# Aromatic Herbal Products Analysis by Laser-Induced  Breakdown Spectroscopy

Authors: Jesús Anzano; Beatriz Bonilla; Beatriz Montull-Ibor; Justiniano Casas
Journal: Scholarly Research Exchange
 (2008)

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## Abstract

Quick online classification allows the installation of the equipment in the line of work. In this sense, the laser-induced breakdown spectroscopy (LIBS) technique offers all the possible advantages: speed, possibility of online analysis, nondestructive, and so on. For the present work, a 350 mJ laser and Mechelle spectrograph were used. Analysis of the data with principal component analysis (PCA) allows an automatic classification of the aromatic herbal products like tea and camomile in a nondestructive and fast way.


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## Body

## 1. Introduction

Laser-induced breakdown spectroscopy (LIBS) is a type of atomic emission spectroscopy
which utilises a highly energetic laser pulse as the excitation source. LIBS operates by
focusing the laser onto a small area at the surface of the specimen; when the
laser is discharged it ablates a very small amount of material, in the range of
nanograms to picograms, which instantaneously generates a plasma plume with temperatures of about 10 000–20 000 K. At these temperatures,
the ablated material dissociates (breaks down) into excited ionic and atomic species.
During this time, the plasma emits a continuum
of radiation which does not contain any useful information about the species
present, but within a very small timeframe the plasma expands at supersonic
velocities and cools. At this point, the characteristic atomic emission lines
of the elements can be observed. It has been successfully applied before in qualitative analysis and to
sort different kinds of materials like paper [1], plastics [2], glasses [3], cinematographics films [4], and so on.
This technique has been used because it is powerful, has online possibilities, performs
quick classification, and so on.The application of multivariant
techniques in LIBS has been previously studied. In this way, principal
component analysis (PCA) and cluster analysis (CA) were used by Amador-Hernández et al. [5] to classify the
spectral information provided by all the components in order to obtain
two-dimensional plots of the sample composition. Multivariate statistical
analysis (MVA) techniques are coupled with laser-induced breakdown spectroscopy
(LIBS) to identify preservative types and to predict elemental content in
preservative-treated wood where principal component analysis (PCA) was used to
differentiate the samples treated with different preservative formulations by
Martin et al. [6]. PLSR (partial least-squares regression) was also used in [7]
to predict the composition of the samples. Partial least-squares (PLS)
regression was used
with LIBS technique to quantify samples in the jewellery industry [8] and for
their characterization [9]. Also, PLS was used to study the matrix effects in
steel samples [10]. In LIBS, PCA was also applied in forensic and environmental
fields [11], in soil samples [12], and for detection of biological contaminants
on surfaces [13].In this work, we present a new, fast,
and easy method for aromatic herbal products classification with LIBS using a
multivariant technique called principal component analysis (PCA).


## 2. Theoretical

PCA is a useful statistical technique that has found application in fields such as
face recognition and image compression, and is a common technique for finding
patterns in data of high dimension. So it is a way of identifying patterns in
data, and expressing the data in such a way as to highlight their similarities
and differences. Here we will present a little explanation for the technique
but more details for this analysis can be found anywhere [14, 15].The mathematics for this model are
based on the consideration of a data matrixX with N×K dimensions, where N are
the samples and K the variables, expressed as product of two matrices with their more
significant characteristics: X=t⋅p′+E where
t are the scores (N×q), p the loadings (K×q), and q the number
of principal components (PC) (q≤min(N,K)). In PCA model, the
importance of a variable is indicated by its variance, and E are the residuals.
The variance explained by every PC is the algebraic concept of the associated
eigenvalue.It is important to point out that
the PCs are a new set of new variables with several important properties [16]:
(i) the information explained by a PC is not present in the other PCs; (ii) the
first PC contributes with most of the information, the second less, and so
forth; (iii) a very reduced number of PCs carry almost all the information that
was in the original set of atomic peaks. It derives from the stems out property
that noise and unrelevant spectral data are left out for the last PCs, which is
very advantageous.


## 3. Experimental

### 3.1. Instrumental Setup

The instrumentation used
consisted of an Nd:YAG laser, anxyz stage carrying the sample (Standa 011957), a spectrograph, and an intensified charged coupled device
(ICCD) detector. An Nd:YAG laser (Brilliant Quantel, Q-Switched) with a 115 mJ laser pulse energy at the second harmonic of
532 nm, a 4.4 nanoseconds pulse duration, and 0.7 Hz repetition frequency was
used. The crater diameter was 0.4 mm. The plasma light was collected and
transported to the spectrograph by lens and optical fiber. The lens position
was adjusted by a diode laser (Andor, HE-OPI-0009). An Echelle spectrograph
(Andor Mechelle ME5000, focal length 195 mm, F/7, λ/Δλ 5000, spectral range from 200 to
975 nm) was coupled to an ICCD detector (Andor iStar DH734, 1024×1024 pixels,
13.6×13.6μm2/pixel, 18 mm intesificator diameter). This system was calibrated
by an Hg:Ar lamp (Ocean Optics, HG-1, Hg-Ar lines 253–922 nm). A measurement gate delay time of 1 microsecond
and an integration time of 500 nanoseconds were optimized.


### 3.2. Samples and Sample Preparation

No sample preparation was necessary preventing sample contamination. This makes the
analysis faster and easier. This kind of analysis makes this technique perfect
for fast analysis and its application online.Different kinds of samples of commercial
aromatic herbal products (dry pulverized leaves) were analyzed: camomile, anise camomile, large-leaved
lime 1, large-leaved lime 2, tea 1, tea 2, tea 3, tea 4, and tea 5. An
average of ten shots in one point for each sample (the samples did not break through
the leaves) was used for the analysis. A spectrum of one sample to show the
data obtained is shown in Figure1.Figure 1
Camomile sample spectrum.


## 3.1. Instrumental Setup

The instrumentation used
consisted of an Nd:YAG laser, anxyz stage carrying the sample (Standa 011957), a spectrograph, and an intensified charged coupled device
(ICCD) detector. An Nd:YAG laser (Brilliant Quantel, Q-Switched) with a 115 mJ laser pulse energy at the second harmonic of
532 nm, a 4.4 nanoseconds pulse duration, and 0.7 Hz repetition frequency was
used. The crater diameter was 0.4 mm. The plasma light was collected and
transported to the spectrograph by lens and optical fiber. The lens position
was adjusted by a diode laser (Andor, HE-OPI-0009). An Echelle spectrograph
(Andor Mechelle ME5000, focal length 195 mm, F/7, λ/Δλ 5000, spectral range from 200 to
975 nm) was coupled to an ICCD detector (Andor iStar DH734, 1024×1024 pixels,
13.6×13.6μm2/pixel, 18 mm intesificator diameter). This system was calibrated
by an Hg:Ar lamp (Ocean Optics, HG-1, Hg-Ar lines 253–922 nm). A measurement gate delay time of 1 microsecond
and an integration time of 500 nanoseconds were optimized.


## 3.2. Samples and Sample Preparation

No sample preparation was necessary preventing sample contamination. This makes the
analysis faster and easier. This kind of analysis makes this technique perfect
for fast analysis and its application online.Different kinds of samples of commercial
aromatic herbal products (dry pulverized leaves) were analyzed: camomile, anise camomile, large-leaved
lime 1, large-leaved lime 2, tea 1, tea 2, tea 3, tea 4, and tea 5. An
average of ten shots in one point for each sample (the samples did not break through
the leaves) was used for the analysis. A spectrum of one sample to show the
data obtained is shown in Figure1.Figure 1
Camomile sample spectrum.


## 4. Results and Discussion

### 4.1. Data Preprocesssing

The preprocessing of data is a usual
practice prior to the application of PCA because the results are based on the
variance patterns of the original data. There are four preprocessing
strategies: mean centering, autoscaling, range scaling, and variance scaling.
In this work, the preprocesssing strategy used by the software was mean
centering. Mean centering is almost always applied
when calculating any multivariate calibration model. This involves calculating
the average spectrum of all the spectra in the training set and then
subtracting the result from each spectrum. In addition, the mean concentration
value for each constituent is calculated and subtracted from the concentrations
of every sample.


### 4.2. Principal Components Analysis (PCA)

The Unscrambler v.9.1, chemometrics
software package by Camo, allows a bigger depth in the study by PCA, and was
used for this study mainly because it allows the introduction of the complete
spectrum (27000 pixels) for its direct study by PCA, without carrying out a
previous selection by visual inspection of wavelength ranges. The graphs can
also be obtained in 3 dimensions, representing the first 3 PCs together that improve
the interpretation and visualization of the data clouds. Another difference
with other programs is that it is possible to carry out the PCA by covariance
method. It also allows graphics between PC1 and PC2, PC2 and PC3, and so on.Three principal components were used
to perform the classification. The first PC explains the 42% of the variance,
the second PC the 28%, and the third one the 10% (80% variance total
explained).In Figure2, the three-dimension
score plot obtained after performing PCA analysis to the samples is presented.
Looking at this graphic, it can be deduced that with PC1 tea can be
differentiated from camomile and large-leaved lime;
also, with PC2 camomile can be differentiated from camomile large-leaved lime. So, with just one quick analysis these three kinds of aromatic herbal products can be easily sorted.Figure 2
3D scores plot.
1: camomile; 2: anise camomile; 3: large-leaved lime sample 1; 4: large-leaved lime sample 2; 5: tea sample 1; 6: tea sample 2; 7: tea sample 3; 8: tea sample 4; 9: tea sample 5.It is also convenient to search what
range of wavelengths made this classification. These ranges are obtained with
the loadings plot, and they are presented in Table1. Loadings plot shows the
variables (wavelengths in this case) that produce the separation. This plot is
similar to the scores one, and the wavelength corresponding to one sample is in
the same position than the score point for that sample. In posterior analysis, it
is enough to study these ranges for an optimal classification of new samples. With
this model, it is easy to sort new samples introducing them in the PCA.Table 1
Range wavelengths that produce the classification.
Tea (nm)Large-leaved lime (nm)Camomile (nm)764 to 768442 to 445588 to 591768 to 773610 to 614570 to 573614 to 618737 to 741


## 4.1. Data Preprocesssing

The preprocessing of data is a usual
practice prior to the application of PCA because the results are based on the
variance patterns of the original data. There are four preprocessing
strategies: mean centering, autoscaling, range scaling, and variance scaling.
In this work, the preprocesssing strategy used by the software was mean
centering. Mean centering is almost always applied
when calculating any multivariate calibration model. This involves calculating
the average spectrum of all the spectra in the training set and then
subtracting the result from each spectrum. In addition, the mean concentration
value for each constituent is calculated and subtracted from the concentrations
of every sample.


## 4.2. Principal Components Analysis (PCA)

The Unscrambler v.9.1, chemometrics
software package by Camo, allows a bigger depth in the study by PCA, and was
used for this study mainly because it allows the introduction of the complete
spectrum (27000 pixels) for its direct study by PCA, without carrying out a
previous selection by visual inspection of wavelength ranges. The graphs can
also be obtained in 3 dimensions, representing the first 3 PCs together that improve
the interpretation and visualization of the data clouds. Another difference
with other programs is that it is possible to carry out the PCA by covariance
method. It also allows graphics between PC1 and PC2, PC2 and PC3, and so on.Three principal components were used
to perform the classification. The first PC explains the 42% of the variance,
the second PC the 28%, and the third one the 10% (80% variance total
explained).In Figure2, the three-dimension
score plot obtained after performing PCA analysis to the samples is presented.
Looking at this graphic, it can be deduced that with PC1 tea can be
differentiated from camomile and large-leaved lime;
also, with PC2 camomile can be differentiated from camomile large-leaved lime. So, with just one quick analysis these three kinds of aromatic herbal products can be easily sorted.Figure 2
3D scores plot.
1: camomile; 2: anise camomile; 3: large-leaved lime sample 1; 4: large-leaved lime sample 2; 5: tea sample 1; 6: tea sample 2; 7: tea sample 3; 8: tea sample 4; 9: tea sample 5.It is also convenient to search what
range of wavelengths made this classification. These ranges are obtained with
the loadings plot, and they are presented in Table1. Loadings plot shows the
variables (wavelengths in this case) that produce the separation. This plot is
similar to the scores one, and the wavelength corresponding to one sample is in
the same position than the score point for that sample. In posterior analysis, it
is enough to study these ranges for an optimal classification of new samples. With
this model, it is easy to sort new samples introducing them in the PCA.Table 1
Range wavelengths that produce the classification.
Tea (nm)Large-leaved lime (nm)Camomile (nm)764 to 768442 to 445588 to 591768 to 773610 to 614570 to 573614 to 618737 to 741


## 5. Conclusions

PCA reduces the dimensionality of a
group of data, offers transformation with a reduced number of variables, but
describes the data in most part. The
analysis of PCs can be used as classifier and detector of “outliers,” since the
representation of the first scores vectors or components offers an image with
the classes of the separate cases. Its
application to the spectra of LIBS that offer a high quantity of information guarantees a correct and
reliable simplification, classification of aromatic herbal products and potentially
of other natural commonly used products.



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*Source: 152607-2008-10-19.xml*