Surface-enhanced Raman spectroscopy method for classification of doxycycline hydrochloride and tylosin in duck meat using gold nanoparticles

Pseudo-Siamese network combined with label-free Raman spectroscopy for the quantification of mixed trace amounts of antibiotics in human milk: A feasibility study

The utilization of antibiotics is prevalent among lactating mothers. Hence, the rapid determination of trace amounts of antibiotics in human milk is crucial for ensuring the healthy development of infants. In this study, we constructed a human milk system containing residual doxycycline (DXC) and/or tetracycline (TC). Machine learning models and clustering algorithms were applied to classify and predict deficient concentrations of single and mixed antibiotics via label-free SERS spectra. The experimental results demonstrate that the CNN model has a recognition accuracy of 98.85% across optimal hyperparameter combinations. Furthermore, we employed Independent Component Analysis (ICA) and the pseudo-Siamese Convolutional Neural Network (pSCNN) to quantify the ratios of individual antibiotics in mixed human milk samples. Integrating the SERS technique with machine learning algorithms shows significant potential for rapid discrimination and precise quantification of single and mixed antibiotics at deficient concentrations in human milk.

A combined experimental and theoretical approach for doxycycline sensing using simple fluorescent probe with distinct fluorescence change in wide range of interferences

Overuse of doxycycline (DOXY) can cause serious problems to human health, environment and food quality. So, it is essential to develop a new sensing methodology that is both sensitive and selective for the quantitative detection of DOXY. In our current research, we synthesized a simple fluorescent probe 4,4'-bis(benzyloxy)-1,1'-biphenyl (BBP) for the highly selective detection of doxycycline by through fluorescence spectroscopy. The probe BBP displayed ultra-sensitivity towards doxycycline due to Forster resonance energy transfer (FRET). Fluorescence spectroscopy, density functional theory (DFT), 1H NMR titration, UV-Vis, and Job's plot were used to confirm the sensing mechanism. The charge transfer between the probe and analyte was further examined qualitatively by electron density differences (EDD) and quantitively by natural bond orbital (NBO) analyses. Whereas the non-covalent nature of probe BBP towards DOXY was verified by theoretical non-covalent interaction (NCI) analysis as along with Bader's quantum theory of atoms in molecules (QTAIM) analysis. Furthermore, probe BBP was also practically employed for the detection of doxycycline in fish samples, pharmaceutical wastewater and blood samples.

Raman spectroscopy coupled with chemometrics for identification of adulteration and fraud in muscle foods: a review

Muscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields.

Histidine-capped copper nanoclusters for in situ amplified fluorescence monitoring of doxycycline through inner filter effect

There is a significant need to accurately measure doxycycline concentrations in view of the adverse effects of an overdose on human health. A fluorescence (FL) detection method was adopted and copper nanoclusters (CuNCs) were synthesized using chemical reduction technology. Based on FL quenching with doxycycline, the prepared CuNCs were used to explore a fluorescent nanoprobe for doxycycline detection. In an optimal sensing environment, this FL nanosensor was sensitive and selective in doxycycline sensing and displayed a linear relationship in the range 0.5-200 μM with a detection limit of 0.092 μΜ. A characterization test demonstrated that CuNCs offered active functional groups for identifying doxycycline using electrostatic interaction and hydrogen bonds. Static quenching and the inner filter effect (IFE) resulted in weakness in the FL of His@CuNCs with doxycycline with great efficiency. This suggested nanosensor was revealed to be a functional model for simple and rapid detection of doxycycline in real samples with very pleasing accuracy.

Highly selective and sensitive determination of doxycycline integrating enrichment with thermosensitive magnetic molecular imprinting nanomaterial and carbon dots based fluorescence probe

Doxycycline (DOX), a typical tetracycline antibiotic, is widely used because of its excellent antibacterial activity. To develop effective method for DOX has attracted much more attention. Herein, a new detection technology integrating magnetic solid phase extraction (MSPE) based on thermosensitive magnetic molecularly imprinted polymers (T-MMIPs) and fluorescence spectrometry based on carbon dots (CDs) was established. Thermosensitive magnetic molecularly imprinted polymers (T-MMIPs) was designed for selective enrichment of trace DOX. The synthesized T-MMIPs showed excellent selectivity for DOX. The adsorption performance of T-MMIPs varied with temperature in different solvents, which could achieve the enrichment and rapid desorption of DOX. In addition, the synthesized CDs had stable fluorescent property and better water-solubility, and the fluorescence of CDs was significantly quenched by DOX due to the internal filtration effect (IFE). Under the optimized conditions, the method resulted in good linearity over the range from 0.5 to 30 μg L-1, and the limit of detection was 0.2 μg L-1. The constructed detection technology was validated with real water samples, and excellent spiked recoveries from 92.5 % to 105.2 % were achieved. These data clearly indicated that the proposed technology was rapid, highly selective, environmentally friendly, and possessed significant potential application and development prospects.

Quantitative detection of phenol red by surface enhanced Raman spectroscopy based on improved GA-BP

Phenol red (PR) is generally used as an acid-base indicator and a printing and dyeing colorant. When its content exceeds a certain concentration in water, it will cause great damage to the human body. Therefore, it is very important to detect the content of PR in water. The advantage of surface enhanced Raman scattering (SERS) is detecting samples quickly, non-destructive and high sensitivity without sample pre-treatment. SERS has attracted great attention in all fields of detection and analysis. In this paper, the method of attaching silver nanoparticles to metallic single-walled carbon nanotubes form carbon nanotubes/silver nanoparticles (CNTs/AgNPs) structure and then combining it with silica sheet is proposed. SERS substrate with silica/carbon nanotubes/silver nanoparticles (SiO₂/CNTs/AgNPs) composite structure has extremely high reinforcement effect. In the quantitative analysis of the detected substance, mathematical fitting or machine learning is used to find the relationship between the intensity of Raman signal and the concentration of the detected substance. The BP neural network optimized by genetic algorithm (GA-BP) is designed in this study. The weights of GA-BP to enhance the robustness of BP neural network, the method of adaptive learning rate and the number of hidden nodes is set to solve the problem that GA-BP is easy to fall into local optimum, thus establishing a quantitative analysis model of PR solution concentration. The model can detect different concentrations of PR solutions with high accuracy quickly, simply and sensitively. Finally, compared with other published quantitative models, GA-BP correlation coefficient R² determined by the training results of the model is 0.99996, and the root mean square error of the prediction is RMSEP = 0.002510, which is 0.0005 higher than the mathematical fitting method, it shows better performance. A reliable idea for the preparation of SERS substrate and online detection of PR concentration in water proposed in this study.

A novel nanostructured organic framework sensor for selective and sensitive detection of doxycycline based on fluorescence enhancement

It is critical for human health to develop sensitive and rapid analytical methods for detecting doxycycline (DOX) residues in food. This paper presents a novel metal-organic framework nanomaterial (Zn-MOF) based on dithiodiglycolic acid and its application in DOX detection by fluorescent probe method. Zn-MOF itself does not fluoresce. When DOX is added, the system exhibits strong fluorescence (100-fold) at 530 nm. The fluorescence intensity displayed an excellent linear relationship with DOX concentration with a detection limit of 2.7 nM. The reaction solution's fluorescence displayed a visible color shift from colorless to yellow that was concentration-dependent. A smartphone was used to detect DOX by recognizing the red, green, and blue values of the reaction solution and the corresponding test paper. The use of smartphones can speed up the detection process and streamline operations, offering a sensitive and visible method for the quantitative detection of DOX residues in actual samples. Interestingly, Zn-MOF can discriminate DOX from other tetracyclines with high selectivity. This material has been used successfully as a fluorescent probe to determine DOX in fish samples with an average spiked recovery of 94.6 % ∼ 95.1 %. The DOX levels in the measured perch samples were 1.25 ∼ 157 μg/kg. There are 2 batches of DOX exceeding the standard in 14 batches.

SERS determination and multivariate classification of antibiotics in chicken meat using gold nanoparticle-decorated electrospun PVA nanofibers

The fabrication of SERS substrate by gold nanoparticle-decorated polyvinyl alcohol electrospun nanofibers which has been used to detect trace sensing of two widely used poultry antibiotics doxycycline hydrochloride and enrofloxacin is demonstrated. The performance of the backscattered Raman signals from the proposed SERS substrate has been initially evaluated with two standard Raman active compounds namely malachite green and rhodamine-6G. The limit of detection of the proposed substrate is estimated to be 7.32 nM. Following this, the usability of the proposed SERS substrate has been demonstrated through the detection of the aforementioned antibiotics in chicken meat samples. The presence of antibiotics in chicken meat sample has been validated with the standard analytical tool of liquid chromatography-mass spectrometry and the results were compared with the proposed sensing technique. Further, principal component analysis has been performed to classify the antibiotics that are present in the field-collected meat samples.

Rapid Detection of Aspergillus flavus and Quantitative Determination of Aflatoxin B1 in Grain Crops Using a Portable Raman Spectrometer Combined with Colloidal Au Nanoparticles

Aspergillus flavus and Aflatoxins in grain crops give rise to a serious threat to food security and cause huge economic losses. In particular, aflatoxin B1 has been identified as a Class I carcinogen to humans by the International Agency for Research on Cancer (IARC). Compared with conventional methods, Surface-Enhanced Raman Scattering (SERS) has paved the way for the detection of Aspergillus flavus and Aflatoxins in grain crops as it is a rapid, nondestructive, and sensitive analytical method. In this work, the rapid detection of Aspergillus flavus and quantification of Aflatoxin B1 in grain crops were performed by using a portable Raman spectrometer combined with colloidal Au nanoparticles (AuNPs). With the increase of the concentration of Aspergillus flavus spore suspension in the range of 102–108 CFU/mL, the better the combination of Aspergillus flavus spores and AuNPs, the better the enhancement effect of AuNPs solution on the Aspergillus flavus. A series of different concentrations of aflatoxin B1 methanol solution combined with AuNPs were determined based on SERS and their spectra were similar to that of solid powder. Moreover, the characteristic peak increased gradually with the increase of concentration in the range of 0.0005–0.01 mg/L and the determination limit was 0.0005 mg/L, which was verified by HPLC in ppM concentration. This rapid detection method can greatly shorten the detection time from several hours or even tens of hours to a few minutes, which can help to take effective measures to avoid causing large economic losses.

Label-free Au NRs-based SERS coupled with chemometrics for rapid quantitative detection of thiabendazole residues in citrus

Citrus is a highly consumed fruit worldwide. However, the excessive use of thiabendazole (TBZ) pesticides during citrus cultivation poses a health risk to people. Hence, a rapid and quantitative method has been established for TBZ determination in citrus by coupling gold nanorods (Au NRs) based surface enhanced Raman scattering (SERS) coupling chemometrics. The results show that support vector machine (SVM) can distinguish TBZ residues of different orders of magnitude with 99.1667% accuracy and that genetic algorithm-partial least squares (GA-PLS) had the best performance in the quantitative prediction of TBZ residues (Rp² = 0.9737, RMSEP = 0.1179 and RPD = 5.85) in citrus. The limit of detection (LOD) was 0.33 μg mL^-1. Furthermore, the proposed method was validated by a standard HPLC method using t-test with no significant difference. Therefore, the proposed Au NRs-based SERS technique can be used for the rapid quantitative analysis of TBZ residues in citrus.