Detection and quantification of adulteration in sandalwood oil through near infrared spectroscopy† Saji Kuriakose,‡ a Xavier Thankappan, a Hubert Joe * a and Venkateswaran Venkataraman b Received 23rd April 2010, Accepted 21st June 2010 DOI: 10.1039/c0an00261e The confirmation of authenticity of essential oils and the detection of adulteration are problems of increasing importance in the perfumes, pharmaceutical, flavor and fragrance industries. This is especially true for ‘value added’ products like sandalwood oil. A methodical study is conducted here to demonstrate the potential use of Near Infrared (NIR) spectroscopy along with multivariate calibration models like principal component regression (PCR) and partial least square regression (PLSR) as rapid analytical techniques for the qualitative and quantitative determination of adulterants in sandalwood oil. After suitable pre-processing of the NIR raw spectral data, the models are built-up by cross- validation. The lowest Root Mean Square Error of Cross-Validation and Calibration (RMSECV and RMSEC % v/v) are used as a decision supporting system to fix the optimal number of factors. The coefficient of determination (R 2 ) and the Root Mean Square Error of Prediction (RMSEP % v/v) in the prediction sets are used as the evaluation parameters (R 2 ¼ 0.9999 and RMSEP ¼ 0.01355). The overall result leads to the conclusion that NIR spectroscopy with chemometric techniques could be successfully used as a rapid, simple, instant and non-destructive method for the detection of adulterants, even 1% of the low-grade oils, in the high quality form of sandalwood oil. Introduction Essential oils are complex mixtures of various terpenoids, alde- hydes, ketones, alcohols, esters and other aromatic substances. Most of the oils are used for flavoring of foodstuffs, in perfume compositions or in mouth care products. 1 Some essential oils containing phenol content are also used in phyto-pharmaceutical products or as additives relating to antibiotic properties. Sandalwood oil is a volatile essential oil obtained by steam distillation of the dried wood from the trunk and roots of the plant Santalum album L (Indian sandalwood) (Kingdom – Plantae, Class – Magnoliopsida, Family – Santalaceae, Genus – Santalum L). This oil appears as a pale yellow/yellow liquid with a characteristic soft, warm, woody odor and a slightly bitter resinous taste (FCC 2003). Sandalwood oil is used as a flavor ingredient, with a daily consumption of 0.0074 mg/kg and as an adjuvant in the food industry. In perfumery also, it is used extensively. The heartwood of mature trees (>10 years old) contains oils whose main constituents are sesquiterpene alcohols, cis-a-santalol, cis-b-santaol etc. 2 This oil is approved for food usage by the United States Food and Drug Administration (FDA), Flavor and Extract Manufactures Association (FEMA) and Council of Europe (COE). 3,4 It is identified that sandalwood oil consists of more than 100 constituents. The a-santalol ($60% of total santalol) and b-santalol ($33% of total santalol) are mainly responsible for the odor depending on the sourced species, 5 although 2-furfuryl pyrrole may also contribute. 6 It also contains sesquiterpene hydrocarbons (60%) 7 that are mostly a-santalene, b-santalene, epi-b-santalene, as well as a-curcumene, b-curcumene, g-curcu- mene, b-bisabolene and a-bisabolol. 8 The other constituents reported are dihydro-b-agarofuran, santene, teresantol, borneol, teresantalic acid, tricycloekasantalal, santalone and santanol. 9 Three new neolignans and a new aromatic ester have been iso- lated from the heartwood of S. album L recently. 10 Sandalwood oil and its major constituents have short sensitive oral and dermal toxicity in laboratory animals. Sandalwood oil is found to have antiviral, anticarcinogenic and bactericidal activity. It is also not mutagenic in spore Rec assay. 11 Sanskrit manuscripts reveal that sandalwood has been in use for over 4000 years. The commercial use of sandalwood oil in the USA began in the early 1800s. Due to its sensory quality, extensive use, and steep rise in the price, sandalwood oil is often adulterated with low grade cost- effective oils and synthetic or semi-synthetic substitutes such as Sandalore Ò . 12,13 Adulteration of sandalwood oil is a serious problem for regulatory agencies, oil suppliers, and a threat to the health of consumers. Substitution and synthetic additives would influence the chemical composition and physical properties of the oil; these factors may affect oil quality and the allergic potential. The common adulterants reported include castor oil, cedarwood oil and low-grade oil from ‘sandalwood’ species other than S. album. 12,14 The most common adulterant is the castor oil (botanical name Ricinus communis of the family Eurphorbiacae). Various authorities have recommended that the oil from S. album should not contain less than 90% w/w of (free) alcohols, a Centre for Molecular and Biophysics, Department of Physics, Mar Ivanios College, Thiruvananthapuram, 695 015, Kerala, India. E-mail: hubertjoe@ gmail.com; [email protected]; Fax: +91 471 2530023; Tel: +91 471 2531053 b Central Electronics Engineering Research Institute, Chennai Centre, CSIR Complex, India † Electronic supplementary information (ESI) available: supplementary table of percentages of castor oil and sandalwood oil in 0–100% adulterated mixtures. See DOI: 10.1039/c0an00261e ‡ Permanent address: St Thomas H.S.S., Pala, Kerala, India. 2676 | Analyst, 2010, 135, 2676–2681 This journal is ª The Royal Society of Chemistry 2010 PAPER www.rsc.org/analyst | Analyst Downloaded by CSIR MADRAS COMPLEX(CSIRM) on 23 September 2010 Published on 03 September 2010 on http://pubs.rsc.org | doi:10.1039/C0AN00261E View Online
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Detection and quantification of adulteration in sandalwood oil through nearinfrared spectroscopy†
Saji Kuriakose,‡a Xavier Thankappan,a Hubert Joe*a and Venkateswaran Venkataramanb
Received 23rd April 2010, Accepted 21st June 2010
DOI: 10.1039/c0an00261e
The confirmation of authenticity of essential oils and the detection of adulteration are problems of
increasing importance in the perfumes, pharmaceutical, flavor and fragrance industries. This is
especially true for ‘value added’ products like sandalwood oil. A methodical study is conducted here to
demonstrate the potential use of Near Infrared (NIR) spectroscopy along with multivariate calibration
models like principal component regression (PCR) and partial least square regression (PLSR) as rapid
analytical techniques for the qualitative and quantitative determination of adulterants in sandalwood
oil. After suitable pre-processing of the NIR raw spectral data, the models are built-up by cross-
validation. The lowest Root Mean Square Error of Cross-Validation and Calibration (RMSECV and
RMSEC % v/v) are used as a decision supporting system to fix the optimal number of factors. The
coefficient of determination (R2) and the Root Mean Square Error of Prediction (RMSEP % v/v) in the
prediction sets are used as the evaluation parameters (R2¼ 0.9999 and RMSEP¼ 0.01355). The overall
result leads to the conclusion that NIR spectroscopy with chemometric techniques could be successfully
used as a rapid, simple, instant and non-destructive method for the detection of adulterants, even 1% of
the low-grade oils, in the high quality form of sandalwood oil.
Introduction
Essential oils are complex mixtures of various terpenoids, alde-
hydes, ketones, alcohols, esters and other aromatic substances.
Most of the oils are used for flavoring of foodstuffs, in perfume
compositions or in mouth care products.1 Some essential oils
containing phenol content are also used in phyto-pharmaceutical
products or as additives relating to antibiotic properties.
Sandalwood oil is a volatile essential oil obtained by steam
distillation of the dried wood from the trunk and roots of the
plant Santalum album L (Indian sandalwood) (Kingdom –
Plantae, Class – Magnoliopsida, Family – Santalaceae, Genus –
Santalum L). This oil appears as a pale yellow/yellow liquid with
a characteristic soft, warm, woody odor and a slightly bitter
resinous taste (FCC 2003). Sandalwood oil is used as a flavor
ingredient, with a daily consumption of 0.0074 mg/kg and as an
adjuvant in the food industry. In perfumery also, it is used
extensively. The heartwood of mature trees (>10 years old)
contains oils whose main constituents are sesquiterpene alcohols,
cis-a-santalol, cis-b-santaol etc.2 This oil is approved for food
usage by the United States Food and Drug Administration
(FDA), Flavor and Extract Manufactures Association (FEMA)
and Council of Europe (COE).3,4
It is identified that sandalwood oil consists of more than
100 constituents. The a-santalol (�$60% of total santalol) and
b-santalol (�$33% of total santalol) are mainly responsible for
the odor depending on the sourced species,5 although 2-furfuryl
pyrrole may also contribute.6 It also contains sesquiterpene
hydrocarbons (�60%)7 that are mostly a-santalene, b-santalene,
epi-b-santalene, as well as a-curcumene, b-curcumene, g-curcu-
mene, b-bisabolene and a-bisabolol.8 The other constituents
reported are dihydro-b-agarofuran, santene, teresantol, borneol,
teresantalic acid, tricycloekasantalal, santalone and santanol.9
Three new neolignans and a new aromatic ester have been iso-
lated from the heartwood of S. album L recently.10
Sandalwood oil and its major constituents have short sensitive
oral and dermal toxicity in laboratory animals. Sandalwood oil is
found to have antiviral, anticarcinogenic and bactericidal
activity. It is also not mutagenic in spore Rec assay.11 Sanskrit
manuscripts reveal that sandalwood has been in use for over
4000 years. The commercial use of sandalwood oil in the USA
began in the early 1800s.
Due to its sensory quality, extensive use, and steep rise in the
price, sandalwood oil is often adulterated with low grade cost-
effective oils and synthetic or semi-synthetic substitutes such as
SandaloreÒ.12,13 Adulteration of sandalwood oil is a serious
problem for regulatory agencies, oil suppliers, and a threat to the
health of consumers. Substitution and synthetic additives would
influence the chemical composition and physical properties of the
oil; these factors may affect oil quality and the allergic potential.
The common adulterants reported include castor oil, cedarwood
oil and low-grade oil from ‘sandalwood’ species other than
S. album.12,14 The most common adulterant is the castor oil
(botanical name Ricinus communis of the family Eurphorbiacae).
Various authorities have recommended that the oil from
S. album should not contain less than 90% w/w of (free) alcohols,
aCentre for Molecular and Biophysics, Department of Physics, Mar IvaniosCollege, Thiruvananthapuram, 695 015, Kerala, India. E-mail: [email protected]; [email protected]; Fax: +91 471 2530023; Tel: +91 4712531053bCentral Electronics Engineering Research Institute, Chennai Centre,CSIR Complex, India
† Electronic supplementary information (ESI) available: supplementarytable of percentages of castor oil and sandalwood oil in 0–100%adulterated mixtures. See DOI: 10.1039/c0an00261e
‡ Permanent address: St Thomas H.S.S., Pala, Kerala, India.
2676 | Analyst, 2010, 135, 2676–2681 This journal is ª The Royal Society of Chemistry 2010
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calculated as santalols.14–17 The acetylation methods18,19
described to asses the santalol content of sandalwood oil gener-
ally lack specificity and accuracy. More recently, the ISO (2002)
has suggested the analysis of S. album oil using gas chromatog-
raphy (GC). However, these reports do not address the detection
of adulterants. Taking into consideration the above facts, there is
an increasing demand for the development of a new, rapid, and
non-destructive method instead of traditional, time consuming
and expensive analysis techniques. Until this date, there is no
standard method that has been explored or reported, for finding
out the adulteration of sandalwood oil.
The application of near infrared (NIR) spectroscopy
combined with chemometric techniques is a relatively new
approach to determine authenticity and to quantify the adul-
teration of essential oils. Recent reports reveal that NIR spec-
troscopy along with chemometrics is widely applied for the rapid
quantitative analysis of a wide range of vital constituents in food
and agricultural products.20 Oliveira et al. proposed partial least
square regression calibration models based on Fourier Trans-
form NIR measurements to evaluate the quality of hydrated
ethyl alcohol fuel and to detect its adulteration with methanol.21
Christy et al. studied NIR spectroscopy to detect and quantify
adulteration of olive oil with soybean, sunflower, corn, walnut
and hazelnut oils.22 Bewig et al.23 and Chen et al.24 used the NIR
spectral profiles to predict quality parameters of vegetable oils.
Multivariate analyses like Principal Component Regression
(PCR)25 and Partial Least Square Regression (PLSR)26,27 have
been applied to NIR spectrometry for quantitative analysis to
extract vital information through non-destructive methods.28
In the present study, both PCR and PLSRmethods are applied
to NIR spectra of pure sandalwood oil and oil adulterated with
various proportions of castor oil. These two multivariate tech-
niques could provide better accuracy, precision and significantly
more information in considerably less time than previous data
analysis methods. To the best of our knowledge, there is no
attempt other than this till now to use near infrared spectroscopy
(NIRS) along with multivariate regression methods for esti-
mating the quantity of adulterants viz. castor oil in sandalwood
oil.
Sample collection and experimental analysis
Samples
Pure sandalwood oil and castor oil (batch nos. 5BB 801622, 5BB
900502, 231) were procured from Khadi Gramodyog Bhavan,
Khadi and Village Industries Commission, Govt. of India.
Source of procurement from Govt. of India food and oil regu-
latory agencies ensures the authenticity of samples.
Chemicals
The solvent (carbon tetrachloride) used in this study was
obtained from Merck. The reagent used is analytical grade
without further purification.
Instrumentation
UV/VIS NIR Spectrophotometer of Cary 5000 (Sl. No:
EL03127331, www.varianinc.com) with a Pbs detector,
wavelength range from ca. 175–3300 nm and 0.01 nm resolution
is used to capture the spectra. A quartz window of 1 mm path
length Camloc cell is used as a sample holder. Serial port
communication is used to capture the raw spectral data. The
monochromator and sample compartments have separate
nitrogen purging capabilities, allowing the sample compartment
to be purged at a higher rate than the instrument.
Sample preparations
The samples are stored in hermetically sealed aluminum bottles
in the dark at 4 �C. The samples are brought to ambient
temperature of 20 �C eight hours prior to measurement. Using an
electromagnetic stirrer, the sandalwood and castor oil samples
are homogenized with proper solvent (1 : 10 v/v) for 15 min in
two separate conical flasks with stoppers. Proper precautions are
taken to avoid loss/change during the process. Samples are
prepared by adding percentile standard low-grade oil in solvent
with standard sandalwood oil in the same solvent. The relative
castor oil fraction (% v/v) in the samples varies from 0 to 100%
(refer to the supplementary table provided†). The oil samples are
blended under normal temperature and pressure. Thus, a set of
56 samples ranging from 0 to 100% (v/v) percentile is prepared.
Out of these, 45 samples are used for calibration and an inde-
pendent set of 11 samples with percentage ranges 0, 1, 5, 8, 12, 20,
25, 50, 70, 85, and 90% are used for prediction respectively.
The samples are labeled as ‘calibration set’ and ‘prediction set’
separately. To ensure a wide range of coverage, proper care is
adopted as norms set by the chemical sample-preparation
procedure. All the samples are kept in glass bottles and stored in
the dark at 3–4 �C. All measurements are carried out at 20 �C in
closed rooms.
Spectral acquisition
Thirty-two scans are performed at 1 nm intervals within the
wavelength range of 700–2200 nm to capture the spectra. The
time to acquire scans is approximately 28 s. The mean spectrum
is computed from the collected data. Background spectra with
reference sample are collected for every sample immediately
before the collection of the sample single-beam spectrum. The
sample spectrum is automatically ratioed against the background
spectrum and that spectrum is automatically stored in the
computer. The spectral data are transformed into ASCII format
by Varian software equipped with the spectrometer. In the
experiment, all of the spectra are recorded in absorbance mode.
During the experiment, the sample cell components are cleaned
with hexane. Thereafter with warm water, rinsed with deionised
water and then with CCl4 at room temperature to avoid oil build-
up on the cell windows. Components are dried using tissue paper.
During experimentation, the quartz cell is dried by exposing it to
a natural source of light to avoid any water film stuck on it
during washing since the presence of OH-groups will influence
the shape of the spectra and hinder the spectral features.
Calibration and quantitative analysis are performed using
PLSR and PCR methods. The root mean square error of cross-
validation (RMSECV) values are calculated for each factor with
the ‘leave-one-out’ cross-validation to determine the optimal
number of factors to be included in the calibration model.
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Chemometrics
Data analysis. Chemometric analysis including detection and
quantification are performed with a Pentium 4 Laptop computer
utilizing PLS Toolbox 5.8.1, March 2010 (The Eigen vector Inc.)
that works under Matlab 7.0.1 environment (Mathworks,
Natick, USA). Detection is performed by principal component
regression technique. Quantification of castor oil adulteration
levels is calculated by partial least square regression. These
involve a calibration step in which the relationship between
spectra and component concentrations is estimated from a set of
reference (measured) samples and a prediction step in which the
results of the calibration are used to estimate the component
concentrations from an unknown sample spectrum.28
Principal component regression (PCR). PCR is the combina-
tion of Principal Component Analysis and Multiple Linear
Regression. Through PCA, the larger number of variables is
reduced to have real contributed components called principal
components that contain most information.29 This is a well-
known technique of multivariate analysis.30–33 In the second step
of PCR, a multiple linear regression is performed on the scores/
loadings obtained in the PCA technique.
Partial least square regression (PLSR). PLSR is another well-
known regression technique for multivariate data, principally
applied for prediction.34 This method is especially useful when
(i) the number of predictor variables is similar to or higher than
the number of observations and (ii) predictors are highly corre-
lated. This tool is applicable when there is partial knowledge of
data, an example being the measurement of protein in wheat by
NIR spectroscopy. The interference and overlapping of the
spectral information may be overcome by PLS techniques to
a certain extent. PLS is a method that uses the full spectral region
selected and is based on the use of latent variables.
Model selection. The model is built by cross-validation method
during the calibration developments. The optimum number of
principal factors can be selected by cross-validation, employing
the cancellation of one sample at a time. This is done by plotting
the number of factors against the root mean square error of
cross-validation (RMSECV) and from this, the optimum number
of factors is selected28,35 for both PCR and PLSR models.
The best model selected is used to determine the concentration
of the samples in the independent prediction set. The relative
performance of the established model is accessed by the root
mean square error of calibration (RMSEC), RMSECV and
multiple coefficient of determination or regression coefficient.
(R2).36 The predictive ability of the model is evaluated from the
root mean square of prediction (RMSEP).37 The lower the
RMSEP value, the higher the degree of accuracy of the predic-
tion result provided by the calibration model.38
Modeling and data pre-processing are carried out using PLS
toolbox 5.8.1, Eigenvector Research39 supported on Matlab.40
The NIRS data from the spectrometer may contain background
information and noise in addition to sample information. Hence,
to obtain reliable, accurate and stable calibration models, it is
necessary to pre-process spectral data before modeling. The pre-
processing methods, in this study, are chosen based on prior
knowledge for each spectroscopic technique combined with
different permutations.31,37,41
Results and discussion
NIR spectra
Fig. 1 shows the average response of the acquired NIR absorp-
tion spectra for pure and blended mixtures of sandalwood oil
over the spectral range of 700–2200 nm at 1 nm spacing. (Spectra
of 45 samples with different relative fractions 0–100% (v/v) of
castor oil in clean sandalwood oil.)
It could be observed that the oil spectra are nearly identical
which makes the calibration problem non-trivial. However, there
are a few subtle but systematic differences in these spectra that
might be amplified by various pre-processing techniques. Fig. 2
shows the NIR spectra of pure sandal oil and castor oil.
Spectra investigation
According to former studies performed on various essential oils,
the NIR spectra of the analyzed oil samples are dominated by
overtones and different combinations of CH stretching and
bending vibrations occurring between 1000 and 2498 nm.42 There
has been much debate as to the importance of finding those
wavelengths that contain significant information, thus reducing
the number of wavelengths, variables, and model complexity. In
this work, the spectral region 700–2200 nm is selected to reduce
the number of insignificant variables and hence the model
complexity.
From Fig. 2, it is observed that the peaks are present at
1179.78, 1387, 1693, 1730, and 1861 nm. Absorption bands
observed at 1179 nm are due to methylene (CH) stretching [2nd
(3n) overtone and 2n combination bands (1135–1215 nm)]. The
peak at 1387 nm was related to methyl (CH) stretching and
bending combination [2nd (3n) overtone and 2n combination
Fig. 1 NIR spectra of pure and blended mixture of sandalwood oil
(at 20 �C; relative castor oil fraction 0–100% (v/v) in pure sandal oil; the
top spectrum represents 0% adulteration, the bottom spectrum represents
100% adulteration and 1–99% adulterations are in order from top to
bottom).
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bands (1375–1399 nm)]. The two peaks around 1693 and
1730 nm are associated with methyl and methylene asymmetric
stretching respectively. The peak centred near 1861 nm is unique
to molecular water (OH) combination vibration.43,44 The report
reveals that the 1st (2n) overtone CH stretching bands are at
1690–1695 nm and 1725–1731 nm.
Spectral pre-processing
The data set is loaded as a matrix with X – block and Y – block.
Most of the peaks are observed in the wavelength range
1100–1900 nm. The changes in the spectral regions
1350–1450 nm and 1550–1850 nm are exploited since significant
differences between NIR spectra of sandal and castor oils are
observed in this region. Hence, more emphasis is given to this
region for extracting required information through the optimal
calibration model.
In this study, several spectral pretreatments including auto-
scale, mean centre, none (without pre-processing), multiplicative
scatter correction (msc) and smoothing (Savitzky–Golay filters)
coupled with autoscale and with mean centre are investigated
(see Table 1). The root mean square error of calibration
(RMSEC), the root mean square error of cross-validation
(RMSECV), the root mean square error of prediction (RMSEP)
and the coefficient of determination (R2) are used to investigate
the methods and for model development.
The qualities of the results are compared using RMSEC/
RMSECV, RMSEP and R2 values. Since the Savitzky–Golay
method (window 15 pts, order 2) coupled with autoscale
produced the lowest RMSEC/RMSECV and RMSEP values and
the highest R2 value, this pre-processing method is chosen as the
best. Other pre-processing methods have not yielded good result
for this application since these produced comparatively high
RMSEP/RMSECV values (low values yield good results) and
low R2 values (high value is good).
Calibration and cross-validation
Optimum number of components. A calibration and quantita-
tive analysis is performed using PCR and PLS methods. Forty-
five samples are used to develop the calibration and eleven
independent samples are used as a prediction set for both
methods. To determine the optimal number of factors to be
included in the calibration model, the RMSECV/RMSEC values
are calculated using the Leave-One-Out (LOO) cross-validation.
The number of principal components (PCs)/latent variables
(LVs) to be used in each case is determined by the lowest
Fig. 2 NIR spectra of pure sandal oil and pure castor oil (as an adul-
terant).
Table 1 RMSECV/RMSEP values for PCR and PLS with variouspre-processing methods
Fig. 3 (a) Principal components and RMSECV,RMSEC through PCR
for sandal oil, and (b) latent variables and RMSECV,RMSEC through
PLS for sandal oil.
Fig. 4 (a) Trends in principal components in adulteration (relative
percentile 0–100% v/v), and (b) trends in latent variables in adulteration
(relative percentile 0–100% v/v).
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RMSECV, RMSEC.37,45 The PC/LV vs. RMSEC plots are
shown in Fig. 3.
From Fig. 3, it is clear that the optimum number of PCs/LVs
that could be suggested is 2 for both PCR and PLS models.
Table 1 shows the RMSECV, RMSEP values with 2 PCs for
PCR and 2 LVs for PLS.
Model building using pre-processed data. Two calibration
models are built in order to predict the adulterant content in
blends with sandalwood oil using the pre-processed data, namely
PCR and PLSR. The cumulative variance for the first two
components (99.99%) for both themodels is found to be the same.
The first two PCs/LVs account for 99.99% of the variation in
the spectra. In PCR, PC1 explains 96.62% and PC2 explains
3.37%. In PLSR, LV1 explains 98.19% and LV2 explains 1.80%
of the total variance between the samples. Fig. 4 shows the first
two PC/LV scores plotted in a scatter diagram for both the
models.
From Fig. 4, it is observed that 45 samples with different
adulterant concentrations (0–100%, v/v) in pure sandalwood oil
are grouped into two classes. The first group with negative scores
values represents the samples with less adulterant contamination
and more sandal oil.
The second group with positive scores values indicates
samples with more adulterant contamination (>50%) and less
sandal oil character. It is seen that the PLSR model can be
used to separate the samples (pure and blended) in a better
way; even 1% of adulteration in sandalwood oil could be
identified.
Prediction/validation by the models
The Fig. 5 reflects the accuracy and the performances of the
models. The plot of the measured values of concentrations
against the predicted values of concentrations reveals the
accountability of the models.
The statistics of results obtained from the calibration models is
shown in Table 2 below.
The correlation coefficient (R2) is the intensity measure of
the correlation between the measured values and the values
predicted by the model. This may range from 0 to +1. The
closer the value to +1, the higher the correlation between the
data.38 Both PCR and PLSR models shown, in this study,
have very good correlation between the real and predicted
concentrations with coefficient of determination (R2) values
equal to 0.99985 and 0.99986 respectively, a good linear fit
(see Fig. 5). For the two models presented here, the number
of variables significantly reduced to 2 principal/latent variables
that could explain 99.99% of the total variances. It is also
revealed that the RMSEP value (0.01364 for PCR and
0.01355 for PLSR) for each model is minimum. The lower
RMSEP value has a higher degree of accuracy of prediction
by the model.37,38 Both PCR and PLSR give almost the same
R2 value. On the closest examination of the scores plot,
RMSEP and R2 values, the PLSR model is found to be the
best.
Conclusion
In this work, near infrared (NIR) spectroscopy combined with
chemometric techniques is used for screening analysis to
identify sandal oil samples adulterated with low-cost and low-
grade oils like castor oil. This method is accurate and reliable
to detect a deceit and can assist the laboratories, the service of
inspection and quality control of essential oils. For the models
proposed in this work, PCR and PLSR show the lowest
RMSECV and RMSEP values and high correlation between
the measured and predicted concentrations. The methodology
of NIR spectra associated with PCR and PLS techniques is
proven to be suitable as a practical analytical tool to predict
the adulterant content in sandal oil in the range 0–100% (v/v).
Even 1% of contamination can be measured precisely. This
technique may be used for discriminating the counterfeit
effectively. It is also observed that the shift of samples in one
quadrant from the other in the scores plot reflects the real
percentile of adulteration. This information is very helpful in
determining the percentage of adulteration in a non-destructive
manner.
Based on the above findings, for the future we suggest NIR
spectroscopy through chemometrics as a detection tool for
quantitative as well as qualitative analysis of adulterations in
essential oils.
Fig. 5 (a) PCR model measured vs. predicted sandal oil, and (b) PLS
model measured vs. predicted sandal oil.
Table 2 The prediction summary of PCR and PLSR modelsa
Statistical parameters PCR PLSR
RMSEC 0.0011 0.002051RMSECV 0.002592 0.002888RMSEP 0.01364 0.01355Bias 0.002361 0.001920R2 Cal 0.99982 0.99931R2 CV 0.99978 0.99937R2 Pred 0.99985 0.99986
a PCR: Principal Component Regression; PLSR: Partial Least SquareRegression. RMSEC: Root Mean Square Error of Calibration;RMSECV: Root Mean Square Error of Cross-Validation; RMSEP:Root Mean Square Error of Prediction; R2: coefficient of determination.
2680 | Analyst, 2010, 135, 2676–2681 This journal is ª The Royal Society of Chemistry 2010
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Acknowledgements
The authors would like to thank Mr Jeremy M. Shaver, Chief of
Technology Development, Help desk, Eigenvector Research,
Inc. and Mr S. Valiathan, Quaero, CSG systems, Inc, Bellevue,
WA, USA for the technical assistance and help.
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Author's personal copy
Analytical Methods
Qualitative and quantitative analysis in sandalwood oils using near infrared
spectroscopy combined with chemometric techniques
Saji Kuriakose 1, Hubert Joe ⇑
Centre for Molecular and Biophysics, Mar Ivanios College, Thiruvananthapuram, Kerala, India
a r t i c l e i n f o
Article history:
Received 30 April 2011
Received in revised form 16 May 2011
Accepted 15 April 2012
Available online 21 April 2012
Keywords:
Essential oils
Sandalwood oil
Near infrared spectroscopy
Chemometric techniques
Support vector machine regression
Qualitative
Quantitative differences
a b s t r a c t
Sandalwood oil is an essential oil which finds very wide application in the flavor and fragrance, pharma-
ceutical industry. The objective of this study is to use the potential of near infrared spectroscopy as a
rapid analytical technique for the qualitative and quantitative assessment of purity in sandalwood oils.
The quality and efficacy of sandalwood oils, even though come from the same species, are somewhat dif-
ferent according to growing conditions (origin) and poor extraction methods. Classification of sandal oils
based on their NIR spectra is performed by principal component analysis, hierarchical cluster analysis
and self organising map (Kohonen neural network). All these techniques clearly differentiate the oils
according to the area from which the sandalwood has been cut. Support vector machine regression
(SVM R) is used to predict the purity of the oils.
Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction
Sandalwood oil is an essential oil obtained by the distillation of
the heartwood and roots of the plant Santalum album (family – San-
talaceae). S. album is a small hemiparasitic tree of great economic
value, growing in Southern India, Sri Lanka, Australia and Indonesia.
Its trunk contains resins and essential oils particularly the a and b-
santalols, santalenes and many other minor sesquiterpenoids
(Jones, Ghisalberti, Plummer, & Barbour, 2006). These sesquiterpe-
noids are responsible for the unique sandalwood fragrance.
Sandalwood oil is used in the food industry as a flavour ingredient.
This oil serves as a fixative for many high – end perfumes. A number
of aromatic and phenolic compounds have also been identified in
the oil S. album (Kim et al., 2005). The quantity of oil produced in
a tree varies considerably according to location (environmental fac-
tors) and age of the tree, even in nearly identical growing conditions
(Jones, Plummer, & Barbour, 2007). It should also be noted that
santalol composition can vary depending on the method of oil
extraction (Piggott, Ghisalberti, & Trengove, 1997). There has been
serious decline in the population of santalum in India due to com-
plex cultivation requirements and non-stop harvesting (especially
from smuggling) associated with limited regeneration (Fox, 2000;
The dendrogram shows that the five clusters of oil samples are
well separated from each other. Cluster E is unique and is sepa-
rated from other four classes. There is no overlap between the clas-
ses of oils. This means that 100% correct classification is possible by
HCA.
3.5. Self organising map (SOM)
The self-organising map proposed by Kohonen is also suitable
and efficient for performing an unsupervised clustering. A SOM
consists of a two-dimensional grid of the computational units
called neurons in the network. SOM is similar to the PCA method
that performs dimensionality reduction and classification. The dif-
ference between the two approaches is that the SOM performs a
nonlinear lower dimensional mapping while PCA is a linear map-
ping technique. SOM is a ‘‘map’’ of the training data, dense where
there is a lot of data and thin where the data density is low. The
map constitutes of neurons located on a regular map grid. The lat-
tice of the grid can be either hexagonal or rectangular. Each neuron
(hexagon) has an associated prototype vector (weight). The algo-
rithm trains the SOM iteratively. After training, neighbouring neu-
rons have similar prototype vectors.. In each training iteration, a
sample vector X is selected from the input data set and the grid
Fig. 1. Trends in principal components in classification of sandal oils.
Fig. 2. Contour plot of cross validation accuracy for SVM regression.
Fig. 3. Actual vs. predicted concentration by using radial basis kernel of support vector machines.
216 S. Kuriakose, H. Joe / Food Chemistry 135 (2012) 213–218
Author's personal copy
node that is nearest to X (also called ‘‘best matching unit’’, BMU) is
determined. The BMU of a data vector is the unit on the map whose
model vector best resembles the data vector. In practise the simi-
larity is measured as the minimum distance (commonly evaluated
using the Euclidean metric) between data vector and each model
vector on the map. In the next step, the weight vector of the
BMU and those of its grid neighbours are moved closer to the input
vector X using the Kohonen learning rule. The result of such reor-
ganization is that similar weight vectors are brought closer to each
other while leaving apart the dissimilar ones. Implementing this
procedure iteratively forces the randomly initialized weight vec-
tors to mimic the distribution of input data patterns in the output
space.
A visual inspection of the trained SOM is provided by a widely
used method described below. The unified distance matrix
(UDM) provides significant information in the form of distances
between nodes of the SOM grid. In this method, a matrix of dis-
tances (called ‘‘U-matrix’’) between the d-dimensional weight vec-
tors of neighbouring nodes of the two-dimensional SOM is
computed. The U-matrix distances can be used to unravel the
structure of the data clusters present in the data set under study.
The density of the weight vectors is illustrative of the density of
the input data patterns. Accordingly, the UDMmeasuring distances
between the weight vectors is indicative of the said density and a
proper representation such as grey level or colour imaging can be
designed to interpret the distances between two neighbouring grid
nodes. The optimum size of the two-dimensional SOM grid is se-
lected by training the SOM with different grid sizes and pre-spec-
ified number of training iterations. The optimum grid size obtained
thereby contains an array of [10 � 10] nodes. Here, the SOM algo-
rithm is run for 10,000 training iterations. The results of the SOM-
based classification are portrayed in the form of a U-matrix plot in
Fig. 5. In this figure, the actual data points are also plotted as dark
coloured hexagons. In the U-matrix plot, a dark coloured node indi-
cates that its weight vector is at a higher distance from those of the
adjoining light coloured nodes.
The Labeled matrix is shown in Fig. 6.
Both Figs. 5 and 6 show distinct differentiation of the 5 classes
of sandalwood oils based on their origin.
4. Conclusion
The results of this research confirm that near infrared spectros-
copy can be used for the qualitative as well as quantitative analysis
of compositions of sandalwood oils. Sandalwood oils of the same
species produced by various extraction methods, either developed
or under developed and collected from different regions (origin)
can be easily discriminated by the difference in their NIR spectra.
Near infrared spectroscopy assisted by multivariate chemometric
techniques viz. principal component analysis, hierarchical cluster
analysis or self organising map can be successfully applied to the
classification of sandalwood oils according to their quality. Quanti-
fication of the constituents, especially adulterants if any, in sandal-
wood oils is achieved by support vector machine regression with
proper combinations of data pretreatments.
In a nutshell, NIR spectroscopy technique has high potential to
determine the qualitative differences and to quantify simulta-
neously the compositions of essential oils.
Acknowledgements
The authors are grateful to Mr. Xavier T. S., Research Scholar,
Center for Molecular and Biophysics Research. Mar Ivanios College,
Fig. 4. HCA dendrogram of sandal oils using Mahalanobis distances: (A–E indicate various classes of oils).
Fig. 5. Unified distance matrix or SOM neighbour distances.
Fig. 6. Labeled matrix or SOM sample hits.
S. Kuriakose, H. Joe / Food Chemistry 135 (2012) 213–218 217
Author's personal copy
Thiruvananthapuram, Kerala, India for the valuable suggestions
and also to STIC, Cochin University of Science and Technology (CU-
SAT), Kerala, India for the technical assistance and help.
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The nonlinear method LWR for multivariate y using the local
PLS algorithm is employed to detect and quantify the adulteration
and to compare with the PLS model. This nonlinear model is also
stable, defined by two principal components with low errors and
high prediction values.
In brief, the results from this study (Figs. 1–3, and Table 1 can be
chosen as a basis for a new exercise) indicate that it is possible to
detect sample authenticity and counterfeits by exploring near
infrared spectral data in the wavelength range 1850–1800 nmwith
prominent biomarker peak 1836 nm that are attributed to methy-
lene, methyl and ethenyl asymmetric stretching (1st overtone C–H
stretch bands) of sandalwood oil or any other essential oil samples.
Acknowledgements
The authors wish to thank the efforts of Mr. Joseph Kuriakose,
Bloomfield, Gillen, Alice spring, NT 0870, Australia for his contribu-
tions to this manuscript. The authors are also grateful to Mr. Sony
George, Research scholar, Department of Photonics, Cochin Univer-
sity of Science and Technology (CUSAT), Kochi, Kerala, India for the
technical assistance and help. The authors also express their sin-
cere thanks to Manonmaniam Sundaranar University, Thirunelveli,
Table 1
Model performance for full and sequential spectrum wavelength data for each data pre-treatment. Best models are in bold. Feasibility of using near infrared spectroscopy to
detect and quantify an adulterant in a high quality sandalwood oil.
Data pretreatment Full spectrum Partitioned/sequential spectrum
a LV – Latent Variables.b RMSEC – Root Mean Square Error of Calibration.c RMSEP – Root Mean Square Error of Prediction.d R2 cal. – Coefficient of Determination for Calibration.e R2 pred. – Coefficient of Determination for Prediction.
Fig. 3. Measured versus predicted adulteration levels; PLSR model with 2 factors
showing the prediction data (pre treatment smoothing + mean centre, spectrum
1850–1800 nm range).
572 S. Kuriakose, I.H. Joe / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 115 (2013) 568–573
Tamil Nadu, India for having given an opportunity to do the
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