Abstract

This paper presents the first part of the automation of the thin layer chromatography technique whose separation and identification of molecules present in a mixture are currently done manually and laboriously [1]. We have therefore found an interest in automating this technique. In this part, the method implemented comprises 2 steps. Firstly the preparation of the extracts and the acquisition of the images of the spots (the spots are obtained after migration of the mixture on the plate) on the chromatographic plate, secondly the calculation of the frontal report from an algorithm via the software Matlab families of molecules (coumarin, terpene, tannin, flavonoid, polyphenol, etc.). The values of the frontal ratios obtained were compared with the values of the frontal ratios obtained by the manual calculation made in the laboratories. The results of this comparison showed a correlation between these two methods. The values of the frontal ratios are substantially identical.

1. Introduction

Traditional medicine based on medicinal plants, survives and tends to develop in parallel with modern medicine in developing countries. According to the World Health Organization (WHO) in 2014, nearly 80% of people in developing countries use traditional medicine for primary health care; hence the need for scientific valorization of medicinal plants ¹. It is important to note that in most African countries, particularly in Côte d'Ivoire, traditional medicine is sometimes the only source of affordable and accessible care, especially for the poorest patients. This medicine uses mostly herbal therapies. However, several anticancer molecules come from medicinal plants ². A majority of the world's population, especially in developing countries, is treated only with traditional herbal remedies ³. From aspirin to taxol, the modern pharmaceutical industry itself still relies heavily on the diversity of plant secondary metabolites to find new molecules with novel biological properties ⁴. Each plant can contain several thousand different compounds ⁵ whose various uses are aimed at overcoming suffering and improving the health of men. ⁴

Moreover, the separation and the identification of the different groups of compounds or molecules in a mixture (extract) are of great interest in the laboratories of natural substances. In these laboratories all research is based on plants (stem, leaves, roots, seeds) and mostly works in the context of the revaluation of traditional medicine based on plants.

However, the separation and identification of molecules is done manually, always referring to the data in the literature, which is slow and very laborious. This is why devices such as the gas chromatograph, the high-performance liquid chromatograph have been designed to have reliable results and record time, but these devices remain less used by laboratories because of their prices exorbitant up to 50 million. ⁶ In Côte d'Ivoire, for example, only one laboratory has a gas chromatograph, in particular the scientific police laboratory. Only LANADA, the national laboratory in Treichville and INP-HB each have a high-performance liquid chromatograph.

There is therefore an interest in designing another device that is cheaper and simpler and that makes it possible to identify the molecules effectively.

For this work, we used three (03) plants that are: Alchornea cordifolia, Paullinia pinnata (Sapindaceae), Trema guineensis (Ulmaceae).

The work is organized in three (03) parts, the first part describes the material and methods in which we present the preparation of hydromethanolic extracts from the aforementioned plants as well as the experimentation device allowing the acquisition of digital color images of migrations of the molecules on the chromatographic plate. The second step consists of a low-level image processing of front-end calculations of the families of molecules sought, the presentation of the results and their interpretations. Finally, we end with a conclusion and perspectives.

2. Material and Methods

All the chemicals used are of analytical quality. The solvents and reagents employed were purchased commercially from Polychimie (Ivory Coast). For thin-layer chromatography, we used silica gel 60 F254 chromatoplates on an aluminum support.

The different devices used for our analyzes are as follows:

• A RETSCH brand electric grinder, type SM 100

• A brand balance Sartorius (Germany)

• Burchi RII Rotary Evaporator

• A magnetic stirrer

• A UV-Visible spectrophotometer

•A camera

√ Spatial resolution: 2448x3264 (8 M pixels)

√ Tonal resolution: 24 bits (color)

√ Color space: RGB

√ Camera model: Infinix

√ Focal length: F / 2

√ Exposure time: 1/13 seconds

√ ISO sensitivity: ISO-864

√ Focal distance: 4 mm

√ Picture format: JPG image

• Glassware (round bottom flask, beakers, etc.)

• UV lamp (366 nm, 254 nm).

Plant organs consisting mainly of leaves were all harvested from the forest relic of Nangui Abrogoua University in Côte d'Ivoire (Figure 1). The plant species have been identified according to the herbaria available at the National Center for Floristics (CNF).

This work consisted in the realization of a protocol presented in the following section, then the device of acquisition of the digital images on which the migration of the molecules is presented.

Their sampled organs were dried under permanent air conditioning for a week, then pounded to have fine powders. (Figure 2)

• Hydromethanic maceration

15 g of each of the powders obtained are macerated in 100 ml of 80% (v / v) MeOH for 24 hours under constant stirring. The operation is repeated twice, keeping the same pomace, but with renewal of the solvent. After filtration on Büchner, combined hydromethanic macerates are stored in the refrigerator for 48 hours. The hydromethanolic extracts are again filtered and then concentrated under reduced pressure at 40°C. using a rotary evaporator (MKE 6540 type) to give 03 hydromethanolic crude extracts of the various leaves (Figure 3).

• Selective extraction of some secondary metabolites

The E1-E3 extracts were treated respectively with 3 × 20 ml of hexane, chloroform, ethyl acetate and n-butanol (Figure 4). The various organic fractions were concentrated under reduced pressure on a rotary evaporator and then stored in a refrigerator. Thus, for each crude extract, selective extracts are obtained as follows: hexanic, chloroformic, ethyl acetate and n-butanol, which were used for phytochemical screening (Figure 5).

The photochemical screening was carried out according to the analytical procedures described in the work of ⁷, ⁸ and ⁹.

Drops of each selective extract are deposited using a capillary on the points (01 cm from each other) of the baseline drawn 1 cm from the bottom of the chromatoplates (silica gel 60 F254). The deposits are allowed to dry for a moment before introducing the plates into the chromatograph vessel containing the migration solvent. After migration and drying with a hair dryer, the chrotograms are revealed with reagents specific to the phytochemistry groups sought, then visualized first in the visible and then under a UV lamp at 366 nm (Figure 6). The different developer systems used are:

• Hexane / AcOEt (20: 4, v / v) for hexanic fractions.

• CHCl3 / AcOEt / hexane (10: 10: 5; v / v / v) and CHCl3 / (CH3) 2CO / (Et) 2NH (10: 8: 2; v / v / v)

for chloroform fractions.

• CHCl3 / AcOEt / CH3CO2H (12: 10: 1; v / v / v) and AcOEt / CH3OH / H2O / CHCl3

(18: 2,4: 2,1: 6, v / v / v / v) for the ethyl acetate fractions.

• AcOEt / C2H5OH / HCO2H / H2O (10: 1.1: 0.7: 2.5, v / v / v / v) for the n-butanol fractions.

The developers used were:

• Liebermann-Bürchard reagent for sterols and terpenes (various staining under UV visualization at 366 nm) ¹⁰:

- red for the oleanane and ursane type triterpenes;

- orange-yellow for lupine triterpenes;

- yellow or yellow-green for steroids.

• Sulfuric vanillin for terpenes (purple, pink, orange in the visible) ¹¹

• Godin's universal reagent for sterols, terpenes and flavonoids (various stains in the visible, but ranging from blue to violet respectively for sherols and polyterpenes) ¹².

• Aqueous solution of (AcO) 2 Pb (5%, m / v) specific to coumarins (appearance of spots) ¹³.

- yellow / brown color in the visible immediately or after 15 min;

- intense fluorescence orange, red, yellow, blue, green under UV at 365 nm.

• NH4OH for flavonoids (various staining under UV visualization 366 nm)

- yellow - green, blue for anthocyanins;

- dark yellow coloring for flavonols and other flavonoids ¹⁴

• FeCl3 aqueous solution (2%, m / v) for tannins (gray spots in the visible) and other polyphenols (red, green, blue spots in the visible) ¹⁵

• Dragendor'ff reagent for alkaloids (appearance of orange spots in the visible) ¹⁶.

3. Results and Discussions

For each extract, we obtained images on the chromatographic plates. We present some of these images that will be processed. (Figure 7).

In the various hexanic, chloroformic, ethyl acetate and n-butanol fractions, we identified the families of molecules under UV 366 nm and with developers.

The sterols revealed with the Liebermann-Bürchard reagent appear in the visible in blue and yellow but fluorescent yellow and yellow-green under UV 366 nm ¹⁷. These are spots of Rf = 0.05; 0.91 in E1, Rf = 0.03; 0.54; 0.91 in E2 and Rf = 0.53; 0.57; 0.91 in E3. Triterpenes of lupan type if the spots have yellow-orange fluorescence under UV at 366 nm ¹⁸. Table 1 gives the values of the frontal ratios of the families of molecules and the corresponding colors. These values are in agreement with the work of ¹⁸.

For the capture of the image we first removed the noise on the original image and get the final image ITxyλ by the relation (1) below:

(1)

Capture of the image transmitted by the sample on the plate (Sxyλ);

Capturing the image of the empty plate (Rxyλ); so-called reference measure;

Capture of the image by obstructing the passage of light (Bxyλ): measurement called background noise.

After the acquisition of the images, the algorithm below on the Matlab software made it possible to calculate the frontal ratios of the different families of molecules. (Figure 8).

[filename,pathname]=uigetfile

('*.*','selectionne une image');

cd(pathname)

imshow(filename)

[x,y]=ginput(3);

h₁=y(1)-y(2);

h₂=y(1)-y(3);

Rf=h₂/h₁;

The values of the frontal ratios (Rf ') obtained by the algorithm are compared with the values (Rf) traditionally calculated at the laboratories. (See Table 2)

The Table 2 and the representation of Rf according to Rf’ show that the two techniques give substantially the same results since there is a good correlation between the two values of Rf and Rf '. This will allow us to develop a database containing the values of the frontal reports of families of molecules. It will therefore be sufficient after the acquisition of the images to proceed to the calculations of the frontal reports using our algorithm and compare these values obtained with the values in our database and then identify the family of molecule if this one is part of the database. This technique will avoid the use of developers whose preparation requires resources and is very often exhausting ¹⁹.

However, the only parameter that is the frontal report can not better identify families of molecules, it requires other image segmentation parameters to better discriminate. It will be a question for us to segment each previously identified task and then calculate the parameters such as average pixel intensity, standard deviation, entropy. These values can further enrich the database.

4. Conclusion

Thin layer chromatography is a technique for identifying molecules in plants that is done not only in several steps but also manually. Indeed for the choice of solvent or eluent to obtain better migration or separation of the mixture, always refer to the literature. Which is not always easy. In addition, after the migration of the mixture, it is necessary to find other reagents in the literature having a particular reaction with the different spots obtained on the plate in order to identify the molecules.

All these steps are often very laborious. It was therefore important for us to automate the calculation of the frontal ratios of the families of molecules represented by the spots on the plate by setting up an algorithm by the software Matlab.

In our next work, it will be a question of automating the identification of the spots from their colors under the ultraviolet lamp at 366nm using image segmentation parameters such as the average pixel intensity, the deviation standard, entropy. Thus we elaborate a large database and proceed to the identification of the families of molecules present in our database in any plant.

References