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Applying machine learning to data obtained with the complementary developing solvents protocol

Written by Admin Camag on April 30, 2023. Posted in CBS Articles.

Introduction

In 2022, CAMAG laboratory introduced the concept of complementary developing solvents (CDS) based on the combination of three automated analyses using three developing solvents (DS): one low polarity (LPDS), one medium polarity (MPDS), and one high polarity (HPDS) solvent [1]. With these three DS, any compound is characterized by three R_F values instead of one. Introduced into a database, a large dataset can be compiled from these values, which then may be subjected to data mining and machine learning.

In their paper [2], CAMAG researchers describe how the application of a CDS protocol to a large number of highly diverse individual compounds was used in combination with machine learning to predict the R_F values of individual substances from molecular properties, and to generate proposals for the identity of a zone. Coupled with machine learning, the CDS concept as a very powerful, general, and medium to high throughput technique for routine analysis and sophisticated research, may become the future of HPTLC. It may help replacing common tasks such as visual evaluation and pattern recognition, as well as subjective pass/fail decisions by automated procedures and numerical values generated by suitable algorithms.

Visualization of the CDS and its composite fingerprint

Standard solutions

Individual standard solutions were prepared at a concentration of 1.0 mg/mL (adjusted when necessary). Methanol was used as solvent for iridoids, coumarins, pharmaceutical drugs, flavonoids, triterpenes, sesquiterpenes, steroids, phospholipids and cannabinoids, 50% aqueous acetonitrile for carbohydrates, 50% aqueous methanol for amino acids, and toluene for monoterpenes. System Suitability Test (SST): the ready to use solution of Universal HPTLC Mix (UHM) was prepared in house according to [3] and applied on each plate.

Chromatogram layer

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

Sample application

2.0 μL of sample solutions 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 three developing solvents in the ADC 2 with activation of the plate at 33% relative humidity for 10 min using a saturated solution of magnesium chloride. LPDS (toluene, ethyl acetate 9:1 (V/V)) is used without saturation, whereas MPDS (cyclopentyl methyl ether, tetrahydrofuran, water, formic acid 40:24:1:1 (V/V)) and HPDS (ethanol, dichloromethane, water, formic acid 16:16:4:1 (V/V)) are used 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

Derivatization with anisaldehyde sulfuric acid reagent (10.0 mL of sulfuric acid were carefully added to the ice-cold mixture of 170.0 mL of methanol and 20.0 mL of acetic acid. To this solution, 1.0 mL of anisaldehyde was added) by spraying (Derivatizer, blue nozzle, 3.0 mL, spraying level 3) was followed by 3 min of heating at 100°C. Images of plates derivatized with Fast blue salt B reagent (250.0 mg of Fast blue salt B (o-dianisidine bis(diazotized) zinc double salt) were dissolved in 10.0 mL of water and mixed with 25.0 mL of methanol and 15.0 mL of dichloromethane) were captured within 2 min after spraying (Derivatizer, green nozzle, 3.0 mL, spraying level 5).

For the derivatization with NP reagent (1.0 g of diphenylborinic acid aminoethylester was dissolved in 200 mL of ethyl acetate ) / PEG (10 g of polyethylene glycol 400 (macrogol) were dissolved in 200 mL of dichloromethane), the plates were heated at 100 °C for 3 min, cooled to room temperature, then sprayed with the mixture NP/PEG 1:1 (V/V) (Derivatizer, green nozzle, 3.0 mL, spraying level 3), and dried for 2 min. Derivatization by immersion (Immersion Device, speed 5, time 0) with toluene sulfonic acid reagent (10% of p-toluene sulfonic acid in ethanol) was followed by heating at 150°C for 3 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.

Numerical databases preparation and processing

The open-source software KNIME (version 4.6) was used. The “RDKit KNIME Integration” was applied for curation of the databases and conversion of the chemical structures. 178 chemicals of the learning set were then used to benchmark various machine learning models.

Machine learning

R_F values obtained from peak profiles from images (PPI) or scanning densitometry (PPSD), were used with the Random Forest regressor algorithms including 100 trees.

Results and discussion

For building a powerful model, four steps were taken:

Overview of the machine learning pipeline and its workflow

The first step was the collection of data. For this, a training set consisting of 178 known individual substances was selected from various chemical classes, covering molecular weights (MW) ranging from 75.1 g/mol to 1131.3 g/mol, and computed octanol/water partition coefficients (SlogP) in the range of -7.53 to 13.98. Using the open source software KNIME and its extensions, molecular descriptors (e.g. MW, SlogP, topological polar surface area (TPSA)…) were computed for each substance. In addition, each substance was chromatographed with the CDS, generating 178 x 3 = 534 R_F values. In the second step, the dataset was cleaned by filtering all descriptors for null variance. The third step included the training of the model. For this, the performance of three regressors was evaluated according to their capacity to predict the R_F within the training set. The Random Forest, trained with 100 trees, yielded the best correlation coefficients R² 0.55, 0.72, and 0.64 for the LPDS, the MPDS, and the HPDS, respectively.

For testing of the model, a test set was created with 20 other substances. The suitability of the selected substances was verified by demonstrating that the chemical space of the test set was within the chemical space of the training set.

Chemical space (2D t-SNE projection) covered by the 178 chemicals belonging to the training set (black dots) and the 20 chemicals belonging to the test set (orange squares)

The model was used to predict the R_F values of the compounds in the test set. Most predicted R_F differ by less than 0.1 units from the measured values. R_F in the MPDS and the HPDS are both predicted within the correct range and with very small errors, leading to R² of 0.87, and 0.71, respectively. The variance for each individual prediction (LPDS, MPDS, and HPDS) remains smaller than 10%, except for a few compounds.

Measured and predicted R_F values of the compounds in the test set

A reverse test was also performed. The query molecule defined by its R_F values in LPDS, MPDS and HPDS was compared to the database for a number of rows (four each) matching the specified similarity. To calculate the similarity, the Euclidean distance was selected and the four nearer neighbors (most similar) were displayed in an additional column.

Use of the database for proposal of potential matches

Conclusion

The examples above illustrate the potential of the CDS and its combination with machine learning. In this study, R_F values can be predicted, emphasizing that this feature is encoded within the chemical structure of the molecules. Moreover, the link between chemical structures and R_F allows to generate a list of four molecules likely to correspond to an unknown zone in complex mixtures. This prediction would be even more useful, if additional data such as mass and UV-VIS spectra were added to the database.

Literature

[1] T.K.T. Do, M. Schmid, I. Trettin, M. Hänni, E. Reich, Complementary developing solvents for simpler and more powerful routine analysis by high-performance thin-layer chromatography, JPC – J. Planar Chromatogr. – Mod. TLC. (2022). https://doi.org/10.1007/s00764-022-00185-1.
[2] T.K.T. Do, I. Trettin, M. Hänni, E. Reich, Applying machine learning to the data obtained with the complementary developing solvents protocol, J. Liq. Chromatogr. Relat. Technol. (2023).
[3] T.K.T. Do, M. Schmid, M. Phanse, A. Charegaonkar, H. Sprecher, M. Obkircher, E. Reich, Development of the first universal mixture for use in system suitability tests for High-Performance Thin Layer Chromatography, J. Chromatogr. A. 1638 (2021) 461830. https://doi.org/10.1016/j.chroma.2020.461830.

Further information on request from the authors.

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

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HPTLC – A good technique for extractable and leachable studies of plastics

Written by Admin Camag on April 26, 2023. Posted in CBS Articles.

Dr. Kashyap Thummar, Assistant Professor at Graduate School of Pharmacy (GSP), Gujarat Technological University (GTU), India, employs chromatographic separation techniques, especially HPTLC, to develop new and improved quantitative analytical methods for determination of drugs, impurities, adulteration and naturally occurring compounds in a variety of sample matrices. He prefers HPTLC because it is flexible, inexpensive, time-saving and does not produce toxic waste.

Introduction

Phthalates are esters of phthalic acid that are commonly added to plastics to improve their flexibility, transparency, durability, and longevity. These phthalates are easily released into the environment from plastic and can harm all living organisms. Phthalates enter the body by contact to plastics, e.g. through ingestion, inhalation, skin absorption, and intravenous injection.

For the detection of extractable and leachable phthalates in pharmaceutical products, a simple, rapid, precise, and accurate HPTLC method was developed. It simultaneously estimates the presence of four different phthalates in various pharmaceutical products and containers: dimethyl phthalate (DMP), diethyl phthalate (DEP), dibutyl phthalate (DBP) and di(2-ethylhexyl) phthalate (DEHP). The method was successfully applied for determination of extractables and leachables from 12 parenteral products packed in plastic containers.

Standard solutions

10.0 mg of each phthalate were individually dissolved in 10.0 mL of methanol and subsequently combined to prepare a working standard solution at a concentration of 0.1 mg/mL of each.

Sample preparation

2.0 g of the plastic container (extractable) and 30.0 mL of the product packed in it (leachable) are extracted 3 times with 30.0 mL of n-hexane by 10 min of sonication. The organic layers are collected, evaporate to dryness and reconstituted with 1.0 mL of methanol.

Chromatogram layer

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

Sample application

Samples and standard solutions are applied as bands with the Linomat 5, 20 tracks, band length 5.0 mm, distance from left edge 10.0 mm, distance from lower edge 8.0 mm. 20.0 μL for sample solutions and 1.0–14.0 μL for standard solutions (8 points for calibration) are applied.

Chromatography

Plates are developed in a saturated Twin Trough Chamber (15 min, with filter paper) with n-hexane – ethyl acetate 9:1 (V/V) to a migration distance of 90 mm (from the lower edge), followed by drying with cool air for 5 min.

Documentation

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

Densitometry

Absorbance measurement at 240 nm is performed with the TLC Scanner 4 and winCATS, slit dimension 4.00 mm x 0.30 mm, scanning speed 20 mm/s.

HPTLC chromatogram in UV 254 nm (left) and densitogram measured at 240 nm (right)

Results and discussion

A representative densitogram of samples is shown. During evaluation of the chromatogram, phthalates in simulated and test samples give the same R_F values as the standard and are well separated from matrix components. A simulated sample is a laboratory-made sample that is designed to mimic a real-world product or material.

3D profile of scanned sample and standard tracks (measured at 240 nm)

DMP, DEP, DBP and DEHP are separated at R_F values of 0.23, 0.31, 0.44 and 0.60 respectively. The limit of quantification was in the range of 41.7 to 99.8 ng/band for the four phthalates and linearity was established between 100.0 and 1400.0 ng/band. The presence of individual phthalates was found in 12 pharmaceutical products (all parenteral formulations) in significant amounts. Importantly, of the four phthalates, DEHP was found in all tested samples as an extractable and leachable. The HPTLC method for detection of phthalates is cost-effective as compared to available analytical methods such as HPLC, LCMS/ MS, GCMS/MS etc. as these techniques require more solvent consumption, power consumption, analysis time, and involve complex sample preparation methods. The method can be universally applied to other samples apart from pharmaceutical products like water, food items etc. which are sold in plastic containers.

Literature

[1] T. H. Broschard et al. (2016) Regulatory Toxicology and Pharmacology 81, 201–211.
[2] K. Thummar and N. Sheth, Publication date: 2022/6/7, Patent office: IN, Patent number: 398670.
[3] K. Thummar et al. (2020) Analytical Chemistry Letters 10 (1), 93–103.

Further information on request from the authors.

Contact: Dr. Kashyap Thummar, Assistant Professor, Graduate School of Pharmacy, Gujarat Technological University, Gandhinagar, Gujarat, India, ap_kashya@gtu.edu.in

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White Paper: Typical applications of HPTLC for analysis of food

Written by Admin Camag on April 26, 2023. Posted in White Paper.

HPTLC is a highly versatile, reliable and cost-efficient tool for the rapid in-parallel analysis of multiple samples. Detection of adulteration, identification and purity tests, monitoring stability, and quantification of marker compounds, are fully exploiting the strengths of the technique in the analysis of matrix-rich samples. HPTLC can easily deal with complex and diverse food matrices and is typically used for quality control purposes, to test for additives and to screen for food contaminants. The complexity and diversity of samples in which analytes such as carbohydrates, lipids, proteins, vitamins, and minerals have to be identified and quantified, are a major challenge in food analysis. HPTLC offers high matrix tolerance, allowing rapid authentication and accurate quantification of target analytes in food samples.

Quality control of honey

Honey is a natural mixture of glucose and fructose with many other minor substances. Due to the high price of honey, particularly of mono-floral types, adulteration with other sugars and syrups is often observed in the market. The type and ratio of mono and oligosaccharides in a sample can also provide information about source, handling, and storage of the honey. The quantification of sugars is challenging due to their high polarity, low volatility, and lack of a chromophore. Their common occurrence in complex matrices, may require separation from proteins, lipids, and/or minerals as well as from other matrix constituents prior to analysis. HPTLC can effectively separate and sensitively quantify mono- and oligosaccharides in honey [1].

Conclusion

HPTLC is the method of choice for the analysis of honey, allowing the identification of the floral source, quantification of sugars as well as detection of sugar adulterants in honey.

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HPTLC chromatograms of selected references and samples obtained after derivatization with ADPA reagent in white light RT. Track 1: fructose, track 2: maltose, track 3: sucrose, track 4: glucose, track 5: blossom honey, track 6: agave syrup, track 7: linden blossom honey, track 8: molasses, track 9: honeydew, track 10: sugar from coconut-flowers, track 11: cane sugar, track 12: rice syrup, track 13: syrup from coconut-flowers, track 14: maple syrup, track 15: wild bee honey, track 16: wild bee honey adulterated with maple syrup
02

Calibration curve of sucrose, blue circle shows the amount detected in the samples maple syrup and wild bee honey mixed with maple syrup.
03

Fingerprints of mono-floral honeys from Western Australia in different detection modes [2]; Track 1: UHM (ready to use solution), track 2: 4,5,7- Trihydroxyflavanone, tracks 3-5: Marri Honey, track 6: Wildflower Honey, track 7: Coastal Peppermint Honey, track 8: Brown Mallet Honey

Screening for aflatoxins in tomato extracts

Aflatoxins are natural mycotoxins produced by Aspergillus fungi. The fungal contamination of crops, nuts, dried and fresh fruits/vegetables is quite common, whereas high temperatures and humidity favor the occurrence of molds and thus the production of aflatoxins, which are known to be highly genotoxic and carcinogenic to humans and therefore must be controlled and prevented from use in food products. HPTLC rapidly identifies and reproducibly quantifies aflatoxins in food samples.

Conclusion

HPTLC is suitable for the quantification of aflatoxins B1, G1, B2, and G2 in tomato extract according to the Test for Aflatoxins of USP chapter <561> Articles of Botanical Origin, which limits aflatoxin B1 to 5 ppb and the sum of B1, G1, B2, and G2 to 20 ppb.

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HPTLC chromatograms in UV 366 nm after derivatization. Track 1: tomato extract, track 2: tomato extract spiked with 5 ppb aflatoxins B1 and G1, track 3: tomato extract spiked with 25 ppb aflatoxins B1 and G1, track 4: standards aflatoxin B1 and G1, track 5: standards aflatoxin B1 and G1, track 6: standards aflatoxin B1 and G1, track 7: standards aflatoxin B1 and G1, track 8: tomato paste, spiked with aflatoxins B1, G1, B2, G2
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Densitogram of a tomato extract sample (black) and the same sample spiked with 5 ppb of aflatoxins B1 and G1 (green)

Analysis of illegal dyes in spices

Synthetically manufactured azo dyes are often illegally used for the artificial enhancement of the natural color of spices. Classified as carcinogens, their use as food additives is prohibited in the EU and the United States. Yet they are still used to amplify the color intensity of spices, particularly in countries in which the spices originate. Products offered as non-branded spices as they are available in public and food markets bear a higher risk of adulteration with illegal dyes. HPTLC is suitable for the rapid, sensitive, and reproducible analysis of spices contaminated with illegal dyes [3].

Conclusion

HPTLC is suitable for the reliable identification and accurate quantification of azo dyes in chili, paprika, curry powder and spice mixtures. Moreover, HPTLC is commonly used for dyestuff analysis in forensic, industrial and other applications.

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HPTLC chromatograms of illegal dyes in white light after derivatization. Track 1: Natural Red 25, track 2: Para Red, track 3: Auramine, track 4: Sudan Red B, track: 5: Methyl yellow, track 6: Sudan Red G, track 7: Oil Orange SS, track 8: Natural Red 28, track 9: Toluidine Red, track 10: Sudan Orange G, track 11: Sudan I, track 12: Sudan II, track: 13, Sudan III, track 14: Sudan IV, track 15: Sudan 7B
02

Spiking experiment with “spice preparation for poultry” spiked with Mix 1. Track 1: sample not spiked; tracks 2-3: sample spiked with 50 ppm; tracks 4-5: sample spiked with 10 ppm; tracks 6- 7: sample spiked with 2 ppm. Mix 1: Disperse orange, butter yellow, toluidine red, sudan red G, FD&C orange, Sudan red 7B, Sudan red B
03

Calibration curves for Para Red, Citrus Red, Sudan I-IV at low level

Analysis of milk

Milk is a healthy and nutritious dairy product, consumed by a majority of the world’s population. Among dairy products, human milk is particularly known for its presence of oligosaccharides (HMOs – Human Milk Oligosaccharides) because they are minimally digested in the gastrointestinal tract and reach the colon intact, where they shape the microbiota. Oligosaccharides are important components containing a group of structurally complex, unconjugated glycans. HPTLC is well suited for detection HMOs component, such as for in-process control during fermentation, or for monitoring of purification steps, and QC of finished products like HMOs. All production cycles can be followed by using the same methodology [4].

Milk products such as milk powders are sometimes affected adulteration for economic reasons. The practice of adulterating milk invariably reduces its quality and can introduce harmful substances into the dairy supply chain, thus endangering the health of consumers. In 2008, melamine, found in infant milk products, caused kidney damage and several deaths among children. Melamine (1,3,5-triazine-2,4,6-triamine) may have been illegally added to mask low protein content in fraudulently diluted or low quality milk. Since then, there is a great need for rapid and reliable methods for quality control of milks [5].

Conclusion

In milk, a very complex mixture, HPTLC is suitable for the screening and quantification constituents such as oligosaccharides. Melamine, a dangerous adulterant can be detected with a limit of 20 mg/L.

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HPTLC chromatograms in UV 366 nm (A) and in white light (B) after derivatization with Aniline diphenylamine phosphoric acid reagent. Track 1: LNFP-I, DFL, 2FL; 2: LNnT, lacto-N-triose II, lactulose; 3: para-LNnH, LNT, D-panose, lactose with increasing RF values; 4: HMO sample 1 (finished product) at 0.05% (application volume 1 μL, absolute amount on plate: 0.5 μg); 5: HMO sample 1 at 1% (application volume 1.0 μL, absolute amount on plate: 10.0 μg); 6: HMO sample 5 (finished product) at 0.2% (application volume 2.0 μL, absolute amount on plate: 4 μg); 7: HMO sample 8 (fermentation) at 1% (application volume 4.0 μL, absolute amount on plate: 40.0 μg); contrast 2.0 for both detection modes
02

HPTLC chromatograms in white light after derivatization. Tracks 1-7: melamine standard; track 8: milk sample; track 9: milk spiked with melamine (spiking level 0.01 %), application volume 1.0 μL; track 10: milk spiked with melamine (spiking level 0.01 %), application volume 2.0 μL

Analysis of polyphenols in coffee beans [6]

Coffee beans are a rich source of bioactive phytochemicals such as chlorogenic acids (CGA), including caffeoylquinic acids (CQA), feruloylquinic acids (FQA) and dicaffeoylquinic acids. Because during the roasting process, the chlorogenic acids content changes dramatically, a HPTLC method is used to follow the evolution of chlorogenic acids throughout the process also with the aim of controlling the roasting degree. The HPTLC chromatograms indicate similarities and differences in the composition of the identified compounds during the roasting process. Here, 3-CQA, 4-CQA and 5-CQA show light-blue fluorescence zones, whereby 4-FQA and 5-FQA show deep-blue fluorescence zones and the three dicaffeoylquinic acids like 3,4-di-CQA, 3,5- di-CQA and 4,5-di-CQA show light-green fluorescence zones.

Conclusion

The present study underlines the usefulness of HPTLC as a reliable tool to assess quality and quantity parameters of the coffee roasting process.

Comparative HPTLC fingerprints of the whole roasting process. Left track is the coffee mixtures for CGA with the separated compounds (a) 3-CQA, (b) 5-CQA, (c) 4-CQA, (d) 5-FQA, (e) 4-FQA, (f) 3,4-di-CQA, (g) 4,5-di-CQA, and (h) 3,5-di-CQA illuminated with UV 366 nm, derivatization with NPA reagent

Literature

[1] M.K Islam, T. Sostaric, L.Y. Lim, K. Hammer, C. Locher (2020) Sugar Profiling of Honeys for Authentication and Detection of Adulterants Using High-Performance Thin Layer Chromatography, Molecules 2020, 25, 5289. https://doi.org/10.3390/molecules25225289

[2] https://www.hptlc-association.org/methods/methods_for_identification_of_herbals.cfm

[3] H. Kandler, M. Bleisch, V. Widmer & E. Reich (2009) A Validated HPTLC Method for the Determination of Illegal Dyes in Spices and Spice Mixtures, Journal of Liquid Chromatography & Related Technologies, 32:9, 1273-1288, https://doi.org/10.1080/10826070902858293

[4] https://www.camag.com/sites/default/files/application_notes/A-139.1.pdf

[5] https://www.camag.com/sites/default/files/application_notes/A-88.1.pdf

[6] V. Pedan, E. Stamm, T. Do, M. Holinger, E. Reich, HPTLC fingerprint profile analysis of coffee polyphenols during different roast trials, Journal of Food Composition and Analysis, Volume 94, 2020, 103610, ISSN 0889-1575, https://doi.org/10.1016/j.jfca.2020.103610

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White Paper: Analytical Tasks in the Quality Control of Herbal Drugs

Written by Admin Camag on February 2, 2023. Posted in White Paper.

It is widely accepted that High-Performance Thin-Layer Chromatography (HPTLC) is the method of choice for the analysis of substances in complex matrices involving herbal drugs and herbal drug preparations. High-end instrumentation and standardized procedures enable HPTLC to deliver reproducible and cGMP-compliant results.

The publication of general chapters on HPTLC for the identification of plants and extracts as part of monographs of the United States Pharmacopoeia (USP-NF 2015) and the European Pharmacopoeia (Ph. Eur. 2017) emphasizes today’s role of HPTLC for the identification of samples from botanical origin.

HPTLC delivers a chromatographic fingerprint of the sample in its entirety, a feature that makes the method ideally suited for the analysis of botanical materials, herbal drugs and herbal drug preparations, all of which are highly complex and consist of many different components. Apart from this, the exact chemical composition of botanical products is unknown and may vary widely, both qualitatively and quantitatively.

The quantitative content of known active or inactive components in herbal drugs is not sufficient as a quality criterion: the presence of other substances must also be part of the analysis of a sample.

Why use HPTLC?

The advantages offered by HPTLC add up to a compelling argument for choosing it as an analytical technique.

Visual output

HPTLC is the method of choice for the analysis of substances in complex matrices. The HPTLC fingerprint of herbal drug samples visually either confirms or rejects the plant identity. HPTLC is a highly flexible analytical technique and allows adapting the analytical method to the individual needs in each process step. For each sample, the separated analytes remain on the plate and allow for further post-chromatographic processing.

Multiple detection of separated analytes

Post-chromatographic derivatization permits the use of additional detection modes and makes differences in fingerprints clearly visible. In contrast to other chromatographic techniques, the separated analytes of the sample remain on the plate.

Analysis of multiple samples in parallel without cross-contamination

HPTLC allows for parallel instead of sequential analysis with little to no sample preparation. At least 15 samples can be developed and then analyzed in parallel under identical conditions at the same time. Due to single use of the plate, there is no risk of cross-contamination.

Cost-efficiency

HPTLC offers short run times per sample (e.g. a total analysis time of 30 min for 15 samples means a run time of 2 min/sample) and low solvent consumption per sample, making it a highly cost-effective form of analysis.

Flexibility

HPTLC is an open system, enabling to set and optimize all influencing parameters independently of each other, and offers a nearly unlimited choice of mobile phases.

Disposable plates

HPTLC plates are disposed of after use, eliminating the problems caused by samples with high matrix content, which may block HPLC columns and cause ghost peaks.

Coupling to Mass Spectrometry

MS hyphenated with HPTLC is a powerful additional detection tool and allows the confirmation of identity of targeted analytes. High-resolution MS also enables some structure elucidation.

Compliance

HPTLC instrumentation from CAMAG can be used in a cGMP/cGLP environment. Additionally, software-controlled HPTLC instruments support full compliance with 21 CFR Part 11.

The required conditions for an efficient purification (ca. 75% use silica gel at Oril Industrie) are determined by TLC. Then, the purification progress is checked by preparative column chromatography via HPTLC. Twenty fractions were analyzable within 1 hour. TLC/HPTLC is the method of choice due to its simplicity, rapidness and the successful scale up from TLC to preparative separations. HPTLC-MS helped to quickly resolve the composition of a mixture.

Identification of raw materials and products

HPTLC generates a chromatographic fingerprint of the drug sample in the form of a unique sequence of zones or peaks or due to the components of the sample (as illustrated by the chromatogram of green tea). The fingerprint of botanically authenticated raw material serves as a primary reference against which unknown materials can be characterized. Both, sample and reference material are chromatographed side by side on the same plate. The resulting fingerprints are then compared with respect to the number, sequence, position and color of separated zones.

Example: HPTLC fingerprints of green tea extract and other caffeine containing botanicals

The polyphenol fingerprint of green tea differs significantly from that of other caffeine containing botanicals (top). In UV 254 nm prior to derivatization (bottom), caffeine can be detected.

Conclusion

HPTLC is a reliable technique for identification of green tea extracts based on polyphenols. During the same analysis, the caffeine content of the material can be determined.

Figure 1 : Track assignment – 1 reference substances with increasing RF: epigallocatechin gallate, epigallocatechin, epicatechin gallate, and epicatechin; 2 caffeine; 3–4 green tea extracts, 5 Cola nitida seed (red), 6 Cola nitida seed (white), 7 Coffee been (roasted), 8 Coffee been (green), 9–10 Guarana seed

Track assignment – 1 reference substances with increasing R_F: epigallocatechin gallate, epigallocatechin, epicatechin gallate, and epicatechin; 2 caffeine; 3–4 green tea extracts, 5 Cola nitida seed (red), 6 Cola nitida seed (white), 7 Coffee been (roasted), 8 Coffee been (green), 9–10 Guarana seed

Detecting Adulteration

One problem commonly encountered when controlling the quality of botanicals is the intentional substitution or inadvertent confusion of plant species. Adulteration or falsification becomes critical if the undesired plant species are toxic. Any method used for identification purposes must thus be specific and sufficiently sensitive.

Example: Controlling the quality of Stephania tetrandra

Stephania tetrandra is often confused with or replaced by toxic species of the Aristolochia genus, such as Aristolochia fangji. The very similar Chinese names of the two species are an additional source of confusion. HPTLC can detect the presence of aristolochic acids (AA) down to a concentration of 1 ppm, which means that the adulteration of Stephania with as little as 1% Aristolochia is visible.

Conclusion

HPTLC is a sensitive, rapid and cost efficient technique for detecting adulteration of Stephania tetrandra with aristolochic acids.

An HPTLC-based limit test for aristolochic acid is proposed by the European Pharmacopoeia (chapter 2.8.21).

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Detection of 1 ppm adulteration with aristolochic acid I in Stephania. Track assignment: 1–2 Stephania tetrandra pure (10 and 30 μL), 3–4 Stephania adulterated with 1 μg/g–1 aristolochic acid I (10 and 30 μL), 5–9 increasing amounts of aristolochic acid I
02

HPTLC screening of TCM samples for the presence of AAs [in UV 366 nm after derivatization with tin(II) chloride]. From left to right: A. fangji, 1 and 10 μL; AAs mixture 10 and 50 ng (absolute); S. tetrandra adulterated with 10 % an 1 % A. fangji, 10 μL each; pure S. tetrandra 10 μL (shows no zone).

Quantification of marker compounds

The most commonly used measure of sample quality is the amount of specific active compounds and/or marker compounds. Those are best quantified using either scanning densitometry or image-based evaluation.

Example: Densitometric quantification of oleuropein in Olive leaf dry extract

Oleuropein is a polyphenolic compound often used as marker for extracts and products derived from olive leaf.

Conclusion

HPTLC with scanning densitometry facilitates spectrally selective, sensitive, and precise quantification of substances in plant material. With comprehensive HPTLC fingerprinting, quantitative information about substances is available via peak profiles from images (PPI).

01

Oleuropein and olive leaf extracts; image of tracks in UV 254 (left); Peak Profiles from Scanning Densitometry at 280 nm (middle); Peak Profiles from Image at 254 nm (right)
02

Quantification of oeleuropein, olive leaf extract, and finished product by scanning densitometry (PPSD at 280 nm)
03

Quantification of oeleuropein, olive leaf extract, and finished product by image-based evaluation (PPI at 254 nm)

Other applications

Product development

HPTLC is used to optimize process parameters and detect changes and degradations in the material during formulation. It is a particularly effective type of analysis because it is fast and can be applied to many different samples simultaneously.

Process control

HPTLC is ideal for demonstrating the consistency of product quality because it proves that raw materials retain their integrity and that no decomposition takes place during production.

Stability tests

Thanks to the instrumentation and standardization offered by HPTLC, it is possible for the same process to be repeated over a prolonged period of time. This makes it a suitable method for stability tests in which the samples are compared from plate to plate over protracted periods. The first plate serves as the reference with which all subsequent plates are compared. Another significant advantage is that a large number of different samples can be analyzed quickly, making HPTLC a fast, efficient solution for stability tests.

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Development and validation of an HPTLC-DPPH assay method for the acteoside content of Ribwort ipowder®

Written by Admin Camag on November 15, 2022. Posted in CBS Articles.

The French company PiLeJe Industrie develops liquid and dry plant-derived ingredients using patented procedures and in-house developed processes. Their products are mainly used in the food supplements industry. The long-term collaboration with Chromacim led to the important development of HPTLC in their laboratory. This article addresses the question, whether it is possible to validate the activity assay of one major compound through its activity only, using the best international current standard.

Introduction

Ribwort plantain (Plantago lanceolata L.) is a common grassland plant traditionally used for its therapeutic properties. The leaves are used in many European countries for the symptomatic treatment of colds and inflammation of the mouth and throat. Biological activities of P. lanceolata include antihistaminic, anti-spasmodic, anti-nociceptive, neuroprotective, metabolic, and gastro-protective activities. Acteoside is a phenyl-propanoid glycoside, well known for its antioxidant and antiinflammatory properties, which is commonly used as a marker.

Ribwort ipowder® is a plant infusion concentrated on plant totum, a proprietary 100% plant-based product developed by PiLeJe Industrie, made from dried P. lanceolata according to a patented process [1, 2]. Quantification was already shown with standard detection and published in a previous issue of the CBS. Our goal was to prove that this type of activity detection is reliable and transferable to quality assurance with a proper validation package. This needs a level of knowledge that we have developed rather quickly with the help of our partners. The objectives of this work were 1) to develop an HPTLC method using the 2,2-diphenyl 1-picrylhydrazyle (DPPH*) effect-directed chemical reaction for the detection of the antioxidant activity of acteoside for quality control of industrial dry extracts of P. lanceolata and 2) to demonstrate the applicability of the concept of Life Cycle Management of analytical methods to quantitative HPTLC-DPPH* methods.

Standard solution

An acteoside standard is dissolved in methanol at a concentration of 17.40 μg/mL in methanol.

Sample preparation

500 mg of Ribwort ipowder® (PiLeJe Industrie) are extracted with 40 mL of ethanol – water 50:50 (V/V) by sonication at 60 °C for 10 min, then filtered and transferred to a 50 mL volumetric flask and filled up to the mark. The solution is diluted 5-fold for application.

Chromatogram layer

HPTLC plates silica gel 60, 20 x 10 cm are used.

Sample application

4.0 μL of sample and standard solutions are applied as bands with the Automatic TLC Sampler (ATS 4), 20 tracks, band length 8.0 mm, distance from left edge 22.0 mm, distance from lower edge 8.0 mm.

Chromatography

Plates are developed with ethyl acetate – water – acetic acid – formic acid 100:27:11:11 (V/V) to 70 mm (from the lower edge) in the ADC 2 with chamber saturation (20 min, with filter paper) and after activation at 33 % relative humidity for 10 min using a saturated aqueous solution of magnesium chloride.

Post-chromatographic derivatization

After drying for 10 min, the plates are immersed into DPPH* reagent (0.5 mM methanolic solution of 2,2-diphenyl-1-picrylhydrazyl, immersion speed 5 cm/s, immersion time 5 s) with the Chromatogram Immersion Device 3. The plates are dried at room temperature in the dark for 90 s and then heated at 60 °C for 30 s (TLC Plate Heater 3).

Documentation

Images of the plates are captured with the TLC Visualizer in white light after derivatization.

Densitometry

Fluorescence mode is used for measurement at 517 nm (tungsten lamp) with TLC Scanner 4 and visionCATS to obtain a positive response of the peaks of interest.

Results and discussion

Analytical methods used for quality control of plants and plant extracts are usually based on the identification and quantification of chemical markers to manage batch reproducibility and efficacy. To measure the concentration of acteoside in Ribwort ipowder®, the HPTLC DDPH* assay was applied. The assay determines the free radical scavenging activity of the plant extract in solution.

Figure 1: Antioxidative properties – DPPH

The first step of this work was the selection of the Analytical Target Profile (ATP) and the determination of the Target Measurement Uncertainty (TMU) taking into account the quality control requirements for such extracts and the applicable range of the detection method. Once the desired range was established, an evaluation of the calibration function was conducted using linear, 1/x and 1/x² weighted linear calibration models and those three models were used to assess accuracy of the method (trueness and precision) by means of accuracy profiles [3]. The 1/x² weighted linear calibration function showed the best performance in the tested range, both in terms of accuracy and uncertainty of measurement.

Figure 2: Accuracy profiles obtained with the selected calibration function, for each function the accuracy limits are given by the vertical doted lines

The method requirement was to assay acteoside amounts around 1.0–2.0% (W/W) in industrial dry extracts of Ribwort plantain with an acceptance criterion of ± 20.0% difference to the true value for the ATP (defined as the combination of the trueness (bias) and precision characteristics), and a TMU of less than 20.0–25.0% relative uncertainty, according to the quality control needs.

Figure 3: HPTLC chromatogram in white light and densitograms measured in fluorescence mode at 517 nm with a tungsten lamp after DPPH* assay of standards and plantain leaf extract

Due to the lack of reference samples, spiked samples were used to evaluate the accuracy of the method by means of Total Analytical Error (TAE) determination, using prediction intervals calculation for the selected calibration functions. For quality control, the calibration function with the best performance level in accordance with the product specifications was chosen by estimating the Measurement Uncertainty (MU).

As Life Cycle Management of the method also includes its routine use, the MU was checked on spiked and non-spiked extract samples at different dilution levels, in order to verify the accordance of results between those samples, and to prepare a replication strategy for the routine method. Statistical calculations were performed with NeoLiCy® software for analytical methods’ life cycle statistical assessment (NeoLiCy, Marseille- Mâcon, France). The tested dilutions did not show any significant effect on the calculated spiked amount and any significant impact on the extract calculated concentrations. To take care of the measurement dispersion we included repeated sample preparation and measurement in the analytical procedure.

This work demonstrated that the concept of Life Cycle Management of analytical methods can successfully be applied to a HPTLC-DPPH* method, even in the case of complex matrices such as plant extracts, from the definition of the ATP and TMU to the reflection on the replication strategy to be applied in quality control.

The method developed for the quantification of acteoside in Ribwort plantain is applicable in a working range from 75.0 to 225.0 ng of acteoside and fit for purpose for use in quality control laboratories.

This study showed the suitability of HPTLC in this domain. Furthermore, the partnership with Chromacim and NéoLiCy showed its efficiency, within a cumulative working time of less than two weeks only, including statistics and matrix effect evaluation. This method is therefore ready to be transferred to the quality control laboratory of the PiLeJe Group.

This success proves that we were initially right to select this approach and encourages us to continue in this powerful way to develop HPTLC as a relevant technique for our needs.

Literature

[1] Dubourdeaux, M. Procédé de Préparation d’extraits Végétaux Permettant l’obtention d’une Nouvelle Forme Galénique. 14 January 2009. Available at https://patents.google.com/patent/EP2080436A2/en, accessed on January 21, 2021)
[2] V. Bardot et al. (2020) Food Funct, https://doi.org/10.1039/c9fo01144g
[3] J.M. Roussel et al. (2021) J Chromtogr B, https://doi.org/10.1016/j.jchromb.2021.122923

Further information on request from the authors.

Contact: Dr. Valérie Bardot, Naturopôle Nutrition Santé, Les Tiolans, 03800 Saint-Bonnet de Rochefort, France, v.bardot@pileje.com

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HPTLC routine analysis using complementary developing solvents

Written by Admin Camag on October 1, 2022. Posted in CBS Articles.

Introduction

In quality control with HPTLC, a specific method using optimized developing solvents is generally used for each kind of sample. In order to simplify routine analysis, the lab team at CAMAG has developed the complementary developing solvents (CDS) concept based on one solvent of low polarity (LPDS), one of medium polarity (MPDS), and one of high polarity (HPDS). With these three developing solvents (DS), each on a separate plate and targeting compounds of different polarity, the same complex sample could be spread over up to three times the separation distance on a single plate, making available more information about the sample’s composition. Single substances can be characterized with three R_F values instead of one. Even though this approach triples the analytical workload (3 analyses instead of one), it may be considered, that routine work with multiple and diverse samples can be simplified and maintenance of methods, plates, solvents and standards can be kept to a minimum, particularly if the process is automated. Identification of individual compounds will be more certain. A further advantage of the concept is that all samples can be compared with any other sample that has been previously analyzed with the same CDS and data could be compiled in a database for treatment with advanced algorithms.

In their paper [1] the researchers at CAMAG describe the development, validation and application of a CDS, applicable to a large number of very diverse samples including individual compounds and complex herbal materials. In combination with thorough standardization, the concept could help positioning HPTLC as a very powerful, general, and medium to high throughput technique for routine analysis and sophisticated research.

Visualization of the CDS and its fingerprints

Standard solutions

The Universal HPTLC mix (UHM) was prepared in house according to [2]. With the UHM, HPTLC laboratories have a single solution, applicable as system suitability test to a wide range of chromatographic systems.

Sample preparation

Powdered herbal drugs and finished products were prepared in methanol using 10 min sonication followed by 5 min centrifugation. Ginkgo biloba, Camellia sinensis, Styphnolobium japonica and Piper nigrum were prepared at 100 mg/mL, Curcuma longa at 66.7 mg/mL, Angelica samples at 200 mg/mL, and the poly-herbal formulation at 50 mg/mL.

Chromatogram layer

HPTLC plates silica gel 60 F₂₅₄ (Merck), 20 x 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 three developing solvents in the ADC 2 with activation of the plate at 33% relative humidity for 10 min using a saturated solution of magnesium chloride. LPDS was used without saturation, whereas MPDS and HPDS were used with 20 min chamber saturation (with filter paper). The developing distance for all three methods was 70 mm (from the lower edge). Plates were dried for 5 min.

Post-chromatographic derivatization

Natural product (NP) reagent (1.0 g of 2-aminoethyl diphenylborinate in 100.0 mL of methanol) is used as derivatization reagent for the identification of Camelia sinensis, Styphnolobium japonicum and Ginkgo biloba. For Ginkgo biloba, the derivatization with NP is followed by anisaldehyde sulfuric acid (AS) reagent [slowly and carefully 170.0 mL of ice-cooled methanol are mixed with 20.0 mL of acetic acid and 10.0 mL of sulfuric acid; mixture is allowed to cool to room temperature, then 1.0 mL of anisaldehyde (p-methoxybenzaldehyde) is added]. Only AS reagent is used for the identification of Curcuma longa and Piper nigrum.

Documentation

Images are captured with the TLC Visualizer 2 in UV 254 nm, UV 366 nm, and white light prior to derivatization, and UV 366 nm, and white light after derivatization (when needed).

Densitometry

For the system suitability test using the UHM, TLC Scanner 4 and visionCATS are used in absorbance mode at 254 nm and in fluorescence mode at 366>/400 nm, with slit dimension 5.00 x 0.20 mm and scanning speed of 50 mm/s.

Results and discussion

The solvents selected for the CDS had to meet the following criteria: be of minimal hazard, stable, and easily available, to cover all selectivity groups according to Snyder, and include a broad range of polarity. The composition and properties of the CDS are shown in the table.

Composition and properties of the CDSs

The power of the CDS concept is illustrated with HPTLC fingerprints for identification of herbal drugs, herbal products, and poly-herbal formulations. Green tea leaves, for example, produce a different fingerprint with each of the DS zooming into specific polarities of the sample composition. The composite fingerprint gives complementary information emulating an extended developing distance.

HPTLC fingerprints of green tea leaves obtained with the CDS

HPTLC can easily detect adulteration of one herbal drug with another, using optimized developing solvents. For example, a method from the HPTLC Association [3] detects adulteration of Ginkgo biloba leaves with fruits of Styphnolobium japonicum. The CDS achieves the same goal, but offers even more certainty based on the data obtained with MPDS and HPDS.

Detection of 20% Styphnolobium japonicum fruit in Ginkgo biloba leaves with method [3] and CDS

Poly-herbal formulations such as products containing Curcuma longa and Piper nigrum are generally identified based on specific markers. The CDS can identify with certainty the presence of curcuminoids for turmeric and piperine for black pepper.

Fingerprints of Curcuma longa and Piper nigrum in comparison to those of two poly-herbal products

The CDS has been qualified using the universal HPTLC mix (UHM). A maximum margin of error of 0.014 was determined for the relevant zones.

Separation of the UHM components with the CDS

Conclusion

The examples above illustrate the potential of the CDS for replacing the established methods for identification of herbal materials. Additional information concerning the chromatographic behavior of representative substances from different chemical classes is presented in the original paper [1]. When combined with fully automated chromatography, the CDS concept may become the basis for new applications of HPTLC in routine analysis and sophisticated research.

Literature

[1] T.K.T. Do et al. (2022) JPC, https://doi.org/10.1007/s00764-022-00185-1.
[2] T.K.T. Do et al. (2021) J. Chromatogr. A. 1638, https://doi.org/10.1016/j.chroma.2020.461830.
[3] HPTLC Association, Identification method of Ginkgo biloba, Leaf and leaf extract (flavonoids), (n.d.). https://www.hptlc-association.org/methods/methods_ for_identification_of_herbals.cfm.

Further information on request from the authors.

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

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3D view of diacylglycerid-o-acyltransferase 2

HPTLC – a useful tool for the characterization of enzymes from plant lipid metabolism

Written by Admin Camag on October 1, 2022. Posted in CBS Articles.

The Jean-Pierre Bourgin Institute (IJPB) is the largest center for plant biology at the National Research Institute for Agriculture, Food and Environment (INRAE). It combines resources and multidisciplinary skills in the areas of biology, chemistry, and mathematics. Located in Versailles, it is a Joint Research Unit between INRAE and AgroParisTech. The DYSCOL (Dynamics and structure of Lipid Droplets) team studies various aspects of lipid accumulation in seeds from oil crops and model plants. The Kennedy pathway allows storage of fatty acids in eukaryotes in the form of triacylglycerols (TAG), through successive acylation of a glycerol backbone by acyltransferases. Hundreds of different fatty acids are found in plants. The different fatty acids incorporated into vegetable oils confer them specific physical, chemical and nutritional properties. DGATs (diacylglycerol acyltransferases) incorporate the final fatty acid in position sn-3 of the glycerol skeleton. They catalyze the rate-limiting step of the whole pathway. Due to their impact on oil yield and quality, DGATs are targets of interest for oil engineering. We aimed to identify candidate proteins as DGATs and to understand their substrate specificity. We used various approaches based on recombinant protein expression in different hosts, and analysis of the products of the reaction by complementary approaches, GC and HPTLC.

Introduction

Historically, demonstration of DGAT activity used radioactive precursors of substrates [1] followed by tedious extraction of the products (TAGs) [2] and quantification by liquid scintillation counting. Trans-methylation of TAGs produces fatty acid methyl esters, subsequently extracted, then separated and analyzed by GC. Alternatively, extraction of TAGs by improved methods and their separation from other cellular constituents by TLC is a convenient method avoiding radioactivity. Derivatization permits direct identification and quantification of the TAGs by comparison with standards.

HPTLC represents a valuable improvement of TLC. 12–15 samples are routinely separated at the same time on one plate. The method exhibits high sensitivity and reproducibility, and uses low amounts of organic solvents (< 50 mL for one run). Herein, we describe two methods to evidence DGAT activity and specificity using HPTLC.

Method (1) allows the analysis of extracted lipids by derivatization with phosphomolybdic acid reagent. Method (2) is used to study the activity and specificity of a purified DGAT.

Standard solutions

Solutions of cholesteryl oleate, oleic acid methyl ester, trioleine, oleic acid, cholesterol at 2.7 μg/μL each in CHCl₃ are prepared.
NBD-DOG (1-{N-[(7-nitro-2-1,3-benzoxadiazol- 4-yl)-methyl] amino-decanoyl-2-decanoyl-sn-glycerol) stock solution in chloroform – methanol 2:1 (V/V) at 67.5 ng/μL (between 5.5 ng to 2.7 μg are applied to generate a calibration curve).

Sample preparation

Lipids are extracted from yeast biomass according to Folch [2], dried under nitrogen, and resuspended in 200 μL of chloroform – methanol 2:1 (V/V).
DGAT assay to investigate enzyme specificity: NBD-DOG as a fluorescent DAG acceptor and different acyl donors (lauroyl-CoA, palmitoyl-CoA, stearoyl-CoA, oleoyl-CoA, and linoleoyl-CoA. The reaction is carried out at 31°C for one hour under shaking, then stopped by the addition of chloroform – methanol 2:1 (V/V).

Chromatogram layer

HPTLC plates silica gel 60 (Merck), pre-washed with isopropanol, are used.

Sample application

Between 3.0–6.0 μL for standard solutions and 50.0 μL for sample solutions (corresponding to 400 μg cells, dry weight) are applied as bands with the Automatic TLC Sampler (ATS 3), 15 tracks, band length 5.0 mm, distance from left edge 15.0 mm, distance from lower edge 8.0 mm.

Chromatography

Plates are developed in the Automatic Developing Chamber (ADC 2) with chamber saturation (with filter paper) for 20 min, (1) development with diethyl ether – hexane –methanol – acetic acid 60:40:5:1 (V/V) to the migration distance of 80 mm (from the lower edge), drying for 20 min, (2) development with hexane – diethyl ether – acetic acid 80:20:2 (V/V) to the migration distance of 80 mm (from the lower edge), drying for 20 min.

Post-chromatographic derivatization

The plates are immersed in 5% phosphomolybdic acid in ethanol, then incubated for 30 min at 100 °C using an oven.

Documentation

Images of the plates are captured in white light.

Densitometry

Fluorescence measurement is performed with the TLC Scanner 3 (excitation at 473 nm and emission > 510nm). DGAT activity is expressed as picomoles of TAG formed per minute and per milligram of purified protein, using a calibration curve based on the fluorescent signal of NBD-DOG.

Results and discussion

The following figure shows the results of the separation of lipids extracted from three yeast strains (method 1). The control yeast strain was transformed with an empty vector; a second strain was transformed with the AtDGAT1 sequence encoding for Arabidopsis thaliana DGAT1, and the last strain was transformed with the EgDGAT1 sequence encoding for Elaeis guineensis DGAT1-1, a putative DGAT [3]. Both cassettes encoding for plant DGAT1 restored TAG accumulation in the Yarrowia lipolytica mutant strain [4]. Thus, we conclude that EgDGAT1 sequence was encoding for an active E. guineensis DGAT1-1.

Figure 1: HPTLC – a useful tool for the characterization of enzymes from plant lipid metabolism

Separation of lipids extracted from yeasts strains expressing plant type 1 DGATs (1: standards, 2: empty cassette, 3: AtDGAT1, 4: EgDGAT1-1).

To generate a calibration curve (method 2), the fluorescence of the NBD-DOG standard applied in different amounts is measured. The standard curve is depicting the dependence of the intensity of the fluorescence of NBD-DOG (minus blank value).

Figure 2: Calibration curve of NBD-DOG (scanned at 473 nm in fluorescence mode) depicting the dependence of the intensity of the fluorescence of NBD-DOG (top) as function of the amount separated on HPTLC plate (bottom)

Calibration curve of NBD-DOG (scanned at 473 nm in fluorescence mode) depicting the dependence of the intensity of the fluorescence of NBD-DOG (top) as function of the amount separated on HPTLC plate (bottom)

In another experiment, purified recombinant DGA1, a type 2 DGAT from the yeast Yarrowia lipolytica was incubated with NBD-DOG and acyl-CoA with various acyl chain lengths (C12:0, C16:0; C18:0).

Separation of TAG synthetized by purified recombinant yeast type 2 DGAT (DGA1) using acyl-CoA with different chain lengths.

In the absence of an acyl donor, no NBD-TAG was formed (blank track). In the presence of acyl donors (C12:0, C16:0; C18:0–CoA), NBD-TAGs were formed. Noticeably, the migration of the reaction product depended on the length of the acyl chain incorporated. NBD-TAG with C18:0 migrate over a longer distance by comparison to NBD-TAG with C16:0 or C12:0.

HPTLC is a fast (within two hours), reproducible, and robust method to evidence acyltransferases activities. In vivo (1), complementation of microbial strains affected in neutral lipid metabolism by sequences coding for DGAT led to accumulation of TAGs. The products were extracted, separated by HPTLC and identified directly by comparison with standard molecules. In vitro (2), DGATs transferred various acyls to fluorescent DAG acceptors. Fluorescent products of the reaction (TAGs) were quantified using a substrate calibration curve. The method was sensitive enough to distinguish TAGs differing by only two carbons [5]. Both approaches avoided the use of radioactive labeled products.

Literature

[1] Erickson, S.K. and Fielfing, P.E. J Lipid Res 27 (1986) 875–883
[2] Folch, J. et al. J Biol Chem 226 (1957) 497–509
[3] Aymé, L., et al. PLoS One (2015) 10: e0143113
[4] Beopoulos, A., et al. (2012) Appl Microbiol Biotechnol 93: 1523–1537
[5] Haili N., et al. (2016) J Biol Chem 277: 6478–6482

Further information on request from the authors.

Contact: Dr. Laure Aymé and Dr. Thierry Chardot, Institut Jean-Pierre Bourgin, INRAE, AgroParisTech, Université Paris- Saclay, 78000, Versailles, France, laure.ayme[at]recherche.gouv.fr, thierry.chardot@inrae.fr

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Use of TLC, HPTLC, and HPTLC-MS during production and purification processes of active ingredients and their impurities

Written by Admin Camag on October 1, 2022. Posted in CBS Articles.

Didier Rigollet, Amélie Havard and Daniel Dron are working at the R&D department Analytical Innovative Technologies of the Industrial Research Centre at Oril Industrie, in Bolbec, France (Servier group). The use of HPTLC in Oril started even earlier than the foundation of Chromacim, including trainings at CAMAG organised by Pierre Bernard-Savary, where Daniel Dron participated already more than 20 years ago. The R&D team is specialized in purification processes of intermediates and active pharmaceutical ingredients (APIs) for toxicological, galenical or clinical studies. Part of their work is devoted to the isolation of impurities and production of APIs or impurity reference batches. In 2018, the team launched their preparative chromatography service InnoPrepTM, dedicated to small and large-scale purifications. Intermediates, APIs and impurities are characterized by MS and NMR. Quantitative analysis by NMR is also performed on these molecules.

Introduction

Servier, an independent international pharmaceutical group, is committed to therapeutic progress for the benefit of patients.

Their goal is to speed up the development of new molecules in order to bring a new molecular entity to market every 3 years, particularly in the field of oncology. Preparative chromatography is, therefore, a method of choice in R&D to provide pure products for the first pharmacological, toxicological and clinical studies in a very short time. This technique also makes it possible to isolate impurities present at low levels in active ingredients whose complex structure does not allow rapid synthesis and to provide a batch within the time limits set for toxicological studies.

About 75% of the purifications at Oril Industrie are done with silica gel. The necessary conditions for an efficient purification are determined using TLC. Then, the purification progress by preparative column chromatography is checked by HPTLC. Twenty fractions can be analyzed within one hour. TLC/HPTLC is the method of choice due to its simplicity, rapidness and the successful scale up from TLC to preparative separations. The HPTLC-MS carried out beforehand allows targeting the molecule sought in often very complex mixtures.

Sample preparation

Crude product (0.05 g) is dissolved in 5.0 mL of ethyl acetate.

Chromatogram layer

For method development (optimization of purification conditions) TLC plates silica gel 60 F₂₅₄ (Merck), 20 x 5 cm are used, while quantification and coupling to mass spectrometry is done on HPTLC plates silica gel 60 F₂₅₄ s (Merck), 20 x 10 cm.

Sample application

Samples are applied as bands with the Automatic TLC Sampler (ATS 4), two tracks for TLC and up to 20 tracks for HPTLC, band length 8.0 mm, sample volumes of 1.0 –15.0 μL.

Chromatography

Plates are developed 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 developing solvents to the separation distance of 100 mm (from the lower edge) for TLC and 50 mm for HPTLC, followed by drying in a stream of cold air for 5 min.

Documentation

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

Mass spectrometry

Zones are eluted with the TLC-MS Interface (oval elution head) at a flow rate of 0.2 mL/min with methanol – water 1:1 (V/V) into a Q-TOF-MS (Xevo® G2-S QTof, Waters), operating in positive ionization mode (m/z 50 –1200).

Results and discussion

The objective of this study was to isolate a sufficient quantity of an impurity present at a content of 0.35% in a batch of an intermediate in a product under development. The aim was to confirm its structure and to carry out toxicological tests. The nature of this impurity was determined beforehand by LC-MS. Its structure being complex, a synthesis would be too time-consuming. The conditions used in RP-HPLC are too complex for direct transfer to preparative chromatography (expensive stationary phase). The mobile phase used was water + 0.1% methane sulfonic acid and acetonitrile +0.1% methane sulfonic acid is too complexed for the isolation after preparative chromatography.

RP-LC-UV Chromatogram of the crude product

TLC was selected for method development to separate the major impurity from the other compounds with a reasonable R_F value allowing an efficient purification.

TLC chromatograms of the crude product in UV 254 nm obtained with different developing solvents

Mass spectra were recorded to characterize the different compounds (main substance at R_F 0.25 and impurity at R_F 0.38).

Figure 3: Instruments used for TLC-MS (left); TLC chromatogram of the main substance and the impurity in white light (middle); mass spectra of the selected zones (a: impurity, b: main substance) measured in positive ionization mode (right)

Instruments used for TLC-MS (left); TLC chromatogram of the main substance and the impurity in white light (middle); mass spectra of the selected zones (a: impurity, b: main substance) measured in positive ionization mode (right)

The purification of the crude product (two injections of 90g dissolved in toluene) on a 20-cm column (packed at 40 bars with 6 kg silica gel 60, 15–40 μm, Merck) at a flow rate of 2.0 L/min with toluene – ethyl acetate 95:5 (V/V), was monitored online with UV 290 nm detection and in parallel offline by HPTLC.

$Figure 4: Online monitoring of the purification process by LC-UV (290 nm, left) versus offline by HPTLC-UV (HPTLC chromatogram of the individual fractions at 254 nm, right; C2-C9 are fractions collected during purification and C5 corresponds to the impurity)$

Online monitoring of the purification process by LC-UV (290 nm, left) versus offline by HPTLC-UV (HPTLC chromatogram of the individual fractions at 254 nm, right; C2-C9 are fractions collected during purification and C5 corresponds to the impurity)

The different fractions of the target impurity were collected, and the combined fractions were analyzed by NMR. 670 mg of impurity was obtained (yield: 0.35%) purity > 99%. The quantity obtained after purification on column was also consistent with the estimated content in analytical HPLC. For Oril, the use of HPTLC has a big positive impact on the production costs, with a benefit of thousands of Euros per year. This is due to the use of HPTLC for various optimizations of the manufacturing process and of raw materials external supply.

Further information on request from the authors.

Contact: Amélie Havard, Daniel Dron, Oril Industrie (Servier), Industrial Research Centre, Department of Analytical Innovative Technologies R&D, 13 rue Auguste Desgenétais, CS 60125, 76210 Bolbec, France, amelie.havard@servier.com, daniel.dron@servier.com

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High-Performance Thin-Layer Chromatography in Practice

Written by Admin Camag on September 15, 2022. Posted in White Paper.

Standardization & SOP for HPTLC

The great advantage of TLC is its flexibility. Understanding the effects of each parameter on the outcome of the final chromatogram allows adjustments to the methodology in order to obtain the desired result. However, that degree of freedom can become a problem for reproducing a method, especially if not all parameters were documented and adhered to.

For obtaining predictable and reproducible HPTLC results, it is essential to establish a standardized methodology in the form of a standard operation procedure (SOP).

The United States Pharmacopoeia (USP-NF 2015) and the European Pharmacopoeia (Ph. Eur. 2017) published general chapters on HPTLC, which are the basis for HPTLC methods of identification as part of monographs on herbal materials. All parameters are based on an SOP adopted by the HPTLC Association in 2012.

HPTLC parameters compliant with USP and Ph. Eur.

Plate material:

HPTLC glass plates, 20 x 10 cm, Silica gel 60 F₂₅₄ (2–10 μm, average 5 μm).

Sample application:

8.0 mm bands, 8.0 mm from lower edge, 20.0 mm from left and right edges. The minimum distance between tracks is 11.4 mm (center to center);
Maximum 15 tracks per plate;
Track 1 is used for SST;
Volumes of less than 2.0 μL and more than 20.0 μL should be avoided. Optimum application volumes are 2.0–10.0 μL.

Developing chamber and development:

Twin Trough chamber (20 x 10 cm);
Use 10.0 mL of developing solvent in the front trough (equivalent to a solvent level of 5 mm) and 25.0 mL for saturation in the rear trough fitted with a saturation pad (filter paper of defined thickness);
Developing distance 7.0 cm from the lower edge of the plate or 6.2 cm from the application position;
Prior to development, condition the plate at a relative humidity of 33% using a saturated solution of magnesium chloride for 10 minutes;
With the lid closed, saturate the chamber for 20 minutes (with saturation pad). For development, introduce the plate in a vertical position into the front trough. The silica gel faces the saturation pad;
When the developing solvent achieves 7.0 cm, remove the plate from the tank and dry in a vertical position with a stream of cold air for 5 min.

Derivatization:

Automatic spraying or dipping/immersion whenever possible.

Documentation: record digital images in

Short-wave UV light (254 nm), and white light before application as “clean plate image” for image correction;
Short-wave UV light (254 nm), long-wave UV light (366 nm), and white light after development;
Long-wave UV light (366 nm) and white light after derivatization.

Converting TLC/HPTLC methods into standardized HPTLC methods

As the general pharmacopoeial chapters on HPTLC were only recently published, most of the literature methods from before 2015/2017 are not harmonized with those chapters, and thus, may not be considered “standardized HPTLC methods”.

Nevertheless, all TLC or HPTLC methods can be converted into standardized HPTLC methods. In a preliminary experiment, the standard and sample solutions of the original method are kept as well as developing solvent and derivatization reagent. All other parameters are set to “standard”:

Plate material (HPTLC Silica gel 60 F₂₅₄ 20 x 10 cm);
Plate layout (15 tracks, 8.0 mm bands, 8.0 mm from lower edge, 20.0 mm from left and right edges);
Twin trough chamber, saturated for 20 minutes with saturation pad;
Plate activation (plate conditioned at a relative humidity of 33% for 10 minutes);
Developing distance (7.0 cm from the lower edge);
Drying step after development (5 min with cold air);
Derivatization (automatic spraying or dipping/immersion whenever possible);
Digital documentation in short-wave UV light (254 nm), longwave UV light (366 nm), and white light before derivatization, long-wave UV light (366 nm), and white light after derivatization.

Other parameters can be adjusted: usually, application volumes (or concentrations of sample and standard solutions) of TLC methods are reduced to about one-fifth for HPTLC plates. Optimum application volumes for HPTLC are between 2.0 and 10.0 μL.

The preparation and use of derivatization reagents should follow the pharmacopoeias or the General SOP of the HPTLC Association. Special derivatization reagents may be evaluated.

Sample preparation may be optimized if needed and simplified if possible. Cumbersome methods and harmful solvents should be avoided. A system suitability test (SST) (e.g. based on the Universal HPTLC Mix [1]) must be introduced.

Comprehensive HPTLC fingerprinting

HPTLC is a standard technique for chemical identification of herbal drugs adopted by many pharmacopoeias. Resulting HPTLC fingerprints are usually visually evaluated. When generated with a standardized methodology, using suitable instruments and software, digital HPTLC fingerprints offer the necessary reproducibility for deeper exploitation of the data and for quantitative evaluation.

HPTLC fingerprint

The HPTLC fingerprint is the digital image of the visual HPTLC chromatogram. It represents the identity of a sample and consists of a sequence of separated zones with a certain color and intensity, and may be a stack of multiple images from different detection modes. The HPTLC fingerprint also includes any zones at the application and front positions, which are usually not detected in other chromatographic techniques.

Additionally, the HPTLC fingerprint is part of the digital image of the entire HPTLC plate. That HPTLC plate contains information regarding other samples and standards, the quality of the chromatography (assessed with an SST), and the chromatographic conditions during all steps. All information can be stored in an analysis file. All fingerprints from a plate that has passed the SST can be compared to fingerprints from other plates developed with the same method, which have also passed the SST.

Peak Profile from Image (PPI)

Unlike scanning densitometry, which measures the absorbance or fluorescence of a zone using a single wavelength per scan, image analysis evaluates the pixels of the three channels red (R), green (G), and blue (B). For each track, the RGB values of the pixel lines (R_F position) of 50% of the length of the zones can be averaged and used to calculate the luminance L with the equation L = (1/3 R) + (1/3 G) + (1/3 B). Plotting the luminance as function of RF generates the Peak Profile from Image. Information on peak height and area contained in PPI data can be used for quantitative assessments.

Figure 1: Transformation of the digital image of the HPTLC chromatogram into the corresponding peak profile from image (PPI). Adapted from [2]

The concept of “comprehensive HPTLC fingerprinting”

Conversion of images into peak profiles results in a loss of color information. Because the PPI formula weights the channels equally, the relative intensity of the zones observed in the image may differ from those of the PPI. Therefore, a comprehensive evaluation of a fingerprint includes the analysis of the PPI and the corresponding image. This is taken into account in the concept of “comprehensive HPTLC fingerprinting”.

In comprehensive HPTLC fingerprinting, tests for identity, purity, and content of an herbal drug, preparation, or product are performed in a single analysis. Qualitative and quantitative information from the HPTLC fingerprints (images in different detection modes) and PPI are combined.

Peak profiles from scanning densitometry (PPSD) can offer complementary, spectrally selective information but do not belong to the core data of comprehensive HPTLC fingerprinting.

Figure 2: The combination of HPTLC images and peak profiles from images allows testing for identity, purity, and content in a single analysis.

Example: comprehensive HPTLC fingerprinting of Angelica gigas root

The concept of comprehensive HPTLC fingerprinting was developed for the quality control of the herbal drug Angelica gigas root, and can be expanded to other fields of analysis.

Criteria for the identification of A. gigas root were established based on the evaluation of multiple samples of cultivated material. An average fingerprint (pooled sample) was then generated. That fingerprint represents the typical characteristics of the herbal drug and is used as reference. The selected method is also able to distinguish A. gigas root from the roots of 27 related plant species.

Two potential confounding herbal drugs of A. gigas root, the roots of Angelica acutiloba and Angelica sinensis, which carry the same common name “Dang gui”, were investigated in a test for purity. The presence of either confounding material in a mixture is detectable at levels as low as 1%, based on the detection of z-ligustilide. This zone, characteristic of the two and nine other related herbal drugs, is absent in A. gigas root.

Figure 3: Identification – evaluation of multiple samples of Angelica gigas root samples, and average, representative fingerprint (track A) in short-wave UV light 254 nm (upper) and in long-wave UV light 366 nm (bottom). Adapted from [2]
Figure 4: Purity – detection of z-ligustilide (characteristic of the confounding species) in A. gigas root. Image and PPI evaluations. Red bars: peak height of z-ligustilide in mixtures of A. acutiloba and A. gigas root. Blue bars: peak height of z-ligustilide in mixtures of A. sinensis and A. gigas root. Adapted from [2]
Figure 5: Test MC against a reference standard equivalent to the MC. Image and PPI evaluations, and % content of D/DA, of 24 samples of A. gigas root. Adapted from [2]

Literature

[1] T. K. T. Do et al. (2021) Journal of Chromatography A, 1638. DOI: 10.1016/j.chroma.2020.461830

[2] S. Cañigueral et al. Chapter 7: High performance thin-layer chromatography (HPTLC) in the quality control of herbal products. pp 119 – 136. In: Recent Advances in Pharmaceutical Sciences VIII. 2018.

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Parameters of High-Performance Thin-Layer Chromatography (Part 2)

Written by Admin Camag on September 15, 2022. Posted in White Paper.

Documentation

In contrast to other chromatographic techniques, HPTLC offers the unique advantage to visualize the chromatographic result directly for the human eye, allowing a convenient qualitative evaluation of multiple samples on the same plate. Substances with absorption at 254 nm can be visualized on plates with fluorescence indicator F₂₅₄ by exposure to short-wave UV light (254 nm). The fluorescence of some substances can be excited by long-wave UV light (366 nm). Substances with or without chromophore can be derivatized for improved detectability.

Figure 1a: Image of chromatogram in white light
Figure 1b: Image of chromatogram in short-wave UV light (254 nm)
Figure 1c: Image of chromatogram in long-wave UV light (366 nm)

With a suitable device (e.g. imaging and documentation system, UV cabinet and digital camera), electronic images of the chromatogram can be captured in different illumination modes. In white light illumination, the light reflected from the layer background is captured. In long-wavelength UV light (366 nm), substances with inherent or reagent induced fluorescence appear as bright spots, often differently colored, on a dark background. When short-wavelength UV light (254 nm) is used, substances absorbing UV 254 appear as dark zones on a bright green or pale blue background, provided the layer contains a fluorescence indicator F₂₅₄ or F₂₅₄s (fluorescence quenching).

Using dedicated HPTLC software, these images can be captured, annotated, and archived in compliance with cGxP, and evaluated against the descriptions provided e.g. by pharmacopoeias or other standards. Furthermore, profiles generated from the captured images build the basis for “Comprehensive HPTLC Fingerprinting”.

Instrumentation

CAMAG® TLC Visualizer 2

The visual presentation of the complete chromatogram showing all samples and standards side by side is one of the most convincing arguments for HPTLC. The TLC Visualizer 2 is a professional imaging and documentation system for HPTLC chromatograms, ensuring highest reproducibility in obtaining high-quality images acquired under homogeneous illumination conditions in UV 254 nm, UV 366 nm, and white light in transmission, reflection, and reflection plus transmission mode.

The image enhancement tools featured in the visionCATS software exploit the full potential of the high-end device. Images are automatically captured based on an optimized control of the illumination and parameters specified in the HPTLC method. Sophisticated algorithms guarantee the highest image quality for identification of even the weakest zones. With the Comparison Viewer, tracks originating from the same or different plates and/or different illumination modes can be compared on the same screen side by side, which allows the creation of virtual plates.

Additionally, the visionCATS software enables an image-based evaluation of chromatograms obtained with the TLC Visualizer 2.

Figure 2: CAMAG® TLC Visualizer 2

CAMAG® UV Cabinet 4

The CAMAG® UV Cabinet 4 consists of the UV Lamp 4 and the Viewing Box 4 and is designed for visual inspection of chromatograms in UV light 254 nm and UV light 366 nm in a bright environment. The interior is accessible via a roller shutter on the front effectively minimizing the influence of ambient light. A glass filter in the viewing window protects the eyes against UV light reflected during visual inspection through the observation port.

Figure 3: CAMAG® UV Cabinet 4

CAMAG® BioLuminizer® 2

The BioLuminizer® 2 is a detection system specifically designed to detect bioluminescence on HPTLC plates. The system consists of a compartment excluding any extraneous light, climate controlled for extended stability of the plate, and a 16 bit CCD digital camera of high-resolution and high-quantum efficiency. Hyphenating HPTLC and bioassay is an excellent tool for identification of single toxic compounds in complex sample matrices. The method is suitable for the detection of toxins in foodstuff, beverages, cosmetics, wastewater, drinking water, and for the detection of bioactivity in natural products. After chromatographic separation of the complex sample, the plate is immersed in a suspension of bioluminescent bacteria Allivibrio fischeri. The reaction takes place within a very short time. All zones with inhibitory or toxic effects appear as dark zones on the luminescent plate background. This stand-alone detection system is operated with BioLuminizer® 2 software.

Figure 4: CAMAG® BioLuminizer® 2

Derivatization

The possibility of convenient chemical derivatization of substances separated on the plate is a strong advantage of planar chromatographic techniques. There are specific and non-specific reagents. An extensive list of reagents and their targeted groups can be found in the two-volume collection “Thin-Layer Chromatography: Reagents and Detection Methods” [1] [2]. It is possible, in some cases, to use subsequently a specific reagent and a universal reagent on the same plate. For example, the first derivatization with natural products reagent (2-aminoethyl diphenylborinate), specific for phenolic compounds like flavonoids, can be followed with anisaldehyde reagent, which is rather universal.

Reagent Transfer onto the Plate

Most derivatization reagents are solutions that are sprayed onto the developed plate or into which the plate is dipped. In a few cases, the reagent is a gas that can be generated, e.g. in the rear trough of a TTC (ammonia or HCl from the respective aqueous solution, iodine from iodine crystals), while the plate is in the empty front trough of the chamber.

Automated spraying: It takes advantage of dedicated instruments, which control the transferred volume of reagent and minimize the release of fumes. About 2-3 mL of reagent are typically used for a standard HPTLC plate. A wide range of reagents is compatible with this technique.
Manual spraying: Is a quick, inexpensive and universally applicable method of reagent transfer, performed with a simple glass apparatus and a hand-held rubber ball pump or compressed air. The principal drawbacks are the considerable skills required to achieve homogenous and reproducible results and the generation of toxic fumes.
Automated dipping/immersion: Utilizes an immersion device to achieve a very homogenous and reproducible reagent transfer. Rather large volumes of reagent (up to 200 mL), which can be re-used a couple of times, are provided in a dipping tank. The stability of reagents and possible contamination during possible re-use must be considered as well as changes in the concentration.

The preparation of reagents for dipping and spraying may be different. Dipping may require reduced reagent concentration and solvent adaptation to avoid washing off the separated samples during immersion.

Completion of the Derivatization

The majority of the chemical reactions require heating of the plate in an oven or on a hot plate (plate heater). For reproducible results, the temperature and duration of heating must be controlled. Proper timing is required for documentation of the result of the derivatization because colors obtained in the process may change or fade with time and temperature.

Figure 5a: Image of chromatogram in white light after derivatization
Figure 5b: Image of chromatogram in long-wave UV light 366 nm after derivatization

Instrumentation

CAMAG® HPTLC PRO Module DERIVATIZATION

The degree of automation and productivity are key factors for the HPTLC laboratory. The Module DERIVATIZATION is part of the HPTLC PRO SYSTEM, a fully automated sample analysis and evaluation system using HPTLC plates (20 x 10 cm), which is best suited for routine quality control of analytes, extracted from complex matrices and provides reproducible and reliable results.

Designed for the fully automatic derivatization of HPTLC glass plates (20 x 10 cm), the Module DERIVATIZATION combines two steps in a single device: high-precision spraying of derivatization reagents and heating of the plate. Employing the patented micro-droplet spraying technology, the Module DERIVATIZATION enables maximum homogeneity in applying derivatization reagents. The integrated plate heating unit ensures a uniform heat distribution across the plate.

To suit the viscosity of the spraying reagents, three different nozzles are available. Equipped with a fully automated nozzle changer and cleaning station, the Module DERIVATIZATION effectively avoids cross-contamination.

Figure 6: CAMAG® HPTLC PRO Module DERIVATIZATION

CAMAG® Derivatizer

Designed for automated reagent transfer onto HPTLC plates, the Derivatizer ensures highest reproducibility and safe handling. Employing the patented micro-droplet spraying technology, the Derivatizer guarantees unsurpassed homogeneous reagent distribution at low consumption. The stand-alone device enables reproducible and user-independent results and is suitable for a wide range of spraying reagents.

Figure 7: CAMAG® Derivatizer

CAMAG® TLC Plate Heater 3

The TLC Plate Heater 3 is a device for heating HPTLC plates, permitting the optimal heating during the derivatization reaction. It features a NEXTREMA® heating surface, which is resistant to all common reagents and is easily cleaned. The heating surface has a grid to facilitate correct positioning of the plate. The temperature is selectable between 25 and 200 °C, the programmed and the actual temperature are displayed.

Figure 8: CAMAG® TLC Plate Heater 3

Densitometry

HPTLC chromatograms can be recorded with scanning densitometry showing peaks of the separated compounds. In a densitometer, a vertical beam of monochromatic light between 190-900 nm moves along the individual tracks of the plate. Part of that light is reflected from the plate and measured by a detector. The obtained signal is processed and plotted as a function of position (R_F), generating the densitogram or Peak Profile from Scanning Densitometry (PPSD) used for qualitative and quantitative evaluation.

Figure 9: Densitograms measured at different wavelengths

Modern scanning densitometers allow measurements in two modes:

Absorbance mode: Measures the amount of light absorbed by the zones. The particles of the stationary phase reflect light (baseline). Zones present in the track can absorb part of that light lowering the signal received in the detector. For plotting the PPSD, the signal is typically inverted.

Figure 10: Simplified scheme of absorbance mode

Fluorescence mode: Measures the fluorescence of the zones excited at a specific wavelength. From a molecular point of view, fluorescence happens when a photon is absorbed, causing an electronic transition from the ground state to an excited state. When the molecule returns to its ground state, the energy is dissipated at a higher wavelength. To selectively detect the response, densitometers are usually equipped with a cut-off filter placed between the plate and the detector (yellow rectangle in the Figure 11). The cut-off filter blocks the reflected short wave light used for excitation so that only longer wavelengths can reach the detector (photomultiplier).

Figure 11: Simplified scheme of fluorescence mode

Fluorescence measurements are up to 100 times more sensitive than absorbance measurements and generally feature a straight baseline because the cut-off filter blocks reflected light from the plate. Only a few substances are naturally fluorescent. Fluorescence can be induced by chemical derivatization, however the fluorescence of zones is not always stable and might decrease over time. Commercial densitometers feature three light sources:

Deuterium lamp
Tungsten lamp
Mercury lamp

Figure 12: Emission spectra of commonly used light sources

Instrumentation

CAMAG® TLC Scanner 4

Designed for the densitometric evaluation of HPTLC chromatograms, the TLC Scanner 4 measures the reflection of separated compounds in absorption and/or fluorescence mode. The spectral range of light from 190 to 900 nm is available for selecting single or multiple wavelengths. Detection can thus be fine-tuned to match the spectral properties of the analyte to its optimized specificity and sensitivity of the detection. The visionCATS software controls the TLC Scanner 4 and enables quantitative evaluation of the generated densitometric data. To determine the substance concentration in a sample, five different quantification functions are available. Several scanning steps, e.g. scanning the plate after development and scanning the same plate after derivatization and up to five different evaluations can be performed with data obtained from single wavelength, multiple wavelengths or a combination of measurements in absorption and fluorescence detection mode.

Figure 13: CAMAG® TLC Scanner 4

Literature

[1] Thin-Layer Chromatography: Reagents and Detection Methods, Physical and Chemical Detection Methods: Fundamentals, Reagents I; Vol. 1a, H. Jork et al., VCH, 1990

[2] Thin-Layer Chromatography: Reagents and Detection Methods. Physical and Chemical Detection Methods: Activation Reactions, Reagent sequences, Reagents II. Vol. 1b; H. Jork et al., VCH, 1994

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