Trace analysis of organic compounds in foods with surface‐enhanced Raman spectroscopy: Methodology, progress, and challenges

Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach

Raman spectroscopy is a general and non-destructive detection technique that can obtain detailed information of the chemical structure of materials. In the past, when using chemometric algorithms to analyze the Raman spectra of mixtures, the challenges of complex spectral overlap and noise often limited the accurate identification of components. The emergence of deep learning has introduced a novel approach to qualitative analysis of mixed Raman spectra. In this paper, we propose a deep learning-based Raman spectroscopy qualitative analysis algorithm (RST) by borrowing the ideas of convolutional neural network and Transformer. By transforming the Raman spectrum into 64 word vectors, the contribution weights of each word vector to the components are obtained. For the 75 spectral data used for validation, the positive identification rate can reach 100.00 %, the recall rate can reach 99.3 %, the average identification score can reach 9.51, and it is applicable to the fields of Raman and surface-enhanced Raman spectroscopy. Furthermore, compared with traditional CNN models, RST has excellent accuracy and robustness in identifying components in complex mixtures. The model's interpretability has been enhanced, aiding in a deeper understanding of spectroscopic learning patterns for future analysis of more complex mixtures.

Characterization and classification of oleogels and edible oil using vibrational spectroscopy in tandem with one-class and multiclass chemometric methods

Oleogel represents a promising healthier alternative to act as a substitute for conventional fat in various food products. Oil selection is a crucial factor in determining the technological properties and applications of oleogels due to their distinct fatty acid composition, molecular weight, and thermal properties, as well as the presence of antioxidants and oxidative stability. Hence, the relevance of monitoring oleogel properties by non-destructive, eco-friendly, portable, fast, and effective techniques is a relevant task and constitutes an advance in the evaluation of oleogels quality. Thus, the present study aims to classify oleogels rapidly and reliably, without the use of chemicals, comparing two handheld near infrared (NIR) spectrometers and one portable Raman device. Furthermore, two different multivariate methods are compared for oleogel classification according to oil type. Three types of oleogels were prepared, containing 95 % oil (sunflower, soy, olive) and 5 % beeswax as a structuring agent, melted at 90 °C. Polarized light microscopy (PLM) images were acquired, and fatty acid composition, peroxide index and free fatty acid content were determined using official methods. A total of 240 oleogel and 92 oil spectra were obtained for each instrument. After spectra pretreatment, Principal Component Analysis (PCA) was performed, and two classification methods were investigated. The Data Driven - Soft Independent Modelling of Class Analogy (DD-SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) models demonstrated 95 % to 100 % of accuracy for the external test set. In conclusion, the use of vibrational spectroscopy using handheld and portable instruments in tandem with chemometrics showed to be an efficient alternative for classifying oils and oleogels and could be extended to other food samples. Although the classification of vegetable oils by NIR is widely used and known, this work proposes the classification of different types of oil in oleogel matrices, which has not yet been explored in the literature.

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

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

Rapid detection of thiabendazole residues in apple juice by surface-enhanced Raman scattering coupled with silver coated gold nanoparticles

In recent years, the excessive use of pesticides has posed significant hazards to the ecological environment and human health in the pursuit of high crop yields. In this work, we developed a simple, sensitive, and eco-friendly approach for rapid detection of thiabendazole in apple juice using surface-enhanced Raman scattering (SERS) coupled with silver-coated gold nanoparticles (Au@Ag NPs). The developed Au@Ag NPs exhibited excellent sensitivity, allowing for the detection of thiabendazole in standard solutions at a minimum concentration of 50 ng/mL. Furthermore, two sample preparation methods were compared for detecting thiabendazole in apple juice. As the direct detection method for SERS analysis failed to detect thiabendazole at levels below the maximum residue limit based on the Chinese standard (3000 ng/mL), the effects of main matrix components in apple juice on the detection of thiabendazole were further investigated. The results revealed that both sugars and organic acids in apple juice interfered with the SERS measurement to varying degrees. Consequently, we optimized the QuEChERS method for sample preparation and achieved a higher sensitivity with a minimum detectable concentration of 250 ng/mL, a limit of detection of 0.06 mg/L and the recoveries of spiked samples were ranged from 80.2 % to 108.6 %. This study demonstrated the feasibility of proposed SERS method for pesticide residue analysis, addressing the need for food safety monitoring.

Trace analysis of food by surface-enhanced Raman spectroscopy combined with molecular imprinting technology: Principle, application, challenges, and prospects

Surface-enhanced Raman spectroscopy (SERS) is a rapid detection method with high sensitivity and simple pretreatment, but can be affected by interference from matrix components. By incorporating molecularly imprinted polymers (MIPs) that recognize specific targets, MIP-SERS sensors effectively overcome the interference of complex matrices and offer improved stability and sensitivity. This review provides a comprehensive understanding of the applications of MIP-SERS sensors for the detection of trace toxic substances in food. The underlying mechanism and development of SERS technology and the principle and classification of MIPs technology are discussed. Furthermore, the types of MIP-SERS sensors are introduced, with their advantages and disadvantages systematically illustrated. Recent advances in MIP-SERS technology for the detection of mycotoxins, additives, prohibited dyes, pesticides, veterinary drug residues, and other hazardous substances in food are highlighted. Finally, this review discusses the challenges associated with MIP-SERS technology and proposes future development prospects.

Modified paper-based substrates fabricated via electrostatic attraction of gold nanospheres for non-destructive detection of pesticides based on surface-enhanced Raman spectroscopy

BACKGROUND

Flexible surface-enhanced Raman spectroscopy (SERS) substrates such as paper-based substrates show great potential for rapid detection of residual chemicals on food surfaces. However, controlling the density and distribution of metallic nanoparticles adsorbed on the paper is still challenging.

RESULTS

The amount of gold (Au) nanospheres (51 ± 4 nm) attached on the filter paper modified with 3-aminopropyltriethoxysilane (APTES) was tunable, increasing as the level of APTES (2.5-15.0 g kg-1 ) applied for paper modification increased. Moreover, the Au nanospheres were relative evenly distributed on the filter paper modified with 2.5-10.0 g kg-1 of APTES, which resulted in excellent intra- and inter-reproducibility of SERS signals for pesticides including thiram, diquat dibromide, and paraquat dichloride (relative standard deviation = 2.2-10.1%). The modified paper-based substrate could be used to detect as low as 0.05-0.2 mg L-1 of pesticides in standard solutions, and as low as 5-20 ng cm-2 of residual pesticides on apple skins with minimum sample pretreatment.

CONCLUSION

This paper-based substrate with tunable feature for the density and distribution of nanoparticles is applicable for rapid SERS detection of residual pesticides in fruits and vegetables. © 2023 Society of Chemical Industry.

Recent advancements in nanomaterial based optical detection of food additives: a review

Food additives have become a critical component in the food industry. They are employed as preservatives to decelerate the negative effects of environmental and microbial factors on food quality. Currently, food additives are used for a variety of purposes, including colorants, flavor enhancers, nutritional supplements, etc., owing to improvements in the food industry. Since the usage of food additives has increased dramatically, the efficient monitoring of their acceptable levels in food products is quite necessary to mitigate the problems associated with their inappropriate use. The traditional methods used for detecting food additives are generally based on standard spectroscopic and chromatographic techniques. However, these analytical techniques are limited by their high instrumentation cost and time-consuming procedures. The emerging field of nanotechnology has enabled the development of highly sensitive and specific sensors to analyze food additives in a rapid manner. The current article emphasizes the need to detect various food additives owing to their potential negative effects on humans, animals, and the environment. In this article, the role of nanomaterials in the optical sensing of food additives has been discussed owing to their high accuracy, ease-of-use, and excellent sensitivity. The applications of nanosensors for the detection of various food additives have been elaborated with examples. The current article will assist policymakers in developing new rules and regulations to mitigate the adverse effects of toxic food additives on humans and the environment. In addition, the prospects of nanosensors for the optical detection of food additives at a commercial scale have been discussed to combat their irrational use in the food industry.

A highly sensitive surface-enhanced Raman scattering sensor with MIL-100(Fe)/Au composites for detection of malachite green in fish pond water

Concerns about food safety have been arisen due to the improper use of chemicals in aquaculture. Malachite green (MG) has attracted attention because of its illegal usage and its potential negative impacts on the environment and public health. Surface-enhanced Raman scattering (SERS) platforms coupled with different SERS substrates have been employed for rapid analysis of MG residues in food. However, the most commonly used SERS substrates were non-reusable and showed limited detection sensitivity. In this study, a novel SERS substrate with a good recyclability and a high sensitivity was prepared by electrostatically assembling together a metal-organic framework material called materials of institute lavoisie-100(Fe) (MIL-100(Fe)) and Au NPs. The lowest detectable concentration of MG was 10^-13 M based on the optimal substrate. The SERS sensor was applied for the detection of the trace MG in fish pond water, which was accomplished with the correlation coefficients R² = 0.991-0.996 in a concentration range of 10^-6-10^-13 M. Moreover, MIL-100(Fe)/Au was recycled at least five times, realizing a "detection to degradation", showing great potential for food contamination monitoring due to its distinguished performance.

New preservation and detection technologies for edible mushrooms: A review

Edible mushrooms are nutritious, tasty, and have medicinal value, which makes them very popular. Fresh mushrooms have a high water content and a crisp texture. They demonstrate strong metabolic activity after harvesting. However, they are prone to textural changes, microbial infestation, and nutritional and flavor loss, and they therefore require appropriate post-harvest processing and preservation. Important factors affecting safety and quality during their processing and storage include their quality, source, microbial contamination, physical damage, and chemical residues. Thus, these aspects should be tested carefully to ensure safety. In recent years, many new techniques have been used to preserve mushrooms, including electrofluidic drying and cold plasma treatment, as well as new packaging and coating technologies. In terms of detection, many new detection techniques, such as nuclear magnetic resonance (NMR), imaging technology, and spectroscopy can be used as rapid and effective means of detection. This paper reviews the new technological methods for processing and detecting the quality of mainstream edible mushrooms. It mainly introduces their working principles and application, and highlights the future direction of preservation, processing, and quality detection technologies for edible mushrooms. Adopting appropriate post-harvest processing and preservation techniques can maintain the organoleptic properties, nutrition, and flavor of mushrooms effectively. The use of rapid, accurate, and non-destructive testing methods can provide a strong assurance of food safety. At present, these new processing, preservation and testing methods have achieved good results but at the same time there are certain shortcomings. So it is recommended that they also be continuously researched and improved, for example through the use of new technologies and combinations of different technologies. © 2023 Society of Chemical Industry.

The overview of analytical methods for studying of fossil natural resins

The review presents methods that are used frequently for multi-analytical study of fossil resins. The preliminary characterization relies on physical methods such as microhardness, density and fluorescence in UV light measurements. The spectroscopic methods: infrared spectroscopy, Raman spectroscopy, fluorescence spectroscopy are also presented in the paper. Besides that, the review also contains examples of the application of chromatographic methods: gas chromatography, thin layer chromatography, high-performance liquid chromatography, two-dimensional gas chromatography coupled to time-of-flight mass spectrometry as well as sample preparation methods for chromatographic studies such as pyrolysis. Additionally, thermal methods such as thermogravimetric analysis and differential scanning calorimetry also are covered by the review. Beside the examples of application, a detailed description with development history and perspective for further improvement are presented for each method. Moreover, fit-for-purpose assessment of each method is illustrated based on many examples from literature. The paper also contains examples of the application of multivariate statistical analysis and chemometric methods for comparing multiple properties of different fossil resin specimens for differentiation and classification purposes.

Detection of thiram on fruit surfaces and in juices with minimum sample pretreatment via a bendable and reusable substrate for surface-enhanced Raman scattering

BACKGROUND

Surface-enhanced Raman scattering (SERS) substrates based on metallic nanoparticles locked in some flexible materials have great potential for rapid detection of pesticide residues in foods, but these substrates are generally not reusable.

RESULTS

A bendable and reusable sponge based on polydimethylsiloxane (PDMS) and Au nanospheres was synthesized and employed as SERS substrate to analyze thiram on the surfaces of apples and grapes (20-1000 ng cm^-2 ) and in their juices (0.5-5.0 mg L^-1 ) with minimum sample pretreatments. The lowest detectible concentrations for thiram in fruit juices and on fruit skins were 0.5 mg L^-1 and 20 ng cm^-2 , respectively. The Au-PDMS substrate had acceptable intra-reproducibility for SERS analysis of thiram in fruit juices and on fruit skins, resulting in 3.6-16.9% relative standard deviation (RSD) for the SERS signal of the primary peak of thiram. Moreover, the Au-PDMS substrate exhibited distinguished reusability and stability, which could provide a reproducible SERS signal of thiram in apple juice even after the substrate being reused ten times (RSDs for the three major characteristic peaks of thiram were 2.7-10.5% during the ten reused cycles).

CONCLUSION

This flexible and reusable Au-PDMS SERS substrate for thiram detection could be readily extended to the analysis of other trace chemicals in a broad range of foods, providing a new possibility for SERS application. © 2022 Society of Chemical Industry.

Hydrophobic Wafer-Scale High-Reproducibility SERS Sensor Based on Silicon Nanorods Arrays Decorated with Au Nanoparticles for Pesticide Residue Detection

High sensitivity and reproducibility are highly desirable to a SERS sensor in diverse detection applications. Moreover, it is a great challenge to determine how to promote the target molecules to be more concentrated on the hotspots of the SERS substrate by engineering a surface with switching interfacial wettability. Along these lines, wafer-scale uniformly hydrophobic silicon nanorods arrays (SiNRs) decorated with Au nanoparticles were designed as the SERS substrate. Typically, the SERS substrate was fabricated by enforcing the polystyrene (PS) sphere self-assembly, as well as the plasma etching and the magnetron sputtering techniques. Consequently, the SERS substrate was treated by soaking within a n-dodecyl mercaptan (NDM) solution at different times in order to obtain adjustable wettabilities. By leveraging the electromagnetic enhancement resulted from the Au nanostructures and enrichment effect induced by the hydrophobicity, the SERS substrate is endowed with efficient SERS capabilities. During the detection of malachite green (MG), an ultralow relative standard deviation (RSD) 4.04-6.14% is achieved and the characteristic signal of 1172 cm^-1 can be detected as low as 1 ng/mL. The proposed SiNRs' structure presents outstanding SERS activity with sensitivity and reproducibility rendering thus an ideal candidate for potential application in analytical detection fields.

Fusion of binary split allosteric aptasensor for the ultra-sensitive and super-rapid detection of malachite green

The complicated labeling procedure and high cost of split aptasensors have hitherto limited their application in the detection of hazardous substances. Herein we report the first examples of label-free aptasensors based on the fusion of a binary split G-quadruplex and malachite green (MG) aptamer, transducing recognition events into fluorescent signals through the allosteric regulation of the aptamer to achieve selective and sensitive detection. Specifically, RNA MGA was successfully converted into DNA MGA with comparable affinity and improved stability, thereby overcoming the limitations of poor stability and high expense. We subsequently split the DNA MGA and attached them to a G-rich DNA sequence at the 5' and 3' ends, to construct the binary split allosteric aptasensor. The performance of the binary split aptasensor for MG detection was significantly improved based on proposed allosteric regulation strategy, and the reconfiguration capability of the aptamers upon binding with targets was proven, providing the binary split aptasensor with superior sensitivity and selectivity. This sensing method has a wide dynamic detection range of 5 nmol·L^-1 to 500 μmol·L^-1, with a low limit of detection (LOD) of 4.17 nmol·L^-1, and achieves the ultra-sensitive and super-rapid detection of MG. This newly proposed aptasensor is equipped with the advantages of being label-free, time saving and economical. More importantly, this successful construction of a fused aptasensor expands the principles of split aptasensor design and provides a universal platform for the detection of environmental contaminants.

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

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

Design, Fabrication and Applications of Electrospun Nanofiber-Based Surface-Enhanced Raman Spectroscopy Substrate

Surface-enhanced Raman spectroscopy (SERS) is an advanced and powerful analysis tool. Due to the advantages of high sensitivity, high resolution, and nondestructive testing, it has been widely used in physics, chemistry, material science and other fields. In recent years, substantial progress has been made in developing flexible platforms for the design and fabrication of SERS substrates. One important kind of the flexible platforms is based on electrospun nanofibers. Electrospun nanofibers not only have unique advantages such as easy preparation, high porosity and large specific surface area, but also can increase the number of hotspots when combined with precious metal nanomaterials, thereby enhancing the SERS signal and expanding the application scope. In this review, we firstly focus on two strategies for the fabrication of metal nanostructure decorated in/on the electrospun nanofibers, namely in-situ and ex-situ. Then the applications of these SERS substrates in the fields of quantitative analysis, monitoring chemical reactions and recyclable detection are introduced in detail. Finally, the challenges as well as perspectives are presented to offer a guideline for the future exploration of these SERS substrates. We expect that it will provide new inspiration for the development of electrospun nanofiber-based SERS substrates.

Nanomaterials Enabled and Bio/Chemical Analytical Sensors for Acrylamide Detection in Thermally Processed Foods: Advances and Outlook

Acrylamide, a food processing contaminant with demonstrated genotoxicity, carcinogenicity, and reproductive toxicity, is largely present in numerous prominent and commonly consumed food products that are produced by thermal processing methods. Food regulatory bodies such as the U.S. Food and Drug Administration (U.S. FDA) and European Union Commission regulations have disseminated various acrylamide mitigation strategies in food processing practices. Hence, in the wake of such food and public health safety efforts, there is a rising demand for economic, rapid, and portable detection and quantification methods for these contaminants. Since conventional quantification techniques like liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) methods are expensive and have many drawbacks, sensing platforms with various transduction systems have become an efficient alternative tool for quantifying various target molecules in a wide variety of food samples. Therefore, this present review discusses in detail the state of robust, nanomaterials-based and other bio/chemical sensor fabrication techniques, the sensing mechanism, and the selective qualitative and quantitative measurement of acrylamide in various food materials. The discussed sensors use analytical measurements ranging from diverse and disparate optical, electrochemical, as well as piezoelectric methods. Further, discussions about challenges and also the potential development of the lab-on-chip applications for acrylamide detection and quantification are entailed at the end of this review.

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

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

Strategies for SERS Detection of Organochlorine Pesticides

Organochlorine pesticides (OCPs) embody highly lipophilic hazardous chemicals that are being phased out globally. Due to their persistent nature, they are still contaminating the environment, being classified as persistent organic pollutants (POPs). They bioaccumulate through bioconcentration and biomagnification, leading to elevated concentrations at higher trophic levels. Studies show that human long-term exposure to OCPs is correlated with a large panel of common chronic diseases. Due to toxicity concerns, most OCPs are listed as persistent organic pollutants (POPs). Conventionally, separation techniques such as gas chromatography are used to analyze OCPs (e.g., gas chromatography coupled with mass spectrometry (GC/MS)) or electron capture detection (GC/ECD). These are accurate, but expensive and time-consuming methods, which can only be performed in centralized lab environments after extensive pretreatment of the collected samples. Thus, researchers are continuously fueling the need to pursue new faster and less expensive alternatives for their detection and quantification that can be used in the field, possibly in miniaturized lab-on-a-chip systems. In this context, surface enhanced Raman spectroscopy (SERS) represents an exceptional analytical tool for the trace detection of pollutants, offering molecular fingerprint-type data and high sensitivity. For maximum signal amplification, two conditions are imposed: an efficient substrate and a high affinity toward the analyte. Unfortunately, due to the highly hydrophobic nature of these pollutants (OCPs,) they usually have a low affinity toward SERS substrates, increasing the challenge in their SERS detection. In order to overcome this limitation and take advantage of on-site Raman analysis of pollutants, researchers are devising ingenious strategies that are synthetically discussed in this review paper. Aiming to maximize the weak Raman signal of organochlorine pesticides, current practices of increasing the substrate's performance, along with efforts in improving the selectivity by SERS substrate functionalization meant to adsorb the OCPs in close proximity (via covalent, electrostatic or hydrophobic bonds), are both discussed. Moreover, the prospects of multiplex analysis are also approached. Finally, other perspectives for capturing such hydrophobic molecules (MIPs-molecularly imprinted polymers, immunoassays) and SERS coupled techniques (microfluidics-SERS, electrochemistry-SERS) to overcome some of the restraints are presented.

Effect of pH and Nanoparticle Capping Agents on Cr (III) Monitoring in Water: A Kinetic Way to Control the Parameters of Ultrasensitive Environmental Detectors

In this work, we apply surface-enhanced Raman spectroscopy (SERS) to study the kinetics of chromium Cr (III) detection in solution using EDTA and silver nanoparticles (AgNPs). We examine for the first time the effect of pH and nanoparticles’ capping agent on the kinetic mechanism of Cr (III) detection using SERS temporal variations. The full width at half maximum (FWHM) and Raman shift variations show that the mechanism of detection is composed of two steps: a first one consisting of chemical coordination between Cr (III) and AgNPs that leads to exalted chemical and electromagnetic enhancement and the second one is an aggregation process with an important optical enhancement. The obtained results showed that the first step in the detection at lower pH was five times faster than in a basic medium using citrate capped silver nanoparticles (Cit-AgNPs). On the other hand, using a capping agent with dicarboxylate groups such as oxalate (Oxa-AgNPs) led to an important enhancement in SERS detection signal (more than 30 times) compared with Cit-AgNPs, although the detection kinetic’s mechanism was slower.

Rapid and ultrasensitive detection of food contaminants using surface-enhanced Raman spectroscopy-based methods

With the globalization of food and its complicated networking system, a wide range of food contaminants is introduced into the food system which may happen accidentally, intentionally, or naturally. This situation has made food safety a critical global concern nowadays and urged the need for effective technologies capable of dealing with the detection of food contaminants as efficiently as possible. Hence, Surface-enhanced Raman spectroscopy (SERS) has been taken as one of the primary choices for this case, due to its extremely high sensitivity, rapidity, and fingerprinting interpretation capabilities which account for its competency to detect a molecule up to a single level. Here in this paper, we present a comprehensive review of various SERS-based novel approaches applied for direct and indirect detection of single and multiple chemical and microbial contaminants in food, food products as well as water. The aim of this paper is to arouse the interest of researchers by addressing recent SERS-based, novel achievements and developments related to the investigation of hazardous chemical and microbial contaminants in edible foods and water. The target chemical and microbial contaminants are antibiotics, pesticides, food adulterants, Toxins, bacteria, and viruses. In this paper, different aspects of SERS-based reports have been addressed including synthesis and use of various forms of SERS nanostructures for the detection of a specific analyte, the coupling of SERS with other analytical tools such as chromatographic methods, combining analyte capture and recognition strategies such as molecularly imprinted polymers and aptasensor as well as using multivariate statistical analyses such as principal component analysis (PCA)to distinguish between results. In addition, we also report some strengths and limitations of SERS as well as future viewpoints concerning its application in food safety.