Review of Thin-Layer Chromatography Tandem with Surface-Enhanced Raman Spectroscopy for Detection of Analytes in Mixture Samples

HPTLC sequentially coupled to UV and SERS: A cost-effective tool for confirmative identification and quantitation of sildenafil in falsified herbal products

The detection of falsified drugs usually requires multi-disciplinary analysis for confirmative identification. Among hyphenated techniques with high specificity detection, thin-layer chromatography coupled with surface-enhanced Raman spectroscopy (TLC-SERS) is an efficient choice, especially for herbal products with diversified matrix. In this study, HPTLC was coupled to two detection techniques: UV absorption and Raman scattering with silver colloid enhancement for the analysis of sildenafil adulterated in herbal products. With this approach, orthogonal UV and SERS spectral data was collected, so that confirmative results could be obtained within a single TLC analysis. How this approach helped to reduce chances of false positive or false negative results was also discussed. The HPTLC sequentially coupled to UV and SERS (HPTLC-UV-SERS) method was developed and validated parallelly on the UV and SERS signals. To improve the repeatability of the SERS signal, several analytical conditions were optimized, so that direct quantitation with TLC-SERS was feasible without chemometric data extrapolation. The determination was done with UV scanning at 304 nm for HPTLC and with SERS signal at 1580 cm-1 (excitation 633 nm). The TLC-SERS method had a detection limit of 1.65 ng/spot, 95 times lower than HPTLC method (157 ng/spot). The HPTLC-UV-SERS method was applied on 24 real herbal samples collected from the market, among which 3 real samples were positive to sildenafil, and quantitation results by UV and SERS were in consistency. Not only this method was proved feasible for practical applications, but the recommendations for TLC-SERS procedures could also be useful in TLC-SERS method development for other compounds.

Facile fluorescence detection of malachite green in fish using molecularly imprinted polymers doped CdTe quantum dots based system

Malachite green (MG) possesses high toxicity, therefore, the detection of MG in fish tissues is of vital importance. A novel core-shell MIPs doped CdTe quantum dots coated silica nanoparticles (CdTe-MIP/SiO2 NPs) were synthesized via a simple one-pot strategy. The materials were characterized carefully. The resulting CdTe-MIP/SiO2 NPs were coated on the thin layer chromatography plate, and coupled with miniaturized fluorimeter for fluorescence detection of MG in fish samples. The resulting CdTe-MIP/SiO2 NPs based system possessed good linearity (0.01 ∼ 20 μmol/L), high recoveries (98.36 %∼101.45 %) and low detection limit (3.7 nmol/L) for MG. Furthermore, CdTe-MIP/SiO2 NPs based system were employed to measure fish samples spiked with MG, meanwhile, HPLC was utilized to evaluate the accuracy and reliability. And the paired t-test was conducted to evaluate differences between fluorescence method and HPLC, P > 0.05 means no significant difference was observed, the results demonstrated that both fluorescence method and HPLC are suitable for MG analysis.

A solid-liquid microextraction approach from a spot in an adsorbent layer of a chromatographic plate

A new approach to extracting substances from a spot on a chromatographic plate for subsequent liquid chromatography-mass spectrometry analysis is described. This method involves extraction in a solid phase (an adsorbent layer of a chromatographic plate) - a liquid system using a simple device. For a single extraction of six selected coccidiostats from the adsorbent layer on the chromatographic plate with silica gel, 50 µL of methanol was used for 5 min. The data from the extraction experiments and liquid chromatography-mass spectrometry measurements demonstrated a good correlation between the ratio of the peak areas of the coccidiostats to the internal standard and the concentration of the substances in the range of two orders of magnitude. The coefficients of determination for the mentioned correlations range from 0.962 to 0.999. Moreover, the repeatability and reproducibility, expressed as the percentage values of relative standard deviation, do not exceed 7.5 % for any of the coccidiostats.

Identification and quantification of pigments in plant leaves using thin layer chromatography-Raman spectroscopy (TLC-Raman)

Carotenoids are yellow, orange, and red pigments commonly found in plants. In leaves, these molecules are essential for photosynthesis, but they also play a major role in plant growth and development. Efficiently monitoring concentrations of specific carotenoids in plant tissues could help to explain plant responses to environmental stressors, infection and disease, fertilization, and other conditions. Previously, Raman methods have been used to demonstrate a correlation between plant fitness and the carotenoid content of leaves. Due to solvatochromatic effects and structural similarities within the carotenoid family, current Raman spectroscopy techniques struggle to assign signals to specific carotenoids with certainty, complicating the determination of amounts of individual carotenoids present in a sample. In this work, we use thin layer chromatography-Raman spectroscopy, or TLC-Raman, to identify and quantify carotenoids extracted from tomato leaves. These quick and accurate methods could be applied to study the relationship between pigment content and a number of factors affecting plant health.

Quantitative monitoring ofloxacin in beef by TLC-SERS combined with machine learning analysis

Ofloxacin is one kind of quinolone antibiotic drugs, the abuse of ofloxacin in livestock and aquaculture may bring bacterial resistance and healthy problem of people. The illegally feeding cattle with ofloxacin will help it keep health, but the sedimentation of ofloxacin could bring problem in food safety. The accurate, simple and instant monitoring ofloxacin from beef by portable sensor was of vital issue in food quality. A simple and reliable method was proposed for instant and quantitative detecting ofloxacin in beef, in which the thin-layer chromatography (TLC) -surface-enhanced Raman scattering (SERS) spectroscopy was in tandem with machine learning analysis base one principal component analysis-back propagation neural network (PCA-BPNN). The TLC plate was composed with diatomite, that was function as the stationary phase to separate ofloxacin from beef. The real beef juice was directly casted onto the diatomite plate for separating and detecting. The directly monitor ofloxacin from beef was achieved and the sensitivity down to 0.01 ppm. The PCA-BPNN was used as reliable model for quantitative predict the concentration of ofloxacin, that shown superior accuracy compared with the traditional model. The results verify that the diatomite plate TLC-SERS combined with machine-learning analysis is an effective, simple and accurate technique for detecting and quantifying antibiotic drug in meat stuff to improve the food safety.

Dual-Mode Separation and SERS Detection of Carbaryl with PA-6/AuNRs@ZIF-8 Films

Appropriate separation and enrichment steps can enhance the performance of SERS assays. For rapid, in-situ detection of carbaryl, a novel PA-6/AuNRs@ZIF-8 film that can be applied to dual-mode separation and SERS detection, has been developed. In the film, PA-6 was used as a TLC substrate for the initial separation of the substance to be measured. ZIF-8 provides chemical enhancement in SERS as well as enrichment and secondary separation of the analytes. Utilizing this film, we have successfully implemented a TLC-SERS rapid detection scheme, resulting in a detection limit for carbaryl as low as 1 × 10-9 M in lake water in 15 min, which is significantly lower than existing standards. Additionally, the manufacturing cost of one PA-6/AuNRs@ZIF-8 film can be kept within the range of $0.20-$0.40 economically, presenting substantial financial advantages. The method is highly promising for pesticide detection as well as forensic in-situ testing.

Medicinal Plants against Viral Infections: A Review of Metabolomics Evidence for the Antiviral Properties and Potentials in Plant Sources

Most plants have developed unique mechanisms to cope with harsh environmental conditions to compensate for their lack of mobility. A key part of their coping mechanisms is the synthesis of secondary metabolites. In addition to their role in plants' defense against pathogens, they also possess therapeutic properties against diseases, and their use by humans predates written history. Viruses are a unique class of submicroscopic agents, incapable of independent existence outside a living host. Pathogenic viruses continue to pose a significant threat to global health, leading to innumerable fatalities on a yearly basis. The use of medicinal plants as a natural source of antiviral agents has been widely reported in literature in the past decades. Metabolomics is a powerful research tool for the identification of plant metabolites with antiviral potentials. It can be used to isolate compounds with antiviral capacities in plants and study the biosynthetic pathways involved in viral disease progression. This review discusses the use of medicinal plants as antiviral agents, with a special focus on the metabolomics evidence supporting their efficacy. Suggestions are made for the optimization of various metabolomics methods of characterizing the bioactive compounds in plants and subsequently understanding the mechanisms of their operation.