Quality alert from direct discrimination of polycyclic aromatic hydrocarbons in edible oil by liquid-interfacial surface-enhanced Raman spectroscopy

Trends in authentication of edible oils using vibrational spectroscopic techniques

The authentication of edible oils has become increasingly important for ensuring product quality, safety, and compliance with regulatory standards. Some prevalent authenticity issues found in edible oils include blending expensive oils with cheaper substitutes or lower-grade oils, incorrect labeling regarding the oil's source or type, and falsely stating the oil's origin. Vibrational spectroscopy techniques, such as infrared (IR) and Raman spectroscopy, have emerged as effective tools for rapidly and non-destructively analyzing edible oils. This review paper offers a comprehensive overview of recent advancements in using vibrational spectroscopy for authenticating edible oils. The fundamental principles underlying vibrational spectroscopy are introduced and chemometric approaches that enhance the accuracy and reliability of edible oil authentication are summarized. Recent research trends highlighted in the review include authenticating newly introduced oils, identifying oils based on their specific origins, adopting handheld/portable spectrometers and hyperspectral imaging, and integrating modern data handling techniques into the use of vibrational spectroscopic techniques for edible oil authentication. Overall, this review provides insights into the current state-of-the-art techniques and prospects for utilizing vibrational spectroscopy in the authentication of edible oils, thereby facilitating quality control and consumer protection in the food industry.

Polycyclic Aromatic Hydrocarbons' Impact on Crops and Occurrence, Sources, and Detection Methods in Food: A Review

Polycyclic aromatic hydrocarbons (PAHs) represent a category of persistent organic pollutants that pose a global concern in the realm of food safety due to their recognized carcinogenic properties in humans. Food can be contaminated with PAHs that are present in water, air, or soil, or during food processing and cooking. The wide and varied sources of PAHs contribute to their persistent contamination of food, leading to their accumulation within these products. As a result, monitoring of the levels of PAHs in food is necessary to guarantee the safety of food products as well as the public health. This review paper attempts to give its readers an overview of the impact of PAHs on crops, their occurrence and sources, and the methodologies employed for the sample preparation and detection of PAHs in food. In addition, possible directions for future research are proposed. The objective is to provide references for the monitoring, prevention, and in-depth exploration of PAHs in food.

Liquid Interfacial Coassembly of Plasmonic Arrays and Trace Hydrophobic Nanoplastics in Edible Oils for Robust Identification and Classification by Surface-Enhanced Raman Spectroscopy

The ubiquity of micro-/nanoplastics poses a visible threat to the environment, aquatic organisms, and human beings and has become a global concern. Here, we proposed a liquid interface-based strategy using surface-enhanced Raman spectroscopy to coassemble nanoplastics and gold nanoparticles into a dense and homogeneous plasmonic array, thereby enabling the rapid and sensitive detection of trace nanoplastics. In addition, due to the uniqueness of the oil-water immiscible two-phase interface, we achieved ideal results for the detection of nanoplastics in a complex matrix (e.g., aqueous environment and edible oil) with a detection limit of μg/mL. With the aid of the principal component analysis algorithm, the differentiation and identification of multiple nanoplastic components (e.g., polystyrene, polyethylene, and polyethylene terephthalate) in aqueous environments and common hazards (e.g., Bap and Phe) in edible oil were achieved. Therefore, our self-assembled plasmonic arrays are expected to be used for monitoring environmental pollution and food safety.

Incidents and Potential Adverse Health Effects of Serious Food Fraud Cases Originated in Asia

Food fraud has long been regarded as a major issue within the food industry and is associated with serious economic and public health concerns. Economically motivated adulteration, the most common form of food fraud, has consequences for human health, ranging from mild to life-threatening conditions. Despite the potential harm and public health threats posed by food fraud, limited information on incidents causing illness has been reported. Enhancing the food control system on the Asian continent has become crucial for global health and trade considerations. Food fraud databases serve as valuable tools, assisting both the food industry and regulatory bodies in mitigating the vulnerabilities associated with fraudulent practices. However, the availability of accessible food fraud databases for Asian countries has been restricted. This review highlights detrimental food fraud cases originating in Asian countries, including sibutramine in dietary supplements, plasticizer contamination, gutter oil, and the adulteration of milk. This comprehensive analysis encompasses various facets, such as incident occurrences, adverse health effects, regulatory frameworks, and mitigation strategies.

Molecularly imprinted fluorescence assay based on lead halide perovskite quantum dots for determination of benzo(a)pyrene

Molecularly imprinted polymers with methylammonium lead halide perovskite quantum dots (MIP@MAPbBr3 PQDs) have been prepared and applied to the determination of benzo(a)pyrene (BaP) for the first time. The photoluminescence (PL) of MIP@MAPbBr3 PQDs was enhanced due to the surface passivation of defects by BaP. PL excitation and emission spectra, X-ray diffraction, Fourier transform infrared, and time-resolved PL studies suggest that the interaction between MIP@MAPbBr3 PQDs and BaP is a dynamic process. After MIP@MAPbBr3 PQDs were incubated with BaP, the benzene ring in the molecular structure of BaP can interact with MIP@MAPbBr3 PQDs through π electrons, which reduces non-radiative recombination of MIP@MAPbBr3 PQDs and lengthens excited state lifetime. The PL intensity of the MIP@MAPbBr3 PQDs-BaP system was monitored at 520 nm with 375 nm excitation. Under optimized conditions, the PL intensity of MIP@MAPbBr3 PQDs is linear with the concentration of BaP in the 10 to 100 ng·mL-1 range, with a detection limit of 1.6 ng·mL-1. The imprinting factor was 3.9, indicating excellent specificity of MIP@MAPbBr3 PQDs for BaP. The MIP@MAPbBr3 PQDs were subsequently applied to the PL analysis of BaP in sunflower seed oil, cured meat, and grilled fish samples, achieving recoveries from 79.3 to 107%, and relative standard deviations below 10%. This molecularly imprinted fluorescence assay improves the selectivity of BaP in complex mixtures and could be extended to other analytes.

Monitoring the lipid oxidation and fatty acid profile of oil using algorithm-assisted surface-enhanced Raman spectroscopy

Deep-fat frying of food develops lipid oxidation products that deteriorate oil and pose a health risk. This necessitates the development of a rapid and accurate oil quality and safety detection technique. Herein, surface-enhanced Raman spectroscopy (SERS) and sophisticated chemometric techniques were used for rapid and label-free determination of peroxide value (PV) and fatty acid composition of oil in-situ. In the study, plasmon-tuned and biocompatible Ag@Au core-shell nanoparticle-based SERS substrates were used to obtain optimum enhancement despite matrix interference to efficiently detect the oil components. The potent combination of SERS and the Artificial Neural Network (ANN) method could determine the fatty acid profile and PV with upto 99% accuracy. Moreover, the SERS-ANN method could quantify the low level of trans fats, i.e., < 2%, with 97% accuracy. Therefore, the developed algorithm-assisted SERS system enabled the sleek and rapid monitoring and on-site detection of oil oxidation.

Determination of benzo(a)pyrene in peanut oil based on Raman spectroscopy and machine learning methods

Benzo(a)pyrene (BaP) generated in the production process of oil is harmful to human severely as a kind of carcinogenic substance. In this study, the qualitative and quantitative detection of BaP concentration in peanut oil was investigated based on Raman spectroscopy combined with machine learning methods. The glass substrates and magnetron sputtered gold substrates for the Raman spectra were compared and the data preprocessing methods of principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were used to process Raman signal. Back propagation neural network (BPNN), partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) algorithms were developed to obtain the qualitative and quantitative detection model of BaP concentration in peanut oil. The results showed that the Raman spectra with the glass substrate was more suitable for the BaP detection than magnetron sputtered gold substrates. RF combined with t-SNE could achieve an accuracy of 97.5% in the qualitative detection of BaP concentration levels in model validation experiment, and the correlation coefficient of the prediction set (R_p) in the quantitative detection was 0.9932, the root mean square error (RMSEP) was 0.8323 μg/kg and the bias was 0.1316 μg/kg. It can be concluded that Raman spectroscopy combined with machine learning methods could provide an effective method for the rapid determination of BaP concentration in peanut oil.

Detection of 1-OHPyr in human urine using SERS with injection under wet liquid-liquid self-assembled films of β-CD-coated gold nanoparticles and deep learning

1-Hydroxypyrene (1-OHPyr), a typical hydroxylated polycyclic aromatic hydrocarbon (OH-PAH), has been commonly regarded as a urinary biomarker for assessing human exposure and health risks of PAHs. Herein, a fast and sensitive method was developed for the determination of 1-OHPyr in urine using surface-enhanced Raman spectroscopy (SERS) combined with deep learning (DL). After emulsification, urinary 1-OHPyr was separated using simple liquid-liquid extraction. Gold nanoparticles with β-cyclodextrin (β-CD@AuNPs) were synthesized, and homogeneous and ordered β-CD@AuNP films were prepared through a liquid-liquid interface self-assembly process. The separated 1-OHPyr was injected under wet assembled films for SERS detection. Concentration as low as 0.05 μg mL^-1 of 1-OHPyr in urine could still be detected, and the relative standard deviation was 5.5 %, and this was ascribed to the adsorption of β-CD and the high-probability contact between 1-OHPyr molecules and the nanogap of assembled films under the action of capillary force. Meanwhile, a convolutional neural network (CNN), a classical DL network architecture, was adopted to build the prediction model, and the model was further simplified by genetic algorithm (GA). CNN combined with a GA obtained optimized results with determination coefficient and a root mean square error of prediction sets of 0.9639 and 0.6327, respectively, outperforming other models. Overall, the proposed method achieves fast and accurate detection of 1-OHPyr in urine, improves the assessment human exposure to PAHs and is expected to have applications in the analysis of other OH-PAHs in complex environments.

General approach to surface-accessible plasmonic Pickering emulsions for SERS sensing and interfacial catalysis

Pickering emulsions represent an important class of functional materials with potential applications in sustainability and healthcare. Currently, the synthesis of Pickering emulsions relies heavily on the use of strongly adsorbing molecular modifiers to tune the surface chemistry of the nanoparticle constituents. This approach is inconvenient and potentially a dead-end for many applications since the adsorbed modifiers prevent interactions between the functional nanosurface and its surroundings. Here, we demonstrate a general modifier-free approach to construct Pickering emulsions by using a combination of stabilizer particles, which stabilize the emulsion droplet, and a second population of unmodified functional particles that sit alongside the stabilizers at the interface. Freeing Pickering emulsions from chemical modifiers unlocks their potential across a range of applications including plasmonic sensing and interfacial catalysis that have previously been challenging to achieve. More broadly, this strategy provides an approach to the development of surface-accessible nanomaterials with enhanced and/or additional properties from a wide range of nano-building blocks including organic nanocrystals, carbonaceous materials, metals and oxides.

Construction, expression, purification, characterization, and structural analysis of microbial transglutaminase variants

Microbial transglutaminase (MTG, EC 2.3.2.13) derived from Streptomyces mobaraensis is widely used in the food and pharmaceutical industry because of its ability to synthesize isopeptide bonds between the proteinogenic side chains of glutamine and lysine. The half-life (t_1/2 ) of the activated wild-type enzyme at 60°C is 2 min. To improve the activity and thermostability of MTG for higher temperature application, three variants (Mut1, Mut2, and Mut3) were obtained by combining key amino acid mutations on the basis of previous research results. The best variant Mut2 with a specific combination of five of seven substitutions (S2P-S23V-Y24N-R215A-H289Y) shows a 10-fold increased half-life at 60°C (t_1/2  = 27.6 min), and a 2.4-fold increased specific enzyme activity (39.3 U/mg). As measured by circular dichroism, the curve of Mut2 was basically the same as that of MTG-WT. The structural simulation of Mut2 shows that the overall structure is discoid with a crack, but the crack openings are wider than that of MTG-WT. Furthermore, structural analysis of Mut2 showed that there were seven hydrogen bonds and one π-anion interaction between Mut2 and its adjacent amino acids, and the number of hydrogen bonds was one more than that of MTG-WT (six hydrogen bonds).

Fabrication of Ag@Au (core@shell) nanorods as a SERS substrate by the oblique angle deposition process and sputtering technology

Gold (Au) and silver (Ag) are the main materials exhibiting strong Surface-Enhanced Raman Scattering (SERS) effects. The Ag nano-rods (AgNRs) and Au nano-rods (AuNRs) SERS substrates prepared using the technology of the oblique angle deposition (OAD) process have received considerable attention in recent years because of their rapid preparation process and good repeatability. However, AgNR substrates are unstable due to the low chemical stability of Ag. To overcome these limitations, an Ag@Au core-shell nano-rod (NR) array SERS substrate was fabricated using the OAD process and sputtering technology. Moreover, simulation analysis was performed using finite-difference time-domain calculations to evaluate the enhancement mechanism of the Ag@Au NR array substrate. Based on the simulation results and actual process conditions, the Ag@Au core-shell NR array substrate with the Au shell thickness of 20 nm was studied. To characterize the substrate's SERS performance, 1,2-bis(4-pyridyl)ethylene (BPE) was used as the Raman probe. The limit of detection of BPE could reach 10-12 M. The Ag@Au NR array substrate demonstrated uniformity with an acceptable relative standard deviation. Despite the strong oxidation of the hydrogen peroxide (H2O2) solution, the Ag@Au NR array substrate maintains good chemical stability and SERS performance. And long-term stability of the Ag@Au NR substrate was observed over 8 months of storage time. Our results show the successful preparation of a highly sensitive, repeatable and stable substrate. Furthermore, this substrate proves great potential in the field of biochemical sensing.