Separation and determination of basic orange II, acid orange II and auramine O in soybean products based on ionic liquid reverse micelle microextraction and ultra-high-performance liquid chromatography

Recent advances and applications of ionic covalent organic frameworks in food analysis

Ionic covalent organic frameworks with both crystallinity and charged sites have attracted significant attention from the scientific community. The versatile textural structures, precisely defined channels, and abundant charged sites of ionic COFs offer immense potential in various areas such as separation, sample pretreatment, ion conduction mechanisms, sensing applications, catalytic reactions, and energy storage systems. This review presents a comprehensive overview of facile preparation methods for ionic covalent organic frameworks (iCOFs), along with their applications in food sample pretreatment techniques such as solid-phase extraction (SPE), magnetic solid-phase extraction (MSPE), and dispersive solid-phase extraction (DSPE). Furthermore, it highlights the extensive utilization of iCOFs in detecting various food contaminants including pesticides, contaminants from food packaging, veterinary drugs, perfluoroalkyl substances, and poly-fluoroalkyl substances. Specifically, this review critically discusses the limitations, challenges, and future prospects associated with employing iCOF materials to ensure food safety.

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.

Theoretical predictions and experimental verifications of SERS detection in colorants

Synthetic colorants added during food processing not only fail to provide nutrients, but also can be harmful to human health when used in excess. To establish a simple, convenient, rapid and low-cost surface-enhanced Raman spectroscopy (SERS) detection method for colorants, an active surface-enhanced substrate of colloidal gold nanoparticles (AuNPs) was prepared in this study. The density functional theory (DFT) method of B3LYP with 6-31G(d) was applied to determine the theoretical Raman spectra of erythrosine, basic orange 2, 21 and 22, and to attribute their characteristic spectral peaks. The SERS spectra of the four colorants were pre-processed using local least squares (LLS) and morphological weighted penalized least squares (MWPLS), and multiple linear regression (MLR) models were established to quantify the four colorants in beverages. The results showed that the prepared AuNPs with a particle size of about 50 nm were reproducible and stable, with a good enhancement of the SERS spectrum of rhodamine 6G at 10^-8 mol L^-1. The theoretical Raman frequencies were in good agreement with the experimental Raman frequencies, and the peak position differences of the main characteristic peaks of the four colorants were within 20 cm^-1. The MLR calibration models for the concentrations of the four colorants showed relative errors of prediction (REP) of 2.97-8.96%, root mean square errors of prediction (RMSEP) of 0.03-0.94, R² of 0.973-0.999, and limits of detection of 0.06 μg mL^-1. The present method could be used to quantify erythrosine, basic orange 2, 21, and 22, revealing its wide range of applications in food safety.

Post-synthetic modification of a magnetic covalent organic framework with alkyne linkages for efficient magnetic solid-phase extraction and determination of trace basic orange II in food samples

Efficient magnetic solid phase extraction using covalent organic frameworks (COFs) can find important applications in food safety. In this work, a sulfonate-functionalized magnetic COF (Fe₃O₄@COF-SO₃Na) was synthesized by self-polycondensation of two-in-one monomer 1,6-bis(4-formylphenyl)-3,8-bis((4-aminophenyl) ethynyl)) pyrene (BFBAEPy) on the surface of aminated Fe₃O₄ and a thiol-yne click reaction. It was further adopted as an adsorbent for the efficient magnetic solid-phase extraction (MSPE) of basic orange II. The selective adsorption experiment indicated that it displayed selective adsorption ability to basic orange II due to the ion exchange, hydrogen bonds, and π-π interactions. Under the optimized conditions, the proposed MSPE method coupled with HPLC-DAD showed excellent linearity in the range of 0.05-0.5 µg/mL (R² = 0.9997) for basic orange II. The lower limits of detection (LODs) for basic orange II were 1.0-1.4 µg/L for three food samples: yellow croaker, paprika and dried bean curd. The recoveries were 90.1-98.8% with relative standard deviations (RSDs) below 4.2%. Therefore, this work provides an effective strategy to modify magnetic COFs as absorbents in MSPE. Due to the tunability of functional groups in thiol‑yne click reactions, the functional groups of magnetic COFs can be readily designed to enrich their multifunctional applications. Meanwhile, this work proposed a new method to detect trace amounts of basic orange II in food samples.

A review on recent advances in the applications of composite Fe3O4 magnetic nanoparticles in the food industry

Fe3O4 magnetic nanoparticles (MNPs) have attracted tremendous attention due to their superparamagnetic properties, large specific surface area, high biocompatibility, non-toxicity, large-scale production, and recyclability. More importantly, numerous hydroxyl groups (-OH) on the surface of Fe3O4 MNPs can provide coupling sites for various modifiers, forming versatile nanocomposites for applications in the energy, biomedicine, and environmental fields. With the development of science and technology, the potential of nanotechnology in the food industry has also gradually become prominent. However, the application of composite Fe3O4 MNPs in the food industry has not been systematically summarized. Herein, this article reviews composite Fe3O4 MNPs, including their properties, modifications, and physical functions, as well as their applications in the entire food industry from production to processing, storage, and detection. This review lays a solid foundation for promoting food innovation and improving food quality and safety.