Recent Developments in Surface-Enhanced Raman Spectroscopy and Its Application in Food Analysis: Alcoholic Beverages as an Example

Estimating baselines of Raman spectra based on transformer and manually annotated data

Raman spectroscopy is a powerful and non-invasive analytical method for determining the chemical composition and molecular structure of a wide range of materials, including complex biological tissues. However, the captured signals typically suffer from interferences manifested as noise and baseline, which need to be removed for successful data analysis. Effective baseline correction is critical in quantitative analysis, as it may impact peak signature derivation. Current baseline correction methods can be labor-intensive and may require extensive parameter adjustment depending on the input spectrum characteristics. In contrast, deep learning-based baseline correction models trained across various materials, offer a promising and more versatile alternative. This study reports an approach to manually identify the ground-truth baselines for eight different biological materials through extensively tuning the parameters of three classical baseline correction methods, Modified Multi-Polynomial Fit (Modpoly), Improved Modified Multi-Polynomial Fitting (IModpoly), and Adaptive Iteratively Reweighted Penalized Least Squares (airPLS), and combining the outputs to best fit the training data. We designed a one-dimensional Transformer (1dTrans) tailored to fit Raman spectral data for estimating their baselines, and evaluated its performance against convolutional neural network (CNN), ResUNet, and three aforementioned parametric methods. The 1dTrans model achieved lower mean absolute error (MAE) and spectral angle mapper (SAM) scores when compared to the other methods in both development and evaluation of the manually labeled original raw Raman spectra, highlighting the effectiveness of the method in Raman spectra pre-processing.

Innovative quantum dots-based SERS for ultrasensitive reporting of contaminants in food: Fundamental concepts and practical implementations

Food contamination poses serious health risks, compelling the discovery of new methods to guarantee regulatory compliance and build consumer conviction. Surface Enhanced Raman Spectroscopy (SERS) has come into sight as a sophisticated approach for the ultrasensitive discovery of toxins in food and water, proposing non-destructive, quick, and precise analysis. Instantaneously, quantum dots (QDs) are astonishing nanomaterials, characterized by distinctive attributes such as quantum confinement and optical photostability. This article extends a decisive outline of SERS technology, pointing out its amalgamation with QDs and discussing numerous augmentation approaches i.e., chemical enhancement, electromagnetic enhancement, Van Hove singularities, the Brus equation, Förster resonance energy transfer, band gap energy, and quantum yield. The amalgamation of SERS with QDs commands an important promise in international food security and conservational sustainability. Nevertheless, QDs provide several compensations, they also aspect a few concerns, counting probable toxicity, stability problems, and predisposition to interference. To tackle these items, further research is required to synthesize safer, more stable QD materials and to refine protocols for practical real-world applications. While some reviews on SERS have been published recently, to our knowledge, the current review is the first one dedicated to QDs-assisted SERS in food safety.

Heavy metal immobilization and radish growth improvement using Ca(OH)2-treated cypress biochar in contaminated soil

Heavy metal contamination poses a significant threat to soil quality, plant growth, and food safety, and directly affects multiple UN SDGs. Addressing this issue and offering a remediation solution are vital for human health. One effective approach for immobilizing heavy metals involves impregnating cypress chips with calcium hydroxide (Ca(OH)2) to enhance the chemical adsorption capacity of the resulting woody charcoal. In the present study, un-treated cypress biochar (UCBC) and calcium-treated cypress biochar (TCBC), were introduced into pristine and contaminated soil, at rates of 3, 6, and 9% (w/w). Both BCs were alkaline (UCBC pH: 8.9, TCBC pH: 9.7) with high specific surface area, which improved the soil properties (pH, EC, and OM). Radish (Raphanus sativus) cultivated in pots revealed that both UCBC and TCBC demonstrated significant improvements in growth attributes and heavy metal immobilization compared to the control, with TCBC exhibiting superior effects. The TCBC surface showed highly active nanosized precipitated calcium carbonate particles that were active in immobilizing heavy metals. The application of TCBC at a rate of 9% resulted in a substantial reduction in Zn and Cu uptake by radish roots and shoots. In contaminated soil, Zn uptake by radish roots decreased by 55% (68.3-31.0 mg kg-1), and shoots by 37% (49.3-31.0 mg kg-1); Cu uptake decreased by 40% (38.6-23.2 mg kg-1) in roots and 39% (58.2-35.2 mg kg-1) in shoots. Uptake of Pb was undetectable after TCBC application. Principal component analysis (PCA) highlighted the potential of TCBC over UCBC in reducing heavy metal concentrations and promoting radish growth. Future research should consider the long-term effects and microbial interactions of TCBC application.

A review of SERS coupled microfluidic platforms: From configurations to applications

Microfluidic systems have attracted considerable attention due to their low reagent consumption, short analysis time, and ease of integration in comparison to conventional methods, but still suffer from shortcomings in sensitivity and selectivity. Surface enhanced Raman scattering (SERS) offers several advantages in the detection of compounds, including label-free detection at the single-molecule level, and the narrow Raman peak width for multiplexing. Combining microfluidics with SERS is a viable way to improve their detection sensitivity. Researchers have recently developed several SERS coupled microfluidic platforms with substantial potential for biomolecular detection, cellular and bacterial analysis, and hazardous substance detection. We review the current development of SERS coupled microfluidic platforms, illustrate their detection principles and construction, and summarize the latest applications in biology, environmental protection and food safety. In addition, we innovatively summarize the current status of SERS coupled multi-mode microfluidic platforms with other detection technologies. Finally, we discuss the challenges and countermeasures during the development of SERS coupled microfluidic platforms, as well as predict the future development trend of SERS coupled microfluidic platforms.

Fabrication of Ag nanostar and PEI-based SERS substrate for sensitive and rapid detection of SO2: Application for detection of sulfite residues in beer

Because of sulfite's potential toxicity, there is a growing concern about detecting and controlling its concentration in foods, alcoholic beverages, pharmaceuticals, and environmental samples to ensure public health. A branched polyethyleneimine-coated silver nano-star (AgNS@PEI) surface-enhanced Raman scattering (SERS) substrate was synthesized in this study for use as a sensitive, simple, rapid, stable, and reproducible non-destructible sulfite detection analytical technique. The seed morphology of the nano-star was created by using hydroxylamine (NH2OH) solution as a primary reducing agent, followed by a slow secondary reduction by trisodium citrate dihydrate (HOC(COONa)(CH2COONa)2 2H2O), resulting in the complete growth of the silver nano-star. For extra stability and selective absorption of sulfur dioxide from the headspace extraction of SO2 from sulfites, the nano-stars were thin coated with branched polyethyleneimine (b-PEI). The results showed that the thin-coated plasmonic substrates selectively absorb sulfur dioxide molecules, allowing sulfites in beer samples to be detected with a detection limit of 0.48 mg/L. Furthermore, the PEI-coated silver nano-star demonstrated increased stability and reproducibility, allowing for longer use of the substrate. Recovery experiments with recovery rates ranging from 95 to 112% and relative standard deviations ranging from 1.55 to 8.1% demonstrated that headspace extraction, selective SO2 absorption by the synthesized substrate, and subsequent SERS detections were reliable and valid for practical applications. Finally, this study developed an SO2-sensitive, selective, and robust Si@AgNS@PEI substrate for effective SERS detection and monitoring of sulfite levels in real-world environmental samples.

Rapid Indentification of Auramine O Dyeing Adulteration in Dendrobium officinale, Saffron and Curcuma by SERS Raman Spectroscopy Combined with SSA-BP Neural Networks Model

(1) Background: Rapid and accurate determination of the content of the chemical dye Auramine O(AO) in traditional Chinese medicines (TCMs) is critical for controlling the quality of TCMs. (2) Methods: Firstly, various models were developed to detect AO content in Dendrobium officinale (D. officinale). Then, the detection of AO content in Saffron and Curcuma using the D. officinale training set as a calibration model. Finally, Saffron and Curcuma samples were added to the training set of D. officinale to predict the AO content in Saffron and Curcuma using secondary wavelength screening. (3) Results: The results show that the sparrow search algorithm (SSA)-backpropagation (BP) neural network (SSA-BP) model can accurately predict AO content in D. officinale, with Rp2 = 0.962, and RMSEP = 0.080 mg/mL. Some Curcuma samples and Saffron samples were added to the training set and after the secondary feature wavelength screening: The Support Vector Machines (SVM) quantitative model predicted Rp2 fluctuated in the range of 0.780 ± 0.035 for the content of AO in Saffron when 579, 781, 1195, 1363, 1440, 1553 and 1657 cm-1 were selected as characteristic wavelengths; the Partial Least Squares Regression (PLSR) model predicted Rp2 fluctuated in the range of 0.500 ± 0.035 for the content of AO in Curcuma when 579, 811, 1195, 1353, 1440, 1553 and 1635 cm-1 were selected as the characteristic wavelengths. The robustness and generalization performance of the model were improved. (4) Conclusion: In this study, it has been discovered that the combination of surface-enhanced Raman spectroscopy (SERS) and machine learning algorithms can effectively and promptly detect the content of AO in various types of TCMs.

Development of an automated Raman system and use of principal component analysis to classify real and counterfeit liquors

We developed an automated Raman measurement platform for the customized design of various solution containers. We used the software LabVIEW to integrate the entire automatic measurement process. By designing an intuitive human-machine interface, the user only needs to input a few setting parameters and can efficiently operate the machine in automation mode for an array of solutions containing real or counterfeit liquors such as kaoliang liquor, vodka, rum, gin, rice wine, ethanol, and methanol. In this study, data from various alcoholic beverage solutions were subjected to principal component analysis (PCA) to distinguish from the low-concentration counterfeit liquors (methanol <50 g L-1). Moreover, several brands of liquors with the same alcohol concentration were successfully classified into different groups based on a combination of Raman spectroscopy and PCA analysis.

Fabrication of Ag-CaCO3 Nanocomposites for SERS Detection of Forchlorfenuron

In this study, Ag-CaCO3 nanocomposites were synthesized using silver nitrate as the precursor solution based on calcium carbonate nanoparticles (CaCO3 NPs). The synthesis involved the reaction of calcium lignosulphonate and sodium bicarbonate. The properties of Ag-CaCO3 nanocomposites were studied by various technologies, including an ultraviolet-visible spectrophotometer, a transmission electron microscope, and a Raman spectrometer. The results showed that Ag-CaCO3 nanocomposites exhibited a maximum UV absorption peak at 430 nm, the surface-enhanced Raman spectroscopy (SERS) activity of Ag-CaCO3 nanocomposites was evaluated using mercaptobenzoic acid (MBA) as the marker molecule, resulting in an enhancement factor of 6.5 × 104. Additionally, Ag-CaCO3 nanocomposites were utilized for the detection of forchlorfenuron. The results demonstrated a linear relationship in the concentration range of 0.01 mg/mL to 2 mg/mL, described by the equation y = 290.02x + 1598.8. The correlation coefficient was calculated to be 0.9772, and the limit of detection (LOD) was determined to be 0.001 mg/mL. These findings highlight the relatively high SERS activity of Ag-CaCO3 nanocomposites, making them suitable for analyzing pesticide residues and detecting toxic and harmful molecules, thereby contributing to environmental protection.

Recent developments in modification of biochar and its application in soil pollution control and ecoregulation

Soil pollution has become a real threat to mankind in the 21st century. On the one hand, soil pollution has reduced the world's arable land area, resulting in the contradiction between the world's population expansion and the shortage of arable land. On the other hand, soil pollution has seriously disrupted the soil ecological balance and significantly affected the biodiversity in the soil. Soil pollutants may further affect the survival, reproduction and health of humans and other organisms through the food chain. Several studies have suggested that biochar has the potential to act as a soil conditioner and to promote crop growth, and is widely used to remove environmental pollutants. Biochar modified by physical, chemical, and biological methods will affect the treatment efficiency of soil pollution, soil quality, soil ecology and interaction with organisms, especially with microorganisms. Therefore, in this review, we summarized several main biochar modification methods and the mechanisms of the modification and introduced the effects of the application of modified biochar to soil pollutant control, soil ecological regulation and soil nutrient regulation. We also introduced some case studies for the development of modified biochars suitable for different soil conditions, which plays a guiding role in the future development and application of modified biochar. In general, this review provides a reference for the green treatment of different soil pollutants by modified biochar and provides data support for the sustainable development of agriculture.