The analysis of spirits involves both chemical and sensory evaluations to ensure product authenticity, quality, and compliance with regulations. Common spirits such as whisky, vodka, rum, tequila, and gin contain ethanol, water, and a diverse range of trace compounds that influence their flavor, aroma, and physical properties.

Advanced analytical techniques are typically employed in spirit analysis. Gas Chromatography (GC) is widely used for volatile compounds, while High-Performance Liquid Chromatography (HPLC) targets non-volatile constituents. In this study, High-Performance Thin-Layer Chromatography (HPTLC) was used as a versatile tool capable of analyzing both volatile and non-volatile compounds.

Glycerol prepared at 0.5 mg/mL in methanol.

Spirit samples were used without any pre-treatment and directly transferred into vials.

HPTLC plates silica gel 60 F₂₅₄ (Supelco, Germany), 20 × 10 cm were employed.

20.0 µL (for MPDS and HPDS) and 25.0 µL (for LPDS) of sample solutions and 2.0 µL of standard solutions are applied as 6.0 mm bands with the Automatic TLC Sampler 4 (ATS 4).

For general screening, the complementary developing solvent (CDS) approach was applied using three solvents of different polarities [1]:

• low-polarity developing solvent (LPDS): cyclohexane – butyl acetate 88:12 (V/V)
• medium-polarity developing solvent (MPDS): formic acid – cyclopentyl methyl ether, tetrahydrofuran, water 1:40:24:1 (V/V)
• high-polarity developing solvent (HPDS): dichloromethane – ethanol – formic acid – water 16:16:1:4 (V/V)

All developments were performed in the ADC 2, with a 70 mm migration distance. Relative humidity was adjusted to 33% for 10 min. Chamber saturation was maintained for 20 min for MDS and HPDS.

For detecting glycerol, a separate mobile phase using acetone – 25% ammonia solution – toluene – water – 85:1:5:9.5 (V/V) was used. Relative humidity was adjusted to 33% for 10 min, and chamber saturation was maintained for 20 min [2].

General screening: 3.0 mL of anisaldehyde sulfuric acid reagent was sprayed using the CAMAG Derivatizer (blue nozzle, spraying level 3) and heated at 100°C for 3 min.

Glycerol detection: 3.0 mL of potassium permanganate (KMnO₄) reagent was sprayed (yellow nozzle, spraying level 3), and dried in a cold airflow for 4 min.

Plates were documented using the CAMAG TLC Visualizer 2 at the following stages:

Before derivatization: UV 254 nm and UV 366 nm.

After derivatization with anisaldehyde sulfuric acid: UV 366 nm with white light reflection-transmission (WRT).

After derivatization with KMnO₄ reagent: white light reflection (WR) and white light reflection-transmission (WRT) 30 min after spraying.

Densitometric measurements for glycerol detection were performed using the CAMAG TLC Scanner 4 in fluorescence mode at 520 nm (deuterium lamp), with a 5.0 x 0.2 mm slit and 20 mm/s scanning speed.

A wide variety of spirits, including cognac, rum, bourbon, vodka, tequila, whisky, and gin, were analyzed.

Before derivatization, samples developed with the CDS systems showed multiple distinct zones, revealing the presence of various classes of compounds. Blue fluorescent zones observed under UV 366 nm (Detection C) seem to be characteristic of barrel-aged spirits such as cognac (tracks 1-2), amber rum (tracks 3-4), bourbon (tracks 5-6), aged tequila (Añejo, reposado; tracks 9-10), and whisky (tracks 11-12). These zones were notably absent in clear spirits like vodka (tracks 7-8) and gin (tracks 13-14), supporting the hypothesis that such fluorescence originates from compounds absorbed during barrel aging, such as lignin-derived congeners.

Interestingly, analysis of two gin samples – one regular (track 13) and one alcohol-free (track 14) – revealed compositional differences. The alcohol-free gin displayed a unique zone under UV 254 nm (Detection B), suggesting the addition of non-volatile components or flavoring agents specific to its formulation.

Numerous spirit samples, including cognac, rum, bourbon, vodka, tequila, whisky, and gin, were collected, and tested.

Post-chromatographic derivatization with anisaldehyde reagent (Detection D) significantly enhanced the zone visibility. Brown-colored zones below R_F 0.3, particularly in HPDS-developed plates, were observed across all samples – including vodka and gin– indicating the possible presence of carbohydrates such as glucose or added sugars. Their presence in clear spirits, which are not typically sweetened, raises questions about additives or flavor enhancers.

For glycerol detection, the specific developing system followed by KMnO₄ derivatization enabled the identification of glycerol-related zones. These zones fluoresced under UV 366 nm in white light reflection mode (WRT), and densitometric analysis confirmed their presence based on retention factor (R_F) and fluorescence profile. Spirits known to contain added glycerol to enhance mouthfeel – such as certain flavored vodkas and specialty liqueurs – showed distinct signals corresponding to glycerol. This method is particularly valuable, as glycerol (E422) is permitted as a food additive in alcoholic beverages within the EU and Switzerland, but its use must comply with specific regulatory limits [3].

This study highlights the strong potential of HPTLC as a practical and powerful analytical technique for the food and beverage industry. In the context of spirits, it offers clear advantages: the ability to analyze multiple samples simultaneously, detect a broad range of compounds – from natural aging markers to added substances like sugars and glycerol – and deliver results quickly with minimal sample preparation.

The successful application of HPTLC to detect glycerol and differentiate between various spirit types underscores its reliability and versatility. It is particularly well-suited for routine screening, quality control, and regulatory compliance, where rapid and reproducible results are essential.

[1] CBS 129: HPTLC routine analysis using complementary developing solvents.

[2] CBS 134: Detection and limit test of diethylene glycol and ethylene glycol impurities in syrup by HPTLC.

[3] https://www.efsa.europa.eu/en/efsajournal/pub/4720 (accessed on 23/07/2025)

Contact:

Robert Gibson, Sazerac Switzerland GmbH, 6005 Lucerne, Switzerland, rgibson@sazerac.com

Tiên Do, CAMAG, 4132 Muttenz, Switzerland, tien.do@camag.com

Mr. Parth Trivedi is an application specialist at Anchrom Enterprises Pvt. Ltd., Mumbai, India, working under the guidance of Mr. Akshay Charegaonkar, Managing Director at Anchrom Enterprises Pvt. Ltd., Mumbai, India. The company specializes in instrumental HPTLC and is dedicated to developing innovative, quantitative and regulatory-compliant analytical methods for a wide range of products, including pharmaceutical formulations, APIs, herbal products, food items, organic intermediates and dyes.  

Mr. Trivedi utilizes the advantages of HPTLC due to its rapidity, cost-effectiveness, versatility, and ability to perform simultaneous analysis of multiple samples with minimal solvent use. His work focuses on the application of HPTLC for the detection of non-permitted substances, such as Rhodamine B, in food and cosmetic products, ensuring safety, quality control and regulatory compliance. 

Rhodamine B is a synthetic, water-soluble dye belonging to the xanthene family, characterized by its bright pink to reddish-purple appearance [1]. Widely used in textiles, paper, inks and biological research, it is recognized for its intense color and fluorescence. Its illegal use is often observed in brightly colored sweets, street foods and colored beverages. In cosmetics, it is frequently found in low-cost or unbranded products. However, Rhodamine B is not approved for use in any food or cosmetic products due to its toxic effect [2].

The Food Safety and Standards Authority of India (FSSAI) explicitly prohibits its presence in food, while the Drugs and Cosmetics Act 2006 bans its use in cosmetics. Rhodamine B is classified as a possible human carcinogen and mutagen, it has shown to cause damage to DNA, liver, kidney toxicity and even tumor formation in experimental studies [3]. HPTLC serves as a reliable analytical technique for the detection and identification of Rhodamine B in various food and cosmetic samples. Despite these restrictions, its unauthorized use persists, especially in low-regulated sectors, for coloring sweets, street foods and inexpensive cosmetic products.

10.0 mg Rhodamine B dissolved in 10.0 mL methanol – water 4:1 (V/V) (1.0 mg/mL stock).

Dilute 1.0 mL of this stock to 10.0 mL with the same solvent to obtain 0.1 mg/mL Rhodamine B standard solution.

All food and cosmetic samples are prepared at a concentration of 500.0 mg/ml. Weigh 2500.0 mg of the sample and add 5.0 mL of methanol. Vortex thoroughly for at least 2-3 min. Sonicate the solution for 15 min, then centrifuge at 3000 rpm for 5 min. Collect the supernatant and use it for application.

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

1.0 µL of Rhodamine B standard, 2.0 µL of each cosmetics sample and 5.0 µL of each food sample are applied with Automatic TLC Sampler 4 (ATS 4) as 8.0 mm bands, 15 tracks, track distance 11.4 mm, distance from left edge 20.0 mm, distance from the lower edge 8.0 mm.

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 tetrahydrofuran – toluene – formic acid – water 16:8:2:1 (V/V) to the migration distance of 70 mm (from the lower edge), followed by drying for 5 min.

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

In the analysis of food and cosmetic samples for Rhodamine B using HPTLC, several important observations were made. The Rhodamine B standard showed a distinct band at R_F 0.50, which was visible under white light, UV 254 nm, and UV 366 nm, with a maximum (λ_max) at 552 nm. This band served as a reference for comparison with the test samples. Among the tested samples, sugar-coated jelly candy and cotton candy clearly showed bands matching the Rhodamine B standard, indicating the presence of the dye. Similarly, sindoor (traditional red powder used in Hindu culture, liquid lipstick, blusher, eyeshadow and nail polish also exhibited bands at the same R_F value, confirming contamination with Rhodamine B. In contrast, tutti frutti (candied fruit mix), jelly, halwa barfi (traditional Indian sweet), and lipstick did not show any corresponding bands, suggesting that these samples were free from the dye.

Spectral analysis further supported these results, as the spectra of positive samples closely overlapped with that of the Rhodamine B standard, confirming its identity. These observations highlight that HPTLC is a reliable and effective method for the detection of Rhodamine B in both food and cosmetic products. The study shows the importance of such testing for quality control and regulatory compliance to prevent harmful adulteration and ensure consumer safety.

[1] Shabir, G. et al. Mini-Reviews in Organic Chemistry (2018) 166.

[2] Puttegowda, S. K. B. et al. Ind J Pharm Pract (2024) 8.

[3] Knudsen, K. B. et al. Journal of nanoparticle research (2014) 2221.

Contact:

Mr. Akshay Charegaonkar, A-101, Shree Aniket Apartments, Navghar Road, Mulund East, Mumbai, Maharashtra 400081, India, hptlc@anchrom.in, www.anchrom.in

This research represents a collaborative effort between experts in pharmacognosy, analytical chemistry, and natural products from Brazil and the United States.

Dr. Flávio L. Beltrame is an Associate Professor at the State University of Ponta Grossa (Brazil). He holds a Ph.D. in Organic Chemistry from São Carlos Federal University (Brazil) and completed a postdoctoral fellowship in pharmacology of plant secondary metabolites at the University of Mississippi (USA). His expertise spans ethnopharmacology, natural products chemistry, and pharmaceutical technology, applied to complex matrices.

Dr. Wilmer H. Perera has been the Laboratory Director at CAMAG Scientific Inc. (USA), where he develops and validates HPTLC methodologies, manages analytical projects, and coordinates training courses. He is the Secretary General of the HPTLC Association – North America Chapter, and a member of the Pan-American Expert Panel. With a Ph.D. in Biochemistry and academic training in chemistry and natural products, he has extensive experience in the isolation and identification of bioactive compounds.

Dr. Evelyn A. de Andrade holds a Ph.D. in Pharmaceutical Sciences from the State University of Ponta Grossa (Brazil), with a period at the University of North Carolina Wilmington (UNCW). She is currently a postdoctoral researcher at the Institute of Chemistry of São Carlos (USP, Brazil), and her expertise includes pharmacognosy and natural products chemistry, with a research focus on organic chemistry applied to biological systems.

Together, they applied advanced chromatographic tools to support the quality control and authentication of Kalanchoe species and medicinal plant products.

For centuries, medicinal plants have formed the basis for the discovery and development of therapeutic agents, with numerous species remaining extensively used in traditional medicine. The genus Kalanchoe (Crassulaceae), comprising over 170 recognized species distributed across tropical and subtropical regions, possesses a longstanding history of use in complementary and alternative therapies. Frequently referred to as miracle leaf, a variety of Kalanchoe species are employed in folk medicine to address wounds, infections, inflammatory conditions, and other health concerns [1-3].

Despite their cultural and therapeutic significance, the morphological similarity among Kalanchoe species, together with the frequent use of common trivial names, often results in confusion, misidentification, and even adulteration within commercial products [4,5]. Such issues can compromise the efficacy and safety of herbal remedies, requiring the implementation of rigorous quality control strategies. In addition, some effect-directed assays were performed to detect active constituents in the species.

Analytical methodologies that enable precise authentication and chemical characterization are indispensable. HPTLC represents a rapid, reproducible, and cost-effective technique for generating chemical fingerprints from plant extracts, thereby facilitating species authentication and quality assurance. In the present study, fully automated HPTLC PRO analysis was conducted on five Kalanchoe species of medicinal interest: K. crenata, K. daigremontiana, K. marmorata, K. pinnata, and K. × houghtonii. By establishing clear chromatographic profiles, this approach offers reliable tools for differentiating morphologically similar species and aids in the standardization of herbal raw materials derived from Kalanchoe.

Stock solutions of quercetin, rutin, chlorogenic acid, and kaempferol are prepared at 1 mg/mL each in methanol. Working concentrations: quercetin, kaempferol, and chlorogenic acid at 200 µg/mL, rutin at 400 µg/mL. The Universal HPTLC mix (UHM) is used as a system suitability test (SST).

Fresh leaf and stem materials from five Kalanchoe species (K. crenata, K. daigremontiana, K. marmorata, K. pinnata, and K. × houghtonii) are extracted with water 1:10 (W/V) using turbo-extraction. The extracts are then diluted to 20 mg/mL in water for HPTLC analysis.

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

Samples (10 µL), SST, and standard solutions (2 µL) are applied as bands using the HPTLC PRO Module Application, 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.

Plates are developed in the HPTLC PRO Module DEVELOPMENT after activation at 33% relative humidity with MgCl₂ for 10 min. Conditioning with mobile phase n-butyl acetate – methanol – water –formic acid 7.5:2:1:1 (V/V/V/V) at 25% pump power from 30 to 70 mm developing distance. Plates are dried in the chamber after development.

Plates are heated to 100° C for 90 s. For the first derivatization, 1.5 mL of NP reagent (1 g of 2-aminoethyl diphenylborinate in 100 mL of methanol) is sprayed using Nozzle 1 (Level 3) in the HPTLC PRO Module DERIVATIZATION. For the second derivatization, 1.8 mL of anisaldehyde reagent (85 mL of ice-cooled methanol mixed with 10 mL of acetic acid and 5 mL of sulfuric acid) is sprayed using Nozzle 2 (Level 2), followed by heating at 100 °C for 90 s.

Three types of antioxidant capacity assays are subsequently performed on the developed plates:

1. Folin-Ciocalteau assay – The reagent is diluted 1:10 (V/V) in methanol, and the plate is dipped into the solution using an Immersion device, with the dipping time set to 0 and speed 5. After 30 min, images are taken under white light using the TLC Visualizer 2.
2. DPPH^• assay – The reagent is prepared at 0.05% (W/V) in methanol, and 3 mL of this solution is sprayed onto the plate with a Derivatizer equipped with a blue nozzle at spraying level 3. The plate was kept in the dark for 30 min, after which images are recorded under white light.
3. ABTS^+• assay – The reagent is prepared at 0.04% (W/V) in water and subsequently diluted 1:1 (V/V) in methanol. Then, 3 mL of this solution is sprayed onto the plate using the Derivatizer equipped with a yellow nozzle at spraying level 3. White light images are taken after 30 min of derivatization.

The images arecaptured using the TLC Visualizer 2 in UV 254 nm, UV 366 nm, and white light.

The HPTLC fingerprint of the five Kalanchoe species under UV 366 nm after derivatization with NP reagent showed species-specific banding patterns. Differences in band positions and color intensity enabled clear species discrimination. Quercetin and kaempferol derivatives were observed as the main phenolic compounds. Kalanchoe daigremontiana exhibited greenish zones associated with kaempferol glycosides, while K. crenata showed intense yellow bands corresponding to quercetin derivatives.

HPTLC fingerprints of Kalanchoe species (NP reagent, UV 366 nm and SST UV 254 nm). Track 1: system suitability test (SST); track 2: rutin, chlorogenic acidand quercetin (with increasing R_F); track 3: K. daigremontiana extract; track 4: K. × houghtonii extract; track 5: K. crenata extract; track 6: K. marmorata extract; and track 7: K. pinnata extract.

Post-chromatographic derivatization was also applied to evaluate antioxidant activity directly on the plates. Three complementary assays were performed (Folin-Ciocalteu, DPPH^•, and ABTS^+•), and revealed clear evidence of antioxidant potential across all species. In the Folin Ciocalteu assay, grayish/bluish zones indicated compounds with reducing capacity; the DPPH^• assay revealed zones where the free radical was decoloured, corresponding to strong radical-scavenging compounds. The ABTS^+•assay produced decoloration of the ABTS^+•, confirming the reducing capacity of specific compounds in the extracts. The reactive zones in all assays corresponded closely to the phenolic bands visualized with NP reagent.

Post-chromatographic Folin–Ciocalteu assay on HPTLC plates under white light. Blue zones correspond to phenolic-rich regions. Track 1: SST (UV 254 nm); track 2: rutin, chlorogenic acid and quercetin (with increasing R_F); track 3: kaempferol; track 4: K. daigremontiana extract; track 5: K. × houghtonii extract; track 6: K. crenata extract; track 7: K. marmorata extract; and track 8: K. pinnata extract.

Post-chromatographic antioxidant activity assay of Kalanchoe extracts using DPPH^• before imaging under white light. Yellow bands on a purple background indicate radical-scavenging activity. Track 1: SST (UV 254 nm); track 2: rutin, chlorogenic acid and quercetin (with increasing R_F); track 3: kaempferol; track 4: K. daigremontiana extract; track 5: K. × houghtonii extract; track 6: K. crenata extract; track 7: K. marmorata extract; and track 8: K. pinnata extract.

Post-chromatographic antioxidant activity assay of Kalanchoe extracts using ABTS^+•, detection under white light. Colorless zones on a bluish-green background indicate antioxidant-active areas corresponding to phenolic compounds. Track 1: SST (UV 254 nm); track 2: rutin, chlorogenic acid and quercetin (with increasing R_F); track 3: kaempferol; track 4: K. daigremontiana extract; track 5: K. × houghtonii extract; track 6: K. crenata extract; track 7: K. marmorata extract; and track 8: K. pinnata extract.

The antioxidant active zones detected in the Folin-Ciocalteu, DPPH^• and ABTS^+• assays are in agreement with the phenolic zones visualized with NP derivatization reagent, indicating that those phenolic compounds likely contribute to the reducing capacity of the extracts.

Overall, the combination of HPTLC fingerprinting and antioxidant assays on the plate provided a robust, reproducible, and highly informative approach for the chemical and functional characterization of Kalanchoe species. These findings confirm the suitability of HPTLC PRO as a robust, reproducible platform for species authentication of Kalanchoe materials.

[1] M.A. Quazi et al. Int. J. Pharm. Sci. Res. 9 (2018) 1000.

[2] S.V. Pattewar et al. Int. J. Pharm. Sci. Rev. Res. 12 (2012) 20.

[3] N.P. Yadav et al. Int. J. Ayurveda Res. 4 (2003) 152.

[4] G.F. Smith et al. Bothalia 48 (2018) 1.

[5] B. Descoings. Illustr. Handb. Succulent Plants (2003) 144.

[6] S. Sasidharan et al. Evid. Based Complement. Alternat. Med. 8 (2011) 1.

[7] H. Wagner et al. Plant Drug Anal. (1996) 304.

[8] E. Reich et al. High-Perform. Thin-Layer Chromatogr. (2007) 45.

Contact: Dr. Wilmer H. Perera, CAMAG Scientific, Inc., 515 Cornelius Harnett Drive Wilmington, NC 28401, USA, wilmer.perera@camag.com

The teams of Dr. Miguel Alfonso, molecular biologist and plant geneticist researcher at the Aula Dei Experimental Station (CSIC, Zaragoza, Spain), and that of Dr. Vicente L. Cebolla, analytical chemist researcher at the Instituto de Carboquímica (CSIC, Zaragoza, Spain), are collaborating in biofuel production from plant seeds, in particular from Thlaspi arvense, also known as pennycress. This winter plant is an emerging raw material for producing high-quality, erucic-rich biofuel. Pennycress has favorable agronomic properties, can be cultivated in arid and semi-arid drylands in cold climates, and does not compete with other crops that are edible, such as soybean or sunflower.

The differential fact that gives pennycress seed oil extraordinary properties as a biofuel is the majority composition of their lipids in erucic acid, a mono-unsaturated, long-chain (C22), and other very long chain fatty acids. However, erucic acid is absent or barely present in oils from other related species (e.g., rapeseed, Arabidopsis thaliana) or in other used for biofuel production (soybean, Camelina sativa). To understand why, we set out to elucidate the molecular mechanisms of incorporation of erucic acid into TAG, in function of the maturation of the Pennycress seed. For this, we have carried out an exhaustive lipidomic study based on HPTLC coupled to MS, a choice technique for a rapid and informative lipidomic screening.

HPTLC-MS allowed us for obtaining normalized profiles of TAG subclasses. This was validated and statistically correlated using a standardized quantitative LC-MS based-method. Likewise, we were able to draw structural information about sn-2 position of certain TAG species by HPTLC-MS/MS. This was of importance in this research to determine which metabolic pathways of TAG biosynthesis are favored over others at different stages of seed maturation. This has been possible by crossing lipidomic results with those of an integrative transcriptomic analysis of gene expression (RNA-Seq and qPCR) [1,2]. The research is of interest for genetic improvement of other potentially biofuel-producing plants.

Solutions of individual standards from a variety of lipid families (0.33-2 mg mL^-1), including triacylglycerol (TAG) standards, were dissolved in DCM-MeOH (1:1, V/V) [1,2].

For this study, five developmental stages of pennycress seeds were selected for lipidomic analysis, which correspond to green (G, 12 days after flowering, DAF), green-yellow (GY, 19 DAF), yellow-green (YG, 26 DAF), yellow (Y, 33 DAF), and mature (M, 45 DAF), according to a previous work [1]. They were harvested, frozen in liquid nitrogen, and stored at −80 °C.

Total lipids are extracted from seeds (0.1 g) with CHCl₃-MeOH (6 mL, 2:1, V/V) using the “Bligh and Dyer” method. Samples are dissolved (3-4 mg mL^-1) in DCM-MeOH (1:1, V/V).

HPTLC silica gel 60 plates (20 × 10 cm) without fluorescence indicators are employed (Merck). Before being used, plates are immersed in THF (5 min), dried at 70 °C in a vacuum (50 mbar) for 15 min, and pre-developed up to 90 mm using n-heptane (C7)-t-butyl-methyl-ether (MTBE)‒acetic acid (AcH), 70:30:1 (V/V).

3 μL of standard solutions and 4 μL of sample solutions are applied as 4-mm bands in triplicate on the same plate, on three different plates with the Automatic TLC Sampler 4 (ATS 4), band length 6.0 mm, distance from the left edge 10.0 mm, minimal track distance 6.0 mm, distance from the lower edge 10.0 mm. One or more tracks are left empty, as blanks for chromatography and MS analysis.

The plates are developed in a horizontal developing chamber (20 × 10 cm) up to 70 mm using C7-t-butyl-methyl ether-AcH (70:30:1, V/V).

Detection was carried out using the TLC Scanner 3 at 190 nm in absorbance mode. WinCATS software was used to control and process data from sample application and densitometry.

Zones at 52 mm migration distance were eluted with the TLC-MS Interface 2 (oval elution head) at a flow rate of 0.2 mL/min with methanol into an ion trap Amazon Speed Spectrometer (Bruker Daltonics, Bremen, Germany).

Electrospray ionization in positive ion mode, ESI(⁺)-MS, was selected for TAG analysis. MS spectra were recorded at the same ionization time in all cases. Structural identity of TAG was performed by ESI(⁺)-MS/MS.

Densitograms show that TAG is the main peak in pennycress seeds (88-96% of the total peak area), increasing as a function of maturation, from the different stages from G to M. TAG bands, with a migration distance (m.d.) in HPTLC near to 52 mm, were sent to ESI(⁺)-MS in order to obtain normalized profiles of TAG ion abundance and the corresponding TAG subclasses, which provided similar results to those obtained by LC-MS [2]. In fact, results of both techniques were statistically correlated. Results confirmed the incorporation of 22:1 into TAG already at the initial stage of seed maturation (TAG 58:5), as well as the increase of 22:1 and 20:1 as maturation progresses (TAG 60:4 and TAG 62:4).

The structural identification of TAG molecular subclasses was carried out by obtaining MS/MS spectra directly from the separated TAG band on the chromatographic plate by isolation and fragmentation of selected ions.

A given TAG subclass usually consists of several isobaric species coming from different combinations of FA. This makes impossible a complete positional analysis, i.e., attribution to sn-1, sn-2, and sn-3 positions. However, there are occasions where only one combination of fatty acid composition is present. When a single triad of fatty acids is found in an MS/MS spectrum of a parent ion, we are able to identify the fatty acid in the sn-2 position. Why? It is known that existing fragmentation methods cannot distinguish between sn-1 and sn-3, as fatty acids from these positions in TAG have identical fragmentation efficiencies. Therefore, TAG molecules lose simultaneously and preferentially fatty acyls at positions sn-1 and sn-3. This implies the formation of two ions of similar abundance and more abundant than the ion corresponding to the loss of the fatty acid position at sn-2. Therefore, we can identify this one.

Positional analysis of acyl-groups at sn-1, sn-2, and sn-3 positions in TAG is important to analyze the influence of acyl-CoA pools and their incorporation into lipid species by the different TAG biosynthetic pathways. In general, it is assumed that acyl groups at the sn-1 and sn-3 positions of TAG are more related with the composition of the acyl-CoA pool, while those at sn-2 are directly related to the acyl preference of the lyso-phosphatidyl acyltransferase (LPAT) enzyme, which catalyzes sn-2 acylation in Kennedy’s pathway (de novo synthesis of TAG).

Our results indicated that in most of the major TAG species, 18:2 was detected at the sn-2 position in all stages of seed maturation, indicating that pennycress LPAT showed higher preference for 18:2-CoA substrates rather than for other CoA moieties such as erucoyl-CoA. In other words, this enzyme, does not show specificity for erucic acid.

Operationally, parent ion fragmentation in ion trap must be done in a limited period of time to obtain the corresponding MS/MS spectrum. Since multiple replicates of the same sample can be applied to the plate, and the chromatographic development is performed simultaneously for all applied samples, HPTLC is the preferred technique for attempting to isolate and fragment a given ion to obtain a rapid MS/MS spectra record. If capture fails, another replicate can be attempted, unlike LC-MS, where the sample must be re-injected in a new run and eluted again.

[1] A. Claver et al. Front Plant Sci 15 (2024)1386023.

[2] J.M. Escuín et al. JPC 38 (2025) 455.

Contact: Dr. Vicente L. Cebolla, Instituto de Carboquímica, CSIC, C/ Miguel Luesma Castán, 4.50018 Zaragoza, Spain, vcebolla@icb.csic.es

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.

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.

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.

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

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.

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.

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.

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.

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.

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 R_F value.

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.

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.

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 %.

[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

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

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.

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.

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.

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).

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

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.

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.

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.

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.

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.

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 R_F = 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 R_F 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].

[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.

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

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.

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.

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].

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].

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

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].

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].

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].

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.

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.

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.

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.

[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

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

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.

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).

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.

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

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.

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.

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.

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

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 R_F values for each analyte were as follows:

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

[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

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

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.

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

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.

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

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.

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.

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.

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.

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.

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.

[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.

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