Volatile constituents of benzoin gums: Siam and Sumatra, part 3. Fast characterization with an...

VOLATILE CONSTITUENTS OF BENZOIN GUMS 439

Copyright © 2006 John Wiley & Sons, Ltd. Flavour Fragr. J. 2006; 21: 439–446

FLAVOUR AND FRAGRANCE JOURNALFlavour Fragr. J. 2006; 21: 439–446Published online 16 February 2006 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/ffj.1675

Volatile constituents of benzoin gums: Siam andSumatra, part 3. Fast characterization with anelectronic nose

Xavier Fernandez,1* Cécilia Castel,1 Louisette Lizzani-Cuvelier,1 Claire Delbecque2 andSophie Puech Venzal3

1 Laboratoire Arômes, Synthèses, Interactions, Faculté des sciences de Nice Sophia-Antipolis, parc Valrose, 06108 Nice cedex 2,France

2 CHARABOT, Research Natural Products, 10 avenue Yves-Emmanuel Baudoin, 06130 Grasse, France3 ALPHA MOS, 20 avenue Didier Daurat, 31400 Toulouse, France

Received 15 March 2005; Revised 28 July 2005; Accepted 3 August 2005

ABSTRACT: The quality control of natural raw materials is a challenging issue for the food, cosmetic, perfume and

tobacco industries. The applicability of an electronic nose for the discrimination of origin, qualities and harvesting year

of a natural raw material (benzoin gum) currently used by all those industries was tested. An electronic nose including

18 metal oxide sensors was used to analyse and discriminate 56 benzoin gum samples according to their origin (Siam and

Sumatra), quality grade, variety (mixture of gums traded as benzoin gums) and year of harvesting. Thanks to its sensi-

tivity, the electronic nose based on metal oxide sensors demonstrated a high ability to assess both the quality and the

organoleptic features of the benzoin gum samples. Fast analysis and ease of use make this instrument a good quality

control tool. A comparison with an electronic nose based on fingerprint mass spectrometry was also studied. Copyright

© 2006 John Wiley & Sons, Ltd.

KEY WORDS: Siam benzoin gum; Sumatra benzoin gum; balsamic resin; electronic nose; static headspace; mass spectrometry;

fingerprint; multivariate analysis; metal oxide sensor; quality control; raw materials

* Correspondence to: X. Fernandez, Laboratoires Arômes, Synthèses, Inter-

actions, Faculté des sciences de Nice Sophia-Antipolis, parc Valrose, 06108

Nice cedex 2, France.

E-mail: [email protected]

Contract/grant sponsor: Conseil Régional Provence Alpes Côte d’Azur.

Contract/grant sponsor: Charabot.

Introduction

Various natural raw materials are used in the formulation

of flavour and fragrance products. The quality and the

form of these raw materials are not easy to monitor given

that they can be produced using craft methods, locally

graded by each producer and traded by brokers. More-

over, prices of raw materials are defined according to the

claimed quality creating a crucial need to assess their

quality.

For quality control, well-established methods such as

gas chromatography (GC), gas chromatography–mass

spectrometry (GC-MS) or olfactometry (GC-O) and high-

performance liquid chromatography (HPLC) are currently

used. Olfactive evaluation is also very common, but this

technique has some drawbacks due to the subjectivity of

human panels. These different methods are usually con-

suming both of time and money.

There is a large demand for rapid, cheap and effective

techniques for quality control in flavour and fragrance

products. ‘Electronic noses’ have been developed in this

purpose.1,2 This term refers to an instrument that mimics

human olfaction by combining the response of sensors

with a headspace sample. Two types of electronic noses,

based on a fingerprint technique, have been commer-

cialized.3 The first, developed from the early 1990s,

commonly called a ‘sensor array system’ (SAS), includes

chemical sensors such as metal oxide semiconductors,4

conducting polymers and surface acoustic wave sensors.

The second, more recent and called a ‘fingerprint mass

spectrometer’ (FMS), is based on the use of a mass

detector.5,6 The fingerprints obtained by these two tech-

niques are exploited using statistical analysis.

Benzoin gum is a balsamic resin obtained from

Styracaceae trees and produced mainly in Asiatic coun-

tries. Two varieties of benzoin gums exist: Siam benzoin

gum from Styrax tonkinensis Craib and Sumatra benzoin

gum from Styrax benzoin Dryander.7

Siam benzoin gum has a pleasant, sweet-balsamic

odour with a specific note of vanilla. This is why it is

particularly used in brown flavours such as vanilla,

chocolate and nuts.8 This resin is graded according to the

size of the pieces (or tears). Different grades from 1 (the

size of very large almonds, without any foreign particles)

to 4 or 5 (very small, powder-like) can be found.

Sumatra benzoin gum presents a strong styrax-like odour,

440 X. FERNANDEZ ET AL.


Experimental

Material

Siam benzoin gums (from Styrax Tonkinensis Craib) were

collected in north Laos, and Sumatra benzoin gums (from

Styrax Benzoin Dryander) in Indonesia. All the benzoin gum

samples studied were purchased in 2000 and 2001 from five

different traders (see list in Table 1).

Electronic nose

All gums were analysed using the α-FOX 4000 (Alpha Mos,

France) equipped with a headspace autosampler (Odorscanner

HS100). This instrument includes 18 metal oxide sensors which

measure the change in electrical resistance in the presence of

volatile compounds.

Crushed benzoin gums (0.2 g) were placed into a 10 ml glass

vial and sealed with septa crimped onto the top. Each vial was

quite distinct from the vanilla odour of the Siam variety.

It is often added to soap and detergents, particularly for

its fixative properties. Its robust character is normally pre-

ferred for flavouring tobacco products.8 Sumatra benzoin

gum is also frequently sold under four grades, A–D.

Siam benzoin gum is mainly made up of coniferyl

benzoate (65–75%), p-coumaryl benzoate, cinnamyl

cinnamate, benzoic acid, vanillin and siaresinolic acid.9,10

Sumatra benzoin gum is reported to contain more

cinnamic acid and cinnamates than Siam benzoin gum.11

We have recently presented studies of the chemical com-

position of Siam and Sumatra benzoin gums by the

analysis of volatile extracts12 and the use of headspace

methods such as static-HS, SPME and HSSE.13

Herein, we report our results in the use of electronic

nose technology to determine the quality and olfactive

features of benzoin gums according to the production

origin, the grade, the harvest year and to identify poten-

tial counterfeited benzoin gums.

Table 1. Benzoin gums studied (56 samples)

Entry Variety Grade Harvesting year Category Trader Code

1 Siam 3 2001 Learning 1 E1, E11, E43

2 Siam 5 2001 Learning 1 E2, E14, E44

3 Sumatra B 2001 Learning 1 E3, E12, E45

4 Sumatra D 2001 Learning 1 E4, E15, E46

5 Siam 3 2001 Learning 1 E5, E13

6 Gum mixture 1 — 2000 Learning 1 E6


8 Siam 3 2000 Learning 1 E8

9 Sumatra D 2000 Learning 1 E9

10 Sumatra B 2000 Learning 1 E10


12 Sumatra A 2000 Learning 4 E21

13 Sumatra A 2000 Learning 2 E22


15 Sumatra B 2000 Learning 2 E24, E35

16 Sumatra D 2000 Learning 2 E27, E28


18 Sumatra C 2000 Learning 4 E31, E37



21 Sumatra B 2001 Learning 1 E39

22 Gum mixture 4 — 2000 Unknown 2 E16

23 Sumatra — 2000 Unknown 3 E17, E25

24 Sumatra B 2000 Unknown 1 E18

25 Sumatra — 2000 Unknown 1 E20

26 Siam 3 2001 Unknown 1 E26

27 Sumatra B 2001 Unknown 1 E30


29 Sumatra — 2000 Unknown 2 E36

30 Sumatra D 2001 Unknown 1 E38

31 Sumatra D 2000 Unknown 2 E40


33 Sumatra A 2000 Unknown 4 E42

34 Siama 5 2001 Unknown 1 E47, E48, E49

35 Siama 5 2001 Unknown 1 E50


37 Siama 5 2001 Unknown 1 E52, E53, E54



a Siam benzoin gum harvested in 2001 and stored under modified conditions.



heated for 10 min at 60 °C and agitated at 500 rpm in order to

produce headspace equilibrium. A syringe heated at 65 °C was

automatically filled with the headspace gas (500 µl) that was

injected into the system. The α-FOX was continuously purged

with dry air set at 150 ml/min.

The data obtained were analysed using principal component

analysis (PCA) and discriminant factorial analysis (DFA). These

are two of the statistical data analysis methods included in the

software package provided with the Alpha Mos Electronic Nose

(α-Software version 8.0).

Conditions of optimization

In this work, our goal was to evaluate the efficiency of a fast

characterization method based on the use of an electronic

nose in order to use it for raw materials quality control, under

conditions close to sensorial analysis. All the gums were initi-

ally crushed before sampling in order to obtain homogeneous

samples.

Moderate heating was apply to the samples, without exceed-

ing 80 °C to prevent artefacts formation, and incubation times

below 30 min were chosen to perform rapid analysis. Various

incubation times (10, 20 and 30 min) and temperatures (40, 60

and 80 °C), sample quantities (0.1–0.5 g) and injected volumes

(100–500 µL) were tested on six different benzoin gums (three

Siam and three Sumatra).

The best conditions for all benzoin gums were obtained with

a sample of 0.2 g, heating at 60 °C for 10 min and an injected

volume of 500 µl.

Figure 1 shows the resistance changes for 18 sensors as func-

tion of time for one sample of Siam and Sumatra benzoin

gums. The differences between the two patterns are important,

and these fingerprints are used to characterize samples. In order

Figure 1. (A) Raw sensor signals of Siam benzoin gum grade 3 (left) and Sumatra benzoin gum grade B (right):sensor response ∆R/R0 where R is resistance. (B) Radar plot representing the 18 sensor answers for the Siambenzoin gum grade 3 (clear grey) and Sumatra benzoin gum grade B (dark grey).



Table 2. Results of unknown benzoin gums identification using DFA

Sample Identification Recognition percentage Group

E16 No 69.4% Gum mixture

E17 Yes 100% Sumatra


E20 Yes 98.6% Sumatra


E26 Yes 100% Siam


E34 Yes 100% Siam




E41 Yes 100% Siam


E47 Yes 94.2% Siam

E48–56 Yes 100% Siam

grade B or gum mixture. In these cases, it can be most

difficult to obtain a homogeneous sample and a lower

level of recognition is generated. Siam benzoin gums,

grade 5, stored under modified conditions were all

identified as Siam benzoin gums.

Siam benzoin gum study

PCA performed with 18 sensors on Siam benzoin gum

samples showed good reproductibility. Benzoin gum

grade 2 was well distinguished from other gum grades,

but the discrimination between grade 3 and 5 was not so

obvious (Figure 4). This can be explained by the fact that

quality classification can differ according to traders.

In order to optimize the discrimination, DFA was per-

formed with the six more efficient sensors. This treatment

led to a good differentiation of the various grades. Grades

2, 3 and 5 were well distinguished and classified accord-

ing to their qualities (Figure 5). Identification of unknown

Figure 2. PCA of 33 gum samples.

to obtain the best discrimination, only the maximum responses

from each sensor were considered.

Results and discussion

A set of 33 samples of benzoin gums including 13 Siam

benzoin gums (one grade 2, eight grade 3 and four grade

5), 17 Sumatra benzoin gums (two grade A, seven grade

B, two grade C and six grade D) and three types of gum

mixtures (three samples) sold as benzoin gums were

analysed to build a model.

The 23 unknown benzoin gums were also analysed to

validate the model. Among Siam benzoin gums grade 5,

10 samples obtained from two batches were stored under

modified conditions (Table 1, entries 34–39) and thus

generated other Siam sample types.

Benzoin gums characterization

PCA was applied to 33 gums and showed that these three

types of gums are clearly discriminated from each other

(Figure 2). It is an easy way to detect gum mixtures that

can include counterfeited benzoin gums.

DFA was then used in order to identify the unknown

benzoin gums (Table 2 and Figure 3). Depending on the

distance between the unknowns and the closest clusters

of the training map, a recognition percentage gives the

level of identification of the unknown gums. An accept-

able sample identification should have a recognition per-

centage higher than 90%.14 All the unknown samples,

except E16 and E30, were identified with a recognition

score above 94%. Sample E30 (Sumatra benzoin gum

grade B, Table 1, entry 27) was correctly identified but

with a recognition percentage of 75.4%. Sample E16

(gum mixture 4, Table 1, entry 22) was not identified.

These results could be explained by the difficulty in

sample preparation especially for large tears such as



Figure 3. Discriminant factorial analysis (DFA) of all benzoin gum samples.

concerning harvesting time. This was a recurring problem

for this balsamic resin where the multiple brokers com-

plicate information collection.

Sumatra benzoin gum study

PCA on Sumatra benzoin gum samples was performed

using 18 sensors. The results presented in Figure 7 show

the difficulty in distinguishing efficiently the tear quali-

ties. As mentioned for Siam benzoin gums, Sumatra

benzoin gum quality can vary from trader to trader.

DFA built with the six more effective sensors (Figure

8) showed a very good discrimination between Sumatra

benzoin gum grade D (the lowest quality) and the other

grades. Grade C was weakly separated from grades A

and B. Grades A and B are known to be very close to

each other.

Unknown projection led to the identification of the

different unknowns. Only the sample E25 (Sumatra

benzoin gum whose grade was not provided by the trader,

Table 1, entry 23) was not identified, whereas another

sample of this same benzoin gum (E17) was identified as

grade B with a recognition percentage of 100%.

Performance comparison of SAS and FMSelectronic noses

In a previous work on headspace sampling methods

applied to benzoin gums,13 performances of technology

Figure 4. PCA of all Siam benzoin gum samples. Thethree quality grades (2, 3 and 5) are displayed; theunknown samples are circled (number underlined).

samples was efficient, with a percentage of recognition

between 80 and 100%.

A DFA (using 18 sensors) considering harvesting years

was built as well. Discrimination related to this parameter

was successful (Figure 6). However, identification of un-

knowns was not relevant due to the lack of information



Figure 6. DFA study of Siam benzoin gums harvested in 2000 and 2001.

Figure 5. DFA of the three grades of Siam benzoin gums.

based on a FMS electronic nose have been studied. Several

benzoin gums (nine Siam, thirteen Sumatra and two gum

mixtures) from different grades and origins were class-

ified using this method. It was perfectly able to distinguish

a Siam benzoin gum from a Sumatra and gum mixtures

sold as benzoin. The two gum mixtures were distin-

guished with the FMS electronic nose. This technique

allows us to obtain ionic masses that lead to the classifi-

cation, and generate the identification of quality markers.

It was more difficult to assess grades of the same gum,

especially for Sumatra benzoin gums, than it was previ-

ously using the Sensor array ‘electronic nose’. This last

technology seems to be more sensitive: effective distinc-

tion between benzoin gums was obtained using small

gum quantities with easier and faster headspace sampling

conditions. However it is difficult to compare the per-

formances of these two techniques because different

sampling conditions were used.



Figure 7. PCA of Sumatra benzoin gums.

Figure 8. DFA study of the four grades of Sumatra benzoin gums.

Conclusion

Gas sensor arrays coupled to statistical treatment (‘elec-

tronic noses’) are fast and reliable tools to be used to

predict the quality of a given variety of benzoin gums by

analysing the headspace. With an appropriate sampling

method providing homogeneous samples, an electronic

nose can be a rapid alternative technology to conven-

tional techniques such as chromatographic methods and

sensorial analysis for raw material quality control.

This should be helpful to cosmetic, perfume, food

or tobacco companies in checking the grading of their

traders, selecting the proper quality of benzoin gum and

paying the correct price.



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