High Resolution Spectroscopy of Sweeteners
G. Giubileo, I. Calderari and A. Puiu
ENEA, UTAPRAD-DIM, Via E. Fermi 45, 00044-Frascati, Italy
Keywords: IR Photoacoustic Spectroscopy, Sugar, Sweeteners.
Abstract: The identification of sophistication in beverage and food products has an increasing role in modern society.
Different techniques are currently used for qualitative assessment of food stuff and beverages. Among them
high resolution spectroscopy shown to be able to identify different types of sweeteners such as fructose,
glucose, maltose, sucrose and aspartame. To this purpose, a reliable, fast and easy-to-use screening method
for the optical characterization of these substances was developed. In the present work the Infrared Laser
Photo-Acoustic Spectroscopy was used to record high resolution infrared absorption spectra of common
sugars in the fingerprint region, not previously reported in literature at our knowledge. Spectral data were
obtained by a CO
2
laser based optical apparatus. These preliminary results are the key toward a further
analysis of sweeteners in a complex matrix devoted to detecting adulteration of commercial fruit juices and
light drinks by low cost sweeteners.
1 INTRODUCTION
Nowadays the food safety and consumer protection
requirements are to increase the food and beverage
quality and to adopt faster and easier methods for the
quality determination. In the field of fruit juice
adulteration most of frauds are based on the addition
of water and low cost sweeteners to the product. The
chromatographic methods GC and HPLC are
currently used as accurate reference techniques to
successfully determine fruit juice authenticity by
oligosaccharide profiling (Pan 2002). Unfortunately
they are time consuming, expensive and difficult to
implement in an on-line setting (Leopold 2009).
The Fourier Transform Infrared (FTIR)
spectroscopy approach considers the entire sample
composition and is widely used to identify and study
chemicals by measuring vibrational/ roto-vibrational
frequencies of the excited molecules. Absorption
bands in the MIR range are characteristic of the
bonds and functional groups of a molecule, so the
overall spectrum acts as a fingerprint for a given
compound. This made it possible to apply FTIR
spectroscopy to authenticity issues and composition
profiling (Kelly 2005).
In the field of infrared spectroscopy, the high
resolution spectroscopy based on a laser source may
compete with the FTIR spectroscopy in order to
quantify simultaneously the Fructose, Sucrose and
Glucose content in a juice used as authenticity
biomarkers. A laser spectroscopic techniques can
offer a significant chance in detecting specific
spectral signatures of sugars, and organic
compounds in general, at trace concentration.
Among different possible laser spectroscopic
methods, high resolution Laser Photoacoustic
Absorption Spectroscopy (LPAS) was selected for
identification of sugars in the solid phase. LPAS
(Michaelian 2003) is characterized by roughness,
high sensitivity and high selectivity. The LPAS is an
indirect absorption spectroscopy technique based on
the Photoacustic effect in solid, which was
discovered by Alexander Bell in 1880. Bell showed
that when a periodically interrupted beam of sunlight
illuminates a solid in an enclosed cell, an audible
sound could be heard by means of a hearing tube
attached to the cell. The sunlight energy absorbed by
the sample is transformed into kinetic energy in the
course of energy exchange processes. This results in
local heating and consequently a pressure wave is
generated. The sound obtained in this way represents
the Photoacoustic signal. By measuring the sound at
different wavelengths, a Photoacoustic spectrum of a
sample can be recorded. To this purpose, modern
LPAS systems employ the intensity modulation of a
laser beam in order to allow the generation of
acoustic waves in a resonant photo-acoustic cell.
In the present study, commercial standard
samples of sugars (Fructose, Maltose, Sucrose,
Glucose) and sweeteners (Aspartame) were analyzed
91
Giubileo G., Calderari I. and Puiu A..
High Resolution Spectroscopy of Sweeteners.
DOI: 10.5220/0005336600910095
In Proceedings of the 3rd International Conference on Photonics, Optics and Laser Technology (PHOTOPTICS-2015), pages 91-95
ISBN: 978-989-758-092-5
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
by LPAS. The experimental results were reported in
the paper. A PCA statistical treatment was applied to
the experimental spectra in order to classify the
examined substances. The present paper is the
preliminary step toward the development of a easy-
to-use technique for a real time detection of the
mentioned fraud.
2 MATERIALS AND METHODS
To analyze the sugar samples by LPAS technique
we adopted an optical apparatus realized in ENEA
Frascati Research Centre with a grating tuned 2
Watts line tunable CO
2
laser source (Model Merit-G
from ACCESS LASER), a two-channel power meter
(Rk-5720 model by Laser Probe), a 3 cc volume
Photoacoustic (PA) cell, and a lock-in amplifier (SR
830 model, Stanford Research Systems). The CO
2
laser emission was square wave modulated by a
digital signal generator (SFG 830 by INSTEK) at the
resonant frequency (40 Hz) of the PA cell, with a
50% duty cycle. The PA signal generated inside the
PA cell was detected by a miniaturized sensitive
microphone (Model EK 3033 by Knowles
Electronics Inc., USA) and sent to the lock-in
amplifier, which communicates to PC via GPIB
interface. Data were acquired in the frame of a
software developed in Labview environment. A
general scheme of a LPAS experiment is shown in
the Figure 1. More information on the LPAS system
employed in the present work have been reported in
previous papers (Giubileo et al. 2012, Puiu et al.
2014).
Figure 1: LPAS set-up schematic view.
The sweeteners for the study were purchased as
commercial standard preparation from the suppliers
reported in the Table 1. The sample of sweetener to
be analyzed was placed inside PA cell at RT,
without any pre-treatment. Measurable PA signals
were obtained from a few milligrams of pure
samples, depending on the investigated substance.
The measurements were repeated 10 times for each
one of the 55 laser lines emitted from the laser
source at wavelengths falling in the 9.2 to 10.8 µm
spectral range. For each kind of substance, four
different samples were analyzed. The laser beam
power interacting with the sample was set between
50 mW and 500 mW on the strongest laser lines
with the purpose to maintain the sample temperature
lower than the melting point.
Table 1: List of sugars analyzed in this paper.
Sweetener Chemical
formula
m.p. (°C) Supplier
Fructose C
6
H
12
O
6
100 Merk
Glucose C
6
H
12
O
6
146 Merk
Maltose C
12
H
22
O
11
360 Sigma
Sucrose C
12
H
22
O
11
186 Baker
Aspartame C
14
H
18
N
2
O
5
249 Fluka
3 RESULTS
For each one of the analyzed substances, measurable
PA signals were detected in the investigated spectral
range, while the background signal was verified to
be neglectable with respect to the sample signal.
This allowed to plot a Photoacoustic spectrum for
every analyzed sweetener, without the necessity to
subtract the background signal. The resulting LPAS
infrared absorption spectra of Fructose, Maltose,
Sucrose, Glucose and Aspartame are reported in the
Figures 2 to 6.
Each spectrum in these figures represents the
average of four different PA spectra of the same type
of substance.
The error bars on the graphs come out from the
standard deviation (sd) after averaging multiple
spectra. The recorded sd was under 3%, which
means a very good reproducibility of data and a high
stability of the system.
Figure 2: IR photoacoustic spectrum of fructose.
laser chopper
PA cell
Lock-in
amplifier
power
meter
laser chopper
PA cell
Lock-in
amplifier
power
meter
oscilloscope
computercomputer
9,0 9,2 9,4 9,6 9,8 10,0 10,2 10,4 10,6 10,8 11,0
0,0000
0,0002
0,0004
0,0006
0,0008
Photoacoustic Signal (Arb. Units)
Wavelength (
μ
m)
Fructose
PHOTOPTICS2015-InternationalConferenceonPhotonics,OpticsandLaserTechnology
92
Figure 3: IR photoacoustic spectrum of glucose.
Figure 4: IR photoacoustic spectrum of maltose.
Figure 5: IR photoacoustic spectrum of sucrose.
Absorption data have been processed in order to
make the experimental data not dependent on the
local measurement settings, nor on the sample
adopted amount. In particular, the absorption data
have been normalized to the laser beam power. An
independent record of the laser emission power
spectrum is shown in the Figure 7.
Figure 6: IR photoacoustic spectrum of aspartame.
Figure 7: Spectrum of the laser emission power.
4 DISCUSSION
The present paper was not thought as a detailed
study of the sweeteners IR absorption molecular
transitions. Really it was intended as a first step of a
work aimed to realize a new real-time methodology
for the mentioned fraud detection to be applied on
field. Consequently, the vibrational rotational
transition lines assignment was not approached in
the present paper.
From the graphs reported in the Figures 2 to 6 it
appears that every substance shows a characteristic
spectral pattern, nevertheless a rapid direct
comparison among the spectra is difficult to achieve.
For this reason, a data treatment based on Principal
Component Analysis (PCA) was applied to the
spectral data as a demonstration of the system
capability to distinguishing among different standard
sweeteners.
PCA is a calculation algorithm that allows to
reduce dimensionality of a data set and make it
9,0 9,2 9,4 9,6 9,8 10,0 10,2 10,4 10,6 10,8 11,0
0,0000
0,0002
0,0004
0,0006
Photoacoustic Signal (Arb. Units)
Wavelen
g
th
(
μ
m
)
Glucose
9,0 9,2 9,4 9,6 9,8 10,0 10,2 10,4 10,6 10,8 11,0
0,0000
0,0005
0,0010
0,0015
0,0020
0,0025
0,0030
0,0035
Photoacoustic Signal (Arb. Units)
Wavelength (
μ
m)
Maltose
9,0 9,2 9,4 9,6 9,8 10,0 10,2 10,4 10,6 10,8 11,0
0,0000
0,0001
0,0002
0,0003
0,0004
0,0005
0,0006
0,0007
0,0008
Photoacoustic Signal (Arb. Units)
Wavelength (
μ
m)
Sucrose
9,0 9,2 9,4 9,6 9,8 10,0 10,2 10,4 10,6 10,8 11,0
0,0000
0,0001
0,0002
0,0003
0,0004
0,0005
0,0006
0,0007
Photoacoustic Signal (Arb. Units)
Wavelength (
μ
m)
Aspartame
9,0 9,2 9,4 9,6 9,8 10,0 10,2 10,4 10,6 10,8 11,0
50
100
150
200
250
300
350
Laser beam power (mW)
Wavelength (
μ
m)
HighResolutionSpectroscopyofSweeteners
93
evident statistical differences eventually present
among the analyzed samples. PCA is a powerful
statistical instrument described for the first time in
the year 1901 (Pearson 1901). The PCA algorithm
finds hypothetical variables (called Principal
Components, PC) accounting for as much as
possible of the variance in the multivariate data
(Davis 1986, Harper 1999); the new variables are the
results of a linear combination of the original
variables, where every descriptor has an own
“weight”, or loading. Generally the first few new
variables retain most of the variation of original
variables. The use of PCA appears extremely
suitable for the spectroscopy analysis discussed in
this work. It allows to make it clear the grade of
similarities or differences among the spectra and
point out the most involved emission lines (loading).
The PCA calculations operated on the above spectral
data produced the sample distribution reported as a
3D chart in the Figure 8.
Figure 8: 3D representation of PCA.
The infrared absorption data have been
normalized to their maximum peak value, in order to
make them directly comparable with each other. For
the PCA calculations, also the PA signal from the
empty cell was taken into account.
On each axis, the respective value of one of the
first three principal components is reported: PC1,
PC2 and PC3.
Taking into account the respective contribution
of each principal component to the substances
classification (see the Figure 9), it appears that the
first three PCs can explain 80% of the overall
spectral differences. Moreover, due also to the same
minor impact given by PC2 and PC3, we reduced
the PCA graph to the 2D representation reported in
the Figure 10 that brings out five different groups
corresponding to the analyzed types of sweeteners.
Figure 9: Weights of the principal components.
Figure 10: 2D graph of PCA.
5 CONCLUSIONS
In the present paper, the LPAS technique was
applied to collect the high resolution infrared
absorption spectra of Fructose, Maltose, Sucrose,
Glucose and Aspartame in the 9.2 to 10.8 µm
spectral region. To our knowledge, the high
resolution infrared absorption spectra shown in this
paper have been reported for the first time in
literature.
Further that, we have also shown that the joint
application of LPAS and Chemometrics can be used
to unambiguously classify the analyzed substances
without mis-assignments. This result makes the
method promising for a simple identification of
sophistication in beverage and food products.
The data here reported will be useful in the
future realization of a fast and easy-to-use screening
PHOTOPTICS2015-InternationalConferenceonPhotonics,OpticsandLaserTechnology
94
apparatus for detecting the presence of unwanted
sweeteners in juices and light beverages.
Consequently, the next step will include the LPAS
analysis of sweeteners mixtures and fruit juices to
check the capability of the optical technique to probe
the quality of a unknown sweetener and detect
frauds.
ACKNOWLEDGEMENTS
The authors acknowledge the financial support of
the Italian Ministry for Economic Development in
the frame of the National Project MI01_00182 -
SAL@CQO.
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