REVIEW

Planar Chromatographic Systems in Pattern Recognitionand Fingerprint Analysis

Dusanka Milojkovic-Opsenica • Petar Ristivojevic •

Filip Andric • Jelena Trifkovic

Received: 31 October 2012 / Revised: 28 December 2012 / Accepted: 8 February 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract An overview of opportunities of contemporary

planar chromatography in pattern recognition and finger-

print analysis is presented. The most used chemometric

methods are highlighted and their main advantages and

drawbacks are underlined. In addition a cross section of the

application of planar chromatographic fingerprinting in

food, pharmaceutical, environmental, and forensic analysis

is given.

Keywords Review � Planar chromatography �Fingerprint analysis � Pattern recognition methods

Introduction

Pattern recognition as well as fingerprint analysis can be

considered as a part of a large family of various statistical

methods that fall under the issues of classification, one of

several chemometric sub-disciplines. Depending on the

way how the classification is conducted the methods are

divided into two categories: supervised, which assume a

priori defined sample category, and unsupervised which do

not have such assumption. The problem of classification is

omnipresent in all chemical disciplines, and its application

in chromatography, particularly high-performance liquid

chromatography (HPLC) is well documented [1]. In this

approach the entire chromatogram is treated as unique

multivariate fingerprint, i.e. multidimensional vector,

without special identification of single peaks. This is one of

the reasons why application of chemometrics in high-per-

formance thin-layer chromatography (HPTLC) fingerprint

is increasing in popularity. However, application of che-

mometric tools for classification in thin-layer chromatog-

raphy (TLC) is still very poor, and in most instances

fingerprint analysis is conducted in subjective manner

based on manually noted peak differences. Despite of

several cases [2], there is still lack of comprehensive fin-

gerprinting TLC approaches based on the full chemometric

processing of collected data.

The aim of the current work is to present TLC in the

light of pattern recognition and fingerprint methodology, to

highlight most used chemometric techniques in combina-

tion with TLC, to underline the main advantages and

drawbacks, to favor and encourage their use, and to give

the cross section of the application of TLC fingerprinting in

food, pharmaceutical, environmental, and forensic analysis.

Modern Planar Chromatography

Among widely used chromatographic methods TLC is the

simplest to perform [3]. This rapid, sensitive, and econom-

ical liquid chromatographic method can be used for sepa-

ration, qualitative and quantitative analysis of various

organic and inorganic substances. TLC is a technique which

differs from HPLC in a configuration of stationary phase

while the separation mechanisms for these two methods are

the same. Thus, in numerous studies HPLC is substituted

with equal in efficiency but simpler TLC [4–10].

Published in the topical collection Miniaturized and New FeaturedPlanar Chromatography and Related Techniques with guest editor

Paweł K. Zarzyck.

D. Milojkovic-Opsenica (&) � F. Andric � J. Trifkovic

Faculty of Chemistry, University of Belgrade,

Studentski trg 12-16, 11000 Belgrade, Serbia

e-mail: dusankam@chem.bg.ac.rs

P. Ristivojevic

Innovation Center, Faculty of Chemistry Ltd,

Studentski trg 12-16, 11000 Belgrade, Serbia

123

Chromatographia

DOI 10.1007/s10337-013-2423-9

The intensive development of TLC as a method of

analysis of various mainly organic substances started in the

middle of the twentieth century (Table 1). Soon after

introduction in 1938 by Izmailov and Shraiber, TLC

became widely used low-cost chromatographic method for

analysis of complex mixtures, such as natural compounds,

pharmaceuticals, in a word various organic and inorganic

substances, which required small amounts of samples and

solvents, with little or no sample preparation.

Continuous improvement of TLC regarding intensive

development of theory, practice, and instrumentation has

resulted in the methods termed ‘‘high-performance thin-

layer chromatography’’ and ‘‘instrumental HPTLC’’ [12].

The HPTLC, as modern planar chromatographic technique

widely used in many areas of science and technology,

represents an advanced form of instrumental TLC that

means the use of high-performance adsorbent layers,

adopted instrumentation, standardized methodology for

development, documentation, and optimization, as well as

the use of validated methods [15–17].

Contemporary TLC is an instrumental technique that is

comparable by its accuracy and precision with both gas

chromatography (GC) and HPLC. Moreover, HPTLC has

several preferences over HPLC and other techniques

[18, 19] such as (1) small amount of sample is needed for

analysis; (2) low sensitivity to impurities; (3) wide choices

of adsorbents and developing solvents; (4) there is no

possibility of interference from previous analysis as fresh

stationary phase is used for each analysis; (5) mobile phase

consumption per sample is extremely low; (6) many sam-

ples can be separated in parallel on the same plate resulting

in a high throughput, and a rapid low-cost analysis; (7)

better precision and accuracy caused by simultaneous

analysis of both samples and standards under the same

conditions; (8) instrumentation is simple, relatively inex-

pensive, and easy to handle; (9) the easy and rapid opti-

mization; (10) a short time of analysis.

The continuous development of liquid chromatographic

methods resulted in application of monolithic materials in

chromatography [20, 21]. This revolutionary improvement

led to the introduction of so-called ultra-performance thin-

layer chromatography (UPTLC) at the beginning of twenty-

first century. UPTLC with either monolithic or nanostructured

sorbents appears to have many advantages in comparison to

HPTLC such as faster separations (1–6 min instead 3–20 min

in HPTLC) over shorter distances (1–3 cm instead 3–5 cm)

due to its thinner stationary phases (10 lm in comparison to

100–200 lm) with finer pore sizes (1–2 lm macropores and

3–4 nm mesopores). Consequently, the consumption of

mobile phase decreases dozens of times. In most cases,

UPTLC separations are characterized by lower limit of

detection; however, they also exhibit lower resolution due to

shorter development lengths and lower available specific

surface area [22–24]. This is the reason that HPTLC is still

the most widely used planar chromatographic method

(Fig. 1a).

Classical Approach to Planar Chromatographic

Systems in Fingerprint Analysis

Fingerprint in essence is chemoprofiling which means

establishing a characteristic chemical pattern for the mate-

rial or its cut or fraction or extract, which help in its iden-

tification. A chromatographic fingerprint is commonly

applied method for qualitative and quantitative analysis of

low-molecular mass compounds from complex biological,

pharmaceutical, and environmental samples. Instrumental

chromatography methods like GC and HPLC are exten-

sively replaced by TLC [25]. ‘‘The colorful picture-like TLC

image manifested vividly the specific pattern of the given

species that cannot be described properly by words’’ [26].

The classical fingerprint by TLC is done by visual

inspection of the chromatogram and comparison to a

Table 1 The evolution of planar chromatography [11, 12]

Year Event

1938 ‘‘Spot chromatography’’: an introduction of TLC technique by Izmailov and Shraiber

1950s An introduction of the term ‘‘thin-layer chromatography’’ (E. Stahl); an actual development of the TLC as it is known today

1956 The first paper entitled ‘‘Thin layer chromatography’’ was published [13]

1958 The fabrication of the first commercial TLC plates (produced by Merck)

1960s An introduction of scanning densitometry in TLC practice by Kirchner [14] resulted in the development of quantitative applications

1970s Development and commercial production of uniform, small particle diameter layers

1978 Development of different hydrophilic and hydrophobic surface-modified sorbents

1988 First issue of Journal of Planar Chromatography (Springer)

1990 The introduction of spherical particles layers

2001 The introduction of ultra-performance thin-layer chromatography (UPTLC)

2004 Issue 100 of Journal of Planar Chromatography

D. Milojkovic-Opsenica et al.

123

reference standard. The analyte and reference standard are

chromatographed together on the same plate under the

optimized chromatographic conditions. Comparison can

also be made to the results obtained from other plates or

their images (book, electronic library, etc.) or to a verbal

description of the expected results, or both. The advantage

of TLC technique is reflected when the identity of the

analyte is not known or uncertain and in cases when ref-

erence standards are not available. Hence, it is important to

document in detail and validate the applied TLC fingerprint

method. Also, if several images of the same plate are

generated during multiple detections to increase the cer-

tainty of the analytical result, decisions made for the

identity of the analyte must not contradict each other.

Visual inspection is always subjective, and it is therefore

important to properly document the chromatogram to be

useful later [27]. Even more important is the comprehen-

sive validation procedure that should be carried out with

respect to the stability of the analyte during chromatogra-

phy, in solution, and on the plate, the stability of the

derivatized zones, and the specificity, repeatability, inter-

mediate precision, reproducibility, and robustness [28–31].

However, a large number of reported methods lack the final

validation step.

A numerous papers demonstrate the separation and

detection capability of TLC in fingerprinting analysis of

complex organic matrix extracts. Application of TLC fin-

gerprint analysis on herbal extracts is predominant over its

use in pharmaceutical, environmental, food, and forensic

analysis [32]. According to World Health Organization

more than 80 % of the world’s population, mostly in

developing countries, depends on traditional plant-based

medicines for their primary healthcare. The need for sci-

entific validation of these useful medicinal plants is

essential. In that sense, a series of papers dealing with TLC

fingerprinting of medicinal plant characteristics have been

published recently [33–45]. Good theoretical and technical

information needed to perform reliable and reproducible

TLC to establish the identity, purity, quality, and stability

of raw materials, extracts, and finished botanical products

can be found in several books [46–48]. An adoption of

TLC by different Pharmacopoeia constitutes a clear rec-

ognition of the importance of this technique as the method

of choice for handling complex analytical task involving

herbal drugs and botanicals [49].

The reproducibility of TLC has been improved signifi-

cantly in recent years through the application of HPTLC

[50, 51]. It is often used as alternative to HPLC and TLC

for monitoring the production of extracts and final prod-

ucts. An important characteristic of HPTLC fingerprint

analysis is the large number of samples that can be ana-

lyzed in parallel. Also, it could be used to establish proper

extraction parameters, to standardize and normalize

extracts, and to detect any changes or degradation in the

material during formulation, i.e. to monitor the production

of extracts and finished products. It is important to preserve

the composition of the raw material during process devel-

opment [46].

Recently, biological fingerprinting analysis, as a method

of screening the natural samples for the presence of most

active compounds, has been introduced. It was originally

developed with the use of HPLC, but Ciesla et al. [52]

applied this concept in TLC. They constructed a so-called

‘‘binary chromatographic fingerprint’’ combining chemical

and biological detection systems. In the former case, the

plates were sprayed with vanillin reagent, while in the case

of biological fingerprint methanolic solution of a stable free

DPPH radical was applied. A good review on biological

fingerprinting techniques, including both HPLC and TLC

methods, has been done by Ciesla [53]. The author con-

cluded that biological detection in liquid chromatography

gives an opportunity for comprehensive herbal sample

analysis that is being able to distinguish the bio-active

compounds from among the set of chromatographic and

spectroscopic signals. The TLC and HPLC have been con-

sidered complementary, although the TLC-based screening

techniques, for potential plant-derived enzyme inhibitors

still outperform those based on HPLC separations.

In order to overcome some deficiencies of TLC analysis of

herbal materials connected with unsuitable reproducibility

Fig. 1 The recent trends in publication rate of a HPTLC and b (HP)TLC fingerprint analysis (based on search of the Scopus database)

Planar Chromatographic Systems in Pattern Recognition

123

and relatively low resolution, new stationary phases have

been proposed. Namely, specific sample pretreatment pro-

cedures and the type and saturation of the developing

chamber are crucial in achieving satisfactory TLC finger-

printing results, which are often difficult to control. Cui et al.

[31] developed a new TLC method, microemulsion TLC

(ME-TLC) for the fingerprinting of aqueous extract of lico-

rice. Microemulsions are macroscopically homogeneous,

optically fully transparent fluids having more than one liquid

phase. The separation mechanism of ME-TLC have been

found to differ significantly from conventional TLC due to

different mobile (aqueous phase) and stationary phase

(polyamide adsorbent). Also, this technique shows better

reproducibility, requires less involved sample pretreatment

and development procedures, and offers higher detection

sensitivity because of sharper band images, compared to

conventional TLC.

High-performance thin-layer chromatography, as a

method of chemical fingerprinting, is a suitable for rapid

assessment of the authenticity of the food products as a

chemical composite. As such, the analysis will enable to

distinguish the presence of aberrant chemical components

from adulterants, as well as favorable or unfavorable

chemical changes arising from varied treatments or storage

of the product [54]. HPTLC is, also, useful in determina-

tion of constituent of different pharmaceutical dosage

forms in the presence of their degradation products and

additives, and it is sometimes the only technique of choice

for the determination of drugs in mixtures due to its high-

resolution power [26, 32]. For conventional identification

of pharmaceuticals, HPTLC has been used in almost all

Pharmacopoeias worldwide.

Over the last decade, the (HP)TLC shows a constant

increasing trend in fingerprint analysis of herbal, food, phar-

maceutical, environmental, and forensic samples (Fig. 1b).

Short summary of recent publications according to sample

type and chromatographic conditions is given in the Table 2.

Chromatogram Manipulation in TLC:

Signal Acquisition and Data Pretreatment

Thin-layer chromatogram is a rich source of data. Careful

choice of derivatizing agents in combination with chromato-

gram illumination under visible, 254- or 366-nm UV light can

tremendously enhance selectivity in visualization of target

bands. Choosing appropriate scanning wavelength or storing

information about colors by splitting a photo through red,

green, and blue channel filter can further enhances selectivity.

Chromatographic data can be obtained by the means of (1)

classical densitometry, or (2) by collecting a photo of selected

chromatogram and extracting a densitogram-like signal along

the target track by the means of appropriate software. Each

approach has its own advantages and drawbacks. Several

devices can be used to take photos of TLC plate such as in

house made apparatus equipped with digital camera or com-

mercially available DigiStore 2 device. Main advantage of the

use of digital camera is that it is much cheaper and easily

available [55], while the main disadvantage remains manual

positioning of plate, illumination system, and digital camera.

Repositioning may significantly affect further chemometric

evaluation [56]. Simple computer scanner can be used as well,

assuming that the target zones are visible under white light

[57]. More sophisticated way for documentation is provided

by DigiStore 2 device, equipped with 12-bit CCD Camera

with excellent color fidelity. Device is managed through

WinCats software and has high reproducibility due to easy

image optimization for all illumination modes.

Images are usually stored in Tagged Image File Format

(TIFF) and Joint Photographic Experts Group (JPEG) file

formats. TIFF file is a flexible format that normally saves 8

or 16 bits per color channel (red, green, and blue) for

24- and 48-bit channels in total. It is a lossless way of

storing images and it is recommended whenever a single

piece of information must be retained. In contrast JPEG is a

very efficient (i.e. much information per byte) but destruc-

tively compresses 24-bit bitmap formats. Also, the same

image saved as a TIFF and a JPEG will have different

brightness values as well. Each time a JPEG image is re-

saved, it is compressed again, consequently leading to dis-

torted information unsuitable for quantitative work. TIFF

format is preferred instead, and distribution of images in

JPEG format is possible if needed. However, many devices

store the photographs in JPEG format and some had

obtained very good results using high-quality JPEGs [55].

Stored images could be further processed with several

softwares such as ImageJ (ver. 1.43q Wayne Rasband, National

Institutes of Health, USA; http://rsb.info.nih.gov/ij), JustTLC

(Sweeday, Lund, Sweden, http://www.sweday.com/Products.

spx) or Sorbfil TLC Videodensitomer (Ivan Mihtarov, Sorbfil,

Russia, http://www.sorbfil.com/en/index.htm), etc. ImageJ is a

freely available Java-based program for digital picture

manipulation. Featured with user friendly graphical envi-

ronment it can be used for simple picture transformations

such as resizing, cropping, and rotating or advanced ones

such as filtering, smoothing, background subtraction, auto-

balance, or grayscale conversion and other signal transfor-

mations [32, 58]. Furthermore, it provides an option for

plotting intensity associated with each pixel, as well as raw

data export which is particularly useful for further chemo-

metric data handling. Sorbfil Video Densitometer and Just-

TLC are of limited power in signal profiling and data

exporting but can be very useful in quantitation and peak

identification.

Once the data have been acquired the usual pretreatment

procedures are as follows: denoising, normalization, and

D. Milojkovic-Opsenica et al.

123

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Planar Chromatographic Systems in Pattern Recognition

123

baseline removal, followed by warping/registering if the

picture of chromatogram is to be analyzed [56, 59].

In the case of densitograms, WinCats software already

provides options for noise reduction, smoothing, filtering,

baseline removal, peak identification, etc. The densitometer

noise is, in most cases, pink and heteroscedastic, and

Savitzky–Golay filter [60] with proper length is usually

applied. A very useful comparative study on the use of several

algorithms for TLC densitograms denoising has been reported

by Komsta [61]. The author compared some of the classical

signal filtering techniques such as Savitzky–Golay, Adaptive

Degree Polynomial Filter, Fourier denoising, Butterworth,

and Chebyshev IIR filters and the wavelet shrinkage method.

The author suggested Savitzky–Golay filter of appropriate

window width or wavelet shrinkage with Haar wavelet, soft

threshold, and high decomposition level as the best choice.

In the case of collected images, there are several options

for noise reduction, baseline removal, and peak shift cor-

rection. The CCD camera noise is white and homoscedastic,

while the noise of densitograms is pink and heteroscedastic,

therefore demanding different approach [56]. Several filters

such as averaging, circular, Gaussian, median, and Wiener

can be used. Some of them are included in the ImageJ

software package. Gaussian filter as a very efficient mode is

proposed by Daszykowski and coworkers [59].

Removing background and baseline drift caused by inho-

mogeneous illumination is necessary and could be carried out

as simple as using ImageJ built in rolling ball filter for

background subtraction, with ball radius about 100 pixels

[62]. The radius could be adjusted to prevent excessive loss of

information, while maintaining satisfactory signal quality.

Normalization, including autoscaling, is not a necessary

step in the case of chromatographic data pretreatment;

however, it is recommended to compare results with and

without normalization. If there is significant difference then

normalization should be kept. Ogegbo and coworkers [63]

showed that using autoscale gives the best separation for

four popular medicinal herbs.

Random shift of the chromatographic peaks cannot be

fully suppressed and it can cause serious problems in fur-

ther chemometric analysis. Several techniques may be used

to correct the shifts such as correlation optimized warping

(COW), dynamic time warping (DTW), or fuzzy warping

[64, 65]. In the case of captured images, warping is more

difficult and requires registering using splines [62].

Chemometric Methods in TLC Pattern Recognition

and Fingerprint Analysis

Once the data have been properly recorded, extracted, and

pretreated, several chemometric classification methods may

be used. All classification methods fall in two categories:

supervised and unsupervised. The first category does not

assume any a priori determined classes and leads to spon-

taneous grouping of objects according to their mutual sim-

ilarities, and algorithm applied. Principal component

analysis (PCA) and hierarchical cluster analysis (HCA) are

one of examples, mostly used for introductory data analysis.

In the second case at the beginning classes are well defined

and the final model is then constructed and validated. One

example is linear discriminant analysis (LDA). Supervised

techniques can be further classified as those focused on

discrimination among classes such as partial least square

discriminant analysis (PLS-DA), k-nearest neighbors (KNN),

classification and regression trees (CART), and artificial

neural networks (ANN), and those that are enable to model

classes such as soft independent modeling of class analogy

(SIMCA) and unequal dispersed classes (UNEQ).

Choice of particular chemometric technique depends on its

features and the nature of a problem to be solved. Techniques

such as PCA and HCA are usually performed at the begin-

ning, to reduce dimensionality of data hyperspace, visualize

the structure of data, identify important variables, and confirm

the presence of outliers [66]. Other methods such as LDA and

PLS-DA aim to build mathematical models that can be used

in further classification of unknown samples. In the very shy

field of thin-layer fingerprint chromatography, PCA and

Cluster analysis (CA) are the most used techniques, followed

by PLS-DA, KNN, and ANN.

Principal component analysis is probably the most

widespread multivariate chemometric technique. It reduces

the data dimensionality by creating new latent variables,

so-called principal components (linear combinations of

starting variables). In a geometrical sense, the principal

components should be imagined as orthogonal axes that

follow directions of the highest variability in the data. The

first principal component (PC1) accounts for the maximum

of the total variance, the second is uncorrelated with the

first (orthogonal) and accounts for the maximum of

residual variance, etc. For practical reasons only few

principal components that describe the most of original

data variability have to be retained. Coefficients that relate

original variables are called loadings. The greater they are

the higher the impact of that variable on that particular

principal component is. Every object has a score value,

unique for each principal component. The scores and

loadings are usually presented in the form of score and

loading plots and provide easy way for outlier identifica-

tion, grouping of objects, finding most influential variables,

etc. [67, 68].

Cluster analysis is another unsupervised classification

technique usually used in combination with PCA. In CA,

objects are grouped based on their mutual similarities. As

the measure of similarity the distance, correlation, or some

combination of both can be taken. Similarity is inversely

D. Milojkovic-Opsenica et al.

123

related to the distance between samples such as Euclidean,

Manhattan, or Mahalanobis distance. Grouping of the

samples can be performed by different clustering algo-

rithms, such as single linkage (nearest neighbor), complete

linkage (furthest neighbor), average linkages, centroid,

Ward’s method, etc. The last one being quite popular for it

results in well-shaped clusters [69]. However, both meth-

ods are sensitive to data pretreatment.

Zarzycki and co-workers [2] used particularly selected

peaks at specified retentions as input variables in combi-

nation with PCA and CA. Such approach easily distin-

guished genuine and non-genuine samples as well as fresh

from expired commercial products and biodegradation

peaks appearing on micro-TLC profiles. In combination

with fuzzy clustering, PCA and CA could be applied on

entire densitogram-like data obtained by TLC image

analysis [70]. Fuzzy clustering showed to be superior in

classification in the particular case. PLS-DA can be used in

addition to PCA not only to enhance poor or ambiguous

separation obtained by PCA [63], but also to generate

models that can be used for further classification and

quality control. The same authors compared different data

pretreatment methods and successfully identified chemo-

taxonomic marker compounds that differentiate among the

four types of herbs of radix species. Only few papers report

the use of ANN, KNN, and other supervised algorithms in

combination with data provided by TLC or in combination

with HPLC [71, 72]. In the case of multimodal data, i.e.

data recorded on several wavelengths or obtained through

different filters, PARAFAC is the method of choice [73].

Some of recent publications relying on different che-

mometric techniques and data acquisition methods are

summarized in the Table 2.

Conclusion

High-performance thin-layer chromatography, as a method

of chemical fingerprinting, is suitable for rapid assessment

of the authenticity of the food products. It is often used as

alternative to HPLC and TLC for monitoring the produc-

tion of extracts and final products although its application

on herbal extracts are predominant over its use in phar-

maceutical, environmental, food, and forensic analysis.

Considering the introduction of biological fingerprinting

analysis, as a method of screening the natural samples for

the presence of most active compounds, use of chemometric

classification methods, application of powerful scanning and

image capturing and processing devices and algorithms,

advancement in development of novel microstructured and

nano monolithic stationary phases as well as various sepa-

ration modalities, HPTLC fingerprinting is becoming

attractive and fruitful field of separation science.

Development of efficient and reliable fingerprint TLC

method requires chemometric approach at several levels

starting with application of experimental design and opti-

mization techniques to the starting separation step, followed

by data acquisition, and signal manipulation, and finally

solving classification and modeling problem. However,

serious lack in application of aforementioned techniques as

well as adequately performed method validation (regarding

precision, accuracy, and stability studies) still remains a

major shortcoming of majority fingerprint studies.

Acknowledgments This work has been supported by the Ministry

of Education and Science of the Republic of Serbia, Grant No.

172017.

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