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Detection and limit test of diethylene glycol and ethylene glycol impurities in syrup by HPTLC

Detection and limit test of DEG and EG impurities in syrup products

Introduction

In 2023, the Food and Drug Administration (FDA) issued guidance to alert pharmaceutical  manufacturers, compounders, repackers, and suppliers about the potential public health risks associated with diethylene glycol (DEG) and ethylene glycol (EG) contamination. This concern was underscored by multiple incidents reported in 2022 and 2023, where contaminated oral liquid drug products were identified in various countries. For instance, Indonesian health authorities detected DEG and EG in a propylene glycol excipient used in the production of oral liquid medications [1]. To mitigate such risks, the United States Pharmacopeia (USP) has established a safety limit for DEG and EG, requiring that their levels in excipients used for pharmaceutical formulations do not exceed 0.10 % as part of identity testing.

This guidance aims to facilitate the detection of DEG and EG-contaminated drug components, helping to prevent further poisoning incidents. The HPTLC method, adapted from a TLC approach [2], has been optimized to enhance sensitivity and reproducibility.

Sample preparation

1.0 g of syrup sample is dissolved in 10.0 mL of methanol, vortexed for 60 seconds, sonicated for 10 min, and centrifuged for 5 min at 5000 rpm. The supernatant is used as test solution.

Standard preparation

Reference solutions: 0.025, 0.0375, 0.05, 0.075 and 0.100 mg/mL of a 50:50 (m/m) DEG/EG mixture in methanol.

Chromatogram layer

HPTLC silica gel 60 F254 plates (Supelco) are used.

Sample application

Syrup samples and reference solutions are applied as 8.0 mm bands with the Automatic TLC Sampler (ATS 4), 15 tracks, band length 8.0 mm, distance from the left edge 20.0 mm, track distance 11.4 mm, distance from the lower edge 8.0 mm.

Chromatography

Plates are developed in the ADC 2 with chamber saturation (with filter paper) for 20 min and after activation at 33 % relative humidity for 10 min using a saturated solution of magnesium chloride, development with acetone – 25 % ammonia solution – toluene – water 85:1:5:9.5 (V/V) to a migration distance of 70 mm.

Post-chromatographic derivatization

After development, chemical derivatization is performed using potassium permanganate (KMnO4) reagent (dissolve 125 mg of sodium hydroxide, 9.9 g of sodium carbonate decahydrate, and 1.5 g of potassium permanganate in 200 mL of water). The plates are sprayed with 3.0 mL of derivatization reagent using the CAMAG Derivatizer (yellow nozzle, spraying level 3) and then dried in cold airflow for 4 min.

Documentation

Images of the plate are captured with the TLC Visualizer 2 in UV 254 nm (Detection A) prior to derivatization, and in white light reflection WR (Detection B) and white light reflection and transmission WRT (Detection C) after derivatization. Documentation is performed 30 minutes after the derivatization process.

Densitometry

Fluorescence measurement is performed with the TLC Scanner 4 and visionCATS at 520 nm (deuterium lamp), slit dimension 5.0 x 0.2 mm, scanning speed 20 mm/s (Detection D and without filter). Peak profiles from scanning densitometry (PPSD) were analyzed with visionCATS software 4.1.

Results and discussion

The results confirm the effective application of HPTLC in detecting excipients such as sorbitol, glycerol, diethylene glycol, ethylene glycol, and polypropylene glycol. The method yielded clear and reproducible chromatographic fingerprints, with the DEG/EG mixture exhibiting a unique RF value.

HPTLC fingerprints in UV 254 nm (A) prior to derivatization, white light in reflection (B) and white light in reflection and transmission (C) after derivatization. Track 1: UHM; track 2: sorbitol (1.0 mg/mL); track 3: glycerol (0.5 mg/mL); track 4: DEG/EG (0.1 mg/mL); track 5: polypropylene glycol (0.5 mg/mL); track 6: syrup 1 (100 mg/mL); track 7: syrup 2 spiked with 0.1 % DEG/EG (100 mg/mL).

When using profiles from scanning densitometry (PPSD) at 520 nm (detection D), the calibration curve was computed using a linear-2 function over a range of 50 ng to 200 ng. The limit of detection (LOD) for DEG/EG is 21 ng and the limit ofquantification (LOQ) is 45 ng.

5-point calibration curve of DEG/EG (D) (from left to right, 50 ng, 75 ng, 100 ng, 150 ng, 200 ng, red circles) generated from peak profiles from scanning densitometry (PPSD) at 520 nm.

Two syrup samples were spiked with a mixture of DEG/EG. Using the reference solution prepared at 0.1 mg/mL as the limit test, a single-point calibration curve (linear-1) can be applied.

Single-point calibration (D), reference solution (0.1 mg/mL, red circle), samples above limit (100 mg/mL, black circles).

Conclusion

By optimizing the method for HPTLC, the limit test set at 0.1 % of DEG/EG in raw material and oral liquid formulations is easily achieved and can even be lowered to 0.03 %.

Literature

[1] Food and Drug Administration, Testing of Glycerin for Diethylene Glycol, (2023)

[2] A Concise Quality Control Guide on Essential Drugs and other Medicines, Special Edition, Diethylene glycol and ethylene glycol as impurities in liquids for oral use, (2024)

[3] T. K. T. Do et al., J Chrom. A (2021) 1638

Further information on request from the authors.

Contact: Sonja Drobnjak, CAMAG, Sonnenmattstrasse 11, 4132 Muttenz, Switzerland, sonja.drobnjak@camag.com

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Tire particles assessed with HPTLC bioassays

Tire particles assessed with HPTLC bioassays

Alan Bergmann, Benoit Ferrari, and Etienne Vermeirssen at the Ecotox Centre (Dübendorf, Switzerland), Thibault Masset and Florian Breider at EPFL (École Polytechnique Fédérale, Lausanne, Switzerland), and William Dudefoi (currently Aquatox Solutions) and Kristin Schirmer at Eawag (Dübendorf, Switzerland). The group is funded by the World Business Council for Sustainable Development’s (WBCSD) Tire Industry Project (TIP) to investigate the source and mechanisms of biological effects of chemicals from tire particles, the accumulation and trophic transfer of chemicals and particles, and potential solutions. WBCSD’s TIP is a global chief executive officer–led initiative undertaken by leading tire manufacturing companies that drives research on potential human health and environmental impacts of tires throughout their life cycle. The study design, execution, interpretation, and manuscript preparation were conducted solely by the authors.

Introduction

Tire and road wear particles (TRWP) are formed by abrasion of tires with road surface. They currently make up a large proportion of primary microplastics released to the environment [1]. TRWP are of particular concern because of many unbound chemical additives of tire rubber and potential unknown impurities and reaction products, all of which may leach from the particles into the environment [2]. We applied HPTLC bioassays for estrogenicity (YES), genotoxicity (umuC), and bacterial luminescence inhibition (BLIT) to evaluate the toxicity of chemicals extracted and leached from lab-generated cryogenically-milled tire tread (CMTT).

HPTLC-bioassays are useful for (1) sensitive detection of biological activity compared to testing the whole mixture in microtiter plates, (2) revealing differences in the profile of toxicity (i.e. differences in the bands between samples, even if the total toxicity is similar), and (3) comparing to individual standard chemicals to include or exclude chemical candidates as potentially responsible for the toxicity. HPTLC-bioassays are limited in the quantitative assessment of total sample toxicity because of greater variability than microtiter versions. This, and our focus on detecting and identifying hazardous chemicals in higher-than-environmentally relevant concentrations of CMTT, mean our work is a qualitative evaluation of worse case scenarios.

Standard solutions

Eleven tire‐associated chemicals were purchased and prepared in methanol or acetone at 0.5 to 1 g/L. Positive controls for the bioassays (YES: 0.80 pg 17β-estradiol, umuC: 0.31 and 2.5 ng 4-nitroquinoline-N-oxide, BLIT: 630 ng caffeine and 65 ng 3,5-dichlorophenol) are applied as solutions in ethanol, acetone, or methanol, respectively.

Sample preparation

Tire tread is cut from new tires from three manufacturers then cryogenically milled into small particles (CMTT) [3]. Soxhlet extracts in dichloromethane and methanol, leachates into simulated fish digestion fluid, and elutriates into water and artificial sediment are created to 100 g/L CMTT. Leachates are further processed with liquid-liquid extraction to ethyl acetate:n-hexane 9:1 (V/V).

Chromatogram layer

HPTLC plates silica gel 60 (Merck), 20 × 10 cm area used.

Sample application

Up to 50.0 µL of sample solutions and 5.0 µL of standard solutions are applied as bands with the Automated TLC Sampler 4 (ATS 4), band length 6.0 mm, distance from the left edge 20.0 mm, track distance 12.0 mm, distance from the lower edge 10.0 mm.

Chromatography

For initial screening of samples in all bioassays, development with the Automated Multiple Development 2 (AMD 2): twice with methanol to the migration distance of 20.0‐mm, acetone to 40.0 mm, acetone – ethyl acetate 3:1 (V/V) to 50.0 mm, ethyl acetate to 60.0 mm, ethyl acetate – n‐hexane 2:1 (V/V) to 70.0 mm, ethyl acetate – n‐hexane 1:1 (V/V) to 80.0 mm. Atmospheric conditioning solution was 10.0 mL 25 % NH4OH in 200.0 mL distilled deionized water. After an initial screening, the AMD 2 was adjusted to improve separation of estrogenic signals clustered near the solvent front: twice with methanol to 20.0 mm, methanol – ethyl acetate 1:1 (V/V) to 40.0 mm, ethyl acetate – n‐hexane 1:1 (V/V) to 60.0 mm, ethyl acetate – n‐hexane 1:9 (V/V) to 80.0 mm.

Post-chromatographic derivatization

Yeast (YES) or bacteria (umuC and BLIT) suspensions were sprayed with the Derivatizer (red nozzle, level 6). For YES and umuC, a solution of 4-methylumberifferylgalactopyranoside (MUG) was sprayed after 3 or 2 hours of incubation at 30 or 37 °C, respectively.

Documentation

Responses of YES and umuC were documented with the Visualizer 2 in UV 366 nm.

Luminescence from the BLIT was recorded immediately after spraying bacteria with the Bioluminizer using 1 min exposures.

Densitometry

Optional: Fluorescence measurement (for umuC and YES) is performed with the TLC Scanner 4 and visionCATS at 366>/400 nm (mercury lamp), slit dimension 5.00 mm x 0.20 mm, scanning speed 20 mm/s.

Results and discussion

An image of tire extracts, digestates, and elutriates tested with the HPTLC-YES is shown. Estrogenic chemicals are present in all extracts and leachates of CMTT. Similar retention factors between some bands in the samples suggest that similar chemicals are responsible for estrogenicity, however the relative intensity of the bands varies.

High-performance thin-layer chromatography (HPTLC) bioassay images of tire particle extracts, digestates, and water/sediment leachates. (A) Yeast estrogen screen (YES), positive control: 17β-estradiol 4 pg. (B) umuC, positive control: 4-nitroquinoline-N-oxide 2.5 ng. (C) Bacterial luminescence inhibition test (BLIT), positive control: caffeine 625 ng. Cryogenically milled tire tread (CMTT) Soxhlet extract at 0.5 mg CMTT equivalent (YES), 1 mg (umuC), and 0.5 mg (BLIT). Aqueous samples (digestates, water, and sediment leachates and corresponding process controls) were extracted with liquid–liquid extraction into ethyl acetate:n-hexane prior to HPTLC bioassay testing and applied at 5 mg CMTT equivalent. Reprinted from Creative Commons Attribution 4.0 International [4].

A blank sample, representing background chemicals from the simulated digestive fluid was also estrogenic. The toxicity profile of the blank digestate shows which bands in the CMTT digestate originate from digestion materials, not CMTT. One band in the CMTT digestate at about RF = 0.7 is likely from the CMTT, and more may remain co-retained with the digestate background estrogens. We linked the blank estrogenicity to biologically sourced components of the digestates: porcine bile and pancreatin. HPTLC allowed us to distinguish some of this background from chemicals coming from CMTT. There may be additional CMTT chemicals obscured by the digestates background estrogenicity.

HPTLC-YES of artificial digestate and its components. HEPES (0.04 M), pancreatin (4 mg/mL in saline solution), porcine bile extract (10 mg/mL in electrolyte solution), and pepsin (5 mg/mL in 0.25 M HCl), were prepared individually approximately following Masset et al. Pancreatin, Porcine bile extract, and pepsin were passed through 0.45 ul PTFE filters to remove slight precipitation, then processed with LLE as described in the main text. Fifty uL of the extracts were applied to the HPTLC plates. Reprinted from Creative Commons Attribution 4.0 International [4].

Ten of eleven chemicals were active in at least one bioassay. Some of these chemicals had similar retention factors to bioactive bands from the CMTT samples. For example, some benzothiazoles and 6PPD were retained in the region RF 0.7-0.85. These results help implicate benzothiazoles as drivers of in vitro toxicity of CMTT to bacteria. However, confirmation of individual benzothiazoles as the responsible toxicants would be needed, possibly through chemical analysis of the active CMTT bands. Subsequently, any risk to organisms in the environment requires evaluation of environmentally relevant concentrations and quantitative toxicological analysis. Our work is an early step in the risk assessment process by expanding the understanding of potential hazards from tire particles, and prioritizing chemicals as potentially responsible for their effects.

Chemicals detected in cryogenically milled tire tread (CMTT) extracts and their bioactivity in high‐performance thin‐layer chromatography (HPTLC) bioassays

High-performance thin-layer chromatography–bacterial luminescence inhibition test of active single chemicals compared with cryogenically milled tire tread samples. Chemical amounts are nominally 0.55 μg (HBT), 0.50 μg (ABT), 5.15 μg (BT), 0.5 μg (MTBT), 0.66 μg (SBT), 0.7 μg (6PPD), 0.14 μg (MBTS), and 4.2 μg (ANI). See Figure 1 for representative negative and positive controls. CMTT = cryogenically milled tire tread; HBT = 2-hydroxybenzothiazole; ABT = 2-aminobenzothiazole; BT = benzothiazole; MTBT = 2-(methylthio) benzothiazole; SBT = 2-mercaptobenzothiazole; 6PPD = N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine; MBTS = 2-2′-dithiobisbenzothiazole; ANI = aniline. Reprinted from Creative Commons Attribution 4.0 International [4].

Literature

[1] Sieber, R.; Kawecki, D.; Nowack, B., Dynamic probabilistic material flow analysis of rubber release from tires into the environment. Environmental Pollution 2020, 258, 113573.

[2] Wik, A.; Dave, G., Occurrence and effects of tire wear particles in the environment – A critical review and an initial risk assessment. Environmental Pollution 2009, 157 (1), 1-11.

[3] Masset, T.; Ferrari, B. J. D.; Dudefoi, W.; Schirmer, K.; Bergmann, A.; Vermeirssen, E.; Grandjean, D.; Harris, L. C.; Breider, F., Bioaccessibility of Organic Compounds Associated with Tire Particles Using a Fish In Vitro Digestive Model: Solubilization Kinetics and Effects of Food Coingestion. Environmental science & technology 2022, 56 (22), 15607-15616.

[4] Bergmann, A. J.; Masset, T.; Breider, F.; Dudefoi, W.; Schirmer, K.; Ferrari, B. J. D.; Vermeirssen, E. L. M., Estrogenic, Genotoxic, and Antibacterial Effects of Chemicals from Cryogenically Milled Tire Tread. Environ Toxicol Chem 2024, 43 (9), 1962-1972.

Further information on request from the authors.

Contact: Dr. Alan Bergmann, Swiss Centre for Applied Ecotoxicology, Dübendorf, Switzerland, alanjames.bergmann@oekotoxzentrum.ch

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Machine learning for botanical identification

Machine learning for botanical identification

Nathan Stern, an analytical chemist at Amway / Nutrilite (Ada, USA), specializes in development and validation of various types of chromatographic test methods. He develops both quantitative (UHPLC) and qualitative (HPTLC) phytochemical tests that are designed to ensure ingredient quality and authenticity. He is also experienced in using machine learning techniques, especially as they apply to analytical chemistry.

Introduction

The primary goal of Nathan’s research was to develop a machine learning system to automate the evaluation of HPTLC-generated botanical fingerprints for the determination of botanical identity. The current industry approach involves manually comparing these HPTLC images with botanical reference materials of both authentic species and common adulterants. Machine learning techniques, such as machine vision, can enable faster and more accurate identification of botanicals.

The developed machine vision system has demonstrated high accuracy in correctly identifying Ginger and its closely related species or adulterants. It can evaluate and classify the correct species for any number of images in only a few seconds, significantly reducing analyst workload and enhancing confidence in botanical identification. Additionally, this software system was validated using two different approaches, showing that it is both accurate and robust.

HPTLC Image Data

All HPTLC images were obtained from the online and publicly available HPTLC Association Atlas repository. This includes 77 total image files for the following species: Alpinia officinarum, Boesenbergia rotunda, Kaempferia galanga, Kaempferia parviflora, Zingiber montanum, Zingiber officinale, and Zingiber zerumbet [2].

Sample preparation

To 1.0 g of each powdered sample 10 mL of methanol are added, followed by 10 minutes of sonication. The samples are centrifuged, and the supernatant is used as test solution [2].

Chromatogram layer

HPTLC plates silica gel 60 F254 (Merck), 20 × 10 cm are used.

Sample application

2.0 μL of sample and standard solutions are applied as bands with the Automatic TLC Sampler (ATS 4), 15 tracks, band length 8.0 mm, distance from the left edge 20.0 mm, track distance 11.4 mm, distance from lower edge 8.0 mm [2].

Chromatography

Plates are developed in the Automatic Developing Chamber (ADC 2) with chamber saturation (with filter paper) for 20 min and after activation for 10 minutes at a relative humidity of 33% using a saturated magnesium chloride solution, development with toluene – ethyl acetate 3:1 (V/V) to the migration distance of 70 mm (from lower edge), followed by drying for 5 min [2].

Post-chromatographic derivatization

The plates are derivatized using an anisaldehyde reagent prepared by adding 10 mL of acetic acid, 5 mL of sulfuric acid, and 0.5 mL of anisaldehyde to 85 mL of icecooled methanol. The derivatization reagent (3 mL) is sprayed using the Derivatizer (blue nozzle, level 3). The plates are heated at 100°C for 3 min and allowed to cool before detection [2].

Documentation

Images of the plates are captured with the TLC Visualizer 3 in UV 254 nm, UV 366 nm, and white light after development, and again after derivatization in UV 366 nm and white light.

Machine learning hardware and software

The machine vision model was created on a computer system utilizing an Nvidia GeForce RTX 3070 GPU for computation. The machine learning software system was implemented in Python using Visual Studio Code as the IDE and PyTorch as the machine learning framework. The machine vision system is comprised of several different neural networks, including a deep conditional generative adversarial network (DCGAN) made up of a discriminator and a generator, as well as a deep convolutional neural network (deep CNN).

The role of the DCGAN in the system is to augment the limited dataset by creating a large number of synthetic HPTLC images for each species, based on a partition of real HPTLC image data. This synthetic data was then used to train the deep CNN model, which was validated separately against both real and synthetic HPTLC image datasets.

Results and discussion

The machine vision system successfully generated realistic synthetic HPTLC images using DCGAN. These synthetic images were effectively employed to train a deep CNN, which demonstrated a high level of accuracy in botanical species identification.

HPTLC images for each of the evaluated botanical species – Zingiber officinale, Alpinia officinarum, Boesenbergia rotunda, Kaempferia galanga, Kaempferia parviflora, Zingiber montanum, and Zingiber zerumbet –were processed and classified.

The system demonstrated 98.7 % accuracy when tested on real HPTLC images, correctly classifying 76 out of 77 botanical samples. The only misclassified image was identified as Zingiber montanum instead of Kaempferia galanga.

Cropped, representative HPTLC images for each of the species that were evaluated for the machine learning system. From left to right: Zingiber officinale, Alpinia officinarum, Boesenbergia rotunda, Kaempferia galanga, Kaempferia parviflora, Zingiber montanum, and Zingiber zerumbet [1]

High-level overview of the general architecture of a deep CNN, similar to that used to create the botanical ID machine vision model [1].

Example of synthetic data as created by the DCGAN. Top image: 5×5 matrix of 25 DCGAN generated synthetic images; bottom image: 5×5 matrix of real HPTLC images [1].

Examples of classification and probability outputs as provided by the botanical ID machine learning system [1].

The synthetic dataset accuracy was recorded at 100 %, indicating that the CNN was capable of effectively learning and distinguishing features within the controlled dataset. To further validate system robustness, an additional test using a held-out validation dataset of real HPTLC images achieved 97.3 % classification accuracy.

Comparison between real and synthetic HPTLC images confirmed the high fidelity of the generated data. The system’s predictive capabilities were also assessed by outputting species probability scores for each classification, providing an additional measure of confidence in the machine vision model’s results.

Overall, this automated system demonstrated significant improvements in speed and accuracy for botanical species identification compared to traditional manual analysis methods, eliminating human subjectivity while maintaining high reliability.

Literature

[1] Stern N, Leidig J, Wolffe G. Proof of Concept: Autonomous Machine Vision Software for Botanical Identification. J AOAC Int. 2024 Nov 19. DOI: 10.1093/jaoacint/qsae091

[2] HPTLC Association, The International Atlas for Identification of Herbal Drugs, https://www.hptlc-association.org/atlas/hptlc-atlas.cfm

Further information on request from the authors.

Contact: Nathan Stern, Sciences Department – Innovation and Science, Amway Corp, 7575 Fulton St E, Ada, MI, USA, nathan.stern@amway.com

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Simultaneous determination of flavonoids and anthraquinones in seven Cassia species

Simultaneous determination of flavonoids and anthraquinones in seven Cassia species

Introduction

Cassia is a genus of the Fabaceae family, widely distributed in tropical and subtropical regions across the globe, with approximately 500 species reported [1]. It has a wide array of pharmacological applications, including purgation and laxatives [2]. Various parts of these species have been explored to date; however, some species have not been fully explored for their phytochemical attributes. Hence, the developed HPTLC method was applied for the chromatographic identification of flavonoids and anthraquinones in leaves and flowers, covering thirteen compounds with excellent separation. This method could further be applied for bioprospection to the other Cassia species and related botanicals [3].

HPTLC is one of the easiest ways to qualitatively and quantitatively determine phytochemicals present in various plant parts, raw materials, extracts, dietary supplements, nutraceuticals, and adulterants. It is a reliable, rapid, green, and low-cost technology used across numerous disciplines. Researchers have shown remarkable interest in HPTLC-based analysis over the past few decades.

Recent advancements in HPTLC instrumentation by CAMAG include the introduction of an automated derivatizer for uniform spraying of derivatizing reagents and a mass spectroscopy (MS) interface, which aligns with the MS system for molecular mass confirmation. In the present investigation, we report a simple, specific, and reliable separation method for the phytochemical fingerprinting of leaves and flowers of seven different Cassia species from India.

Standard solutions

The stock solution for each reference standard was separately prepared at a concentration of 1.0 mg/mL for isovitexin (1), and 0.5 mg/mL for the other standards: luteolin-7-O-glucoside (2), 3’,4’,7-trihydroxy isoflavone (3), 4’,7-dihydroxyflavone (4), luteolin (5), apigenin (6), kaempferol (7), rhein (8), biochanin A (9), emodin (10), obtusifolin (11), physcion (12), and chrysophanol (13).

Sample preparation

Two different plant parts from seven different Cassia species were extracted with methanol at a concentration of 50 mg/mL. Before concentrating, the lipid portion of the filtrate was removed using n-hexane. The resulting extract was used for further analysis.

Chromatogram layer

HPTLC plates silica gel 60 F254 (Merck) are used.

Sample application

2.0 µL of sample and standard solutions are applied as bands with the Automatic TLC Sampler (ATS 4), 13 tracks for standards, 15 tracks for samples, band length 8.0 mm, distance from the left edge 20.0 mm, track distance 11.4 mm, distance from the lower edge 8.0 mm.

Chromatography

Plates are developed in the ADC 2 with chamber saturation (with filter paper) for 20 min, and after activation at 33 % relative humidity for 10 min using a saturated solution of magnesium chloride, development with toluene – ethyl acetate – formic acid 55:42:6 (V/V) to the migration distance of 70 mm (from the lower edge), followed by drying for 5 min.

Post-chromatographic derivatization

The plate was immersed into NP-PEG reagent using the Chromatogram Immersion Device, immersion speed 3 cm/s and immersion time 6 s, dried for 30 s with cold air, and heated at 140°C for 30 min using the TLC Plate Heater.

Documentation

Images of the plate are captured with the TLC Visualizer in UV 366 nm.

Results and discussion

An accurate analysis plays acrucial role in natural product research and in fields involving complex mixtures. The selection of rapid and precise analytical techniques, such as HPTLC, has become increasingly popular and is now widely accepted by various food agencies and regulatory bodies. The HPTLC-based analysis provides authentic, precise, and reliable data, using a green methodology, saving both time and manpower.

In this investigation, we developed an HPTLC method that effectively resolved the complexity of the substances present in Cassia leaves and flowers. The separation revealed two distinct functional groups – flavonoids and anthraquinones – each exhibiting characteristic coloration.

The RF values for each analyte were as follows:

luteolin-7-O-glucoside (RF 0.12), 3’,4’,7- trihydroxyflavone (RF 0.28), 4,7 dihydroxyflavone (RF 0.36), luteolin (RF 0.38), apigenin (RF 0.44), kaempferol (RF 0.46), biochanin A (RF 0.61), rhein (RF 0.55), obtusifolin (RF 0.69), emodin (RF 0.67), chrysophanol (RF 0.79), and physcion (RF 0.77).

Detection was performed at UV 366 nm after development due to the fluorescence of the compounds, as the fluorescence quenching bands of most flavonoids are not visible under a 254 nm wavelength. After derivatization, the analytes appeared as brighter zones with different colors depending on their chemical structures.

The developed HPTLC method enabled the simultaneous qualitative identification of targeted analytes in multiple Cassia samples. This study illustrates that generating contrasting chemical fingerprints facilitates taxonomic

Chromatograms of reference standards (track 1-13) before derivatization at 366 nm (A) and after derivatization with NP-PEG reagent at 366 nm (B) and samples (track 1-16) before derivatization at 366 nm (C) and after derivatization with NP-PEG reagent at 366 nm (D).

Literature

[1] Lodha S.R. et al. J Adv Pharm Technol Res 1(3), (2010) 330–333.

[2] Verma L. et al. (2010) Indian J Pharmacol 42(4), (2010) 224–228.

[3] Girme A, Saste G, Kureshi AA, Jagtap S, Kamble S, Wadye SD, Hingorani L. Integrated Multiplatform Analysis and Separation of Thirteen Flavonoids and Anthraquinones in Seven Medicinal Cassia Species. J AOAC Int. 2024 Jul 4;107(4):714-726. doi: 10.1093/jaoacint/qsae028. PMID: 38648754

Further information on request from the authors.

Contact: Ganesh Saste, Analytical Development and Innovation Center, Pharmanza Herbal Pvt. Ltd., Plot # 214, Borsad-Tarapur Road, Nr. Vadadla Patiya, At and PO: Kaniya-388430, Ta: Petlad, Dist: Anand (Gujarat) India, ard@pharmanzaherbals.com

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HPTLC for quality differentiation of functional mushrooms

Nammex specializes in the production of high-quality, certified organic mushroom extract powders for the food and dietary supplement (DS) industries. As a result of the rapid growth of the functional mushroom market, we have observed the introduction of many new products of varying quality. Nammex has a long-standing history of leading the industry in product analysis, with a focus on ensuring product authenticity and efficacy [1]. Our laboratory has developed an innovative HPTLC method for the identification and quality control testing of diverse species used in DS products. With this method, we aim to enhance the overall reliability and transparency of quality testing in the industry.

Introduction

The functional mushroom market is experiencing significant growth, driven by factors like increased DS usage and ongoing medical research. Despite the market’s size, only one validated HPTLC mushroom identification method has been published (USP Ganoderma lucidum monograph) [2], and its indiscriminate use across other species may lead to misidentification, undermining the reliability of the identification process and creating a need for more comprehensive testing solutions.

HPTLC is widely recognized for its effectiveness in botanical identification, making it an ideal method for mushroom analysis. In the absence of validated methods, consumers risk exposure to mislabeled or adulterated products. For instance, products containing tempeh-like mycelium (i.e. vegetative body) fermented grain are often marketed as mushrooms (i.e. fruiting bodies) despite significant compositional differences. Additionally, concentrated mushroom extracts may be deficient in specific marker compounds due to processing conditions.

HPTLC offers a robust, highly selective approach for mushroom differentiation. This new method ensures that characteristic compounds from diverse chemical classes in mushrooms are clearly separated, supporting accurate species identification. The advantages of HPTLC in this context include its specificity, versatility, and ability to detect adulteration in complex products.

Standard solutions

Standard stock solutions are prepared at 0.5 mg/mL in methanol.

Sample preparation

Samples consist of 250 mg of mushroom extract powder or finely milled whole mushrooms. These are extracted in 5.0 mL of methanol, vortexed for 10 s, sonicated for 10 min at room temperature, and centrifuged at 3500 rpm for 10 min. The supernatant is then transferred to vials.

Chromatogram layer

HPTLC plates silica gel 60 F254 Premium Purity (Supelco, Merck), 20 × 10 cm are used.

Sample application

10.0 μL of sample solutions and 2.0 μL of standard solutions are applied as bands with the Automatic TLC Sampler (ATS 4), 15 tracks, band length 8.0 mm, distance from the left edge 20.0 mm, track distance 11.4 mm, distance from the lower edge 8.0 mm.

Chromatography

Plates are developed in the ADC 2, with chamber saturation (with filter paper) for 20 min and after activation at 33 % relative humidity for 10 min using a saturated magnesium chloride solution, development with toluene – methanol – and acetic acid 85:10:5 (V/V) to the migration distance of 70 mm (from the lower edge), followed by drying for 5 min.

Post-chromatographic derivatization

The plates are immersed into p-anisaldehyde sulfuric acid reagent using the Chromatogram Immersion Device (immersion speed: 5 cm/s, immersion time: 0 s). After derivatization, the plates are heated at 100 °C for 4 min using the TLC Plate Heater.

Documentation

Images of the plates are captured with the TLC Visualizer 3 in UV 254 nm, UV 366 nm, and white light after development, and again after derivatization in UV 366 nm and white light.

Results and discussion

The high selectivity of the HPTLC method is demonstrated through distinct chromatographic fingerprints obtained for each species. These fingerprints display characteristic bands under multiple detection modes, providing a reliable means of differentiating between species and product types such as mushroom extracts and mycelia fermented grain powders.

Key marker compounds for each species were identified through literature, playing a critical role in distinguishing between the mushroom and the mycelium. Specifically, the mushroom is known to exhibit a different profile of compounds than the mycelium. HPTLC comparisons of mushroom extracts, supported by these chemical markers, effectively demonstrate these differences.

HPTLC comparisons between Chaga conk, pure mycelium, and fermented grain forms reveal significant compositional differences, with fermented grain fingerprints closely matching grain reference materials. Importantly, Chaga triterpenoid markers are absent in fermented grain, which instead shows high concentrations of triglycerides and linoleic acid. These chromatograms highlight the clear differences between Chaga conk, 1:1 extract, brown rice and oats, and fermented grain products, underscoring HPTLC’s effectiveness in detecting potential adulteration and verifying product authenticity.

While fermented grain products are expected to contain grain, the lack of sufficient mycelium or relevant compounds, along with unclear labeling practices, raises concerns about product authenticity. Many fermented grain products prominently display “mushroom” on the front label, along with images of mushrooms, but only disclose their myceliated grain content on the back, with some brands failing to identify the grain entirely. This inconsistency in labeling, coupled with the compositional differences identified through HPTLC, underscores the urgent need for more transparent and stringent quality control measures in the mushroom supplement industry.


  • hptlc-for-quality-differentation-of-functional-mushrooms-fig1

    01

    HPTLC chromatograms of whole mushroom, conk, or sclerotium vouchers from 12 species, highlighting compositional differences between species under various detection modes. Images after derivatization are shown in UV 366 nm (A) and white light (B). Chromatograms captured after development are displayed in white light (C) and 254 nm UV light (D).

  • HPTLC chromatograms at UV 254 nm of the crude product (10 g/L, 1 μL versus 100 g/L,15 μL) and mass spectra (left) versus 1H NMR spectra of isomeres (right)

    02

    HPTLC comparisons between Chaga conk voucher and fermented grain forms reveal significant compositional differences, with fermented grain fingerprints closely matching grain reference materials. Key Chaga marker compounds – such as inotodiol (

In conclusion, the development of the innovative HPTLC method for the differentiation of functional mushrooms offers a significant advancement in ensuring product authenticity and quality within the growing mushroom supplement market. By providing clear, reliable chromatographic fingerprints for various species, this method enhances the ability to detect adulteration and verify product composition, particularly in distinguishing between mycelia fermented grain-based products marketed as mushrooms. As the market continues to expand, the implementation of robust, transparent quality control measures like HPTLC will be critical in maintaining consumer trust and safeguarding product efficacy.

Literature

[1] Chilton, Jeff. White Paper. Redefining Medicinal Mushrooms: A New Scientific Screening Program for Active Compounds. Nammex, 2015. jeff@nammex.com

[2] United States Pharmacopeia (USP). Ganoderma lucidum Fruiting Body Monograph. USP 43-NF 38, United States Pharmacopeial Convention, Rockville, MD, 2020.

Further information on request from the authors.

Contact: Coleton Windsor, Nammex, Box 1780, Gibsons, British Columbia, Canada, coleton@nammex.com

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Developing HPTLC identification methods for pharmacopoeia monographs

For the past 20 years, CAMAG Laboratory has been a key contributor to pharmacopoeias worldwide, developing identification methods for botanicals, herbal drugs, and extracts. As a pioneer in standard-setting efforts, Dr. Eike Reich played a crucial role as an HPTLC expert in numerous pharmacopoeia committees. Now, as he transitions into retirement, Dr. Reich passes the torch to Dr. Tiên Do and her team, who continue to advance this important work.

Introduction

To effectively support the pharmacopoeia committees, all members of the laboratory undergo extensive training in working with standardized methodologies. Delivering HPTLC methods tailored to the specific requirements of pharmacopoeias involves more than just standardized HPTLC; each scientist must also understand and follow a general method development process. This process encompasses several key stages, illustrated in this paper using the European Pharmacopoeia (Ph. Eur.) monograph on Epimedium leaf as an example.

As the preferred chromatographic technique for the identification of herbal drugs, HPTLC aims to determine a characteristic chromatogram (fingerprint) based on the relative position, color, and intensity of specific zones. According to Ph. Eur., HPTLC must adhere to the Ph. Eur.’s general chapter 2.8.25., which specifies all steps and parameters of the HPTLC process. This document describes in detail the specific points relevant to the development of an HPTLC identification method.

Discussion

Developing a suitable identification method involves several steps:

Step 1: definition

The scope of the method must clearly specify the article (e.g. the medicinal plant) to be identified. In addition to the Latin plant name, the definition should include the accepted plant part(s) and the process by which the article is obtained (drying, cutting, extracting, etc.). Ideally, an identification method is specific for the article of the monograph and distinguishes related articles that may be considered adulterants.

Various monographs on “Epimedium” target the whole or fragmented dried leaf or herb of several species (see table) according to availability in different markets. The Ph. Eur. monograph on Epimedium leaf includes whole or fragmented dried leaf of the major species E. koreanum Nakai, E. brevicornum Maxim., and E. pubescens Maxim., including mixtures thereof.

Acceptance criteria for the herbal drug “Epimedium leaf” must include the selected drugs and exclude all others (e.g. E. sagittatum).

Step 2: collection of samples

Samples of different origins and related species are collected by the pharmacopeia group and distributed to various collaborating laboratories. Each laboratory also collects its own samples. A wide range of samples is crucial to ensure that the method is applicable to routine analysis of market samples.

Step 3: development / evaluation of HPTLC method(s)

Using standard HPTLC conditions, methods from pharmacopoeias are evaluated for reproducibility, practicality, and fitness for purpose. Other methods can also be considered. For Epimedium leaf, several methods have been proposed, each with specific advantages and limitations.

A first proposal was made to the Traditional Chinese Medicine (TCM) Working Party by the Shanghai Institute for Materia Medica (SIMM), using water, formic acid, n-butanol, ethyl acetate 1:1:3:6 (V/V/V/V) as developing solvent. During the peer review in our laboratory, the RF values were lower and the colors of zones slightly different.

This prompted us to optimize sample preparation, developing solvents, and detection, based on a previously established method for separation of flavonoids, using ethyl acetate – formic acid – water 8:1:1 (V/V) and derivatization with NP/PEG reagents.

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  • method-development-monograph-fig1

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    Evaluation of the first proposal

  • method-development-monograph-fig2

    02

    Second proposal

In parallel, a third method with good reproducibility, using ethanol – ethyl acetate – water 2:1:8 (V/V) was developed for consideration by the United States Pharmacopoeia by the Korean group led by Prof. Jang (Kyung Hee University). For compliance with Ph. Eur. Chapter 2.8.25, we included a System Suitability Test (SST) and intensity markers.

method-development-monograph-fig3

Third proposal

Third proposal

Step 4: method selection and acceptance criteria definiton

In several iterations, the experts compare the submitted proposals and reach agreement on the most suitable one. With this method, multiple samples are analysed, and the results are described in table format. The data is included in the monograph and published for public comment. In the case of Epimedium leaf, species can be clearly discriminated. The result table describes only the features common to the species covered by the monograph.

method-development-monograph-fig4

Data included in the monograph and published for public comment

Data included in the monograph and published for public comment

Step 5: public comments and finalization of method

Comments received from various stakeholders are reviewed by the expert committee before the monograph is presented to the pharmacopoeia commission for adoption. After publication in the Ph. Eur., the HPTLC fingerprints are shown in the EDQM knowledge database.

For CAMAG Laboratory, the involvement in the development and refinement of HPTLC methods not only contributes to global pharmacopoeia standards but also strengthens the scientific rigor and consistency in the identification of herbal drugs. The ongoing collaboration with international groups ensures that these methods are both practical and scientifically sound.

Further information on request from the authors.

Contact: Dr. Tiên Do, Sonnenmattstrasse 11, 4132 Muttenz, Switzerland, tien.do@camag.com

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Oil adulteration evaluation using HPTLC

The research team at Nestlé Research in Lausanne, Switzerland, develops innovative solutions for food quality and authenticity. Their work, particularly in detecting adulteration in edible oils, plays a key role in ensuring the authenticity of the global food supply chain. By employing advanced chromatographic techniques, the team enhances analytical methods, making a significant contribution to food quality and authenticity. Tiên Do from CAMAG collaborated on this project, contributing to the development of the methods.

Introduction

The evaluation of edible oil authenticity has become increasingly important due to rising incidents of oil adulteration, where low-quality or non-edible oils are mixed with premium oils for economic gain. Such fraudulent practices not only erode consumer trust but also pose health risks. As adulteration methods become more sophisticated, reliable and efficient detection methods are needed.

This study evaluates the use of HPTLC as a cost-effective and efficient tool for monitoring oil authenticity. Both untargeted (fingerprint profiling) and targeted (mineral oil detection) methods were applied to palm, sunflower, and rapeseed oils, demonstrating the capability to detect adulteration at levels between 5% and 25%.

HPTLC offers numerous advantages, including the ability to analyze multiple samples simultaneously with lower solvent consumption. It is also adaptable to different detection protocols and highly reproducible across laboratories. As a result, HPTLC is positioned as an ideal method for industrial applications requiring rapid and user-friendly solutions for oil quality monitoring.

Sample preparation

Edible oils, including sunflower, rapeseed, and palm oil, were collected from various suppliers and prepared for analysis. Authentic oil batches were diluted using cyclopentyl methyl ether (CPME) as the solvent (25.0 μL of oil in 3.0 mL of CPME). The samples were vortexed for 5 seconds, and 1.0 mL of the resulting solution was transferred to a vial for single-use analysis.

Chromatogram layer

HPTLC silica gel 60 F254 plates (Merck) were used for vegetable oil analysis, while RP18 F254 plates (Merck) were employed for mineral oil adulteration detection. For mineral oil method, the plates were prewashed with methanol and heated at 110 °C for 15 minutes before application.

Sample application

Oil samples were applied as 6.0 mm bands onto the plates using an Automatic TLC Sampler 4.

Chromatography

Plates were developed in the ADC 2 to a migration distance of 70 mm for edible oils and 30 mm for mineral oil detection. A mixture of acetonitrile and CPME (7:3 V/V) was used as the developing solvent for vegetable oils, and cyclohexane was used for mineral oil detection. Relative humidity was adjusted to 33% for 10 minutes only for the edible oil method, and chamber saturation was maintained for 20 minutes for both methods.

Post-chromatographic derivatization

After development, chemical derivatization was performed using anisaldehyde reagent for edible oils and primuline reagent for mineral oils. The plates were sprayed with the respective derivatization reagent using the Derivatizer. In the case of anisaldehyde reagent the plates were heated at 100 °C for 3 minutes, and after primuline at 40 °C for 3 min.

Documentation

The plates were documented using the TLC Visualizer 2 at UV 366 nm for mineral oils after derivatization with primuline, and in white light (transmission) for edible oils after derivatization with anisaldehyde reagent. Peak profiles from images (PPIs) were analyzed with the visionCATS software, and peak heights were recorded to assess the presence of adulterants.

Data analysis

Statistical analysis was conducted to assess batch variability and adulteration detection. The peak heights from RF values ranging between 0.2 and 0.8 were used to evaluate oil authenticity. The detection limit for adulteration was established at 5% for both edible oils and mineral oils.

Results and discussion

The results demonstrate the successful application of HPTLC in detecting adulteration in edible oils. The method provided clear and reproducible chromatographic fingerprints for sunflower, rapeseed, and palm oils. Each oil type exhibited unique RF values, enabling the differentiation of authentic oils from adulterated ones.

oil-adulteration-evaluation-using-hptlc-1

Fingerprints of tested oils with corresponding RF (represented with a red line), HPTLC plate in white light (transmission) after derivatization with anisaldehyde reagent; sunflower oil (A), rapeseed oil (B), and palm oil (C); (https://creativecommons.org/licenses/by/4.0/legalcode)

Fingerprints of tested oils with corresponding RF (represented with a red line), HPTLC plate in white light (transmission) after derivatization with anisaldehyde reagent; sunflower oil (A), rapeseed oil (B), and palm oil (C); (https://creativecommons.org/licenses/by/4.0/legalcode)

The following HPTLC chromatograms reveal the detection of adulteration in sunflower oil. Samples adulterated with cotton, safflower, corn, sesame, and soy oils were analyzed, and the corresponding RF values for each adulterant are marked with dashed lines. Adulteration was detected at RF values specific to each adulterant, such as RF 0.38 for cotton oil and RF 0.49 for sesame oil. The clear distinction between authentic and adulterated sunflower oil samples demonstrates the sensitivity of the HPTLC method, which successfully detected adulteration at levels as low as 5%.

oil-adulteration-evaluation-using-hptlc-2

HPTLC chromatograms in white light (transmission) after derivatization with anisaldehyde reagent: Sunflower oil adulterated with cotton oil (A), safflower oil (B), corn oil (C), sesame oil (D), and soy oil (E) with the corresponding adulteration RF’s (represented with a dash lines); (https://creativecommons.org/licenses/by/4.0/legalcode)

HPTLC chromatograms in white light (transmission) after derivatization with anisaldehyde reagent: Sunflower oil adulterated with cotton oil (A), safflower oil (B), corn oil (C), sesame oil (D), and soy oil (E) with the corresponding adulteration RF’s (represented with a dash lines); (https://creativecommons.org/licenses/by/4.0/legalcode)

Adulteration was detected at RF values around 0.8 for mineral oil and paraffin wax, clearly distinguishing them from the authentic palm oil sample. The high sensitivity of the HPTLC method allowed for the detection of adulteration at levels below 5%, demonstrating its effectiveness in identifying hazardous non-edible oil contaminants such as mineral oils.

oil-adulteration-evaluation-using-hptlc-3

HPTLC chromatograms in UV 366 nm after derivatization with primuline reagent: Palm oil adulterated with mineral oil (A) and paraffin wax (B); (https://creativecommons.org/licenses/by/4.0/legalcode)

HPTLC chromatograms in UV 366 nm after derivatization with primuline reagent: Palm oil adulterated with mineral oil (A) and paraffin wax (B); (https://creativecommons.org/licenses/by/4.0/legalcode)

Adulteration was detected at RF values around 0.8 for mineral oil and paraffin wax, clearly distinguishing them from the authentic palm oil sample. The high sensitivity of the HPTLC method allowed for the detection of adulteration at levels below 5%, demonstrating its effectiveness in identifying hazardous non-edible oil contaminants such as mineral oils.

Conclusion

HPTLC proved to be a valuable tool for detecting adulteration in edible oils, offering a high-throughput, reliable, and relatively simple method. The method is well-suited for industrial applications, ensuring food quality and authenticity in the global edible oil market.

Literature

[1] Paul Rogeboz et al. Food Analytical Methods (2024) 17:1336–1347

Further information on request from the authors.

Contact: Paul Rogeboz, Société des Produits Nestlé SA, Nestlé Research, 1000, Lausanne, Vers-Chez-Les-Blanc, Switzerland, paul.rogeboz@rd.nestle.com

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HPTLC fingerprint profiling for determination of bioactive ingredients in Indian propolis

Sandeep Sankaran*, PhD Scholar from the Department of Quality Assurance Techniques at Poona College of Pharmacy, BVDU, carried out his research work focusing on the systematic evaluation of the chemical profile and its correlation to neuroprotective activity for Indian bee propolis. The research team under the supervision of Dr Sathiyanarayanan worked comprehensively on deriving the chemical profile of Indian propolis extracts through the HPTLC fingerprinting methodology developed inhouse, extending to marker-based standardization and HPTLC-effect-directed analysis.

Introduction

Bee propolis is a valuable yet often neglected therapeutic resource made up of a combination of plant resins gathered during foraging, mixed with the bees’ own salivary secretions deposited in the beehives. The chemical composition is highly heterogeneous and depends on the vegetation in and around the hive, climatic conditions, and the bee species. Various analytical techniques have been used to evaluate the quality of propolis, including the use of high-end instruments in combination with chemometric modeling for deriving the complete chemical profile. However, these methods are costly and hard to replicate in quality control labs. A more feasible approach is to standardize based on markers that correlate with the specific biological activity of that propolis variant. The present study was therefore designed to focus on fingerprint profiling for identifying the propolis type, screening for the antioxidant and anticholinesterase components directly on the plate through a new developed, validated and sustainable HPTLC methodology.

To identify the propolis type, a simplified, rapid, low-cost, low-environmental impact, and easily adoptable analytical methodology was developed, extending to the standardization of selected neuroprotective components in Indian propolis. The versatility of HPTLC, with various derivatizing reagents and orthogonal detection capabilities, allows for increased applications. With the advent of thin-layer chromatography-effect directed analysis, it enables direct screening on the TLC plate, establishing preliminary evidence of the biological activities. Thus, this HPTLC method is valuable for rapid chemical profiling and simultaneous screening of antioxidant and anticholinesterase activities of Indian propolis. Also, educating beekeepers about its medicinal value can help them generate additional revenue.

Standard solutions

Stock solutions (1.0 mg/mL) are prepared in methanol, except dimethyl sulfoxide was used for initial solubilization of chrysin. The subsequent working solutions are prepared in methanol, i.e., chrysin (0.10 mg/mL), p-coumaric acid (0.05 mg/mL), pinocembrin (0.10 mg/mL), luteolin (0.10 mg/mL), and galangin (0.20 mg/mL).

Sample preparation

Indian propolis extracts and the marketed samples (2.0 mg/mL or 3.0 mg/mL) are prepared by weighing 20.0 mg or 30.0 mg and dissolving in 10.0 mL of ethanol. The samples are sonicated, centrifuged and filtered before TLC analysis.

Chromatogram layer

HPTLC plates silica gel 60 F254 (Merck), 20 x 10 cm are used.

Sample application

1.0-10.0 μL of standard solutions (7-point calibration) and 2.0 and 5.0 μL of sample solutions are applied as bands with the Linomat 5 (with N2). Plate layout: 15 tracks, band length 6.0 mm, distance from left plate edge 15.0 mm, track distance 11.4 mm, distance from the lower edge 8.0 mm.

Chromatography

Plates are developed in the twin-trough chamber with chamber saturation for 30 min (with filter paper) and development with toluene ‒ ethyl acetate ‒ formic acid 74:26:5 (V/V) to the migration distance of 80 mm (from the lower edge), followed by drying for 5 min.

Post-chromatographic derivatization

The developed plate is first heated at 110 °C for 2 min and then placed in the immersion device containing Natural product reagent (NP or 2-aminoethyl diphenylborinate – 1% (W/V) in ethyl acetate). The developed plate is immersed in anisaldehyde sulfuric acid reagent (ASR – prepared fresh by combining 1.0 mL p-anisaldehyde with 20.0 mL glacial acetic acid, followed by 170 mL methanol and 10.0 mL concentrated sulfuric acid) and then heated at 100 °C for 5 min. The developed plate is immersed in Ferric chloride solution (FeCl3 – 2 % (W/V) in methanol) and then heated for 2 min at 110 °C.

Note: The derivatization was conducted on three different developed plates.

Post-chromatographic bioautography

The developed plate is immersed into a 2,2-diphenyl-1-picryl hydrazyl solution (DPPH – 0.25 % (W/V) in methanol), stored in the dark for 30 min. The yellow zones captured against purple background are an indicator of antioxidant components when visualized in white light. The Ellman assay protocol was used wherein the developed plate is first immersed in a solution of 5,5′-dithiobis-2-nitrobenzoic acid (DTNB) and acetylthiocholine iodide (ATCI) (1 mM DTNB and 1 mM ATCI in buffer A) until the plate was saturated, dried for 5 min and then around 3-4 mL of acetylcholinesterase enzyme solution (Electrophorus electricus – AChE – 3 U/mL) is sprayed onto the plate. The white band on the plate is an indicator of acetylcholinesterase inhibition.

Documentation

Images of the plate are captured with the TLC Visualizer 2 in UV 254 nm, UV 366 nm, and white light.

Densitometry

Absorbance measurement is performed with the TLC Scanner 3 and visionCATS at 268 nm (chrysin), 297 nm (p-coumaric acid and pinocembrin) and 352 nm (luteolin and galangin), slit dimension 5.00 mm x 0.45 mm, scanning speed 20 mm/s, spectra scanned from 200 to 450 nm.

Mass spectrometry

The selected bands are eluted with the TLC-MS Interface 2 at a flow rate of 0.5 mL/min with methanol (with 0.1 % formic acid) into an Electrospray ionization (ESI)-Triple Quadruple Mass Analyzer (Agilent 6460) in the negative ionization mode.

Results and discussion

The HPTLC fingerprint image of the various propolis extracts is shown, and the profiles are key indicators of the diversity in vegetation across different regions. The sample coded HAR was mainly of ‘O-type’ propolis due to the presence of flavonoids like chrysin, galangin, pinocembrin, as well as non-flavonoids like p-coumaric acid, matching the characteristic bands of the standard when derivatized with various reagents. Interestingly, the applicability of the method on two marketed products presented a similar fingerprint to that of the HAR extract.

The optimized method is found to be precise (%RSD ≤ 2.0 %), accurate (90‒110 %), linear over the concentration ranges (r2 ≥ 0.995), sensitive and robust resulting in the RF values of 0.235, 0.353, 0.552, 0.606, and 0.655 for luteolin, p-coumaric acid, chrysin, galangin, and pinocembrin, respectively. Pinocembrin (2.30 ± 0.12 % W/W) and galangin (5.78 ± 0.30 % W/W) are found in the highest concentrations in the HAR sample. The m/z values of the molecular ion and fragment ions from the isolated sample bands matched those of the standards, further confirming the identity of the peaks. The bands with RF values corresponding to chrysin, galangin, and pinocembrin showed strong antioxidant activity, as indicated by bright yellow zones against a purple background, while the white bands in the extract fingerprint that appeared along the plate following the Ellman’s assay are indicative of acetylcholinesterase inhibitors.

Thus, the developed analytical method with orthogonal capabilities can be universally applied to different propolis extracts and formulated propolis products as a quick screening method for fingerprint and neuroprotective profiling.

HOW IT WORKS

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  • HPTLC profiling for bioactive ingredients in Indian propolis Fig 1

    01

    HPTLC fingerprint image of propolis extracts collected from different regions in India and marketed samples in UV 254 nm and in modified UV 366 nm before derivatization (enhanced contrast)

  • HPTLC profiling for bioactive ingredients in Indian propolis Fig 2

    02

    HPTLC fingerprints of HAR extracts pre- and post-derivatization in different illumination modes

Acknowledgements
The authors would like to express their gratitude to Poona College of Pharmacy (Bharati Vidyapeeth Deemed to be University), Central Bee Research and Training Institute (CBRTI, Pune), All-India Council for Technical Education (AICTE), Anchrom Enterprises Pvt. Ltd. (Mumbai), Bee Basket Enterprises Pvt. Ltd and the Centre of Food Testing Laboratories, (Pune) for all the assistance and support in the work.
Literature

[1] Sankaran, S. et al. (2024) J Planar Chromat 37 (3), 233–245

[2] Bankova, V. et al. (2019) J Apic Res 58, 1–49

[3] Sankaran, S. et al. (2023) J Biol Active Prod Nat 13, 76–93.

Further information on request from the authors.

Further information is available in the article published “Sustainable instrumental thin-layer chromatography-based methodology for standardization of neuroprotective components in propolis collected from India” J Planar Chromat 37, 233–245 (2024). https://doi.org/10.1007/s00764-024-00307-x or on request from the authors.

Contact: Sandeep Sankaran, Department of Quality Assurance Techniques, Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to be) University, Pune, Maharashtra 411038, India, sandeepsss1992@gmail.com

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Detection and identification of chemical warfare agents using HPTLC

Introduction

Chemical warfare agents present a considerable threat to human health, inducing a spectrum of symptoms ranging from irritation to fatality. It is imperative for law enforcement agencies and military personnel to possess the knowledge and tools required to detect and prevent exposure to these hazardous substances. There are various methods to categorize chemical warfare agents, one common approach is to categorize them based on the primary symptoms they cause. Nerve agents, for instance, are organic chemicals that disrupt the mechanisms through which nerves convey messages to organs. This disruption arises from the inhibition of acetylcholinesterase (AChE), an enzyme facilitating the breakdown of acetylcholine.

Blistering agents, also known as vesicants, are chemical warfare agents that induce skin blisters, eye damage, and respiratory harm. Typically, these agents manifest as oily liquids that can persist on surfaces for extended durations. Exposure to blistering agents can lead to severe burns, lung damage, and even death. In contrast, irritant agents elicit irritation on the skin, eyes, and respiratory system. Although less lethal than nerve agents and blistering agents, irritant agents can still inflict significant harm on exposed individuals. Examples of irritant agents include substances like chlorine gas, phosgene gas, and tear gas.

Arsenic agents represent another category of chemical warfare agents capable of causing substantial harm to human health. Exposure to arsenic agents can result in symptoms ranging from irritation to death.

HPTLC is a reliable and widely used analytical technique for the identification of chemical warfare. HPTLC separates the individual components of a mixture, making it possible to identify specific nerve agents such as Russian VX (RVX), O-ethyl S-(2-diisopropylaminoethyl) methylphosphonothioate (VX), Soman (GD), Tabun (GA), cyclosarin (GF), and sarin (GB) based on their characteristic retention factor (RF) values [1]. TLC methods were transferred to HPTLC.

For six blistering agents and irritants, namely sulfur mustard (HD), HN-3 (TTA), 2-chlorobenzylidenemalononitrile (CS), 2-chloroacetophenone (CN), bromobenzyl cyanide (CA), and benzyl bromide (CB) [2], as well as three arsenic agents Lewisite (L), Clark 1 (DA), and Adamsite (DM) [3], their initial TLC methods were successfully transferred to HPTLC. This underscores the adaptability and efficacy of HPTLC in extending the capabilities of traditional TLC methods for the comprehensive analysis of chemical warfare agents.

Standard solutions

Individual standard solutions were prepared according to the table below, and for quantification purposes each solution was applied at different application volumes to generate a calibration curve.

System Suitability Test (SST): the ready-to-use solution of Universal HPTLC mix (UHM) was prepared in house according to [4] and applied on track 8 of each plate.

Chromatogram layer

HPTLC plates silica gel 60 F254 (Supelco), 20 × 10 cm are used.

Sample application

Samples are applied as bands with the Automatic TLC Sampler (ATS 4), 15 tracks, band length 8.0 mm, distance from left edge 20.0 mm, distance from lower edge 8.0 mm.

Chromatography

Plates were developed with the following three developing solvents in ADC 2 with activation of the plate at 33 % relative humidity for 10 min using a saturated solution of magnesium chloride. For nerve agents, acetone – cyclohexane – ethyl acetate – methanol 1:5:3:0.2 (V/V), for blistering agents and irritants, toluene, and for arsenic agents, cyclohexane – dichloromethane – methanol 7:2:1 (V/V), are used as developing solvents with 20 min chamber saturation (with saturation pad). The developing distance for all three methods was 70 mm (from the lower edge). Plates were dried for 5 min.

Post-chromatographic derivatization

For nerve agents: 

1. Spraying solution A: Acetylcholinesterase

Reagent preparation:
Dissolve 55.0 mg of acetylcholinesterase (55 mg = 150 U) in 100.0 mL of buffer solution (dissolve 19.0 g of Na2HPO4 x 12 H2O and 1.8 g of KH2PO4 in 1.0 L of de-ionized water (pH approx. 7.4)).

Reagent use:
Spray the plate with 4.0 mL of spraying solution A with the Derivatizer, yellow nozzle, spraying level 4, and leave the plate (horizontal; outside of the Derivatizer) for 15 min at room temperature.

[Note]: with 4.0 mL, the plate should not dry out.

2. Spraying solution B: Fast blue salt

Reagent preparation:
Mix 40.0 mL of fast blue solution (100.0 mg of fast blue salt B in 40.0 mL of de-ionized water) with 10.0 mL of 1-naphthyl acetate solution (25.0 mg of 1-naphthylacetate in 10.0 mL of ethanol).

Reagent use:
Spray the plate with 2.0 mL of praying solution B with the Derivatizer, yellow nozzle, spraying level 4, and record the images after 30 min.

For blistering agents and irritants (optional):

1. Spraying solution C: 4-(4’-Nitroenzyl)-pyridine solution

Reagent preparation:
Dissolve 5.0 g of 4-(4’-nitrobenzyl)-pyridine in 100.0 mL of ethanol.

2. Spraying solution D: Benzofurazan-(1)-oxide solution

Reagent preparation:
Dissolve 1.0 g of benzofurazan-(1)-oxide in 100.0 mL of ethanol.

Reagent use:
Spray the plate with 2.0 mL of spraying solution B with the Derivatizer, yellow nozzle, spraying level 4, and record the images after 30 min.

3. Spraying solution E: NaOH solution

Reagent preparation:
Dissolve 4.0 g of NaOH in a mixture of 50.0 mL of de-ionized water and 50.0 mL of methanol.

Reagent use:
Spray the plate with spraying solution C with the Derivatizer (yellow nozzle, 3.0 mL, spraying level 4), heat at 150 °C for 30 s, and immediately record images. Spray the plate with spraying solution D with the Derivatizer (yellow nozzle, 3.0 mL, spraying level 3), and then with spraying solution E with the Derivatizer (yellow nozzle, 3.0 mL, spraying level 6), and record the images within the next 2 min.

Documentation

TLC Visualizer in UV 254 nm, UV 366 nm, and white light prior to derivatization, and UV 366 nm, and white light after derivatization (as needed).

Densitometry

For the UHM, TLC Scanner 4 and visionCATS, absorbance measurement at 254 nm, slit dimension 5.00 mm x 0.20 mm, scanning speed 50 mm/s, and in fluorescence mode at 366>/400 nm. For the other substances, each standard is detected at their maximum of absorption as described in the following table.

table agents and irritants

Results and discussion

For each method, the UHM was used as SST and the RF values to obtain for each method are described as follows:


  • RF values to obtain for SST using the UHM for each method

    01

    RF values to obtain for SST using the UHM for each method

  • HPTLC chromatograms at UV 254 nm of the crude product (10 g/L, 1 μL versus 100 g/L,15 μL) and mass spectra (left) versus 1H NMR spectra of isomeres (right)

    02

    HPTLC chromatograms and RF values for each nerve agent, blistering agents and irritants, and arsenic agents

For nerve agents, a large-scale untargeted screening of samples was developed, involving the detection of toxic substances without specific identification

In this approach, each sample (utilizing reference substances in our example) was applied at different Y positions, forming a zone equivalent to a 1.0 mm band, with varying application volumes. In our example, the screening was applied on a 20 x 10 cm plate, but the screening could also be applied to a 20 x 20 cm plate.

Following the application, no development was conducted, but the entire plate underwent derivatization. Positive zones were observed as yellow against a pink/violet background. This test revealed that each nerve agent was still detectable at very low absolute quantities (amount on the plate):

  • GA, VX, RVX < 0.25 ng
  • GB < 0.125 ng
  • GD < 0.025 ng
  • GF < 0.01 ng
396 samples graph

Protocol developed for large-scale untargeted screening of samples for detection of nerve agents (top), and example with reference substances in white light after derivatization (bottom). RVX (0.5 ng/μL) was applied at Y = 10 mm, VX (0.5 ng/μL) at Y = 20 mm, GB (0.25 ng/μL) at Y = 30 mm, GA (0.5 ng/μL) at Y = 40 mm, GF (0.02 ng/μL) at Y = 50 mm, and GD (0.05 ng/μL) at Y = 60 mm

Conclusion

The examples above show that HPTLC is a valuable tool for identifying nerve agents, blistering agents and irritants, as well as arsenic agents which are important for law enforcement and military personnel in preventing chemical warfare. HPTLC’s format preserves the separated zones, allowing for further investigation including bioassays like acetylcholinesterase inhibition. Additionally, the use of HPTLC instruments reduces the need for analysts to physically interact with toxic samples, enhancing safety.

Literature

[1] CAMAG Application note A-142.1: Identification and quantification of arsenics agents L, DA and DM by HPTLC.

[2] CAMAG Application note A-143.1: Identification and quantification of blistering agents and irritants HD, TTA, CS, CN, CA and CB by HPTLC.

[3] CAMAG Application note A-144.1: Identification and quantification of nerve agents RVX, VX, GD, GA, GF and GB by HPTLC, and methodology for a large-scale untargeted screening.

[4] T. K. T. Do et al., J Chromatogr A (2021) 1638

Further information on request from the authors.

Contact: Dr. Tiên Do, Sonnenmattstrasse 11, 4132 Muttenz, Switzerland, tien.do@camag.com

mentioned products

The following products were used in this case study

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Gas phase control in the HPTLC PRO Module DEVELOPMENT

HPTLC is a straightforward analytical technique that offers numerous advantages. While the technique follows the same concept of separating mixture components between two phases (mobile phase and stationary phase), it differs from other liquid chromatographic techniques in the fact that a gas phase is present during and, indeed, influences the development process [1]. This property has always posed a challenging issue for controlling the outcome of the separation. Moreover, the fact that a broad spectrum of solvents can be used means gas phase control holds great promise for resolving complex matrix separations.

In order to investigate this, the Module DEVELOPMENT (a component of the CAMAG® HPTLC PRO System [2]) was employed in this study. The Module not only allows the generation and introduction of a gas phase of varying composition into the development chamber but also provides control over the timing and power settings of the pump used to build up the gas phase. The Module is equipped with three separate solvent bottles that enable the generation of gas phase from either the same solvents used for plate development or from different solvents. Additionally, the Module can be configured to introduce the generated gas phase at two distinct stages, prior to the start of the development (referred to as pre-conditioning) and/or during the development process (referred to as conditioning). These features provide useful tools to control the gas phase throughout the development process.

This study aims to investigate whether it is possible to manipulate the gas phase to attain a desired chromatographic separation. To achieve this objective, we sought to control the gas phase in a way that we can obtain RF values based on the Universal HPTLC mix (UHM), a mixture of chemicals for system suitability testing, that are comparable to (ΔRF ≤ 0.05) those previously measured using the ADC 2 [3].

Standard solutions

The ready to use solution of UHM was prepared in house according to [4] and applied on track 8 (middle track) of each plate.

Chromatogram layer

HPTLC plates silica gel 60 F254 (Supelco), 20 × 10 cm are used.

Sample application

2.0 µL of UHM solution are applied as bands with the HPTLC PRO Module APPLICATION, 15 tracks, band length 8.0 mm, distance from the left edge 20.0 mm, track distance 11.4 mm, distance from the lower edge 8.0 mm. The default settings of methanol as sample solvent are used. The first rinsing step (bottle 1 solvent) is done with methanol – acetonitrile – iso-propanol – water – formic acid 250:250:250:250:1 (V/V) and the second rinsing step (bottle 2 solvent) is done with methanol – water 7:3 (V/V).

Chromatography

Images of the plates are captured with the TLC Visualizer in UV 254 nm.

Documentation

In the Twin Trough Chamber 20 x 20 cm (TLC) or 20 x 10 cm (HPTLC) with chamber saturation (with filter paper) for 20 min with different solvents to the migration distance of 100 mm for TLC and 50 mm for HPTLC (both from the lower edge), drying in a stream of cold air for 5 min

Results and discussion

It is known that the gas phase surrounding the HPTLC plate during the development process can significantly influence the chromatographic separation. The HPTLC PRO Module DEVELOPMENT has a unique chamber design compared to the chambers used in the ADC 2 or for manual development. These differences can lead to changes in the rate of evaporation and the concentration of the developing solvent, which may result in differences in the pattern of separations. Therefore, it may be expected that the RF values measured using the HPTLC PRO Module DEVELOPMENT will exhibit some deviations from those obtained through ADC 2 or manual development.

To explore the effects of the gas phase on compound separations, our goal in this study was to achieve RF values similar to those obtained using the ADC 2 method.

Key aspects of the study involve:

  • Examining the impact of gas phase composition, while keeping the developing solvent and activation constant at 33 % rH.
  • Setting a limit of ΔRF at ± 0.05, meaning that the absolute difference between RF values in the ADC 2 and the HPTLC PRO Module DEVELOPMENT should not exceed 0.05.

An initial experiment (HPTLC PRO M1) was conducted without using pre-conditioning or conditioning. In comparison to the ADC 2 results, the overall RF values were different. However, compounds [e], [f], and [h] exhibited average RF values within the specified control limits. Notably, ΔRF was higher for compound [g] (~ 0.06).

These findings suggest that the migration pattern for all compounds does not behave uniformly. The development without the use of the gas phase leads to increased RF values for compounds in the lower half of the plate, and to decreased RF values for compounds in the upper half of the plate.

The challenge now is to control the retention of each of the four compounds individually on the plate solely based on gas phase control.

HPTLC chromatogram of UHM after development

HPTLC chromatogram of UHM after development. 1: Experiment ADCRF 2, 2: Experiment HPTLC PRO M1

Based on this initial information, three methods were developed to evaluate the effect on the ΔRF.

Methods used to study active gas phase control

Methods used to study active gas phase control

The method HPTLC PRO M2 focused on using the same solvent for both, the developing solvent and for gas phase generation. Initially, conditioning with the developing solvent was employed. However, the experiment revealed that initiating conditioning at various migration distances (while keeping the pump power constant) significantly affected the RF values of individual substances.

For example, beginning conditioning at either 0 or 30 mm resulted in a substantial reduction of the RF value for zones located in the upper part of the plate, while starting conditioning at 50 mm exhibited less impact on these zones. Consequently, we decided to initiate conditioning after 50 mm, leading to an improvement of the RF values for most zones, except for compound [f], which required the use of a pre-conditioning step. Previous studies have shown that conditioning in normal phase HPTLC usually increases the RF values and pre-conditioning lowers them.

Ultimately, increasing the pre-conditioning duration from 10 to 30 s corrected the RF value for compound [f], but this came at the expense of reduced RF values for compounds [g] and [h].

Those data highlight the various parameters that can be used to regulate the gas phase. It also reveals that substances respond differently to each given experimental condition, indicating that the chemical properties of the compounds play a role in regulating the gas phase.

HPTLC chromatograms show results obtained at different conditions

HPTLC chromatograms show results obtained at different conditions with method HPTLC PRO M2

Similar optimization processes were employed in the other two approaches (optimization data not shown). However, in these two approaches we demonstrated how to control the gas phase with solvents that are different from the developing solvent. One approach involved entirely different solvents, adopted from [3] (referred to as HPTLC PRO M3), while the other maintained the same composition but different solvent proportions (referred to as HPTLC PRO M4).

Notably, the fourth approach (HPTLC PRO M4), which uses ethyl acetate – toluene 3:7 (V/V) for pre-conditioning, yielded the most favorable outcome. In this approach, no conditioning is required and in contrary to the common tendency for pre-conditioning to decrease RF values (due to the known building of virtual fronts), our study revealed an anomalous outcome where RF values for compounds other than [g] experienced an increase in RF value. By exploring these alternative solvent combinations, we can expand our understanding about the effect of the gas phase composition and its subsequent impact on chromatographic performance.

HPTLC chromatograms of the UHM after development with different conditions

HPTLC chromatograms of the UHM after development with different conditions (A): track 1: ADC 2 (standard conditions), track 2: HPTLC PRO M1, track 3: HPTLC PRO M2, track 4: HPTLC PRO M3, track 5: HPTLC PRO M4; Control chart for ΔRF (B)

RF values obtained from methods conducted in this study

RF values obtained from methods conducted in this study

Conclusion

This study emphasizes the essential role of the gas phase in regulating the development process and extends its significance beyond the establishment of standardized chromatographic procedures for HPTLC analysis.

Furthermore, this study shows, that it is possible to control the gas phase. By optimizing the composition of the gas phase, the pump power used to build up the gas phase, and the duration of the gas phase using the HPTLC PRO Module DEVELOPMENT, we demonstrated how the control of the gas phase allows the customization of the retention of each of the target compounds in specific regions of the chromatogram. This results in the achievement of the desired separation pattern through three distinct approaches.

This groundbreaking work highlights the critical role of the gas phase in controlling the development process, introducing new possibilities for strengthening and enhancing the selectivity of the gas phase on the development. These concepts, previously not fully explored, represent a significant step towards a deeper understanding of the complexities involved in pre-conditioning and conditioning processes within Thin-Layer Chromatography systems.

Notably, the fourth approach (HPTLC PRO M4), which uses ethyl acetate – toluene 3:7 (V/V) for pre-conditioning, yielded the most favorable outcome. In this approach, no conditioning is required and in contrary to the common tendency for pre-conditioning to decrease RF values (due to the known building of virtual fronts), our study revealed an anomalous outcome where RF values for compounds other than [g] experienced an increase in RF value. By exploring these alternative solvent combinations, we can expand our understanding about the effect of the gas phase composition and its subsequent impact on chromatographic performance.

Literature

[1] E. Reich et al., High-Performance Thin-Layer Chromatography for the Analysis of Medicinal Plants (2007).

[2] CAMAG CBS 123. Introducing CAMAG HPTLC PRO.

[3] T. K. T. Do et al. J. Planar Chromatogr. – Mod. TLC (2022) 299

[4] T. K. T. Do et al., J Chromatogr A (2021) 1638

Further information on request from the authors.

Contact: Dr. Ehab Mahran, CAMAG, Sonnenmattstrasse 11, 4132 Muttenz, Switzerland, ehab.mahran@camag.com

mentioned products

The following products were used in this case study

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