Green reduction of silver nanoparticles for cadmium detection in food using surface-enhanced Raman spectroscopy coupled multivariate calibration

Photoreduced Ag+/sodium alginate supramolecular hydrogel as a sensitive SERS membrane substrate for rapid detection of uranyl ions

BACKGROUND

In the fields of environmental monitoring and nuclear emergency, in order to obtain the relevant information of uranyl-induced environmental pollution and nuclear accident, it is necessary to establish a rapid quantitative analytical technique for uranyl ions. As a new promising technique, surface-enhanced Raman scattering (SERS) is hopeful to achieve this goal. However, uranyl ions are easily desorbed from SERS substrates under acidic conditions, and the structures of SERS substrates will be destroyed in the strong acidic aqueous solutions. Besides, the quantitative detection ability of SERS for uranyl ions needs to be promoted. Hence, it is necessary to develop new SERS substrates for accurate quantitative detection of trace uranyl in environmental water samples, especially in acidic solutions.

RESULTS

In this work, we prepared silver ions/sodium alginate supramolecular hydrogel membrane (Ag+/SA SMH membrane), and the Ag+ ions from the membrane were transformed into Ag/Ag2O complex nanoparticles under laser irradiation. The Raman signal of uranyl was strongly enhanced under the synergistic interaction of electromagnetic enhancement derived from the Ag nanoparticles and charge transfer enhancement between uranyl and Ag2O. Utilizing the peak of SA (550 cm-1) as an internal standard, a quantitative detection with a LOD of 6.7 × 10-9 mol L-1 was achieved due to a good linear relation of uranyl concentrations from 1.0 × 10-8 mol L-1 to 2 × 10-6 mol L-1. Furthermore, foreign metal ions hardly affected the SERS detection of uranyl, and the substrate could determine trace uranyl in natural water samples. Particularly, the acidity had no obvious effect on SERS signals of uranyl ions. Therefore, in addition to the detection of uranyl ions in natural water samples, the proposed strategy could also detect uranyl ions in strong acidic solutions.

SIGNIFICANCE AND NOVELTY

A simple one-step method was used to prepare an Ag+/SA SMH membrane for rapid quantitative detection of uranyl ions for the first time. The proposed substrate successfully detected uranyl ions under acidic conditions by immobilizing uranyl ion in hydrogel structure. In comparison with the previous studies, a more accurate quantitative analysis for uranyl ions was achieved by using an internal standard, and the proposed strategy could determine trace uranyl in either natural water samples or strong acidic solutions.

Navigating the development of silver nanoparticles based food analysis through the power of artificial intelligence

In the ongoing pursuit of enhancing food safety and quality through advanced technologies, silver nanoparticles (AgNPs) stand out for their antimicrobial properties. Despite being overshadowed by other nanoparticles in food sensing applications, AgNPs possess inherent qualities that make them effective tools for rapid and selective contaminant detection in food matrices. This review aims to reinvigorate the interest in AgNPs in the food industry, emphasizing their sensing mechanism and the transformative potential of integrating them with artificial intelligence (AI) for enhanced food safety monitoring. It discusses key AI tools and principles in the food industry, demonstrating their positive impact on food analytical chemistry. The interplay between AI and biosensors offers many advantages and adaptability to dynamic analytical challenges, significantly improving food safety monitoring and potentially redefining the landscape of food safety and quality assurance.

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

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

Development of a Sensitive SERS Method for Label-Free Detection of Hexavalent Chromium in Tea Using Carbimazole Redox Reaction

Tea plants absorb chromium-contaminated soil and water and accumulate in tea leaves. Hexavalent chromium (Cr⁶⁺) is a very toxic heavy metal; excessive intake of tea containing Cr⁶⁺ can cause serious harm to human health. A reliable and sensitive surface-enhanced Raman spectroscopy (SERS) method was developed using Au@Ag nanoparticles as an enhanced substrate for the determination of Cr⁶⁺ in tea. The Au@AgNPs coated with carbimazole showed a highly selective reaction to Cr⁶⁺ in tea samples through a redox reaction between Cr⁶⁺ and carbimazole. The Cr⁶⁺ in the contaminated tea sample reacted with methimazole-the hydrolysate of carbimazole-to form disulfide, which led to the decrease in the Raman intensity of the peak at 595 cm^-1. The logarithm of the concentration of Cr⁶⁺ has a linear relationship with the Raman intensity at the characteristic peak and showed a limit of detection of 0.945 mg/kg for the tea sample. The carbimazole functionalized Au@AgNPs showed high selectivity in analyzing Cr⁶⁺ in tea samples, even in the presence of other metal ions. The SERS detection technique established in this study also showed comparable results with the standard ICP-MS method, indicating the applicability of the established technique in practical applications.

Aflatoxin detection technologies: recent advances and future prospects

Aflatoxins have posed serious threat to food safety and human health. Therefore, it is important to detect aflatoxins in samples rapidly and accurately. In this review, various technologies to detect aflatoxins in food are discussed, including conventional ones such as thin-layer chromatography (TLC), high performance liquid chromatography (HPLC), enzyme linked immunosorbent assay (ELISA), colloidal gold immunochromatographic assay (GICA), radioimmunoassay (RIA), fluorescence spectroscopy (FS), as well as emerging ones (e.g., biosensors, molecular imprinting technology, surface plasmon resonance). Critical challenges of these technologies include high cost, complex processing procedures and long processing time, low stability, low repeatability, low accuracy, poor portability, and so on. Critical discussion is provided on the trade-off relationship between detection speed and detection accuracy, as well as the application scenario and sustainability of different technologies. Especially, the prospect of combining different technologies is discussed. Future research is necessary to develop more convenient, more accurate, faster, and cost-effective technologies to detect aflatoxins.

Non-destructive prediction of colour and water-related properties of frozen/thawed beef meat by Raman spectroscopy coupled multivariate calibration

Freeze-thaw accelerated the colour deterioration of beef with the increase of colour b* and the decrease of colour a* values (P < 0.05). The maximum exudate loss reached 22 % after the seventh freeze-thaw. A strong correlation between the transversal relaxation time T₂₁ and thawing loss may mean that T₂₁ water contributed to the exudate loss during freeze-thaw. Afterwards, competitive adaptive reweighted sampling-partial least square (CARS-PLS) has the best prediction in thawing loss of frozen/thawed beef with correlation coefficients of prediction (R_p) of 0.971, and root mean square error of prediction (RMSEP) of 1.436. Besides, Uninformative variable elimination-partial least squares (UVE-PLS) showed good prediction effects on colour values (R_p = 0.932 - 0.994) and water content (R_p = 0.928, RMSEP = 0.582) of frozen/thawed beef. Therefore, this work demonstrated that Raman spectroscopy coupled with multivariate calibration has a good ability for non-destructive prediction in colour and water-related properties of frozen/thawed beef.

Raman spectroscopy: Principles and recent applications in food safety

Food contaminant is a significant issue because of the adverse effects on human health and economy. Traditional detection methods such as liquid chromatography-mass spectroscopy for detecting food contaminants are expensive and time-consuming, and require highly-trained personnel and complicated sample pretreatment. Raman spectroscopy is an advanced analytical technique in a manner of non-destructive, rapid, cost-effective, and ultrasensitive sensing various hazards in agri-foods. In this chapter, we summarized the principle of Raman spectroscopy and surface enhanced Raman spectroscopy, the methods to process Raman spectra, the recent applications of Raman/SERS (surface-enhanced Raman spectroscopy) in detecting chemical contaminants (e.g., pesticides, antibiotics, mycotoxins, heavy metals, and food adulterants) and microbiological hazards (e.g., Salmonella, Campylobacter, Shiga toxigenic E. coli, Listeria, and Staphylococcus aureus) in foods.

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

Emerging Trends in Advanced Translational Applications of Silver Nanoparticles: A Progressing Dawn of Nanotechnology

Nanoscience has emerged as a fascinating field of science, with its implementation in multiple applications in the form of nanotechnology. Nanotechnology has recently been more impactful in diverse sectors such as the pharmaceutical industry, agriculture sector, and food market. The peculiar properties which make nanoparticles as an asset are their large surface area and their size, which ranges between 1 and 100 nanometers (nm). Various technologies, such as chemical and biological processes, are being used to synthesize nanoparticles. The green chemistry route has become extremely popular due to its use in the synthesis of nanoparticles. Nanomaterials are versatile and impactful in different day to day applications, resulting in their increased utilization and distribution in human cells, tissues, and organs. Owing to the deployment of nanoparticles at a high demand, the need to produce nanoparticles has raised concerns regarding environmentally friendly processes. These processes are meant to produce nanomaterials with improved physiochemical properties that can have significant uses in the fields of medicine, physics, and biochemistry. Among a plethora of nanomaterials, silver nanoparticles have emerged as the most investigated and used nanoparticle. Silver nanoparticles (AgNPs) have become vital entities of study due to their distinctive properties which the scientific society aims to investigate the uses of. The current review addresses the modern expansion of AgNP synthesis, characterization, and mechanism, as well as global applications of AgNPs and their limitations.

Rapid and sensitive detection of zearalenone in corn using SERS-based lateral flow immunosensor

Zearalenone (ZEN) is a universal mycotoxin contaminant in corn and its products. A surface-enhanced Raman scattering (SERS) based test strip was proposed for the detection of ZEN, which had the advantages of simplicity, rapidity, and high sensitivity. Core-shell Au@AgNPs with embedded reporter molecules (4-MBA) were synthesized as SERS nanoprobe, which exhibited excellent SERS signals and high stability. The detection range of ZEN for corn samples was 10-1000 μg/kg with the limit of detection (LOD) of 3.6 μg/kg, which is far below the recommended tolerable level (60 μg/kg). More importantly, the SERS method was verified by HPLC in the application on corn samples contaminated with ZEN, and the coincidence rates were in the range of 86.06%-111.23%, suggesting a high accuracy of the SERS assay. Therefore, the SERS-based test strip with an analysis time of less than 15 min is a promising tool for accurate and rapid detection of ZEN-field contamination.