A novel suitable TLC-SERS assembly strategy for detection of Rhodamine B and Sudan I in chili oil

Determination of fifteen illegal colorants in traditional Chinese medicines by two hydrophobic DES-based microextraction methods coupled with an HPLC-DAD

The dyeing and adulteration of traditional Chinese medicines (TCMs) are continuously updated. Valuable analytical methods for the daily inspection of illegal colorant additives in TCMs and the preparations are in demand. Two deep eutectic solvent (DES)-based vortex-assisted liquid-liquid microextraction (VA-LLME) and ultrasonic-assisted solid-liquid microextraction (UA-SLME) were developed for the sample pretreatment of ten water-soluble colorants and five water-insoluble colorants, respectively, followed by an HPLC-DAD detection. Fifteen colorants were analyzed at four detection wavelengths within 40 min of gradient elution. The optimal DES of VA-LLME and UA-SLME were screened from 23 homemade DESs. The factors affecting the extraction efficiency of VA-LLME and UA-SLME were optimized systematically. Under the optimal conditions, ten water-soluble colorants analyzed by DES-based VA-LLME-HPLC-DAD showed good linearity (R ≥ 0.9995) within the optimal linear range. The LODs and LOQs were 0.2-1.0 μg g-1 and 0. 5-5.0 μg g-1, respectively. The recoveries of spiked samples were 80.2%-104.7 %, with RSDs ≤ 4.39 %. Five water-insoluble colorants of Sudan I‒IV and Sudan 7B analyzed by DES-based UA-SLME-HPLC-DAD showed good linearity (R ≥ 0.9995) within the optimal linear range. The LODs and LOQs were 0.8-8.0 μg g-1 and 4.0-40.0 μg g-1, respectively. The recoveries of spiked samples were 94.2%-103.1 %, with RSDs ≤ 4.81 %. The proposed DES-based VA-LLME-HPLC-DAD was successfully applied to analyze six water-soluble yellow colorants in Cuscutae Semen, salted Cuscutae Semen, and four water-soluble red colorants in Schisandrae Chinensis Fructus. The proposed DES-based UA-SLME-HPLC-DAD was successfully applied to analyze five water-insoluble red colorants in Dieda pills. The study provides analytical method options for routine tests of water-soluble, water-insoluble, or both water-soluble/-insoluble illegal colorant additives in herbal medical materials and preparations by the relevant proposed DES-based sample pretreatment method or a combination of the two proposed DES-based methods.

Automated identification of pesticide mixtures via machine learning analysis of TLC-SERS spectra

Identification of components in pesticide mixtures has been a major challenge in spectral analysis. In this paper, we assembled monolayer Ag nanoparticles on Thin-layer chromatography (TLC) plates to prepare TLC-Ag substrates with mixture separation and surface-enhanced Raman scattering (SERS) detection. Spectral scans were performed along the longitudinal direction of the TLC-Ag substrate to generate SERS spectra of all target analytes on the TLC plate. Convolutional neural network classification and spectral angle similarity machine learning algorithms were used to identify pesticide information from the TLC-SERS spectra. It was shown that the proposed automated spectral analysis method successfully classified five categories, including four pesticides (thiram, triadimefon, benzimidazole, thiamethoxam) as well as a blank TLC-Ag data control. The location of each pesticide on the TLC plate was determined by the intersection of the information curves of the two algorithms with 100 % accuracy. Therefore, this method is expected to help regulators understand the residues of mixed pesticides in agricultural products and reduce the potential risk of agricultural products to human health and the environment.

Screening method for the analysis of Rhodamine B in chili powder

Rhodamine B is a synthetic dye known to enhance the visual appearance of chili powder. Due to its toxicity and carcinogenicity, chromatographic methods have been developed to monitor its presence in adulterated chili powder, but their assays are laborious, time consuming and expensive for screening purposes. The present studies propose an alternative for screening Rhodamine B in chili powder samples. The method combines thin layer chromatography (TLC) to solid surface room-temperature fluorescence spectroscopy. The scrape-dissolution procedure common to the instrumental analysis of TLC procedures was replaced with a fiber optic probe coupled to a commercial spectrofluorometer. The determination of Rhodamine B on the chromatographic plate is based on its retardation factor and maximum excitation and emission wavelengths. The limit of detection (1.9 ng.mL-1) and the limit of quantitation (5.2 ng.mL-1) are well below the usual contamination of Rhodamine B in adulterated foods.

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.

Thin layer chromatography coupled with surface enhanced Raman scattering for rapid separation and on-site detection of multi-components

The separation and detection of multi-component mixtures has always been a challenging task. Traditional detection methods often suffer from complex operation, high cost, and low sensitivity. Surface enhanced Raman scattering (SERS) technique is a high sensitivity, powerful and rapid detection tool, which can realize the specific detection of single substance components, but it must solve the problem that multi-component mixtures cannot be accurately determined. Thin layer chromatography (TLC) technology, as a high-throughput separation technology, uses chromatographic plate as the stationary phase, and could select different developing phases for separation experiments. The advantages of TLC technology in short distance and rapid separation are widely used in protein, dye and biomedical fields. However, TLC technology has limitations in detection ability and difficulty in obtaining ideal signal intensity. The combination of TLC technology and SERS technology made the operation procedure simple and the sample size small, which can achieve rapid on-site separation and quantitative detection of mixtures. Due to the rapid development of TLC-SERS technology, it has been widely used in the investigation of various complex systems. This paper reviews the application of TLC-SERS technology in food science, environmental pollution and biomedicine.

Aflatoxin detection technologies: recent advances and future prospects

Aflatoxins have posed serious threat to food safety and human health. Therefore, it is important to detect aflatoxins in samples rapidly and accurately. In this review, various technologies to detect aflatoxins in food are discussed, including conventional ones such as thin-layer chromatography (TLC), high performance liquid chromatography (HPLC), enzyme linked immunosorbent assay (ELISA), colloidal gold immunochromatographic assay (GICA), radioimmunoassay (RIA), fluorescence spectroscopy (FS), as well as emerging ones (e.g., biosensors, molecular imprinting technology, surface plasmon resonance). Critical challenges of these technologies include high cost, complex processing procedures and long processing time, low stability, low repeatability, low accuracy, poor portability, and so on. Critical discussion is provided on the trade-off relationship between detection speed and detection accuracy, as well as the application scenario and sustainability of different technologies. Especially, the prospect of combining different technologies is discussed. Future research is necessary to develop more convenient, more accurate, faster, and cost-effective technologies to detect aflatoxins.

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.

Separation of Amino Acids, Dyes, and Pigments Using Novel Pressurized Circular TLC Assembly for Secure Medical Imaging Applications

A novel pressurized flow system for circular thin-layer chromatography (PC-TLC) has been successfully established and employed for the separation of amino acids, dyes, and pigments for safe medical imaging applications. In this system, the mobile phase is applied to a regular TLC plate through the tube and needle of an intravenous infusion set. The needle was fused in a hole underneath the center of the plate, while the second side end of the tube was connected to a microburette containing the solvent. This new assembly proved itself better in terms of separation time (within 5 minutes) and controlled flow of the solvent and horizontal movement of analyte components over chromatograms with better separation and R _f values (glutamine: 0.26, valine: 0.44, phenylalanine: 0.60, chlorophyll a: 0.52, chlorophyll b: 0.43, xanthophyll: 0.18, carotenoid: 0.97, and pheophytin: 0.60) when a number of samples of amino acids, dyes, and pigments were separated by the developed apparatus and the conventional TLC procedure. The developed method was found distinctly rapid, precise, and eco-friendly (less solvent consuming) as compared to traditional ascending TLC.