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

Dual signal output detection of acetamiprid residues in medicine and food homology products via nanopore biosensor

Acetamiprid (ACE) is used in plant cultivation and their accumulation is a threat to ecology and health. Herein, a fluorescence/ion current dual-readout nanopore biosensor was designed for ACE detection. The carboxyfluorescein (FAM) labeled ACE aptamer was hybridized with complementary DNA (cDNA) to form partially hybridized double stranded DNA (dsDNA), and modified on the nanopore surface. ACE could specifically interact with FAM-aptamer and compete it against the nanopore surface. The probe changed from dsDNA to single-stranded DNA (ssDNA), which triggering the ionic current signal switched from "closed" to "open" and the fluorescence signal changed from "on" to "off". The sensor exhibited linear range 0.5-100 ng/mL and with detection limit of 0.12 ng/mL. Meanwhile, the sensor can be employed for the detection of ACE residues in medicine and food homology (MFH) products. More importantly, the dual signal output detection results can be cross-verified, ensuring the accuracy of the detection.

Integrative plasmonics: optical multi-effects and acousto-electric-thermal fusion for biosensing, energy conversion, and photonic circuits

Surface plasmons, a unique optical phenomenon arising at the interface between metals and dielectrics, have garnered significant interest across fields such as biochemistry, materials science, energy, optics, and nanotechnology. Recently, plasmonics is evolving from a focus on "classical plasmonics," which emphasizes fundamental effects and applications, to "integrative plasmonics," which explores the integration of plasmonics with multidisciplinary technologies. This review explores this evolution, summarizing key developments in this technological shift and offering a timely discussion on the fusion mechanisms, strategies, and applications. First, we examine the integration mechanisms of plasmons within the realm of optics, detailing how fundamental plasmonic effects give rise to optical multi-effects, such as plasmon-phonon coupling, nonlinear optical effects, electromagnetically induced transparency, chirality, nanocavity resonance, and waveguides. Next, we highlight strategies for integrating plasmons with technologies beyond optics, analyzing the processes and benefits of combining plasmonics with acoustics, electronics, and thermonics, including comprehensive plasmonic-electric-acousto-thermal integration. We then review cutting-edge applications in biochemistry (molecular diagnostics), energy (harvesting and catalysis), and informatics (photonic integrated circuits). These applications involve surface-enhanced Raman scattering (SERS), surface-enhanced infrared absorption (SEIRA), surface-enhanced fluorescence (SEF), chirality, nanotweezers, photoacoustic imaging, perovskite solar cells, photocatalysis, photothermal therapy, and triboelectric nanogenerators (TENGs). Finally, we conclude with a forward-looking perspective on the challenges and future of integrative plasmonics, considering advances in mechanisms (quantum effects, spintronics, and topology), materials (Dirac semimetals and hydrogels), technologies (machine learning, edge computing, in-sensor computing, and neuroengineering), and emerging applications (5G, 6G, virtual reality, and point-of-care testing).

Assessment of in-house reference materials for multi-pesticide residue analysis in three food matrices by LC-MS/MS: development, homogeneity, and stability

In-house reference materials (ihRM) are an alternative to the limited supply of reference materials for method validation and assurance of the validity of pesticide residue results. Currently, limited information exists on producing ihRM of pesticide residues in food for laboratory testing purposes for the desired matrix/analyte/concentration combination. This study aimed to develop in-house reference materials for three food matrices: banana, rice, and green coffee spiked with a total of 22 pesticides, which were selected for their relevance in food matrices, with pKOW values ranging from -0.13 to 5.8 following the guidelines of ISO Guide 80:2014. In this study, we assessed the impact of spiking (solvent) and drying conditions (freeze-dried and oven) on homogeneity uncertainty. The final step of this work involved evaluating the homogeneity and kinetics for long-term stability (4 °C) according to the ISO 33405 guidelines, using a fully validated method based on QuEChERS and measurement by LC-MS/MS through a completely randomized block design with two-way ANOVA, and simple linear regression analysis for homogeneity and stability, respectively. In general, for the three matrices, it was found that using a predominantly aqueous solvent (>70%) and freeze-drying resulted in sufficient homogeneity and no significant processing trends (slope p-value > 0.05), compared to using an organic medium (>70%), which resulted in uncertainties up to 50 times higher for several analytes. On the other hand, uncertainties for long-term stability below 3% were obtained for most of the analytes at 4 °C. The results demonstrate that homogeneous and stable ihRMs can be obtained under the provided preparation conditions, although special attention is required for processing conditions, particularly for the banana at a larger scale.

Discriminative detection of various organophosphorus nerve agents and analogues based on self-trapping probe coupled with SERS

Organophosphorus nerve agents (OPNAs) are highly lethal chemical warfare agents (CWAs), which poses a serious threat to human health and safety. The accurate and rapid identification of OPNAs is crucial for medical diagnosis and effective treatment. However, distinguishing between various OPNAs and their analogues using on-site point-of-care testing (POCT) remains challenging. Herein, we present a novel Raman-enhanced strategy that employs a chemical capture probe through a structural differential amplification derivative probe coupled with handheld Raman spectrometry. In this method, 2-(dimethylamino methyl)-3-hydroxypyridine (2-DMAMPD) was designed and used to capture target OPNAs in the plasmonic hotspot for the first time. The formation of strong Au-N bonds between nanoparticles and pyridine significantly enhances the cross-section and specific Raman intensity of OPNAs, facilitating effective amplification and differentiation of subtle structural variations among different OPNAs. In practical application, the probe solution can be directly sprayed on the surfaces contaminated by agents, allowing the entire detection process to be completed within five minutes, with a detection limit of 2 ng/mL (equivalent to an absolute content of 50 pg). It is worth noting that during the process of detection, highly toxic OPNAs can be quickly transformed into low-toxic or non-toxic derivatives, which is of great significance for green detection and protection of the operator.

Raman spectroscopy - a visit to the literature on plant, food, and agricultural studies

Raman spectroscopy, a fast, non-invasive, and label-free optical technique, has significantly advanced plant and food studies and precision agriculture by providing detailed molecular insights into biological tissues. Utilizing the Raman scattering effect generates unique spectral fingerprints that comprehensively analyze tissue composition, concentration, and molecular structure. These fingerprints are obtained without chemical additives or extensive sample preparation, making Raman spectroscopy particularly suitable for in-field applications. Technological enhancements such as surface-enhanced Raman scattering, Fourier-transform-Raman spectroscopy, and chemometrics have increased Raman spectroscopy sensitivity and precision. These and other advancements enable real-time monitoring of compound translocation within plants and improve the detection of chemical and biological contaminants, essential for food safety and crop optimization. Integrating Raman spectroscopy into agronomic practices is transformative and marks a shift toward more sustainable farming activities. It assesses crop quality - as well as the quality of the food that originated from crop production - early plant stress detection and supports targeted breeding programs. Advanced data processing techniques and machine learning integration efficiently handle complex spectral data, providing a dynamic and detailed view of food conditions and plant health under varying environmental and biological stresses. As global agriculture faces the dual challenges of increasing productivity and sustainability, Raman spectroscopy stands out as an indispensable tool, enhancing farming practices' precision, food safety, and environmental compatibility. This review is intended to select and briefly comment on outstanding literature to give researchers, students, and consultants a reference for works of literature in Raman spectroscopy mainly focused on plant, food, and agronomic sciences. © 2024 Society of Chemical Industry.

Comprehensive Raman Fingerprinting and Machine Learning-Based Classification of 14 Pesticides Using a 785 nm Custom Raman Instrument

Raman spectroscopy enables fast, label-free, qualitative, and quantitative observation of the physical and chemical properties of various substances. Here, we present a 785 nm custom-built Raman spectroscopy instrument designed for sensing applications in the 400-1700 cm-1 spectral range. We demonstrate the performance of the instrument by fingerprinting 14 pesticide reference samples with over twenty technical repeats per sample. We present molecular Raman fingerprints of the pesticides comprehensively and distinguish similarities and differences among them using multivariate analysis and machine learning techniques. The same pesticides were additionally investigated using a commercial 532 nm Raman instrument to see the potential variations in peak shifts and intensities. We developed a unique Raman fingerprint library for 14 reference pesticides, which is comprehensively documented in this study for the first time. The comparison shows the importance of selecting an appropriate excitation wavelength based on the target analyte. While 532 nm may be advantageous for certain compounds due to resonance enhancement, 785 nm is generally more effective for reducing fluorescence and achieving clearer Raman spectra. By employing machine learning techniques like the Random Forest Classifier, the study automates the classification of 14 different pesticides, streamlining data interpretation for non-experts. Applying such combined techniques to a wider range of agricultural chemicals, clinical biomarkers, or pollutants could provide an impetus to develop monitoring technologies in food safety, diagnostics, and cross-industry quality control applications.

Novel analysis based on Raman spectroscopy in nutrition science

Modern research in nutrition science is transitioning from classical methodologies to advanced analytical strategies, in which Raman spectroscopy plays a crucial role. Raman spectroscopy and its derived techniques are gaining recognition in nutrition science for their features, such as high-speed, non-destructive analysis, label-free multiple detection and high sensitivity. Raman-enhancing techniques have further improved the sensitivity of Raman spectroscopy and widely extended its detection and imaging applications in nutrient analysis, as well as in ancillary tasks for nutrition research, such as nutrient status evaluation, nutrient interaction and metabolism studies. Further development of Raman-based analytical approaches lies in the improvement of instruments with higher precision, as well as the incorporation of other analytical techniques and advanced data analysis tools. This paper provides a comprehensive review of the application of nanoscience and nanotechnology, with a specific focus on Raman technology, in the field of food and nutrition science research. Instead of delving into the quantitative or qualitative detection capabilities of Raman technology, we highlight the remarkable food analysis and nutrition research methods established by this technology. Generally, this review introduces the characteristics and applications of Raman technology in nutrition analysis and discusses the limitations and future prospects of Raman spectroscopy for nutrition monitoring.

Surface-enhanced Raman spectroscopy: a half-century historical perspective

Surface-enhanced Raman spectroscopy (SERS) has evolved significantly over fifty years into a powerful analytical technique. This review aims to achieve five main goals. (1) Providing a comprehensive history of SERS's discovery, its experimental and theoretical foundations, its connections to advances in nanoscience and plasmonics, and highlighting collective contributions of key pioneers. (2) Classifying four pivotal phases from the view of innovative methodologies in the fifty-year progression: initial development (mid-1970s to mid-1980s), downturn (mid-1980s to mid-1990s), nano-driven transformation (mid-1990s to mid-2010s), and recent boom (mid-2010s onwards). (3) Illuminating the entire journey and framework of SERS and its family members such as tip-enhanced Raman spectroscopy (TERS) and shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) and highlighting the trajectory. (4) Emphasizing the importance of innovative methods to overcome developmental bottlenecks, thereby expanding the material, morphology, and molecule generalities to leverage SERS as a versatile technique for broad applications. (5) Extracting the invaluable spirit of groundbreaking discovery and perseverant innovations from the pioneers and trailblazers. These key inspirations include proactively embracing and leveraging emerging scientific technologies, fostering interdisciplinary cooperation to transform the impossible into reality, and persistently searching to break bottlenecks even during low-tide periods, as luck is what happens when preparation meets opportunity.

Photocatalytic degradation of the pesticide pyridaben: Identification of degradation pathways using SERS and GC-MS

Degradation pathways of the pesticide pyridaben in tea caused by ultraviolet photocatalytic degradation were identified using SERS and GC-MS. Pyridaben in tea decreased from 4.50 mg/kg to 3.46 mg/kg after 2 h and to 0.62 mg/kg after 5 h, with a degradation rate of 86.22 % and a recovery rate of 90.00 %. The pyridaben degradation process involved C-S cleavage, with a SERS band of ν(C-S) at 710 cm-1 disappearing in the first hour. This was followed by cleavage of the N atom and the attached tert butyl group, resulting in the cleavage of the C-N bond in the ring, causing ring opening degradation. The presence of residual pyridaben was not necessarily related to the content of tea polyphenols; however, the UV light significantly reduced the content of theobromine, EGC, EC, and ECG but increased the content of EGCG. Ultraviolet irradiation slightly reduced the moisture content of oolong tea samples and did not significantly affect the total ash content. The content of water extract increaseing irradiation time due to the influence of moisture. UV irradiation time at the range of 60-360 min could degrade pyridaben while reducing its impact on tea quality.

Multifunctional Surface Enhanced Raman Scattering Substrate Fe 3O4 @AgNPs@MIL-101 for Pretreatment and Rapid Detection of Pesticide Residues on the Surface of Fruit Peels

A multifunctional surface-enhanced Raman scattering substrate Fe3O4@AgNPs@MIL-101 was prepared. Rapid SERS detection of pesticide residues was realized by direct pre-enrichment and separation on the peel surface. MIL-101 has an ortho-octahedral framework and large pore size, which endowed Fe3O4@AgNPs@MIL-101 with the ability to rapidly adsorb and separate positively charged targets. The introduction of tannic acid realized the in situ growth of AgNPs on the backbone, to modulate the electromagnetic enhancement. Pesticide molecules were adsorbed onto the surface of AgNPs mediated by central S atoms, accompanied by the interaction between pesticide molecules and AgNPs, the corresponding SERS signals of different pesticides were observed. Together with the introduction of magnetic coating Fe3O4, the molecules were enriched in the hotspot and separated to further enhance the SERS performance. Magnet instead of centrifugation was used to simultaneously perform surface extraction and sample separation for a noninvasive, rapid, immediate, and portable assay. The method was accomplished in measurement of thiram and thiabendazole on apple and tangerine epidermis, and the limits of detection (LODs) were 20 ng/cm2 and 4 μg/cm2, respectively. The recovery was reasonable, and it showed that the procedure is valuable for the rapid and nondestructive surface analysis of residual chemicals.

Pyrene functionalized organic cation receptor-based "turn-on" fluorescence approach for monitoring of chlorpyrifos in food, soil, and water samples

BACKGROUND

The unregulated use of pesticides by farmers, for crop productivity results in widespread contamination of organophosphates in real environmental samples, which is a growing societal concern about their potential health effects. The conventional approaches for the monitoring these organophosphate-based pesticides which include immunoassays, electrochemical methods, immunosensors, various chromatography techniques, along with some spectroscopic methods, are either costly, sophisticated, or involves the use of different metal complexes. Therefore, there is an urgent need for sensitive, quick, and easy-to-use detection techniques for the screening of widely used organophosphate-based pesticides.

RESULTS

Herein, we demonstrates the metal-free detection of CPF pesticide in aqueous medium, based on the organic nanoparticles of benzimidazole-based cationic receptor (R1-ONPs), and thoroughly analyzed using advanced techniques such as AFM, FESEM, and DLS etc. The photophysical investigations revealed that developed R1-ONPs exhibited high selectivity towards chlorpyrifos with an enhancement in fluorescence emission. Further, the observed pyrene excimer-based "turn-on" fluorescence mechanism, and the interaction between developed sensor and chlorpyrifos has been validated utilizing 1H, and 31P NMR spectroscopy. The developed sensor can effectively quantify chlorpyrifos up to a detection limit of 18.9 nM (3σ method) with a range of 0-120 μM as well as below the cutoff limit set by FAO. Moreover, the real-time application of developed sensor (R1-ONPs) was evaluated to monitor chlorpyrifos in spiked food, water, and soil samples with good (%) recovery.

SIGNIFICANCE

The development of metal-free, pyrene-excimer-based "Turn-On" fluorescent sensor offers a novel, eco-friendly strategy for the detection of chlorpyrifos in aqueous medium. Additionally, its ability to quantify the chlorpyrifos at levels as low as those set by FAO makes it more efficient tool for monitoring the environmental toxicity ensuring better protection for human, and animal health.

A Novel Array-Based Fluorescent Sensing Approach for the Identification and Quantification of Pesticides with High Sensitivity Based on Distinguishable Cross-response Algorithm

The presence of excessive residues of pesticides poses a great threat to ecology and human health. Herein, a novel, low-cost, simple and precise quantification sensing platform was established for differentiating and monitoring four common pesticides in China. Particularly, the array-based ratio fluorescent sensor array detector (ARF-SAD) based on cross-reaction characteristics of porphyrins and other porphyrin derivative was successfully constructed and integrated into the platform. Via acquiring the fluorescent data before and after the reaction of the ARF-SAD with pesticides, a novel, unique, and recognizable pattern of fluorescence changes was developed and utilized for the rapid characterization of pesticides. In addition, after raw data processed through the intervention of machine learning algorithms (hierarchical cluster analysis, principal component analysis, fitting of a polynomial), the selected pesticides and their mixture can be accurately distinguished via the constructed fluorescence fingerprint map by the platform in terms of category. By use of ratio fluorescence strategy, the platform and fluorescent sensor array can provide good sensitivity and selectivity for the monitoring of selected pesticides with LODs less than 10 ppb. Furthermore, the reproducibility, stability and practicability analysis of real sample have been thoroughly validated simultaneously. The findings indicated that the standard recovery rates of the six categories of blended pesticides in Jialing River water samples ranged from 86.13% to 114.84%, with the lowest relative standard deviation (RSD) reaching a remarkable level of only 3.04%. All representations consistently demonstrate that the detector serves as a prompt and viable sensing platform for discriminating and quantitatively analyzing pesticides, thereby showcasing its potential in the fields of pesticide differentiation and detection.

Advancements in reusable SERS substrates for trace analysis applications

Surface Enhanced Raman Spectroscopy (SERS) technique is an effective analytical technique in which fingerprint information about analytes can be obtained, can provide detection limit performance at the single molecule level, and analyzes are performed in a single step without any intermediate steps. SERS technique offers additional benefits rather than other analytical techniques including high selectivity, ultrasensitive detection, uncomplicated protocols, in situ sampling, on-set capability and cost-effectiveness. As a result of the combination of developments in materials and nanotechnology science with the SERS analysis technique, this technique strengthens its use advantage day by day. The most important factor that limited the use of this technique was the fact that the solution containing the desired analyte(s) was dropped onto the SERS substrate and the same substrate could not be reused in subsequent analyses. To solve this problem, scientists have focused on developing reusable SERS substrates in recent years. In these studies, scientists basically used three SERS substrate cleaning applications (1) washing the SERS substrate with a suitable solvent that can elute the analyte from SERS surface after analysis, (2) cleaning the SERS substrate with catalytic degradation of analytes after analysis by modifying them with catalytic active materials and (3) Applying plasma cleaning procedure to SERS substrate after analysis and (4) applying adsorption and desorption procedure prior to SERS analysis. Herein, the aim of this review article is to evaluate the reusable SERS substrates-based methods based on their level of development and their potential to recycle. This review offers a coherent discussion on a wide range of sensing schemes employed in fabricating the SERS substrates. We utilized a critical approach in which elaborative examples were selected to highlight key shortcomings of various experimental configurations. In the same vein, there is a discussion of the advantages and limitations concerning the key instrumental advances and the expansion of the recent methods developed in this area.

Nondestructive Detection of Corky Disease in Symptomless 'Akizuki' Pears via Raman Spectroscopy

'Akizuki' pear (Pyrus pyrifolia Nakai) corky disease is a physiological disease that strongly affects the fruit quality of 'Akizuki' pear and its economic value. In this study, Raman spectroscopy was employed to develop an early diagnosis model by integrating support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) modeling techniques. The effects of various pretreatment methods and combinations of methods on modeling results were studied. The relative optimal index formula was utilized to identify the SG and SG+WT as the most effective preprocessing methods. Following the optimal preprocessing method, the performance of the majority of the models was markedly enhanced through the process of model reconditioning, among which XGBoost achieved 80% accuracy under SG+WT pretreatment, and F1 and kappa both performed best. The results show that RF, GBDT, and XGBoost are more sensitive to the pretreatment method, whereas SVM and CNN are more dependent on internal parameter tuning. The results of this study indicate that the early detection of Raman spectroscopy represents a novel approach for the nondestructive identification of asymptomatic 'Akizuki' pear corky disease, which is of paramount importance for the realization of large-scale detection across orchards.

Proteomics mining of cancer hallmarks on a single-cell resolution

Dysregulated proteome is an essential contributor in carcinogenesis. Protein fluctuations fuel the progression of malignant transformation, such as uncontrolled proliferation, metastasis, and chemo/radiotherapy resistance, which severely impair therapeutic effectiveness and cause disease recurrence and eventually mortality among cancer patients. Cellular heterogeneity is widely observed in cancer and numerous cell subtypes have been characterized that greatly influence cancer progression. Population-averaged research may not fully reveal the heterogeneity, leading to inaccurate conclusions. Thus, deep mining of the multiplex proteome at the single-cell resolution will provide new insights into cancer biology, to develop prognostic biomarkers and treatments. Considering the recent advances in single-cell proteomics, herein we review several novel technologies with particular focus on single-cell mass spectrometry analysis, and summarize their advantages and practical applications in the diagnosis and treatment for cancer. Technological development in single-cell proteomics will bring a paradigm shift in cancer detection, intervention, and therapy.

Regenerated SERS substrate based on Ag/AuNPs-TiO2-oxidized carbon cloth for detection of imidacloprid

Imidacloprid (IMI) are widely used in modern tea industry for pest control, but IMI residues pose a great threat to human health. Herein, we propose a regeneration metal-semiconductor SERS substrate for IMI detection. We fabricated the SERS sensor through the in-situ growth of a nano-heterostructure incorporating a semiconductor (TiO2) and plasmonic metals (Au, Ag) on oxidized carbon cloth (OCC). Leveraging the high-density hot spots, the formed Ag/AuNPs-TiO2-OCC substrate exhibits higher enhancement factors (1.92 × 108) and uniformity (RSD = 7.68%). As for the detection of IMI on the substrate, the limit of detection was lowered to 4.1 × 10-6 μg/mL. With a hydrophobic structure, the Ag/AuNPs-TiO2-OCC possessed excellent self-cleaning performance addressing the limitation of single-use associated with traditional SERS substrates, as well as the degradation capability of the substrate under ultraviolet (UV) light. Accordingly, Ag/AuNPs-TiO2-OCC showcases outstanding SERS sensing and regenerating properties, making it poised for extensive application in the field of food safety assurance.

Enhancing substance identification by Raman spectroscopy using deep neural convolutional networks with an attention mechanism

Raman spectroscopy is widely used for substance identification, providing molecular information from various components along with noise and instrument interference. Consequently, identifying components based on Raman spectra remains challenging. In this study, we collected Raman spectral data of 474 hazardous chemical substances using a portable Raman spectrometer, resulting in a dataset of 59 468 spectra. Our research employed a deep neural convolutional network based on the ResNet architecture, incorporating an attention mechanism called the SE module. By enhancing the weighting of certain spectral features, the performance of the model was significantly improved. We also investigated the classification predictive performance of the model under small-sample conditions, facilitating the addition of new hazardous chemical categories for future deployment on mobile devices. Additionally, we explored the features extracted by the convolutional neural network from Raman spectra, considering both Raman intensity and Raman shift aspects. We discovered that the neural network did not solely rely on intensity or shift for substance classification, but rather effectively combined both aspects. This research contributes to the advancement of Raman spectroscopy applications for hazardous chemical identification, particularly in scenarios with limited data availability. The findings shed light on the significance of spectral features in the model's decision-making process and have implications for broader applications of deep learning techniques in Raman spectroscopy-based substance identification.

Towards solving the reproducibility crisis in surface-enhanced Raman spectroscopy-based pesticide detection

Growing concerns about pesticide residues in agriculture are pushing the scientific community to develop innovative and efficient methods for detecting these substances at low concentrations down to the molecular level. In this context, surface-enhanced Raman spectroscopy (SERS) is a powerful analytical method that has so far already undergone some validation for its effectiveness in pesticide detection. However, despite its great potential, SERS faces significant difficulties obtaining reproducible and accurate pesticide spectra, particularly for some of the most widely used pesticides, such as malathion, chlorpyrifos, and imidacloprid. Those inconsistencies can be attributed to several factors, such as interactions between pesticides and SERS substrates and the variety of substrates and solvents used. In addition, differences in the equipment used to obtain SERS spectra and the lack of standards for control experiments further complicate the reproducibility and reliability of SERS data. This review systematically discusses the problems mentioned above, including a comprehensive analysis of the challenges in precisely evaluating SERS spectra for pesticide detection. We not only point out the existing limitations of the method, which can be traced in previous review works, but also offer practical recommendations to improve the quality and comparability of SERS spectra, thereby expanding the potential applications of the method in such an essential field as pesticide detection.

Surface-Enhanced Raman Spectroscopy and Electrochemistry: The Ultimate Chemical Sensing and Manipulation Combination

One of the lessons we learned from the COVID-19 pandemic is that the need for ultrasensitive detection systems is now more critical than ever. While sensors' sensitivity, portability, selectivity, and low cost are crucial, new ways to couple synergistic methods enable the highest performance levels. This review article critically discusses the synergetic combinations of optical and electrochemical methods. We also discuss three key application fields-energy, biomedicine, and environment. Finally, we selected the most promising approaches and examples, the open challenges in sensing, and ways to overcome them. We expect this work to set a clear reference for developing and understanding strategies, pros and cons of different combinations of electrochemical and optical sensors integrated into a single device.

Se site targeted-two circles antioxidant in GPx4-like catalytic peroxide degradation by polyphenols (-)-epigallocatechin gallate and genistein using SERS

A Se site targeted-two circles antioxidant of polyphenols EGCG and genistein in glutathione peroxidase 4 (GPx4)-like catalytic peroxide H2O2 and cumene hydroperoxide degradation was demonstrated by surface-enhanced Raman scattering (SERS). Se atom's active center is presenting a 'low-oxidation' and a 'high-oxidation' catalytic cycle. The former is oxidized to selenenic acid (SeO-) with a Raman bond at 619/ 610 cm-1 assigned to the νO - Se by the hydroperoxide substrate at 544/ 551 cm-1 assigned to ωHSeC decreased. Under oxidative stress, the enzyme shifted to 'high-oxidation' catalytic cycle, in which GPx4 shuttles between R-SeO- and R-SeOO- with a Raman intensity of bond at 840/ 860 cm-1 assigned to νO[bond, double bond]Se. EGCG could act as a reducing agent both in H2O2 and Cu-OOH degradation, while, genistein can only reduce Cu-OOH, because it binds more readily to the selenium site in GPx4 than EGCG with a closer proximity, therefore may affect its simultaneous binding to coenzymes.

Calixarene-Based Supramolecular Sensor Array for Pesticide Discrimination

The identification and detection of pesticides is crucial to protecting both the environment and human health. However, it can be challenging to conveniently and rapidly differentiate between different types of pesticides. We developed a supramolecular fluorescent sensor array, in which calixarenes with broad-spectrum encapsulation capacity served as recognition receptors. The sensor array exhibits distinct fluorescence change patterns for seven tested pesticides, encompassing herbicides, insecticides, and fungicides. With a reaction time of just three minutes, the sensor array proves to be a rapid and efficient tool for the discrimination of pesticides. Furthermore, this supramolecular sensing approach can be easily extended to enable real-time and on-site visual detection of varying concentrations of imazalil using a smartphone with a color scanning application. This work not only provides a simple and effective method for pesticide identification and quantification, but also offers a versatile and advantageous platform for the recognition of other analytes in relevant fields.

Sandwich-like CuNPs@AgNPs@PSB SERS substrates for sensitive detection of R6G and Forchlorfenuron

The development of a highly sensitive, synthetically simple and economical SERS substrate is technically very important. A fast, economical, sensitive and reproducible CuNPs@AgNPs@ Porous silicon Bragg reflector (PSB) SERS substrate was prepared by electrochemical etching and in situ reduction method. The developed CuNPs@AgNPs@PSB has a large specific surface area and abundant "hot spot" region, which makes the SERS performance excellent. Meanwhile, the successful synthesis of CuNPs@AgNPs can not only modulate the plasmon resonance properties of nanoparticles, but also effectively prolong the time stability of Cu nanoparticles. The basic performance of the substrate was evaluated using rhodamine 6G (R6G). (Detection limit reached 10-15 M, R2 = 0.9882, RSD = 5.3 %) The detection limit of Forchlorfenuron was 10 μg/L. The standard curve with a regression coefficient of 0.979 was established in the low concentration range of 10 μg/L -100 μg/L. This indicates that the prepared substrates can accomplish the detection of pesticide residues in the low concentration range. The prepared high-performance and high-sensitivity SERS substrate have a very promising application in detection technology.

Structure information analysis and relative content determination of protein and chitin from yellow mealworm larvae using Raman spectroscopy

Insect protein extract is one of the high-quality protein sources and is frequently viewed as a potential nutrition alternative. However, a more precise method for protein measurement is still needed due to protein overestimation by the Kjeldahl method due to the presence of a large amount of chitin in insects. Therefore, we demonstrated the monitoring of chitin and protein extracted from yellow mealworm larvae through the information on molecular vibration obtained using Raman spectroscopy and infrared (IR) spectroscopy. The NH vibration at 3475 cm-1 is the characteristic peak of chitin in defatted product observed in the Raman spectra. The nitrogen-to-protein conversion factor in protein extracted from larvae by the Raman method was determined based on the NH vibration and found to be 5.66 ± 0.01. We also compared these experimental data to theoretical Raman and IR spectra and determined the possible reasons for why nitrogen elements in chitin affect the determination of protein content. The method of sequentially removing fat and protein could provide more accurate quantification of protein and chitin. Raman spectroscopy is feasible for various types of insects with high chitin content. Compared with the Kjeldahl method, the Raman method is a faster and more accurate measurement method. Moreover, it provides the content of impurities, purity, and structural information.

Integrated surface-enhanced Raman spectroscopy and convolutional neural network for quantitative and qualitative analysis of pesticide residues on pericarp

Pesticide residue poses a significant global public health concern, necessitating improved detection methods. Here, a novel platform was introduced based on surface-enhanced Raman spectroscopy (SERS) to detect ten distinct types of pesticides. Notably, the sensitivity of this approach is exemplified by detecting trace amounts of 50 pM (10 ppt) thiabendazole. The correlation between the characteristic peak intensity of coexisting pesticides and their concentrations displays an exceptional linear relationship (R2 = 0.9999), underscoring its utility for quantitative mixed pesticide detection. Additionally, qualitative analysis of five mixed pesticides was conducted leveraging distinctive peak labeling. Harnessing machine learning techniques, a model for classifying and predicting pesticides on pericarps was developed. Remarkably, the convolutional neural network achieved classification accuracy of 100 % and prediction accuracy of 99.62 %. This innovative approach accurately identifies and quantifies diverse pesticides, thus offering a feasible scheme for in-situ detection of pesticide residues. Ultimately, this strategy contributes to ensuring food safety and public health.

Ag-coated tetrapod gold nanostars (Au@AgNSs) for acetamiprid determination in tea using SERS combined with microfluidics

Acetamiprid is an organic and highly toxic compound. Despite being widely used as a pesticide agent on a large scale, acetamiprid poses numerous health risks to living organisms, particularly humans. Herein, a strategy for the detection of acetamiprid in tea employing surface-enhanced Raman scattering (SERS) technology incorporated with a microfluidic chip was developed. Significantly, a seed-mediated growth approach was utilized to engineer Ag-coated tetrapod gold nanostars (core-shell Au@AgNSs) with four sharp tips. The synthesized Au@AgNSs showed an enhancement factor of 7.2 × 106. Solid works was used to figure out the two-channel microfluidic chip featuring four circular split hybrid structures, and COMSOL (Software for Multiphysics Simulation) was utilized to model the fusion effect between the substrate (Au@AgNSs) and the sample (acetamiprid). For the first time, the core-shell Au@AgNSs and acetamiprid were fused in the microfluidic channel to facilitate the detection of acetamiprid using SERS. The outcomes pointed out that the standard curve correlation coefficient between SERS intensity (876 cm-1) and the concentration of acetamiprid in tea specimens was calculated as 0.991, while the limit of detection (LOD) was 0.048 ng mL-1, which is well below the minimum limit set by the European Union (10 ng mL-1). Thus, the developed technique combining SERS and microfluidics demonstrated high potential for the rapid and efficient detection of acetamiprid in tea.

Surface-enhanced Raman scattering spectroscopy monitoring and degradation of organic pollutants using a novel nanowire

A multifunctional Ag/AlOOH nanowires (ANW) composite substrate was constructed, which not only accomplishes highly sensitive detection of organic dye molecules, but also has excellent performance in the degradation of pollutants. The ANW in the Ag/ANW substrate possesses a high aspect ratio, which extends the distribution area of Ag and enables a large number of hot spots on the active substrate. Additionally, due to the abundant OH groups on the ANW, there is an increased number of anchor sites for adsorbed metal ions in the Ag/ANW compound, thus contributing to the enhancement and degradation of molecules. Moreover, the constructed multifunctional Ag/ANW nanocomplexes also show great promise for practical applications, providing a reference for the detection and degradation of contaminants.

Pesticide residue detection technology for herbal medicine: current status, challenges, and prospects

The domains of cancer therapy, disease prevention, and health care greatly benefit from the use of herbal medicine. Herbal medicine has become the mainstay of developing characteristic agriculture in the planting area increasing year by year. One of the most significant factors in affecting the quality of herbal medicines is the pesticide residue problem caused by pesticide abuse during the cultivation of herbal medicines. It is urgent to solve the problem of detecting pesticide residues in herbal medicines efficiently and rapidly. In this review, we provide a comprehensive description of the various methods used for pesticide residue testing, including optical detection, the enzyme inhibition rate method, molecular detection methods, enzyme immunoassays, lateral immunochromatographic, nanoparticle-based detection methods, colorimetric immunosensor, chemiluminescence immunosensor, smartphone-based immunosensor, etc. On this basis, we systematically analyze the mechanisms and some of the findings of the above detection strategies and discuss the challenges and prospects associated with the development of pesticide residue detection tools.

Strategies for mitigation of pesticides from the environment through alternative approaches: A review of recent developments and future prospects

Chemical-based peticides are having negative impacts on both the healths of human beings and plants as well. The World Health Organisation (WHO), reported that each year, >25 million individuals in poor nations are having acute pesticide poisoning cases along with 20,000 fatal injuries at global level. Normally, only ∼0.1% of the pesticide reaches to the intended targets, and rest amount is expected to come into the food chain/environment for a longer period of time. Therefore, it is crucial to reduce the amounts of pesticides present in the soil. Physical or chemical treatments are either expensive or incapable to do so. Hence, pesticide detoxification can be achieved through bioremediation/biotechnologies, including nano-based methodologies, integrated approaches etc. These are relatively affordable, efficient and environmentally sound methods. Therefore, alternate strategies like as advanced biotechnological tools like as CRISPR Cas system, RNAi and genetic engineering for development of insects and pest resistant plants which are directly involved in the development of disease- and pest-resistant plants and indirectly reduce the use of pesticides. Omics tools and multi omics approaches like metagenomics, genomics, transcriptomics, proteomics, and metabolomics for the efficient functional gene mining and their validation for bioremediation of pesticides also discussed from the literatures. Overall, the review focuses on the most recent advancements in bioremediation methods to lessen the effects of pesticides along with the role of microorganisms in pesticides elimination. Further, pesticide detection is also a big challenge which can be done by using HPLC, GC, SERS, and LSPR ELISA etc. which have also been described in this review.

Dynamic repulsive interaction enables an asymmetric electron-phonon coupling for improving Raman scattering

Two-dimensional (2D) materials are an excellent platform for surface-enhanced Raman spectroscopy (SERS). For ReS2, the Raman enhancement effect can be highly improved through the dipole-dipole interactions and synergistic resonance effects in the phase-engineering ReS2 films. However, the performance of the substrate can be improved further through regulating the electronic interaction between the ReS2 and probe molecules. Herein, a dynamic coulomb repulsion strategy is proposed to trigger an electronic state redistribution by asymmetric electrostatic interactions. With the phase-engineering ReS2/graphene heterostructure as a prototype, under laser excitation, the generated hot electrons in graphene and ReS2 can repel each other due to Coulomb interaction, which breaks the symmetrical distribution of hot electrons in ReS2, and increases the electronic concentration at the interface between ReS2 and the probe molecule. With R6G as the probe molecule, the asymmetric electron distribution and synergistic resonance effects on their interface improve the limit of detection to 10-12 M with an EF of 2.15 × 108. Meanwhile, the heterostructure also shows good uniformity, stability as well as unique anisotropy. This strategy can be generalized to other 2D heterostructures to obtain the ultrasensitive SERS substrates.

Synthesis and Insecticidal Activity of Novel Anthranilic Diamide Insecticides Containing Indane and Its Analogs

Diamide insecticides have always been a hot research topic in the field of pesticides. To further discover new compounds with high activity and safety, indane and its analogs were introduced into chlorantraniliprole, and a battery of chlorfenil derivatives, including indane and its analogs, were designed and prepared for biological testing. Their characterization and verification were carried out through nuclear magnetic resonance (NMR) and high-resolution mass spectrometry (HRMS). Biological detection showed that all the compounds exhibited good insecticidal activity against Mythimna separata. At 0.8 mg/L, the insecticidal activity of compound 8q against Mythimna separata was 80%, which was slightly better than that of chlorantraniliprole. The results of the structure-activity relationship (SAR) analysis indicated that the indane moiety had a significant effect on insecticidal activity, especially in the R-configuration. The results indicated that chlorantraniliprole derivatives containing indane groups could serve as pilot compounds for the further development of new insecticides.

Different sized gold nanoparticles for array-based sensing of pesticides and its application for strawberry pollution monitoring

The use of pesticides plays an essential role in improving crop quality and yield, however, it causes air, water, and soil pollution and the residue of these pesticides in agricultural products threatens the ecosystem and human life. Therefore, it is highly desirable to develop rapid, simple, and cost-effective methods for regular monitoring of pesticide residues in agricultural products especially strawberry that is consumed fresh and unpeeled. In this study, gold nanoparticles (AuNPs) of varying sizes have been exploited as sensing units to design a non-enzymatic colorimetric sensor array for the detection and discrimination of various pesticides including; bifenazate (BF), paraquat (PQ), diazinon (DZ), thiometon (TM), and carbendazim (CD) and chlorpyrifos (CP). Because of their strong size- and environmentally-dependent properties, AuNPs with different sizes produced distinguished plasmonic patterns in the presence of pesticides at a vast range of concentrations (25-800 ng mL-1). Plasmonic patterns of sensor units have been analyzed by various data visualization (bar plots and heat maps) and pattern recognition methods (linear discriminant analysis (LDA)). The multivariate calibrations showed linear responses ranging from 50 to 800 ng mL-1 for carbendazim, chlorpyrifos, paraquat, and bifenazate and 25-800 ng mL-1 for diazinon and thiometon. The limit of detection (LOD) was calculated to be 17.7, 22.8, 22.4, 9.7, 7.4, and 23.8 ng mL-1 for carbendazim, chlorpyrifos, paraquat, diazinon, thiometon, and bifenazate respectively. Finally, the applicability of the designed sensor was evaluated in real samples comprising tap water, well water, soil, and fruit, leave, drainage water, and culture substrate of strawberry.

Microfluidic advances in food safety control

Food contamination is a global concern, particularly in developing countries. Two main types of food contaminants-chemical and biological-are common problems that threaten human health. Therefore, rapid and accurate detection methods are required to address the threat of food contamination. Conventional methods employed to detect these two types of food contaminants have several limitations, including high costs and long analysis time. Alternatively, microfluidic technology, which allows for simple, rapid, and on-site testing, can enable us to control food safety in a timely, cost-effective, simple, and accurate manner. This review summarizes advances in microfluidic approaches to detect contaminants in food. Different detection methods have been applied to microfluidic platforms to identify two main types of contaminants: chemical and biological. For chemical contaminant control, the application of microfluidic approaches for detecting heavy metals, pesticides, antibiotic residues, and other contaminants in food samples is reviewed. Different methods including enzymatic, chemical-based, immunoassay-based, molecular-based, and electrochemical methods for chemical contaminant detection are discussed based on their working principle, the integration in microfluidic platforms, advantages, and limitations. Microfluidic approaches for foodborne pathogen detection, from sample preparation to final detection, are reviewed to identify foodborne pathogens. Common methods for foodborne pathogens screening, namely immunoassay, nucleic acid amplification methods, and other methods are listed and discussed; highlighted examples of recent studies are also reviewed. Challenges and future trends that could be employed in microfluidic design and fabrication process to address the existing limitations for food safety control are also covered. Microfluidic technology is a promising tool for food safety control with high efficiency and applicability. Miniaturization, portability, low cost, and samples and reagents saving make microfluidic devices an ideal choice for on-site detection, especially in low-resource areas. Despite many advantages of microfluidic technology, the wide manufacturing of microfluidic devices still demands intensive studies to be conducted for user-friendly and accurate food safety control. Introduction of recent advances of microfluidic devices will build a comprehensive understanding of the technology and offer comparative analysis for future studies and on-site application.

One-step fabrication of flexible polyamide@Ag-dodecanethiol membranes for highly sensitive SERS detection of thiram

The surface-enhanced Raman scattering (SERS) is an effective spectral technology based on Raman scattering, but in practice, the commonly used SERS substrates suffer from low sensitivity and poor stability. In order to overcome these limitations, the SERS substrates were prepared from hydrophobic modification of dodecanethiol (C12) coupled with a flexible substrate, which was then used for pesticides detection in water. A flexible PA@Ag-C12 substrate with surface functionalization has been obtained. This work aims to investigate the self-assembly of Ag NPs modified with C12 onto polyamide (PA) membranes. Initially, transmission electron microscopy and scanning electron microscopy were used to analyze the substrate's morphology. Then with the help of an energy-dispersive spectrometer, sulfur content of C12-modified Ag NPs was analyzed. In order to determine the hydrophobicity of the modified Ag NPs, the contact angle was used. The results indicate that the gap between Ag NPs on PA membrane can be effectively controlled in order to prevent Ag NPs from aggregating. Furthermore, the finite-difference time-domain analysis indicated that the PA@Ag-C12 substrate exhibited a stronger electromagnetic enhancement effect than the PA@Ag substrate. By reducing NPs gaps on the PA membrane, the number of 'hot spots' increased, and the SERS performance of the substrate was improved as a result. According to the results of this study, this method can greatly reduce the manufacturing costs and time costs of the SERS substrate while maintaining the original uniformity. The SERS performance of PA@Ag-C12 was found to be three orders of magnitude better than that of PA@Ag direct self-assembled substrate, and the detection limit for Rhodamine 6G (R6G) was approximately 8.47 × 10-14M. On the basis of the PA@Ag-C12 substrate, thiram is detectable at a detection limit of 5.88 × 10-11M with a high degree of sensitivity and repeatability.

Effects of Au6 and Au20 Adsorption Sites of Cyromazine-Au Complexes by Raman Spectroscopy and Density Functional Theory

Cyromazine, when used as an insect growth regulator and low-toxicity insecticide, may degrade into melamine and pose a potential threat to the environment and soil health, which has thus attracted extensive research on eliminating such a harmful effect. In this paper, density functional theory (DFT)/LC-BLYP/6-311G(d,p) is used to optimize the geometric structure and analyze the vibration of cyromazine. The DFT/LC-BLYP/def2-SVP is used for the cyromazine-Au complex optimization and vibration analysis. The molecular electrostatic potential (MEP), frontier molecular orbitals (FMOs), vibration frequency, electrophilicity-based charge transfer (ECT) descriptor, binding energy (BE), polarizability, normal Raman spectroscopy (NRS), and surface-enhanced Raman spectroscopy (SERS) of cyromazine adsorbing on Au6 and Au20 are calculated. The study of the chemical enhancement mechanism of SERS of cyromazine at different adsorption sites of Au6 or Au20 confirms the existence of a charge transfer between cyclopromazine and Au6 and Au20, which can adsorb and form stable cyromazine-Au complexes. The results show that N2, H13, and N4 are the adsorption sites of Au6 and Au20. The Raman spectra of the cyromazine-Au complex can be selectively enhanced with a factor up to 9.07. Compared with those of cyromazine-Au6, the Raman spectra of cyromazine-Au20 are enhanced more significantly.

Fabrication of Flexible Pyramid Array as SERS Substrate for Direct Sampling and Reproducible Detection

Rapid extraction and analysis of target molecules from irregular surfaces are in high demand in the field of on-site analysis. Herein, a flexible platform used for surface-enhanced Raman scattering (SERS) based on an ordered polymer pyramid structure with half-imbedded silver nanoparticles (AgNPs) was prepared to address this issue. The fabrication includes the following steps: (1) creating inverted pyramid arrays in silicon substrate, (2) preparing a layer of AgNPs on the surface of the inverted pyramids, and (3) obtaining a substrate with an ordered polymer pyramids array with half-imbedded AgNPs by the molding method. This flexible substrate is capable of rapid extraction via a simple and convenient "paste and peel off" method. In addition, the substrate exhibits great repeatability and good sensitivity thanks to the uniformity and larger surface area of the ordered pyramids. The density of "hot spots" (local electromagnetic field with high intensity) is increased on the structured surface. Semi-imbedding silver particles in the polymer pyramids makes "hot spots" robust on the substrate. In addition, the preprepared silicon template with the inverted pyramids can be reused, which greatly reduces the production cost. With this substrate, we successfully analyzed thiram molecules on the epidermis of apples, cucumbers, and oranges, and the detection limits are 2.4, 3, and 3 ng/cm2, respectively. These results demonstrate the great potential of the substrate for in situ analysis, which can provide reference for the design of ideal SERS substrates.

Recent Advances of Biosensors for Detection of Multiple Antibiotics

The abuse of antibiotics has caused a serious threat to human life and health. It is urgent to develop sensors that can detect multiple antibiotics quickly and efficiently. Biosensors are widely used in the field of antibiotic detection because of their high specificity. Advanced artificial intelligence/machine learning algorithms have allowed for remarkable achievements in image analysis and face recognition, but have not yet been widely used in the field of biosensors. Herein, this paper reviews the biosensors that have been widely used in the simultaneous detection of multiple antibiotics based on different detection mechanisms and biorecognition elements in recent years, and compares and analyzes their characteristics and specific applications. In particular, this review summarizes some AI/ML algorithms with excellent performance in the field of antibiotic detection, and which provide a platform for the intelligence of sensors and terminal apps portability. Furthermore, this review gives a short review of biosensors for the detection of multiple antibiotics.

Raman ConvMSANet: A High-Accuracy Neural Network for Raman Spectroscopy Blood and Semen Identification

Animal blood and semen analysis plays a significant role in national biological resource management, wildlife conservation, and customs security quarantine. Traditional blood analysis methods have disadvantages, such as complex sample preparation, time consumption, and false positives. Therefore, proposing a rapid and highly accurate analysis method is highly valuable. Raman spectroscopy has been widely used in blood analysis, and efficient and accurate analysis results can be obtained through the machine learning algorithm feature extraction. Recently, the transformer network structure was applied to Raman spectroscopy recognition. However, the multihead self-attention mechanism does not perform well in extracting local feature peaks, although it obtains global feature relations. This paper proposes a neural network based on the combination of one-dimensional convolution and multihead self-attention mechanism (Raman ConvMSANet) to identify 52 species of blood and semen Raman spectra. The network can achieve reliable identification effects in multiclassification and sample imbalance situations, and the average identification accuracy of blood and semen can reach more than 98.5%. The proposed network model can be applied not only to blood and semen identification but also to other biological fields.

Label-free detection of trace level zearalenone in corn oil by surface-enhanced Raman spectroscopy (SERS) coupled with deep learning models

Surface-enhanced Raman spectroscopy (SERS) and deep learning models were adopted for detecting zearalenone (ZEN) in corn oil. First, gold nanorods were synthesized as a SERS substrate. Second, the collected SERS spectra were augmented to improve the generalization ability of regression models. Third, five regression models, including partial least squares regression (PLSR), random forest regression (RFR), Gaussian progress regression (GPR), one-dimensional convolutional neural networks (1D CNN), and two-dimensional convolutional neural networks (2D CNN), were developed. The results showed that 1D CNN and 2D CNN models possessed the best prediction performance, i.e., determination of prediction set (R_P²) = 0.9863 and 0.9872, root mean squared error of prediction set (RMSEP) = 0.2267 and 0.2341, ratio of performance to deviation (RPD) = 6.548 and 6.827, limit of detection (LOD) = 6.81 × 10^-4 and 7.24 × 10^-4 μg/mL. Therefore, the proposed method offers an ultrasensitive and effective strategy for detecting ZEN in corn oil.

Multicolor aptasensors for pesticide multiresidues detection in agricultural products using bioorthogonal surface-enhanced Raman scattering tags

Realizing accurate pesticide multiresidue detection in a complex matrix is still a challenge for point-of-care sensing methods. Herein, we introduced background-free and multicolor aptasensors based on bioorthogonal surface-enhanced Raman scattering (SERS) tags and successfully applied them to analyze multiple pesticide residues. The excellent anti-interference and multiplex capability are due to the application of three bioorthogonal Raman reporters involving 4-ethenylbenzenamine (4-EBZM), Prussian blue (PB) and 2-amino-4-cyanopyridine (AMCP) with alkynyl and cyano groups, which demonstrated apparent Raman shift peaks at 1993 cm^-1, 2160 cm^-1, and 2264 cm^-1 in the biologically Raman-silent region, respectively. Ultimately, a detection range of 1-50 nM for acetamiprid, atrazine and malathion was achieved with detection limits of 0.39, 0.57 and 0.16 nM, respectively. The developed aptasensors were successfully used to determine pesticide residues in real samples. These proposed multicolor aptasensors offer an effective strategy for pesticide multiresidue detection with advantages of anti-interference, high specificity and high sensitivity.

A facile heat-treatment solid phase microextraction method for SERS detection of isocarbophos in tea using a hand-held Raman spectrometer

A facile sensor system based on heat-treatment solid phase microextraction and Surface-Enhanced Raman Scattering (HT-SPME-SERS) was established for in-situ detection of isocarbophos in complex tea matrix. Starting from the action optimization of temperature control unit and air flow control unit, pesticide molecules volatilizing from solution are efficiently captured by substrate and generate real-time SERS signals by a hand-held Raman spectrometer, and the sensor system based on HT-SPME-SERS was finally established. A novel SERS substrate of Cu@rGO@Ag was developed as HT-SPME-SERS material, where reduced graphene oxide (rGO) enriched pesticide molecules by π-π stacking. A superior detection sensitivity brought by the ultra-high enhancement effect of Cu@rGO@Ag substrate was obtained. A good linear relationship between Raman intensity and isocarbophos concentration was obtained and the limit of detection (LOD) was as low as 0.00451 ppm. The detection results obtained from the sensor system have been verified by gas chromatography-mass spectrometer (GC-MS), showing its great application potential for the safety of agricultural products.

Non-Destructive Screening of Sodium Metabisulfite Residue on Shrimp by SERS with Copy Paper Loaded with AgNP

In order to prompt the appearance of the shrimp color, sodium metabisulfite is frequently added in shrimp processing, which is, however, prohibited in China and many other countries. This study aimed to establish a surface-enhanced Raman spectroscopy (SERS) method for screening sodium metabisulfite residues on shrimp surfaces, in a non-destructive manner. The analysis was carried out using a portable Raman spectrometer jointly with copy paper loaded with silver nanoparticles as the substrate material. The SERS response of sodium metabisulfite gives two fingerprint peaks at 620 (strong) and 927 (medium) cm^-1, respectively. This enabled unambiguous confirmation of the targeted chemical. The sensitivity of the SERS detection method was determined to be 0.1 mg/mL, which was equal to residual sodium metabisulfite on the shrimp surface at 0.31 mg/kg. The quantitative relationship between the 620 cm^-1 peak intensities and the concentrations of sodium metabisulfite was established. The linear fitting equation was y = 2375x + 8714 with R² = 0.985. Reaching an ideal balance in simplicity, sensitivity, and selectivity, this study demonstrates that the proposed method is ideally suitable for in-site and non-destructive screening of sodium metabisulfite residues in seafood.

Screening for pesticide residues in cocoa (Theobroma cacao L.) by portable infrared spectroscopy

Rapid assessment of pesticide residues ensures cocoa bean quality and marketability. In this study, a portable FTIR instrument equipped with a triple reflection attenuated total reflectance (ATR) accessory was used to screen cocoa beans for pesticide residues. Cocoa beans (n = 75) were obtained from major cocoa growing regions of Peru and were quantified for pesticides by gas chromatography (GC) or liquid chromatography (LC) coupled with mass spectrometry (MS). The FTIR spectra were used to detect the presence of pesticides in cocoa beans or lipid fraction (butter) by using a pattern recognition (Soft Independent Modeling by Class Analogy, SIMCA) algorithm, which produced a significant discrimination for cocoa nibs (free or with pesticides). The variables related to the class grouping were assigned to the aliphatic (3200-2800 cm^-1) region with an interclass distance (ICD) of 3.3. Subsequently, the concentration of pesticides in cocoa beans was predicted using a partial least squares regression analysis (PLSR), using an internal validation of the PLRS model, the cross-validation correlation coefficient (R_val = 0.954) and the cross-validation standard error (SECV = 14.9 mg/kg) were obtained. Additionally, an external validation was performed, obtaining the prediction correlation coefficient (R_pre = 0.940) and the standard error of prediction (SEP = 16.0 μg/kg) with high statistical performances, which demonstrates the excellent predictability of the PLSR model in a similar real application. The developed FTIR method presented limits of detection and quantification (LOD = 9.8 μg/kg; LOQ = 23.1 μg/kg) with four optimum factors (PC). Mid-infrared spectroscopy (MIR) offered a viable alternative for field screening of cocoa.

Toward microfluidic SERS and EC-SERS applications via tunable gold films over nanospheres

Many promising applications of surface-enhanced Raman scattering (SERS), such as microfluidic SERS and electrochemical (EC)-SERS, require immersion of plasmonic nanostructured films in aqueous media. Correlational investigations of the optical response and SERS efficiency of solid SERS substrates immersed in water are absent in the literature. This work presents an approach for tuning the efficiency of gold films over nanospheres (AuFoN) as SERS substrates for applications in aqueous environment. AuFoN are fabricated by convective self-assembly of colloidal polystyrene nanospheres of various diameters (300-800 nm), followed by magnetron sputtering of gold films. The optical reflectance of the AuFoN and Finite-Difference Time-Domain simulations in both water and air reveal the dependence of the surface plasmon band on nanospheres' diameter and environment. SERS enhancement of a common Raman reporter on AuFoN immersed in water is analyzed under 785 nm laser excitation, but also using the 633 nm line for the films in air. The provided correlations between the SERS efficiency and optical response in both air and water indicate the best structural parameters for high SERS efficiency and highlight a route for predicting and optimizing the SERS response of AuFoN in water based on the behavior in air, which is more practical. Finally, the AuFoN are successfully tested as electrodes for EC-SERS detection of the thiabendazole pesticide and as SERS substrates integrated in a flow-through microchannel format. The obtained results represent an important step toward the development of microfluidic EC-SERS devices for sensing applications.

Iron/iron oxide-based magneto-electrochemical sensors/biosensors for ensuring food safety: recent progress and challenges in environmental protection

Foodborne diseases have arisen due to the globalization of industry and the increase in urban population, which has led to increased demand for food and has ultimately endangered the quality of food. Foodborne diseases have caused some of the most common public health problems and led to significant social and economic issues worldwide. Food quality and safety are affected by microbial contaminants, growth-promoting feed additives (β-agonists and antibiotics), food allergens, and toxins in different stages from harvesting to storage and marketing of products. Electrochemical biosensors, due to their reduced size and portability, low cost, and low consumption of reagents and samples, can quickly provide valuable quantitative and qualitative information about food contamination. In this regard, using nanomaterials can increase the sensitivity of the assessment. Magnetic nanoparticle (MNP)-based biosensors, especially, are receiving significant attention due to their low-cost production, physicochemical stability, biocompatibility, and eco-friendly catalytic characteristics, along with magnetic, biological, chemical and electronic sensing features. Here, we provide a review on the application of iron-based magnetic nanoparticles in the electrochemical sensing of food contamination. The types of nanomaterials used in order to improve the methods and increase the sensitivity of the methods have been discussed. Then, we stated the advantages and limitations of each method and tried to state the research gaps for each platform/method. Finally, the role of microfluidic and smartphone-based methods in the rapid detection of food contamination is stated. Then, various techniques like label-free and labelled regimes for the sensitive monitoring of food contamination were surveyed. Next, the critical role of antibody, aptamer, peptide, enzyme, DNA, cells and so on for the construction of specific bioreceptors for individual and simultaneous recognition by electrochemical methods for food contamination were discussed. Finally, integration of novel technologies such as microfluidic and smartphones for the identification of food contaminations were investigated. It is important to point out that, in the last part of each sub-section, attained results of different reports for each strategy were compared and advantages/limitations were mentioned.

A high-performance Calix@ZnO based bifunctional nanomaterial for selective detection and degradation of toxic azinphos methyl in environmental samples

One of the key tenets of sustainable agriculture and food safety is the removal of toxic pesticides from the environment. However, developing reliable, affordable, and efficient methods for detecting and degrading pesticides into non-toxic degradable products remains an immediate matter of concern. Herein, we attempt to develop a strategy for the detection as well as degradation of highly toxic phosphorodithioate pesticide, Azinphos methyl (AZM), using hybrid zinc oxide nanoparticles (ZnO NPs). Considering the non-selectivity of bare ZnO and receptor R1, we have fabricated the heterocalixarene-based Calix (R1) over zinc oxide (ZnO) surface in situ via the sol-gel process. The synthesized heterocaliaxrene-modified ZnO (R1@ZnO) NPs show an excellent affinity for the selective and sensitive detection of AZM with a tremendously low limit of detection (68 mg L^-1) and no interference from other pesticides. Degradation of AZM was fully supported by fluorescence spectroscopy, scanning electron microscopy (SEM), ¹H NMR titrations, FTIR spectroscopy, cyclic voltammetry, and mass spectroscopy, which unequivocally confirmed the formation of non-toxic products. According to our findings, R1@ZnO NPs are sustainable nanomaterials that can be employed for environmental remediation since they operate in an aqueous medium.

How safe is our food we eat? An electrochemical lab-on-kitchen approach towards combinatorial testing for pesticides and GMOs; A case study with edamame

In our daily life, as consumers we are constantly made aware of the impact of pesticides and other modifications to food products derived from genetically modified organisms (GMO's) that have an impact on human health. In our connected world, there is an immense interest for on-demand information about food quality prior to consumption. The gold standard method to detect pesticides or GMOs residues in food is complex and is not amenable to rapid consumer use. In this study, we demonstrate the feasibility of an electrochemical portable sensing approach for the simultaneous direct detection of spiked pesticides chlorpyrifos (Chlp) and GMOs protein Cry1Ab in real edamame soy matrix. The immunoassay based two-plex sensing platform was fabricated using respective antibody's Chlp on one side and Cry1Ab on other side. A simple lab-on-kitchen level preparation of matrix has been demonstrated and sensor response was tested using non-faradaic electrochemical impedance spectroscopy (EIS), which showed a linear response in Cry1Ab/Chlp concentrations from 0.3 ng/mL to 243 ng/mL with limit of detection 0.3 ng /mL for both the target antigens (Cry1Ab and Chlp) respectively. The spiked and recovery test results fall within ± 20% error in real sample matrix which demonstrates the performance of the our platform with maximum residue limit (MRL) for the given targets. Such electrochemical portable multi-analyte direct sensing tool with simple matrix processing protocol can be a future commercial field-testing tool for use at everyday consumer level.

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.

Functional Micro-/Nanostructures in Agrofood Science: Precise Inspection, Hazard Elimination, and Potential Health Risks

Nanotechnology, biotechniques, and chemical engineering have arisen as new trends with significant impacts on agrofood science development. Advanced analytical techniques with high sensitivity, specificity, and automation based on micro-/nanomaterials for food hazard elimination have become leading research hotspots in agrofood science. Research progress in micro-/nanomaterials has provided a solid theoretical basis and technical support to solve problems in the industry. However, the rapid development of micro-/nanostructures has also raised concerns regarding potential risks to human health. This review presents the latest advances in the precise inspection and elimination of food hazards from micro-/nanomaterials and discusses the potential threats to human health posed by nanomaterials. The theoretical reference was provided for the application trend of micro-/nanomaterials in the field of agrofood science in the future.

Engineering an Ag/Au bimetallic nanoparticle-based acetylcholinesterase SERS biosensor for in situ sensitive detection of organophosphorus pesticide residues in food

Developing simple, efficient, and inexpensive method for trace amount organophosphorus pesticides' (OPs) detection with high sensitivity and specificity is of significant importance for guaranteeing food safety. Herein, an Ag/Au bimetallic nanoparticle-based acetylcholinesterase (AChE) surface-enhanced Raman scattering (SERS) biosensor was constructed for in situ simple and sensitive detection of pesticide residues in food. The principle of this biosensor exploited 4-mercaptophenylboronic acid (4-MPBA)-modified Ag/Au bimetallic nanoprobes as SERS signal probe to improve sensitivity and stability. The combination of AChE and choline oxidase (CHO) can hydrolyze acetylcholine (ATCh) to generate H₂O₂. The product of H₂O₂ selectively oxidizes the boronate ester of 4-MPBA, decreasing the Raman intensity of the B-O symmetric stretching. In the presence of OPs, it could inhibit the production of H₂O₂ by destroying the AChE activity, so the reduction of the SERS signal was also alleviated. Based on the principle, an Ag/Au bimetallic nanoparticle-based AChE SERS sensor was established without any complicated pretreatments. Benefiting from the synergistic effects of Ag/Au bimetallic hybrids, a linear detection range from 5×10^-9 to 5×10^-4 M was achieved with a limit of detection down to 1.7×10^-9 M using parathion-methyl (PM) as the representative model of OPs. Moreover, the SERS biosensor uses readily available reagents and is simple to implement. Importantly, the proposed SERS biosensor was used to quantitatively analyze OP residues in apple peels. The levels of OPs detected in real samples by this method were consistent with those obtained using gas chromatography-mass spectrometry (GC-MS), suggesting the proposed assay has great potential applications for OPs in situ detection in food safety fields.

SERS Resolving of the Significance of Acetate on the Enhanced Catalytic Activity of Nanozymes

Understanding the structure-activity correlation and reaction mechanism of the catalytic process in an acetic acid-sodium acetate (HAc-NaAc) buffer environment is crucial for the design of efficient nanozymes. Here, we first reported a lattice restructuration of Au-LaNiO_3-δ nanofibers (NFs) after acidification with the HAc-NaAc buffer to show a significantly enhanced oxidase-like property. Surface-enhanced Raman spectroscopy (SERS) and density functional theory (DFT) calculation confirm the direct evidence for the formation of specific enhanced intermediate O-O species after acidification, indicating that the insertion of the carboxyl group in the A-Au/LaNiO_3-δ NFs plays crucial roles in both producing vacancies in HAc-NaAc solution from its dissociation during the catalytic process and the protection of the vacancies, which can be directly interacted with oxygen in the environment to produce O-O species, realizing the enhanced oxidation of substrate molecules. The insertion of the carboxyl group increased the oxidase-like catalytic activity by 2.38 times and the SERS activity by 5.27 times. This strategy offers a way to construct an efficient nanozyme-linked immunosorbent assay system for the diagnosis of cancer through the highly sensitive SERS identification of exosomes.