Nanostructured hybrid surface enhancement Raman scattering substrate for the rapid determination of sulfapyridine in milk samples

Rapid qualitative detection of titanium dioxide adulteration in persimmon icing using portable Raman spectrometer and Machine learning

Persimmon icing is the white crystalline powder that adheres to the surface of persimmon cakes when the sugar in the persimmon spills over during processing, which is considered the essence of persimmon. Titanium dioxide is a food additive that is commonly added to the surface of persimmon cakes to impersonate high-quality persimmon cakes. However, excessive titanium dioxide can be harmful to humans, so a quick method is needed to identify persimmon cakes as adulterated. Raman spectroscopy with distinctive advantages of water-insensitivity, real-time, field-deployable, label-free, and fingerprinting-identification has been rapidly developed and used in food quality assurance and safety monitoring. In this study, we investigated Raman spectroscopy integrated with machine learning to assess titanium dioxide adulteration in dried persimmon icing. The adaptive iterative reweighting partial least squares (air-PLS) algorithm as an effective algorithm was used to remove fluorescent background signals in raw Raman spectroscopy. Principal components analysis (PCA) was employed to analyze the spectral data and determine the class memberships, and results showed that 99.9% of information could be explained by PC-1 and PC-2. Compared with extreme learning machine (ELM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), and random forest (RF) models, one-dimensional stack auto encoder convolutional neural network (1D-SAE-CNN) could provide the highest detection accuracy of 0.9825, precision of 0.9824, recall of 0.9825, and f1-score of 0.9824. This study shows that Raman spectroscopy coupled with 1D-SAE-CNN is a promising method to detect titanium dioxide adulteration in persimmon icing.

Silver-Decorated Silicon Nanostructures: Fabrication and Characterization of Nanoscale Terraces as an Efficient SERS-Active Substrate

Rich and highly dense surface-enhanced Raman (SERS) hotspots available in the SERS-active platform are highly anticipated in SERS measurements. In this work, conventional silicon wafer was treated to have wide exposure to terraces available within the silicon nanostructures (Si-NSs). High-resolution field emission scanning electron microscopic (FESEM) investigations confirmed that the terraces were several microns wide and spread over different steps. These terraces were further decorated with silver nanoparticles (Ag-NPs) of different shapes and sizes to achieve SERS-active hotspots. Based on more than 150 events, a histogram of the size distribution of Ag-NPs indicated a relatively narrow size distribution, 29.64 ± 4.66 nm. The coverage density was estimated to be ~4 × 10¹⁰ cm^-2. The SERS-activity of Ag-NPs -decorated Si-NSs was found to be enhanced with reference to those obtained in pristine Si-NSs. Finite difference time domain models were developed to support experimental observations in view of electromagnetic (EM) near-field distributions. Three archetype models; (i) dimer of same constituent Ag-NPs, (ii) dimer of different constituent Ag-NPs, and (iii) linear trimer of different constituent Ag-NPs were developed. EM near-field distributions were extracted at different incident polarizations. Si-NSs are well-known to facilitate light confinement, and such confinement can be cascaded within different Ag-NPs-decorated terraces of Si-NSs.

SERS-Based Methodology for the Quantification of Ultratrace Graphene Oxide in Water Samples

The extensive use of graphene materials in real-world applications has increased their potential release into the environment. To evaluate their possible health and ecological risks, there is a need for analytical methods that can quantify these materials at very low concentrations in environmental media such as water. In this work, a simple, reproducible, and sensitive method to detect graphene oxide (GO) in water samples using the surface-enhanced Raman spectroscopy (SERS) technique is presented. The Raman signal of graphene is enhanced when deposited on a substrate of gold nanoparticles (AuNPs), thus enabling its determination at low concentrations with no need for any preconcentration step. The practical limit of quantification achieved with the proposed method was 0.1 ng mL^-1, which is lower than the predicted concentrations for graphene in effluent water reported to date. The optimized procedure has been successively applied to the determination of ultratraces of GO in water samples.

Rapid detection of sulfonamide antibiotics residues in swine urine by surface-enhanced Raman spectroscopy

Surface enhanced Raman spectroscopy (SERS) combined with rapid pretreatment technique was used to determine sulfonamide antibiotics (sulfadiazine and sulfathiazole) residue in swine urine. Au nanoparticles (AuNPs) were synthesized as Raman enhance substrate and the extraction of swine urine was purified with primary secondary amine (PSA), octadecyl silane (C₁₈) and graphitized carbon (GCB) to eliminate the interference of the matrix and different dosages of adsorbents (PSA, C₁₈, GCB) were investigated. The results showed that the treatment with C₁₈ of 150 mg, GCB of 200 mg and PSA of 200 mg were an excellent approach for rapidly detecting sulfonamide antibiotics residue in swine urine. Combined with density functional theory calculation (DFT), Raman characteristic peaks of 819, 1102, 1173, 1588 cm^-1 and 825, 1127 cm^-1 were selected for qualitative and quantitative assessment of sulfadiazine and sulfathiazole in swine urine, respectively. Based on raman characteristic peak of 819 cm^-1, a good linear relationship between sulfadiazine concentration and Raman intensity was developed with R² = 0.9912, and based on raman characteristic peak of 825 cm^-1, a good linear relationship between sulfathiazole concentration and Raman intensity was developed with R² = 0.9941. And recoveries for five unknown concentration samples predicted were 98.47 ∼ 105.18% with relative standard deviation (RSD) of 1.53% ∼ 5.18%. This study demonstrated that SERS coupled with a quick, easy, cheap, effective, rugged, and safe (QuEChERS) method could be employed to rapidly examine the sulfonamide antibiotics residue in swine urine towards its quality and safety monitoring.

ASSURED Point-of-Need Food Safety Screening: A Critical Assessment of Portable Food Analyzers

Standard methods for chemical food safety testing in official laboratories rely largely on liquid or gas chromatography coupled with mass spectrometry. Although these methods are considered the gold standard for quantitative confirmatory analysis, they require sampling, transferring the samples to a central laboratory to be tested by highly trained personnel, and the use of expensive equipment. Therefore, there is an increasing demand for portable and handheld devices to provide rapid, efficient, and on-site screening of food contaminants. Recent technological advancements in the field include smartphone-based, microfluidic chip-based, and paper-based devices integrated with electrochemical and optical biosensing platforms. Furthermore, the potential application of portable mass spectrometers in food testing might bring the confirmatory analysis from the laboratory to the field in the future. Although such systems open new promising possibilities for portable food testing, few of these devices are commercially available. To understand why barriers remain, portable food analyzers reported in the literature over the last ten years were reviewed. To this end, the analytical performance of these devices and the extent they match the World Health Organization benchmark for diagnostic tests, i.e., the Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment-free, and Deliverable to end-users (ASSURED) criteria, was evaluated critically. A five-star scoring system was used to assess their potential to be implemented as food safety testing systems. The main findings highlight the need for concentrated efforts towards combining the best features of different technologies, to bridge technological gaps and meet commercialization requirements.

Enhanced peroxymonosulfate activation over heterogeneous catalyst Cu0.76Co2.24O4/SBA-15 for efficient degradation of sulfapyridine antibiotic

The largest source of resistant bacteria or viruses is the overuse and misuse of antibiotics in humans and animals. These resistant bacteria or viruses may evolve into superbacteria or superviruses, which causes global plague. Therefore, it is significant to find a highly efficiency and low-cost method to eliminate antibiotics in water environment from inappropriate discharge. Here, a highly active and highly stable heterogeneous catalyst, Cu0.76Co2.24O4/SBA-15 (CCS) was prepared for peroxymonosulfate (PMS) activation in aim of decomposing persistent sulfapyridine (SPD). The reaction mechanism was thoroughly investigated via in situ quenching test and in situ electron paramagnetic resonance. Four reactive species, SO4·-, O2·-, 1O2 and ·OH were generated in Cu0.76Co2.24O4/SBA-15/PMS (CCSP) system. The SO4·- and O2·- were dominant active species responsible for SPD degradation. Co(Ⅱ)↔Co(Ⅲ)↔Co(Ⅱ) redox reaction cycle was constructed due to the different redox potential of Co(Ⅱ)/Co(Ⅲ), HSO5-/SO4∙-, and HSO5-/SO5∙-. Interestingly, Cu(Ⅰ) could urge the redox reaction cycle for PMS activation to be more thermodynamically feasible. Therefore, CCS possessed a highly catalytic activity and excellent stability. Meanwhile, the anions interference test indicated Cl-, NO3-, HCO3-, and H2PO4- had almost no inhibitory effect on SPD degradation over this catalytic system. We sincerely expected that this catalyst system would be applied extensively into antibiotics degradation in real water bodies.

Bayesian Discriminant Analysis of Yogurt Products Based on Raman Spectroscopy

Background

The quality discrimination of dairy products is an important basis on which to achieve quality assurance.

Objective

Taking the discriminant analysis of brand yogurt products as an example, a new rapid discriminant method can be constructed.

Method

The first three principal components were selected as the pattern vectors of the samples. Then, at random, 75% of the samples were collected as a training set, and their mean values and covariance matrices were calculated to construct a Gauss Bayesian discriminant model. The remaining 25% of samples were employed as a test set, and the pattern vectors of each sample were input into the above model. Next, the posterior probability of each sample in relation to each category could be obtained. Results: The category corresponding to the maximum posterior probability as the brand classification of each sample was defined.

Conclusions

We constructed a Gauss Bayesian discriminant model to discriminate these different yogurt products after the principal component feature extraction of Raman properties. The results indicate the rationality and wide application prospects of this approach.

Highlights

A fast dairy product discriminant method based on Gauss Bayesian model and Raman spectroscopy was established.

Synergistic Enhancement Effect for Boosting Raman Detection Sensitivity of Antibiotics

In this paper, a two-step method is used to prepare a regenerative three-dimensional (3D) ZnO/Ag@Au substrate for developing a superior sensitive surface enhanced Raman scattering (SERS) method for detecting antibiotics. A great electromagnetic enhancement is observed from the as-prepared composite substrate, which is triggered by tuning the electron distribution of metals and semiconductor metal oxide. The strong interaction between target sample and the huge surface area of ZnO/Ag@Au composite promotes the charge transfer to produce promising chemical enhancement. The synergistic physical and chemical enhancement mechanisms are validated by density functional theory and finite difference time domain simulation. Additionally, the presence of light "echo effect" in the 3D structure of ZnO support could also amplify the efficiency of light excitation for Raman scattering. The above-stated merits benefit to boost the Raman scattering detection sensitivity for real samples. The ZnO/Ag@Au-based SERS substrate could detect rhodamine 6G molecules with an enhancement factor of up to 1.48 × 10⁹ and the lowest detectable concentration of 10^-10 M. As a real application, antibiotics sulfapyridine in milk is determined by using the proposed SERS protocol, and the limit of detection at 1 × 10^-9 M could be reached. As a prospective, the ZnO/Ag@Au-based SERS method would be extended for food safety and biomedicine analysis.