can be used through Surface-Enhanced Raman Spectrometry, fluorescence, or chemiluminescence methodologies for selective and more precise detection of pesticides. Moreover, NPs assist in the photocatalytic breakdown of various organic and inorganic pesticides. Here, AI models are ideal means to identify, classify, characterize, and even predict the data of pesticides obtained through NP sensors. The present study aims to discuss the environmental contamination and negative impacts of pesticides on the ecosystem. The article also elaborates the AI and NP-assisted approaches for detecting and degrading a wide range of pesticide residues in various environmental and agrecultural sources including fruits and vegetables. Finally, the prevailing limitations and future goals of AI-NP-assisted techniques have also been dissected.

A three-dimensional gold nanoparticles spherical liquid array for SERS sensitive detection of pesticide residues in apple

High-performance plasmonic substrates have recently attracted much research attention. Herein, a three-dimensional gold nanoparticles (AuNPs) spherical liquid array (SLA) with high "hot spots" and tunable nanometer gap by optimizing the proportion of AuNPs colloids over chloroform was synthesized based on a water-oil interfacial self-assembly strategy. The substrate demonstrated excellent surface-enhanced Raman scattering (SERS) performance using tetrathiafulvalene and rhodamine 6G (R6G) as probe molecules. With a simple extraction and soaking pretreatment process, the SLA exhibited high sensitivity for analysing triazophos on apple peels, with a limit of detection (LOD) of 0.005 µg/mL and recovery ranging from 96 to 110 %. Particularly, the chloroform produced an inherent characteristic peak at 665 cm-1, which was used as the internal standard to correct SERS signal fluctuation, leading to an improvement of the corresponding coefficient R2 from 0.97 to 0.99, thus improving the reproducibility. Therefore the SLA substrate possesses the potential for quantitative analysis of food contaminants.

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.

Plasmonic photocatalytic degradation of tebuconazole and 2,4-dichlorophenoxyacetic acid by Ag nanoparticles-decorated TiO2 tracked by SERS analysis

Chemical oxidation technologies have been notably used for the mineralization of organic pollutants from aqueous effluents, been especially relevant for the degradation of pesticides. In this context, both tebuconazole (TEB) and 2,4-dichlorophenoxyacetic acid (2,4-D) pesticides were photodegraded by a combined catalyst of TiO2 and silver nanoparticles irradiated by UV-A light (λmax = 368 nm), and the experiments were tracked by surface-enhanced Raman scattering (SERS) spectroscopy. For 2,4-D, the degradation of about 70% was observed after almost 200 min, while for TEB, a decrease of 80% of the initial concentration was observed after approximately 100 min. The SERS monitoring allowed the proposal of some by-products, such as oxidized aliphatic chain and triazole from TEB besides glycolic, glyoxylic and dihydroxyacetic acids from 2,4-D. Their toxicities were predicted through ECOSAR software, verifying that most of them were not harmful to populations of fish, Daphnia and green algae. Thus, the performed oxidative process was efficient in the photodecomposition of TEB and 2,4-D pesticides, inclusive in terms of the decreasing of the toxicity of contaminated effluents.

Regulated synthesis of an Au NB-DT@Ag bimetallic core-molecule-shell nanostructure for reliable SERS detection

In recent research, anisotropic plasmonic core-shell nanomaterials have gained a lot of attention in surface-enhanced Raman scattering (SERS) due to their brilliant uniformity and optical properties. Herein, a bimetallic core-molecule-shell (CMS) composite nanorod SERS substrate nanomaterial (Au NB-DT@Ag NRs) was designed and synthesized under precise regulation. The inner core is gold nanobipyramids (Au NBs), which possess superior plasmonic properties. Uniform Au NBs of five different sizes were fabricated via a penta-twinned gold seed mediated growth method. The length varied from 160 to 62 nm and the corresponding diameter varied from 60 to 23 nm while the longitudinal surface plasmonic resonance (SPR) changed from 908 to 715 nm. The SERS activity of five Au NBs were compared and the optimally sized one with a length of 78 nm and width of 28 nm was set as the inner core. After modification with the Raman reporter (DT), different amounts of silver were deposited on the surface of Au NB-DTs to form an Au NB-DT@Ag nanocomposite. The shape of the nanostructure gradually became a rod and lengthened while the longitudinal SPR wavelength varied from 733 nm to 664 nm with an increase in the amount of silver nitrate added. The Au NB-DT@Ag NRs with the best SERS activity (b-3) could realize the quantitative detection of the toxic dyes malachite green (MG) and crystal violet (CV) of concentrations as low as 5 × 10-9 M, showing good reproducibility and stability. This work offers a new design strategy for a SERS substrate for reliable quantitative SERS detection applications.

Facile synthesis of fluorescence-SERS dual-probe nanocomposites for ultrasensitive detection of sulfur-containing gases in water and beer samples

A highly structured fluorescent-SERS dual-probe nanocomposites were synthesized for the determination of sulfur-containing gases in water and beer samples. Initially, Au@Ag NPs were prepared by growing the Ag shell on the Au core in situ, modified with surfactant and fabricated with Zn²⁺. Then, MOF-5-NH₂ assembled Au@Ag NPs were obtained through coordination between Zn sites and 2-aminoterephthalic acid. The principle was based on redox reaction between H₂S and Au@Ag NPs, and the fluorescence turn-on effects were due to the charge transfer between SO₂ and amino groups. The SERS intensity was related to the concentration of H₂S (5 ∼ 60 nM), and an ultra-low detection limit of 2.26 nM was achieved. Importantly, the fluorescence performance was applied for SO₂ analysis and exhibited good linear response. Moreover, the platform for H₂S and SO₂ in real samples revealed satisfactory results (95.6 ∼ 101.6% and 99.0 ∼ 104.4%). Therefore, the proposed system offered a precise detection of H₂S/SO₂ in food/environmental settings.

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.

Multiplex Surface-Enhanced Raman Scattering: An Emerging Tool for Multicomponent Detection of Food Contaminants

For survival and quality of human life, the search for better ways to ensure food safety is constant. However, food contaminants still threaten human health throughout the food chain. In particular, food systems are often polluted with multiple contaminants simultaneously, which can cause synergistic effects and greatly increase food toxicity. Therefore, the establishment of multiple food contaminant detection methods is significant in food safety control. The surface-enhanced Raman scattering (SERS) technique has emerged as a potent candidate for the detection of multicomponents simultaneously. The current review focuses on the SERS-based strategies in multicomponent detection, including the combination of chromatography methods, chemometrics, and microfluidic engineering with the SERS technique. Furthermore, recent applications of SERS in the detection of multiple foodborne bacteria, pesticides, veterinary drugs, food adulterants, mycotoxins and polycyclic aromatic hydrocarbons are summarized. Finally, challenges and future prospects for the SERS-based detection of multiple food contaminants are discussed to provide research orientation for further.

Simultaneous, Label-Free and High-throughput SERS Detection of Multiple Pesticides on Ag@Three-Dimensional Silica Photonic Microsphere Array

Rapid identification and quantitative simultaneous analysis for multiple pesticide in real samples based on surface-enhanced Raman spectroscopy (SERS) is still a challenge because of sample complexity, reproducibility, and stability of SERS substrate. With use of colloidal silver nanoparticles loaded three-dimensional (3D) silica photonic microspheres (SPMs) array as the analytical platform, a SERS-based array assay for multiple pesticides was developed in this work. The silver nanoparticles were fixed into the gaps formed by the self-assembled nanospheres of the 3D SPMs to produce "hot spots", on which the Raman enhanced effect was up to 9.86 × 10⁷ and the maximum electric field enhancement effect reached to 9.75 times, ensuring the target pesticides on the surface of the SERS-substrate integrated SPM can be detected sensitively. Using 2,4-dichlorophenoxyacetic acid (2,4-D), glyphosate, and imidacloprid as the testing pesticides, the label-free and high-throughput SERS assay for simultaneous detection of the pesticides was established, giving good linear detection ranges (0.1-204.8 μg/mL for 2,4-D, 0.3-247.9 μg/mL for glyphosate, and 0.2-204.8 μg/mL for imidacloprid) and low detection limits (3.03 ng/mL for 2,4-D, 3.14 ng/mL for glyphosate, and 8.82 ng/mL for imidacloprid). The spiked recovery rates in the real samples were measured in the range of 82-112%, which was consistent with that of the classical standard methods. The label-free 3D SERS array analytical platform provides a powerful tool for high-throughput and low-cost screening of multiple pesticide residues in real samples.

Detection of 2,4-dichlorophenoxyacetic acid in water sample by organosilane based silica nanocomposites

The present study aims to produce nanocomposites of silica based organosilane as sensitive and selective fluorescent sensor for the recognition of 2,4 dichlorophenoxyacetic acid (2,4-D). Hydrazone tethered triazole functionalized organosilane has been synthesized by the condensation reaction of 4-hydroxybenzaldehyde and phenyl hydrazine followed by Cu(I) catalysed cycloaddition of azide with alkyne. The prepared compound has been further grafted over silica surface and the synthesized materials were characterized by FT-IR, NMR (¹H and ¹³C), XRD, mass spectrometry and FE-SEM spectral analyses. The prepared organosilane and its HSNPs have been utilized as an effective emission probe for the selective detection of 2,4 D with good linear relationship in the range of 0-160 μM and 0-115 μM and LOD value of 46 nM and 13.5 nM respectively. In the presence of other active species, the sensor shows minimal interference while the comparison with the previously reported techniques suggests it to be more desirable for the sensitive and selective detection of 2,4 D. Further, the real sample application for detection of 2,4 D was analyzed in field water and the HSNPs based sensing system gave recovery percentage of above 98 %.

A super-hydrophobic perfluoropolyether coated polytetrafluoroethylene sheets substrate for detection of acetamiprid surface-enhanced Raman spectroscopy

In this paper, a hydrophobic substrate as concentrators including an inner layer of polytetrafluoroethylene (PTFE) and an outer layer covered a thin layer of perfluoropolyether (PFPE) was constructed to achieve a higher sensitivity for acetamiprid (AC) SERS detection. The condensation effect of the PTFE-PFPE hydrophobic substrate-induced aggregation of gold nanoparticles (Au NPs) result ''hot spots'' for SERS. The hydrophobic substrate is better reproducibility (RSD < 5%) compared with that on a conventional silicon wafer. A further application of the hydrophobic substrate was demonstrated by the detection of AC in tea samples within a detection range of 0.03 mg/L to 3 mg/L. The hydrophobic substrate eliminates the problem of solution diffusion to avoid the "coffee ring" effect (When a droplet adheres to a solid surface, the suspended molecular particles usually deposit on the edge of the droplet to form a ring).

Intelligent evaluation of free amino acid and crude protein content in raw peanut seed kernels using NIR spectroscopy paired with multivariable calibration

Given the nutritional importance of peanuts, this study examined the free amino acid (FAA) and crude protein (CP) content in raw peanut seeds. Near-infrared spectroscopy (NIRS) was employed in combination with variable selection algorithms after successful reference data analysis using colorimetric and Kjeldahl methods. Ensuing the application of partial least squares (PLS) as a full spectral model, the genetic algorithm (GA), bootstrapping soft shrinkage (BOSS), uninformative variable elimination (UVE), and random frog (RF) models were tested and assessed. A comparison of correlation coefficients of prediction (R_p), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) was performed to appraise the performance of the built models. Using RF-PLS, an unsurpassed outcome was achieved for FAA (R_p = 0.937, RPD = 3.38) and CP (R_p = 0.9261, RPD = 3.66). These findings demonstrated that NIR in combination with RF-PLS could be utilized for quantitative, rapid, and nondestructive prediction of FAA and CP in raw peanut seed samples.

Simultaneous determination of benzimidazole fungicides in food using signal optimized label-free HAu/Ag NS-SERS sensor

Extensively employed pesticide in agriculture causes residue in food products that would threaten public health safety. The surface-enhanced Raman scattering (SERS) signal reliant on double sensing of carbendazim and thiabendazole in a single step is achieved without the aid of any bio-recognition element. A label-free anisotropic bimetallic hollow Au/Ag nanostars (HAu/Ag NS) SERS substrate was synthesized with numerous hot spots for Raman molecule through a galvanic displacement-free deposition. The individual and mixed analyte calibration results were compared based on the identified peak at 1224 (carbendazim) and 778 (thiabendazole) cm^-1 and exhibited insignificant differences. The sensor could detect carbendazim and thiabendazole up to 4.28 × 10^-4 and 6.04 × 10^-4 µg·g^-1 or µg·mL^-1 in both individual and mixture of their extract. The recovery for accuracy and precision analysis was 91.54-98.26 % in rice and water. Finally, validation results were achieved satisfactorily (p > 0.05) with HPLC.

Experimental and theoretical investigation of acetamiprid adsorption on nano carbons and novel PVC membrane electrode for acetamiprid measurement

Acetamiprid removal was investigated by synthesized Graphene oxide, multiwall nanotube and graphite from an aqueous solution. For this propose, FT-IR, XRD, UV–Vis, SEM and EDS were used to characterize the synthesized nano adsorbents and to determine the removal process. A novel PVC membrane electrode as selective electrode made for determining the concentration of acetamiprid. Batch adsorption studies were conducted to investigate the effect of temperature, initial acetamiprid concentration, adsorbent type and contact time as important adsorption parameters. The maximum equilibrium time was found to be 15 min for graphene oxide. The kinetics studies showed that the adsorption of acetamiprid followed the pseudo-second-order kinetics mechnism. All the adsorption equilibrium data were well fitted to the Langmuir isotherm model and maximum monolayer adsorption capacity 99 percent. Docking data of adsorption have resulted in the same as experimental data in good manner and confirmed the adsorption process.

Surface-Enhanced Raman Spectroscopy Substrates for Food Safety and Quality Analysis

Surface-enhanced Raman spectroscopy (SERS) has been identified as a fundamental surface-sensitive technique that boosts Raman scattering by adsorbing target molecules on specific surfaces. The application of SERS highly relies on the development of smart SERS substrates, and thus the fabrication of SERS substrates has been constantly improved. Herein, we investigate the impacts of different substrates on SERS technology including plasmonic metal nanoparticles, semiconductors, and hybrid systems in quantitative food safety and quality analysis. We first discuss the fundamentals, substrate designs, and applications of SERS. We then provide a critical review of the recent progress of SERS in its usage for screening and detecting chemical and biological contaminants including fungicides, herbicides, insecticides, hazardous colorants, and biohazards in food samples to assess the analytical capabilities of this technology. Finally, we investigate the future trends and provide practical techniques that could be used to fulfill the requirements for rapid analysis of food at a low cost.

Rapid Determination of Mixed Pesticide Residues on Apple Surfaces by Surface-Enhanced Raman Spectroscopy

Chlorpyrifos (CPF) and 2,4-dichlorophenoxyacetic acid (2,4-D) are insecticides and herbicides which has been widely used on farms. However, CPF and 2,4-D residues on corps can bring high risks to human health. Accurate detection of pesticide residues is important for controlling health risks caused by CPF and 2,4-D. Therefore, we developed a fast, sensitive, economical, and lossless surface-enhanced Raman spectroscopy (SERS)-based method for pesticide detection. It can rapidly and simultaneously determine the CPF and 2,4-D mixed pesticide residues on an apple surface at a minimum of 0.001 mg L⁻¹ concentration, which is far below the pesticide residue standard in China and the EU. The limits of detection reach down to 1.28 × 10⁻⁹ mol L⁻¹ for CPF and 2.47 × 10⁻¹⁰ mol L⁻¹ for 2,4-D. The limits of quantification are 4.27 × 10⁻⁹ mol L⁻¹ and 8.23 × 10⁻¹⁰ mol L⁻¹ for CPF and 2,4-D. This method has a great potential for the accurate detection of pesticide residues, and may be applied to other fields of agricultural products and food industry.

Catalytic hairpin activated gold-magnetic/gold-core-silver-shell rapid self-assembly for ultrasensitive Staphylococcus aureus sensing via PDMS-based SERS platform

Staphylococcus aureus (S. aureus) has been identified as a marker of food contamination, closely associated with human health. This work designs a sensitive and rapid bio-detection strategy for S. aureus based on hybridization chain reaction-assisted surface enhanced Raman scattering (HCR-assisted-SERS) signal amplification. In this approach, the interaction between the aptamer (Apt) and its partial complementary DNA strands (cDNA) fabricated on the surface of gold-assisted magnetic nanoparticles (Au-MNPs) and the subsequent detachment of the cDNA results in the activation of the HCR process. In the HCR, a pair of hairpin structured DNA probes (H1 and H2) with sticky ends self-assembles to form a long DNA polymer. Subsequently, the output and amplification of the SERS signal were performed by conjugating 4-ATP modified Au@Ag NPs with the obtained DNA polymer via a specific Ag-S bond, and further collected through a self-administered polydimethylsiloxane (PDMS) cone-shaped support array. The precise quantification of S. aureus was performed in the concentration range of 28 to 2.8 × 10⁶ cfu/mL, achieving a detection limit of 0.25 cfu/mL. This strategy was further applied to S. aureus detection in spiked milk samples with good recoveries (91-102%) and the relative standard deviation (4.35-8.41%). The sensing platform also showed satisfactory validation results (p > 0.05) using the traditional plate counting method. The proposed HCR-assisted SERS probe can be extended to other foodborne pathogenic bacteria types via engineering appropriate Apt and DNA initiators, thus, inspiring widespread applications in food safety and biomedical research.

Simultaneous quantification of deoxymyoglobin and oxymyoglobin in pork by Raman spectroscopy coupled with multivariate calibration

Because of the nutritional advantages and customer acceptance, it is vital to ensure pork meat quality. This study examined the quantification of myoglobin proportions (deoxymyoglobin and oxymyoglobin) by coupling Raman spectroscopy with efficient variables selection chemometrics. Prior to acquiring Raman spectroscopic data, the fractions of myoglobin were determined. Afterward, multivariate calibration methods like partial least square (PLS), competitive adaptive reweighted sampling (CARS-PLS), genetic algorithm-PLS (GA-PLS), and random frog-PLS (RF-PLS) were applied and evaluated. The models' performance was assessed using correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The RF-PLS model achieved optimal results for both deoxymyoglobin and oxymyoglobin, with Rp = 0.8936; RMSEP = 2.91 and RPD = 1.97 for the former and Rp = 0.9762; RMSEP = 1.23 and RPD = 4.47 for the latter, respectively. Therefore, this work demonstrated that Raman spectroscopy paired with RF-PLS could be employed for nondestructive, fast, and easy detection of deoxymyoglobin and oxymyoglobin.

Rapid detection and prediction of chloramphenicol in food employing label-free HAu/Ag NFs-SERS sensor coupled multivariate calibration

Considering growing food safety issues, hollow Au/Ag nano-flower (HAu/Ag NFs) nanosensor has been synthesized for label-free and ultrasensitive detection of chloramphenicol (CP) via integrating the surface-enhanced Raman scattering (SERS) and multivariate calibration. As the anisotropic plasmonic nanomaterials, HAu/Ag NFs had numerous nano-chink on their surface, which offered huge hotspots for analytes. CP generated a strong SERS signal while adsorbed on the surface of HAu/Ag NFs and noted excellent linearity with 1st derivative-competitive adaptive reweighted sampling-partial least squares (CARS-PLS) in the range of 0.0001-1000 µg/mL among the four applied multivariate calibrations. Additionally, CARS-PLS generated the lowest prediction error (RMSEP) of 0.089 and 0.123 µg/mL for milk and water samples, respectively, and any CARS-PLS model could be used for both samples according to T-test results (P > 0.05). The intra- and interday recovery for both samples were in the range of 92.62-96.74% with CV < 10%, suggested the proposed method has excellent accuracy and precision.

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.

Recent advances of nanomaterial-based optical sensor for the detection of benzimidazole fungicides in food: a review

The abuse of pesticides in agricultural land during pre- and post-harvest causes an increase of residue in agricultural products and pollution in the environment, which ultimately affects human health. Hence, it is crucially important to develop an effective detection method to quantify the trace amount of residue in food and water. However, with the rapid development of nanotechnology and considering the exclusive properties of nanomaterials, optical, and their integrated system have gained exclusive interest for accurately sensing of pesticides in food and agricultural samples to ensure food safety thanks to their unique benefit of high sensitivity, low detection limit, good selectivity and so on and making them a trending hotspot. This review focuses on recent progress in the past five years on nanomaterial-based optical, such as colorimetric, fluorescence, surface-enhanced Raman scattering (SERS), and their integrated system for the monitoring of benzimidazole fungicide (including, carbendazim, thiabendazole, and thiophanate-methyl) residue in food and water samples. This review firstly provides a brief introduction to mentioned techniques, detection mechanism, applied nanomaterials, label-free detection, target-specific detection, etc. then their specific application. Finally, challenges and perspectives in the respective field are discussed.

Study on starch content detection and visualization of potato based on hyperspectral imaging

Starch is an important quality index in potato, which contributes greatly to the taste and nutritional quality of potato. At present, the determination of starch depends on chemical analysis, which is time consuming and laborious. Thus, rapid and accurate detection of the starch content of potatoes is important. This study combined hyperspectral imaging with chemometrics to predict potato starch content. Two varieties of Kexin No.1 and Holland No.15 potatoes were used as experimental samples. Hyperspectral data were collected from three sampling sites (the top, umbilicus, and middle regions). Standard normal variate (SNV) was used for spectral preprocessing, and three different methods of competitive adaptive reweighted sampling (CARS), iterative variable subset optimization (IVSO), and the variable iterative space shrinkage approach (VISSA) were used for characteristic wavelength selection. Linear partial least-squares regression (PLSR) and nonlinear support vector regression (SVR) models were then established. The results indicated that the sampling site has a considerable impact on the accuracy of the prediction model, and the umbilicus region with CARS-SVR model gave best performance with correlation coefficients in calibration (Rc) of 0.9415, in prediction (Rp) of 0.9346, root mean square errors in calibration (RMSEC) of 15.9 g/kg, in prediction (RMSEP) of 17.4 g/kg, and residual predictive deviation (RPD) of 2.69. The starch content in potatoes was visualized using the best model in combination with pseudo-color technology. Our research provides a method for the rapid and nondestructive determination of starch content in potatoes, providing a good foundation for potato quality monitoring and grading.

In situ and rapid determination of acetamiprid residue on cabbage leaf using surface-enhanced Raman scattering

BACKGROUND

Pesticide residues in agricultural products and foods pose a serious threat to human health, and therefore a simple, rapid and direct method is urgently needed for pesticide residue detection. In addition to realizing the detection of acetamiprid in cabbage extract solution, the main target of this study was to establish an in situ surface-enhanced Raman scattering (SERS) method, which could directly detect acetamiprid residue on cabbage leaf without the need for extraction. Acetamiprid was first used to contaminate the surface of fresh cabbage leaf, and then bimetallic silver-coated gold nanoparticles (Au@AgNPs) were added on the contaminated spots and dried for SERS measurement.

RESULTS

Results suggested that acetamiprid can be detected in cabbage extract and on cabbage leaf surface in situ using the SERS method based on the Au@AgNPs substrate. The limit of detection was 0.08 μg mL^-1 in cabbage extract and 0.14 mg kg^-1 on cabbage leaf, the recovery ranged from 80.5% to 105.5% and the relative standard deviation was in the range 4.37-10.63%.

CONCLUSIONS

The proposed SERS method provides an in situ, nondestructive and rapid way to detect acetamiprid residue on the surface of fruits and vegetables, which could serve as an auxiliary approach for early screening of contaminated produce in field or on site in the future. © 2020 Society of Chemical Industry.

Application of benchtop NIR spectroscopy coupled with multivariate analysis for rapid prediction of antioxidant properties of walnut (Juglans regia)

Benchtop near-infrared (NIR) spectroscopy coupled with multivariate analysis was used for the classification and prediction of antioxidant properties of walnut. Total phenolic content (TPC), total flavonoid content (TFC), ABTS assay and FRAP assay were performed spectrophotometrically. The synergy interval partial least square coupled competitive adaptive reweighted sampling (Si-CARS-PLS) was used for the prediction. A decent discrimination using principal component analysis (PCA) was observed by mean of spectroscopic and antioxidant properties data with total cumulative variance of 99.26% (PC1 = 95.07%, PC2 = 2.98%, PC3 = 1.21%) and 96.60% (PC1 = 64.28%, PC2 = 32.32%) respectively. The Si-CARS-PLS yielded optimal performance, R_P = 0.9616, RPD = 3.807 for TPC, R_P = 0.9657, RPD = 3.367 for TFC, R_P = 0.9683, RPD = 2.728 for ABTS assay, and R_P = 0.914, RPD = 2.669 for FRAP assay. These findings revealed that NIR integrated with Si-CARS-PLS could be used for the prediction of antioxidant properties of walnut.

Rapid detection of mercury in food via rhodamine 6G signal using surface-enhanced Raman scattering coupled multivariate calibration

Considering food safety and limitations of biorecognition elements, this study focused on the development of a novel method for predicting mercury (Hg²⁺) in fish and water samples using surface-enhanced Raman scattering (SERS) coupled wavenumber selection chemometric method. Herein, core-shell Au@Ag nanoparticles (Au@Ag NPs) were synthesized as SERS substrate, and rhodamine 6G (R6G) was used as signaling probe for Hg²⁺. In the presence of Hg²⁺, citrate ion of Au@Ag NPs induced complexation and become amalgam causes desorption of R6G occurred, resulted in decreased SERS signal intensity. Compared to surface Plasmon resonance method, SERS coupled genetic algorithm-partial least squares realized good correlation coefficient (0.9745 and 0.9773) in their prediction over the concentration ranges 1.0 × 10² to 1.0 × 10^-3 µg/g. The recovery (88.45 - 94.73%) and precision (coefficient of variations, 3.28 - 5.76%) exhibiting satisfactory results suggested that the proposed method could be employed to predict Hg²⁺ in fish and water samples towards quality and safety monitoring.

Cellulose paper-based SERS sensor for sensitive detection of 2,4-D residue levels in tea coupled uninformative variable elimination-partial least squares

Food safety is a growing concern in recent years. This work presents the design of a simple and sensitive method for predicting 2,4-D (2,4-dichlorophenoxyacetic acid) residue levels in green tea extract employing surface-enhanced Raman spectroscopy (SERS) coupled uninformative variable elimination-partial least squares (UVE-PLS). Herein, SERS active citrate functionalized silver nanoparticles (AgNPs) with enhancement factor 1.51 × 10⁸ was used to prepare cellulose paper (common office) templated SERS sensor for acquiring SERS spectra of 2,4-D. The principle of the work was based on the interaction between 2,4-D and citrate group of AgNPs via chlorine atoms in the concentration range 1.0 × 10^-4 to 1.0 × 10³ µg/g. Three different wavenumber selection chemometric algorithms were studied comparatively to build an optimum calibration model, among them UVE-PLS showed enhanced performance as evident from the RPD value of 6.01 and Rp = 0.9864. Under optimized experimental condition proposed paper-based SERS sensor exhibited detection limit and RSD of 1.0 × 10^-4 µg/g and <5%, respectively. In addition, the validation results by HPLC method were satisfactory (p > 0.05).

Self-Cleaning-Mediated SERS Chip Coupled Chemometric Algorithms for Detection and Photocatalytic Degradation of Pesticides in Food

Pesticide residues in food have been a grave concern to consumers. Herein, we have developed a dual-mode SERS chip using Cu₂O mesoporous spheres decorated with Ag nanoparticles (MCu₂O@Ag NPs) as both sensing and degradation/clearing unit for rapid detection of pymetrozine and thiram pesticides in tea samples. Three kinds of chemometric algorithms were comparatively applied to analyze the collected SERS spectra of pesticides. In comparison, random frog-partial least squares achieved the best performance with root mean square error of prediction and residual predictive deviation values of 0.9871, 6.17, and 0.9873, 6.64 for pymetrozine and thiram, respectively. Additionally, the prepared SERS chip showed great photocatalytic activity to degrade pesticides under visible light irradiation. Through a facile method, this work presented a novel dual-functional SERS chip for the rapid detection and degradation of low-concentration pesticides in both environmental and food samples.

Rapid on-site identification of pesticide residues in tea by one-dimensional convolutional neural network coupled with surface-enhanced Raman scattering

In this study, a novel analytical approach is proposed for the identification of pesticide residues in tea by combining surface-enhanced Raman scattering (SERS) with a deep learning method one-dimensional convolutional neural network (1D CNN). First, a handheld Raman spectrometer was used for rapid on-site collection of SERS spectra. Second, the collected SERS spectra were augmented by a data augmentation strategy. Third, based on the augmented SERS spectra, the 1D CNN models were established on the cloud server, and then the trained 1D CNN models were used for subsequent pesticide residue identification analysis. In addition, to investigate the identification performance of the 1D CNN method, four conventional identification methods, including partial least square-discriminant analysis (PLS-DA), k-nearest neighbour (k-NN), support vector machine (SVM) and random forest (RF), were also developed on the basis of the augmented SERS spectra and applied for pesticide residue identification analysis. The comparative studies show that the 1D CNN method possesses better identification accuracy, stability and sensitivity than the other four conventional identification methods. In conclusion, the proposed novel analytical approach that exploits the advantages of SERS and a deep learning method (1D CNN) is a promising method for rapid on-site identification of pesticide residues in tea.

Chemometrics coupled 4-Aminothiophenol labelled Ag-Au alloy SERS off-signal nanosensor for quantitative detection of mercury in black tea

Black tea like other food crops is prone to mercury ion (Hg²⁺) contamination right from cultivation to industrial processing. Due to the dangerous health effects posed even in trace contents, sensitive detection and quantification sensors are required. This study employed the surface-enhanced Raman scattering (SERS) enhancement property of 4-aminothiophenol (4-ATP) as a signal turn off approach functionalized on Ag-Au alloyed nanoparticle to firstly detect Hg²⁺ in standard solutions and spiked tea samples. Different chemometric algorithms were applied on the acquired SERS and inductively coupled plasma-mass spectrometry (ICP-MS) chemical reference data to select effective wavelengths and spectral variables in order to develop models to predict the Hg²⁺. Results indicated that Ag-Au/4-ATP SERS sensor combined with ant colony optimization partial least squares (ACO-PLS) exhibited the best correlation efficient and minimum errors for Hg²⁺ standard solutions (R_c = 0.984, R_p = 0.974, RMSEC = 0.157 μg/mL, RMSEP = 0.211 μg/mL) and spiked tea samples (R_c = 0.979, R_p = 0.963, RMSEC = 0.181 μg/g and RMSEP = 0.210 μg/g). The limit of detection of the proposed sensor was 4.12 × 10^-7 μg/mL for Hg²⁺ standard solutions and 2.83 × 10^-5 μg/g for Hg²⁺ spiked tea samples. High stability and reproducibility with relative standard deviation of 1.14% and 0.84% were detected. The potent strong relationship between the SERS sensor and the chemical reference method encourages the application of the developed chemometrics coupled SERS system for future monitoring and evaluation of Hg²⁺ in tea.

SERS based sensor for mycotoxins detection: Challenges and improvements

Surface-enhanced Raman spectroscopy (SERS) has become a growing ultrasensitive analytical technique to quantify toxic molecules in foodstuffs. Monitoring the levels of chemical contaminants not only ensures food security but also offers a guideline on the production, processing, and risk analysis of consumer's health protection. The objective of this study was to point out the possible challenges associated with the detection of mycotoxins in foodstuffs. Herein, we have discussed briefly as to selectivity, accuracy, precision, robustness, ruggedness, non-specific adsorption (NSA), cross-reactivity (for both label-free and the target analyte capture approaches like the application of antibody, aptamer, molecularly imprinted polymer (MIP), linear polymer affinity agents and/or specific surface-modified nanomaterials) and their potential solution.

Pre etched Ag nanocluster as SERS substrate for the rapid quantification of AFB1 in peanut oil via DFT coupled multivariate calibration

The current study extends the use of surface-enhanced Raman spectroscopy (SERS) combined with density functional theory (DFT) and multivariate calibration towards the rapid quantification of aflatoxin B1 (AFB1) in peanut oil samples. It reports the design of pre etched Ag nanocluster as an active SERS substrate for quantifying AFB1, after being impregnated on its surface. The SERS spectra of AFB1@pre etched Ag nanocluster was recorded and its respective theoretical spectrum was calculated by density functional theory (DFT) to assign the characteristic peaks. The baseline drift and rotation effects were masked by the first-order derivative preprocessing method followed by multivariate calibration. The BP-AdaBoost model exhibited optimum prediction (Rp = 0.9283 and 0.9332) ability over the concentration range 5-100 and 100-1000 ngmL^-1, respectively. The limit of detection calculated was 5.0 ngmL^-1 and the obtained recoveries were in the range from 90.4% to 113.1% in spiked peanut oil samples. Additionally, precision analysis revealed an RSD ca. 5%, suggesting the applicability of the pre etched Ag nanocluster SERS substrate towards AFB1 detection. Thus, the proposed SERS platform exploiting DFT and BP-AdaBoost model was found reproducible for the quantification of AFB1 in peanut oil.

Micro-NIR spectrometer for quality assessment of tea: Comparison of local and global models

Near-infrared (NIR) spectroscopy is an effective tool for analyzing components relevant to tea quality, especially catechins and caffeine. In this study, we predicted catechins and caffeine content in green and black tea, the main consumed tea types worldwide, by using a micro-NIR spectrometer connected to a smartphone. Local models were established separately for green and black tea samples, and these samples were combined to create global models. Different spectral preprocessing methods were combined with linear partial-least squares regression and nonlinear support vector machine regression (SVR) to obtain accurate models. Standard normal variate (SNV)-based SNV-SVR models exhibited accurate predictive performance for both catechins and caffeine. For the prediction of quality components of tea, the global models obtained results comparable to those of the local models. The optimal global models for catechins and caffeine were SNV-SVR and particle swarm optimization (PSO)-simplified SNV-PSO-SVR, which achieved the best predictive performance with correlation coefficients in prediction (Rp) of 0.98 and 0.93, root mean square errors in prediction of 9.83 and 2.71, and residual predictive deviations of 4.44 and 2.60, respectively. Therefore, the proposed low-price, compact, and portable micro-NIR spectrometer connected to smartphones is an effective tool for analyzing tea quality.