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Detection of linalool in black tea using a quartz crystal microbalance sensor
Sensors and Actuators B 190 (2014) 318–325
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Sensors and Actuators B: Chemical journal homepage: www.elsevier.com/locate/snb
Detection of linalool in black tea using a quartz crystal microbalance sensor Prolay Sharma a,∗ , Arunangshu Ghosh a , Bipan Tudu a , Lakshi Prasad Bhuyan b , Pradip Tamuly b , Nabarun Bhattacharyya c , Rajib Bandyopadhyay a , Anutosh Chatterjee a a
Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata 700 098, India Tea Research Association, Tocklai Experimental Station, Jorhat, Assam, India c Centre for Development of Advanced Computing, Kolkata 700 091, India b
a r t i c l e
i n f o
Article history: Received 30 October 2012 Received in revised form 24 August 2013 Accepted 26 August 2013 Available online 5 September 2013 Keywords: Quartz crystal microbalance Linalool Polyethylene glycol Black tea Gas chromatography mass spectrometry
a b s t r a c t Linalool is one of the most important volatile constituent that contributes significantly in the aroma of brewed, dry or extracted tea. Thus it is very essential to sense the linalool content in orthodox black tea as that may lead to rapid quality estimation. Quartz crystal microbalance (QCM) type gas sensors are very sensitive and are increasingly being used for many applications. It can be easily fabricated, has less response time and fast recovery characteristics and can be coated with a variety of materials to obtain different sensitivities and selectivities. In the present work, a QCM sensor has been developed with polyethylene glycol (PEG) to detect linalool gas in black tea. Extensive experiments have been carried out with various concentrations of linalool gas and the sensitivity, repeatability and reproducibility of the developed sensors were determined. The sensors were observed to be sensitive and selective to linalool. The developed sensors were exposed to the orthodox black tea aroma and significant correlation is obtained with gas chromatography mass spectrometry (GCMS) estimations. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Black tea is the processed leaves of Camellia sinensis. Tea is of great economic importance and is the most consumed beverage worldwide. The black tea production consists of four stages, namely, withering, rolling, fermentation, and drying [1]. Three main types of tea are generally produced based on the level of fermentation: unfermented (green), semi fermented (oolong), and fermented (black) tea. The aroma of tea is defined by the clonal variety, withering duration and rolling phase. In general, the quality characteristics of tea are determined by both taste and aroma. Volatile components contribute to aroma quality and non-volatile components determine the taste. In tea, even though the volatile organic components (VOCs) are present in very minute quantities, i.e. 0.01% of the total dry weight, they have a very high impact on its aroma. Volatile organic compounds of tea are classified into two groups. While the Group I compounds are mainly non-terpenoids, which impart fresh green flavour, e.g. hexenols, the Group II compounds are terpenoids and impart sweet flowery aroma to tea,
∗ Corresponding author at: Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt Lake Campus, Block LB, Sector III, Plot 8, Salt Lake, Kolkata 700 098, India. Tel.: +91 33 23352587; fax: +91 33 23357254. E-mail address: [email protected] (P. Sharma). 0925-4005/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.snb.2013.08.088
viz., monoterpene alcohols like linalool and geraniol. The presence of Group II compounds is highly desirable. Major chemical compounds responsible for flavour in tea are presented in Table 1 [2]. Large number of reports on characterization of volatile compounds present in different black tea is available. Guth & Grosch studied the odour-active volatiles in a Chinese black tea powder by aroma extract dilution analysis (AEDA) [3]. In this study linalool with the highest flavour dilution (FD) factors has been observed. The flavour dilution factor (FD) of a substance can be defined as the ratio of its concentration of the initial extract to its height dilution at which the substance odour can be detected [4]. The compound with the highest FD factor has the highest impact in that particular extract, and the compound with lowest FD does not contribute much to the final odour impression. By applying the technique of AEDA, Masuda and Kumazawa confirmed that linalool is one of the most odour-active constituents in the black tea beverage prepared from Darjeeling tea [5]. Christian and Peter have done a significant work on the characterization of key aroma compounds in the beverage prepared from Darjeeling black tea [6]. They collected Darjeeling black tea with TGFOP grade and identified the important aroma compounds in the extract prepared from the black tea leaves. Linalool was again reported with the highest FD value. Investigations regarding concentrations of important aroma compounds in black tea leaves have revealed that the presence of linalool is
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Table 1 Flavour quality of volatile compounds responsible for tea aroma. Compounds
Flavour
Linalool, linalool oxide Geraniol, phenylacetaldehyde Nerolidol, benzaldehyde, methyl salicylate, phenyl ethanol Trans-2-hexenal, n-hexanal, cis-3-hexenol -Ionone
Sweet Floral Fruity Fresh Grassy
second highest (6600 g/kg) after hexanoic acid (12,000 g/kg), but the FD value of hexanoic acid is 16 as compared to 256 of linalool. Kawakami, Ganguly, Banerjee & Kobayashi have carried out extensive examination of two types of oolong teas, three types of Darjeeling, two types of Sri Lankan and one type of Chinese black tea [7]. The aroma concentrations were prepared by two extraction methods: the brewed extraction method and the simultaneous steam distillation and extraction method (SDE) and were then analyzed by gas chromatography (GC) and gas chromatography/IR spectrometry/mass spectrometry (GC/FTIR/MS). Gas chromatography mass spectrometry analysis on Darjeeling tea aroma reveals that more than 20% peak area is covered by linalool. On observing the gas chromatograms of Darjeeling SDE extract and four types of brewed black tea extracts, the main components found are linalool, linalool oxides (II, I and IV), geraniol and methyl salicylate. The main components found in the brewed extract are linalool, linalool oxide (I, II and IV), geraniol, trans-geranic acid, (E)-2-hexenoic acid, benzyl alcohol and hexanoic acid. The presence of significant amount of linalool along with nerolidol is also observed on the aroma extraction analysis of the Chinese Oolong teas. The main components of the brewed Sri Lanka clone DT-1 extract are linalool, linalool oxide (II and IV), benzyl alcohol and indole. Rawat et al., have characterized volatile components of Kangra orthodox black tea by two different extraction methods viz. simultaneous distillation extraction (SDE) and hydrodistillation, using GCMS [8]. They have reported that SDE extracts more volatile components from Kangra orthodox tea as compared to hydrodistillation methods. The VOC profile of tea obtained by simultaneous distillation extraction is dominated by terpenoids. Linalool oxide-II (furanoid) constitutes the highest amount (19.06%), followed by geraniol (16.21%) and linalool oxide-I (furanoid) (7.64%). The review reports indicate that linalool is by far the most important compound that defines the headspace of brewed, dry or extracted tea aroma. It is thus important to sense the linalool content in black orthodox tea for its quality estimation. Quartz crystal microbalance (QCM) sensors are very sensitive and are now increasingly being used as gas sensors. QCM sensors are made of a thin plate of quartz crystal blanks with metal electrodes on each side. When an alternating electrical excitation is applied to the electrodes, the deformation and relaxation of crystal faces occurs at natural resonance frequency which depends on the crystal dimensions, physical parameters and type of crystal cut. For sensing applications, QCM sensors are coated with appropriate analyte-sensitive coating. The target analyte is adsorbed on coating surface increasing the mass of QCM sensors and results in a change in its resonance frequency. According to Sauerbrey’s equation [9] the QCM resonance frequency decreases linearly with the adsorbed mass. The target gases with different molecular weights can be detected in terms of different frequency deviations. In other words, for different VOCs, differences in the values of deviations in resonance frequency can indicate the selectivity of sensors and the magnitude of deviations will indicate the sensitivity. In this paper, we investigate the development of a QCM sensor that can detect linalool. PEG is chosen as the adsorbent coating. PEG is a gas chromatographic material and is used extensively to sense aroma from fruits that contain significant amount of linalool
Development of sensor (electrostatic spray)
Gas delivery setup based on static headspace sampling (Single sensor)
Gas delivery setup based on dynamic headspace sampling (Sensor array)
Study of sensitivity and selectivity
Study of repeatability and reproducibility
Study of developed sensors with fresh tea samples
Fig. 1. Schematic of experimental process.
[10–13]. The works carried out is represented schematically in Fig. 1. The sensors were prepared using electrostatic spray method. The sensitivity, selectivity, repeatability and reproducibility of the developed sensors were studied. Finally, to validate the response with tea aroma, the developed sensors are exposed to five different orthodox tea samples with linalool content obtained through GCMS analysis. 2. Experimental procedures 2.1. Materials Polyethylene glycol 200 is procured from Merck Specialities Pvt. Ltd., Mumbai, India. Linalool, linalool oxide and trans-2-hexene1-al are collected from Aldrich, Germany. Geraniol is procured from National Chemicals, India. Five different varieties of orthodox tea samples were collected from Darjeeling Tea Research and Development Center (DTR&DC), Kurseong, India for validation of the developed sensors with tea samples. GCMS was performed at Tea Research Association, Tocklai Experimental Station, Jorhat, Assam, India with a PerkinElmer Clarus 500 GCMS model and a 60 m × 0.25 mm CP-Wax 52 CB capillary column from Agilent. Quantitative estimation of flavour compounds in GCMS instrument was done by the internal standard comparison technique using National Institute of Standards and Technology (NIST), Wiley and Maurer/Pfleger/Weber Drugs Mass spectral Libraries. The dichloromethane, caproic acid, sodium chloride, anhydrous sodium sulphate and GCMS standards for volatile flavour compounds were obtained from Sigma chemicals. 2.2. Sensor development An AT-cut 10 MHz Quartz crystal blank was coated with a gas chromatography-stationary phase material. PEG was chosen as the adsorbent coating. PEG was dissolved in chloroform with
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Teflon Chamber
Exhaust
Glass Syringe
Suction Pump Three way valve
Personal Computer and Data Acquisition
Counter (Agilent 53131 A)
Fig. 2. Measurement setup with single QCM sensor based on static headspace sampling.
concentration 0.17% (w/v) and then deposited on the silver electrodes of QCM (10 MHz, AT-Cut) using electrostatic spraying method [14]. In the electrostatic spray method, a solution of sensor-active material is loaded into a glass syringe. A high DC voltage of about 18 kV was applied between the needle and the electrode of the QCM. The high electric field at the tip of needle spreads out the droplets in a form of finely divided spray, yielding a uniform coating.
the test gas was injected through rubber septa into the air flow path from a side channel. The air flow carried the aroma molecules and facilitates adsorption of them onto the sensor surface. The air flow rate thus needs to be carefully controlled. The responses of all eight sensors were converted into voltages using electronic circuits. The resonance frequencies of the sensors were determined by counting the frequency of the voltage waveform. The real time frequency counting and data acquisition into a PC was performed using counter array of data acquisition card NI 6602.
2.3. Experimental setup Two types of measurement setup and odour delivery schemes were explored. Firstly, the response of PEG coated sensors were studied for sensitivity, selectivity and linearity. Secondly, the repeatability and reproducibility of sensors were studied. 2.3.1. Static headspace sampling with single sensor The measurement setup for the first part of the experiment was based on static headspace sampling method and is shown in Fig. 2. This measurement setup was used for the study of sensitivity and selectivity of developed sensors. The method of static headspace sampling is preferred as the measurement process is simple and free of environmental or experimental variations. The sensor chamber is a 100 ml Teflon chamber kept in a temperature controlled environment. The sample is injected into the sensor chamber using a glass syringe through a rubber septa attached to one of the arms of a three way valve. The gases were carefully dispersed in the chamber so that it affects the sensor in a short time with maximum impact. However, efforts were made to reduce the effect of syringe flow rate. The sampling duration was continued till the sensor response reached equilibrium with the target-gas environment. The QCM sensor was attached to a rigid base from which signal connections were taken to the counter instrument. The counter displays the current frequency of the sensors and also sends the readings to a personal computer for data-logging. After sampling, the sensors were purged for a short duration using a suction pump that exhausts the analyte gases outside the chamber. The purging was continued until the initial sensor baseline was recovered. In order to confirm the selectivity of developed sensor towards linalool, it was also exposed to other common tea volatiles like geraniol, trans-2-hexenal and linalool oxide. The sensitivity curves for three types of gases were studied and the selectivity of the sensor is discussed in Section 3. 2.3.2. Dynamic headspace sampling with sensor array In order to justify the reproducibility of sensor development process and repeatability of the sensors an array based measurement setup is conceived and implemented. The measurement setup for this part of the experiment is based on dynamic headspace sampling method and is presented in Fig. 3. The developed setup can support an array of up to eight sensors. The chamber volume is 500 ml. A suction pump attached to the sensor chamber continuously pulls in fresh air at a rate of 4 l/min. During sampling,
2.4. Study of reproducibility and repeatability for sensors For the study of reproducibility of sensors, eight quartz crystals were coated with PEG using electrostatic spray method and the final coating thickness kept constant by monitoring the change in frequency. After simultaneously exposing the sensor array to standard concentration of gases the frequency deviations were recorded. The relative standard deviations (RSD) of all eight sensors were calculated as the measure of reproducibility. For calculating repeatability, all the sensors are repeatedly exposed to same concentration of linalool vapour. The RSD of sensor responses for all the repeated applications is calculated. The average RSD considering all the eight sensors is the measure of repeatability. These measurements are carried out in experimental setup shown in Fig. 3.
2.5. Extraction and estimation of volatile flavour components (VFC) using GCMS 100 g of orthodox black tea was taken in a 3 l round bottomed flask, 1000 ml de-ionized boiling water was added to it along with 0.125 l caproic acid as internal standard. The mixture was steam distilled for about 45 min. To the steam distillate, 10 g of sodium chloride was added and the flavour components were sequentially extracted with dichloromethane with volumes of 80 ml, 40 ml and 40 ml respectively, consecutively thrice using a separating funnel. Anhydrous sodium sulphate of 10 g was then added to the extract for dehydration purpose and kept for 24 h. The dehydrated extract was then concentrated in Eyala CCA-1110 rotary evaporation concentrator. The solvent was further reduced under purified nitrogen atmosphere to 100 l. The concentrate thus obtained was injected to GCMS instrument. Helium was used as a carrier gas at a flow rate of 12 psi. The injector and detector temperature was set at 250 ◦ C and 300 ◦ C respectively. The column oven was temperature programmed from 60 ◦ C to 200 ◦ C by maintaining a 10 min isothermal condition at 60 ◦ C and 25 min at 200 ◦ C with an increment of 2 ◦ C/min. A runtime of 105 min was maintained. A number of peaks were obtained in the GCMS profile. Out of those peaks, few significant compounds were identified for study. The amount of each component was determined and expressed as the ratio of each peak area to that of the internal standard in the chromatogram.
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Exhaust
Suction Pump
Data Acquisition (NI PCI 6602)
Sensor Chamber and sensor array
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Personal Computer
Data analysis Air inlet
Gas injector channel Selector switch
Fig. 3. Measurement setup with eight QCM sensor array based on dynamic headspace sampling.
2.6. Study with orthodox black tea aroma using QCM sensors The gas delivery setup based on static headspace sampling was used for analysis of tea aroma with experimental conditions as decribed in Section 2.3.1. In total, five orthodox tea samples were procured from DTR&DC, Kurseong. They were labelled as S-1, S-2, S-3, S-4 and S-5. Each tea sample was kept in a 250 ml desiccator for about 15 min to generate sufficient headspace. 50 ml headspace aroma was then collected in a syringe from the desiccator and injected to the sensor chamber. The corresponding sensor responses were observed. 3. Results and discussion 3.1. Sensitivity and selectivity studies with static headspace system The sensor responses with linalool vapour were studied with the static headspace system. The QCM under study was placed in the chamber and the linalool concentration was varied from 10 to 900 ppm. The gases were kept confined in the chamber till it achieved an equilibrium state of interaction with the sensing layer. During the process, the frequency of the sensors decreased and stabilized after some time. The difference between initial and stabilized frequency was noted as information about gas concentration. The typical sensor response profile for 200 ppm linalool is presented in Fig. 4. It was observed that the response of the sensor stabilized after 20 s of application of the gas. The linear pattern of frequency drop is mainly attributed to the syringe flow rate. Once the injection ended at nearly 35 s, the equilibrium frequency was attained after 15 s. The 0 -5 -10
Frequency (Hz)
-15
sniffing cycles for a particular concentration was replicated three times and the averaged steady state deviations were recorded. It may be mentioned that excellent repeatability is observed among replicated readings. The sensitivity curves derived from averaged deviations are presented in Fig. 5. The sensitivity curve for linalool is presented in Fig. 5(a). The sensor produces highly linear response within the range of exposed concentration. The sensitivity obtained is 0.14 Hz/ppm, but linearity is very high as is evident from the high R2 value. As far as selectivity is concerned, the sensor responses are studied against other dominant vapours of tea, viz. geraniol, trans2-hexenal and linalool oxide. The sensitivity curves are presented from Fig. 5(b) through (d). It can be observed that the sensitivity of geraniol is lower than that of linalool. Though the sensitivity values are comparable, there is a difference in the offset term (1.0 for linalool and −0.519 for geraniol). These differences are manifested as differences in frequency deviations when both the sensors are exposed to same concentration e.g. 50 ppm of linalool, linalool oxide, geraniol and trans-2-hexenal vapours. Fig. 6 presents the bar plots of sensor responses to 50 ppm of each of the above mentioned vapours. Linalool has the highest response followed by geraniol. An interesting point that may be made from Fig. 6 is the lower response to linalool oxide. The reason may be the lower polarity of linalool oxide molecules due to more cyclic and symmetric molecular structure as compared to linalool. The absence of hydroxyl groups also reduces the polarity of this molecule. Hence, the behaviour of the sensors can have significant impact on the efficiency of qualitative discrimination among different types of tea aroma with various proportions of linalool and linalool oxide in its headspace. The response of trans2-hexenal is the lowest. This behaviour is also desirable, as the aroma of tea leaves gradually changes from grassy to sweet during tea manufacturing process. The sweet smell characterized by the presence of linalool is expected to be detected by such a sensor. It is however evident that there is a room for improvement as far as sensitivity is concerned. The application of gases under dynamic headspace system may help to increase the sensor responses and thus the sensitivity.
-20
3.2. Studies using dynamic headspace system -25 -30 -35 -40 10
20
30 Time (seconds)
40
50
60
Fig. 4. Response profile for 200 ppm linalool vapour using static headspace sampling.
3.2.1. Sensitivity Experiments were conducted with a dynamic headspace sampling set up as shown in Fig. 4. The air flow rate was set at 4 l/min. In this method an array of eight PEG sensors was exposed to linalool vapour with four standard concentrations, namely 30, 50, 70 and 100 ppm. Typical responses of one such sensor with 30, 50 and 100 ppm are presented in Fig. 7. The drop in QCM resonance frequency is observed during sampling phase when gas molecules adsorb on the sensor surface. Increased frequency deviations are observed for concentrations
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Fig. 5. (a) Sensitivity curve for linalool vapour using static headspace sampling. (b) Sensitivity curve for geraniol using static headspace sampling. (c) Sensitivity curve for trans-2-hexenal using static headspace sampling. (d) Sensitivity curve for linalool oxide using static headspace sampling.
of 50 and 100 ppm. The responses are recorded and the sensitivity curve is drawn out as shown in Fig. 8(a). Each point in the plot is an average of 5 repeated readings. Highly linear responses as indicated by R2 of 0.974 are obtained over a range of 30–100 ppm. The sensitivity obtained as 0.3 Hz/ppm is an improvement over static sampling system. The sensitivity curves for geraniol and trans-2-hexenal is also presented in Fig. 8(b) and (c) respectively. The sensitivity obtained for geraniol following same analysis procedure is 0.213 Hz/ppm while that for trans2-hexenal is 0.210 Hz/ppm. It can again be observed that the sensors are most selective to linalool, followed by geraniol and then trans-2-hexenal.
3.2.2. Repeatability and reproducibility The repeatability of the sensors were tested with 25 repeated applications of 30 ppm linalool. The repeatability is expressed in terms of relative standard deviations (RSD) among the responses from repeated trials. It has been observed that the best repeatability obtained is nearly 14.4% (RSD) and the average repeatability considering all eight sensors is 14.98% (RSD). However, it was observed that more repeatable responses were obtained at higher concentrations. For establishing the reproducibility of sensor preparation process, eight quartz crystals were coated with PEG and simultaeneously subjected to volatiles of linalool. The RSD of responses over
9
0
8
A -5 Frequency deviation (Hz)
Frequency deviation (Hz)
7 6 5 4 3
-10
-15
B
-20
C
-25
2 -30
1 0
10
Trans-2-hexenal
Linalool Oxide
Geraniol
Linalool
20
30
40
50 60 70 Time (seconds)
80
90
100
110
Test gases Fig. 6. Response of sensors at 50 ppm of volatiles.
Fig. 7. Frequency deviation pattern for QCM sensors in dynamic sampling system when exposed to (a) 30 ppm, (b) 50 ppm and (c) 100 ppm of linalool.
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Fig. 8. (a) Sensitivity curve for linalool using dynamic headspace sampling. (b) Sensitivity curve for geraniol using dynamic headspace sampling. (c) Sensitivity curve for trans-2-hexenal using dynamic headspace sampling.
Table 2 Repeatability and reproducibility measures for the developed sensors. Concentration 30 ppm 50 ppm 100 ppm
Repeatability (RSD %)
Reproducibility (RSD %)
14.98 8.95 13.4
14.94 11.81 9.01
eight sensors were calculated. Average reproducibility obtained for 25 repeated trials is 14.94% (RSD). Table 2 presents the average values of repeatability and reproducibility including eight sensors. It can be observed that better reproducibility value expressed in RSD of 9% is obtained. This indicates that the reponses of sensors do not vary much even when they are prepared again and again. Thus, one sensor can be easily replaced by the other one without much change in sensitivity.
3.4. Study with orthodox black tea aroma using static headspace technique Fig. 9 shows the QCM sensor profiles after application of different orthodox tea samples. It can be observed that each of the tea samples produce distinct frequency profiles. The response reaches equilibrium within 90 s of the application of tea aroma. The average frequency deviations of three trials obtained for each tea sample with their respective linalool content estimated by GCMS is presented in Table 4. Fig. 10 presents the bar-plot comparison of responses obtained with the developed QCM sensors and the GCMS
0 -10
3.3. Estimation of major volatile flavour components by GSMS analysis The peaks obtained in the GCMS profile were identified by mass-spectroscopy, out of which a few significant volatile components are presented in Table 3. The amount of each component is expressed as the ratio of each peak area to that of the internal standard in the chromatogram. It may be observed that besides linalool, linalool oxide, benzyl alcohol, benzeneethanol and geranic acid significantly contributes to the aroma headspace. Samples S1–S-5 are arranged according to increasing percentage of linalool in aroma headspace. The headspace of S-1 is composed of lowest proportion of linalool and highest percentage of linalool oxide, followed by that for benzyl alcohol and benzeneethanol. Highest proportion of linalool can be observed in S-5.
Frequency deviation (Hz)
-20 -30 -40 -50 -60 -70 S-1 S-2 S-3 S-4 S-5
-80 -90 10
20
30
40
50
60
70
80
90
Time (seconds) Fig. 9. QCM sensor profiles for different DTR&DC tea samples.
100
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Table 3 Presence of important flavour compounds in tea samples determined through GCMS. Volatile flavoury compounds (VFC)
S-1
S-2
S-3
S-4
S-5
Correlation with QCM response
2-Hexenal, (E)Linalool oxide Linalool Phenylacetaldehyde Benzyl alcohol Geraniol Benzeneethanol Nonanic acid Geranic acid
0.22 3.38 0.07 0.00 1.47 0.00 0.83 0.07 0.34
0.15 1.47 0.95 0.02 0.00 0.89 0.88 0.09 0.35
0.01 0.01 1.14 0.28 0.87 0.39 0.97 0.29 0.34
0.04 0.00 1.16 0.28 1.72 0.10 0.61 0.17 0.64
0.08 0.21 3.55 0.01 1.49 0.00 0.89 0.05 0.33
−0.59 −0.76 0.98 0.03 0.22 −0.21 0.06 −0.10 0.07
Table 4 QCM and GCMS responses for linalool. Tea samples
QCM deviations (Hz)
GCMS estimations for linalool
S-1 S-2 S-3 S-4 S-5
10.13 27.02 31.63 36.20 61.31
0.07 0.95 1.14 1.16 3.55
estimations of linalool. The values along the vertical axis is range normalized between 0.01 and 1. The names of tea samples are arranged according to the increasing values of their linalool content along the horizontal axis. It may be observed that the response of QCM sensors increases along with the increase of linalool content. The correlation coefficient is 0.98 between GCMS estimations and QCM frequency deviations as shown by the scatter plot of Fig. 11. In order to examine the extent of cross-sensitivity of other gases in tea aroma, the correlation coefficients between their GCMS estimations and QCM frequency deviations are reported in Table 3. Interestingly, It may be observed while the highest positive correlation of 0.98 is obtained for linalool, large negative values of −0.76 is obtained for linalool oxide. This may be explained based on the fact that, linalool oxide is obtained after oxidation of linalool. In other words, increase of linalool oxide comes at the cost of decrease in linalool itself. However, tea sample S-1 having lowest amount of linalool produces lowest frequency deviation despite the highest presence of linalool oxide. This indicates a satisfactory rejection for linalool oxide. The content of 2-hexenal generates a correlation of −0.59 with the QCM response. During tea processing, the characteristic aroma of tea leaves changes from grassy to sweet. This is
1 QCM Deviations (Hz) GC Estimates
0.9
Normalised QCM frequency deviations (Hz)
y = 0.95*x + 0.097 R = 0.98 1
0.8
0.6
0.4
0.2
0
-0.2 -0.2
0
0.2
0.4
0.6
0.8
1
1.2
Normalised GCMS estimates of linalool Fig. 11. Scatter plot of QCM response and GCMS estimates for linalool in tea samples.
caused due to the increase of linalool along with the decrease of 2hexenal during tea processing. The proportion of 2-hexenal is also found to be highest in S-1. In the light of above discussions it can be stated that during tea processing, the generation of linalool bears negative correlation with both 2-hexenal and linalool oxide. The correlation with other volatile flavour compounds listed in Table 3 is quite insignificant indicating negligible cross-sensitivity towards other competing gases. The developed sensors can provide a low cost instrumental means to detect linalool in black tea. The sensors can be particularly useful for evaluation of Darjeeling orthodox tea samples as their headspace is characterized by higher linalool content [15]. 4. Conclusion
Normalized responses
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
S-1
S-2
S-3
S-4
S-5
Tea samples Fig. 10. Comparison of QCM response and GCMS estimates for linalool in tea samples.
The development of a QCM sensor for linalool is discussed in this work. The response of the sensors are studied with standard concentration of linalool vapour and the sensitivity is sufficient for electronic detection. It has been found that the sensors are somewhat selective towards linalool as compared to geraniol, followed by a weak response towards linalool oxide and responds very weakly towards trans-2-hexenal. The sensitivity has been improved using dynamic headspace sampling method and significant improvement in the response intensities are obtained. The repeatability and reproducibility measure, expressed in terms of RSD, have been obtained below 15%. The performance of the sensors were tested with orthodox tea samples. It is found that the QCM frequency deviations have a satisfactory correlation with gas chromatographic estimations for linalool. Sufficient rejection is obtained for competing gases like 2-hexenal and linalool oxide. It can be thus be concluded that such a sensor can be used for an array based sensing in an electronic nose. As the Indian aromatic tea has a
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large amount of linalool in its headspace, the developed sensor can be an appropriate choice for devising methodologies for making qualitative differentiation among various types of tea aroma and subsequent grading. Acknowledgements This work is supported by Department of Science & Technology, University Grants Commission, Government of India and DST-CSIR Sensor Hub programme at CGCRI Kolkata (GAP 03/32). The authors are thankful to tea research association (TRA), Tocklai experimental station, Jorhat, Assam for providing infrastructural support and Mr. Udhav Das, research fellow, TRA for his help in biochemical estimations with tea samples. The authors are extremely thankful to the esteemed reviewers for their comments to improve the quality of work. References [1] G.W. Sanderson, H.N. Grahar, On the formation of black tea aroma, J. Agric. Food Chem. 21 (1973) 576–585. [2] P.K. Mahanta, R. Singh, Flavour components of assam and darjeeling teas in relation to agropractices and processing, in: Proceedings of the International Conference on R&D in Tea, 1990, pp. 148–153. [3] H. Guth, W. Grosch, Identification of potent odourants in static headspace samples of green and black tea powders on the basis of aroma extract dilution analysis (AEDA), Flavour Fragr. J. 8 (1993) 173–178. [4] H.-D. Belitz, W. Grosch, P. Schieberle, Food Chemistry, 4th ed., Springer-Verlag, Berlin, Heidelberg, 2009, pp. 350. [5] H. Masuda, K. Kumazawa, The change in the flavor of green and black tea drinks by the retorting process, in: Caffeinated Beverages; ACS Symposium Series 754, American Chemical Society, Washington, DC, 2000, pp. 337–346. [6] S. Christian, S. Peter, Characterization of the key aroma compounds in the beverage prepared from darjeeling black tea: quantitative differences between tea leaves and infusion, J. Agric. Food Chem. 54 (2006) 916–924. [7] M. Kawakami, S.N. Ganguly, J. Banerjee, A. Kobayashi, Aroma composition of oolong tea and black tea by brewed extraction method and characterizing compounds of darjeeling tea aroma, J. Agric. Food Chem. 43 (1995) 200–207. [8] R. Rawat, A. Gulati, G.D.K. Babu, R. Acharya, V.K. Kaul, B. Singh, Characterization of volatile components of Kangra orthodox black tea by gas chromatography–mass spectrometry, J. Agric. Food Chem. 105 (2007) 229–235. [9] G. Sauerbrey, The use of quartz oscillators for weighing thin layers and for microweighing, Z. Phys. 155 (1959) 206–222. ˜ [10] S. Munoz-Aguirre, A. Yoshino, T. Nakamoto, T. Moriizumi, Odor approximation of fruit flavors using a QCM odor sensing system, Sens. Actuators B: Chem. 123 (2007) 1101–1106. [11] U. Herrmann, T. Jonischkeit, J. Bargon, U. Hahn, Q. Yi Li, C.A. Schalley, E. Vogel, F. Vögtle, Monitoring apple flavor by use of quartz microbalances, Anal. Bioanal. Chem. 372 (2002) 611–614. [12] T. Yamanaka, R. Matsumoto, T. Nakamoto, Study of apple flavor using odor recorder with five components, Sens. Actuators B: Chem. 89 (2003) 112–119.
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[13] N. Kasai, I. Sugimoto, M. Nakamura, Discrimination of odorants of definite concentrations by using plasma-organic-film-coated QCR sensors, Sens. Actuators B: Chem. 65 (2000) 114–119. [14] B. Hoyer, G. Sørensen, N. Jensen, D.B.D.B. Nielsen, B. Larsen, Electrostatic spraying: a novel technique for preparation of polymer coatings on electrodes, Anal. Chem. 68 (1996) 3840–3844. [15] L.P. Bhuyan, K.K. Senapati, P. Saikia, M. Hazarika, Characterization of volatile flavour constituents of orthodox black tea of twenty nine Tocklai released cultivars for Darjeeling, Two Bud 59 (2012) 112–118.
Biographies Prolay Sharma received his M.Tech degree in instrumentation and electronics engineering in 2006 from Jadavpur University, Kolkata, India. He is currently perusing his PhD degree at the Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, India. His research interests include electronic nose, quartz crystal microbalance sensor, pattern recognition and signal processing. Arunangshu Ghosh received his M.Tech degree in instrumentation and electronics engineering in 2005 from Jadavpur University, Kolkata, India. He is currently perusing his PhD degree at the Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, India. His research interests include electronic tongue, machine olfaction, Quartz crystal microbalance sensors and pattern recognition. Bipan Tudu received his PhD degree in the year 2011 from Jadavpur University, Kolkata, India. He is currently an associate professor with the Department of Instrumentation and Electronics Engineering, Jadavpur University. His main research interest includes pattern recognition, artificial intelligence, machine olfaction and the electronic tongue. Lakshi Prasad Bhuyan is currently with the Biochemistry department at the Tea Research Association, Tocklai Experimental Station, Jorhat, Assam, India. He is a PhD and his research interests are in the fields of flavour chemistry, tea biochemistry, tea enzymology and quality improvement of tea. Pradip Tamuly is currently the head of biochemistry in the Tea Research Association, Tocklai Experimental Station, Jorhat, Assam, India. He is a PhD and his research interests are in the fields of tea bio-chemistry, flavour chemistry and tea processing science. Nabarun Bhattacharyya received his PhD degree in the year 2008 from Jadavpur University, Kolkata, India. He is currently an associate director with the Centre for the Development of Advanced Computing (CDAC), Kolkata, India. His research areas include agrielectronics, machine olfaction, soft computing and pattern recognition. Rajib Bandyopadhyay received the PhD degree from Jadavpur University, Kolkata, India, in 2001. He is currently a professor with the Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata. His research interests include the fields of machine olfaction, electronic tongue and intelligent systems. Anutosh Chatterjee received the Ph.D. degree from Calcutta University, Kolkata, India, in 1975. He is currently associated with the Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata. His research interests include the fields of electronic nose and tongue, molecular spectroscopy, NMR, EPR and NQR-based instrumentation systems.
Contents lists available at ScienceDirect
Sensors and Actuators B: Chemical journal homepage: www.elsevier.com/locate/snb
Detection of linalool in black tea using a quartz crystal microbalance sensor Prolay Sharma a,∗ , Arunangshu Ghosh a , Bipan Tudu a , Lakshi Prasad Bhuyan b , Pradip Tamuly b , Nabarun Bhattacharyya c , Rajib Bandyopadhyay a , Anutosh Chatterjee a a
Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata 700 098, India Tea Research Association, Tocklai Experimental Station, Jorhat, Assam, India c Centre for Development of Advanced Computing, Kolkata 700 091, India b
a r t i c l e
i n f o
Article history: Received 30 October 2012 Received in revised form 24 August 2013 Accepted 26 August 2013 Available online 5 September 2013 Keywords: Quartz crystal microbalance Linalool Polyethylene glycol Black tea Gas chromatography mass spectrometry
a b s t r a c t Linalool is one of the most important volatile constituent that contributes significantly in the aroma of brewed, dry or extracted tea. Thus it is very essential to sense the linalool content in orthodox black tea as that may lead to rapid quality estimation. Quartz crystal microbalance (QCM) type gas sensors are very sensitive and are increasingly being used for many applications. It can be easily fabricated, has less response time and fast recovery characteristics and can be coated with a variety of materials to obtain different sensitivities and selectivities. In the present work, a QCM sensor has been developed with polyethylene glycol (PEG) to detect linalool gas in black tea. Extensive experiments have been carried out with various concentrations of linalool gas and the sensitivity, repeatability and reproducibility of the developed sensors were determined. The sensors were observed to be sensitive and selective to linalool. The developed sensors were exposed to the orthodox black tea aroma and significant correlation is obtained with gas chromatography mass spectrometry (GCMS) estimations. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Black tea is the processed leaves of Camellia sinensis. Tea is of great economic importance and is the most consumed beverage worldwide. The black tea production consists of four stages, namely, withering, rolling, fermentation, and drying [1]. Three main types of tea are generally produced based on the level of fermentation: unfermented (green), semi fermented (oolong), and fermented (black) tea. The aroma of tea is defined by the clonal variety, withering duration and rolling phase. In general, the quality characteristics of tea are determined by both taste and aroma. Volatile components contribute to aroma quality and non-volatile components determine the taste. In tea, even though the volatile organic components (VOCs) are present in very minute quantities, i.e. 0.01% of the total dry weight, they have a very high impact on its aroma. Volatile organic compounds of tea are classified into two groups. While the Group I compounds are mainly non-terpenoids, which impart fresh green flavour, e.g. hexenols, the Group II compounds are terpenoids and impart sweet flowery aroma to tea,
∗ Corresponding author at: Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt Lake Campus, Block LB, Sector III, Plot 8, Salt Lake, Kolkata 700 098, India. Tel.: +91 33 23352587; fax: +91 33 23357254. E-mail address: [email protected] (P. Sharma). 0925-4005/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.snb.2013.08.088
viz., monoterpene alcohols like linalool and geraniol. The presence of Group II compounds is highly desirable. Major chemical compounds responsible for flavour in tea are presented in Table 1 [2]. Large number of reports on characterization of volatile compounds present in different black tea is available. Guth & Grosch studied the odour-active volatiles in a Chinese black tea powder by aroma extract dilution analysis (AEDA) [3]. In this study linalool with the highest flavour dilution (FD) factors has been observed. The flavour dilution factor (FD) of a substance can be defined as the ratio of its concentration of the initial extract to its height dilution at which the substance odour can be detected [4]. The compound with the highest FD factor has the highest impact in that particular extract, and the compound with lowest FD does not contribute much to the final odour impression. By applying the technique of AEDA, Masuda and Kumazawa confirmed that linalool is one of the most odour-active constituents in the black tea beverage prepared from Darjeeling tea [5]. Christian and Peter have done a significant work on the characterization of key aroma compounds in the beverage prepared from Darjeeling black tea [6]. They collected Darjeeling black tea with TGFOP grade and identified the important aroma compounds in the extract prepared from the black tea leaves. Linalool was again reported with the highest FD value. Investigations regarding concentrations of important aroma compounds in black tea leaves have revealed that the presence of linalool is
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Table 1 Flavour quality of volatile compounds responsible for tea aroma. Compounds
Flavour
Linalool, linalool oxide Geraniol, phenylacetaldehyde Nerolidol, benzaldehyde, methyl salicylate, phenyl ethanol Trans-2-hexenal, n-hexanal, cis-3-hexenol -Ionone
Sweet Floral Fruity Fresh Grassy
second highest (6600 g/kg) after hexanoic acid (12,000 g/kg), but the FD value of hexanoic acid is 16 as compared to 256 of linalool. Kawakami, Ganguly, Banerjee & Kobayashi have carried out extensive examination of two types of oolong teas, three types of Darjeeling, two types of Sri Lankan and one type of Chinese black tea [7]. The aroma concentrations were prepared by two extraction methods: the brewed extraction method and the simultaneous steam distillation and extraction method (SDE) and were then analyzed by gas chromatography (GC) and gas chromatography/IR spectrometry/mass spectrometry (GC/FTIR/MS). Gas chromatography mass spectrometry analysis on Darjeeling tea aroma reveals that more than 20% peak area is covered by linalool. On observing the gas chromatograms of Darjeeling SDE extract and four types of brewed black tea extracts, the main components found are linalool, linalool oxides (II, I and IV), geraniol and methyl salicylate. The main components found in the brewed extract are linalool, linalool oxide (I, II and IV), geraniol, trans-geranic acid, (E)-2-hexenoic acid, benzyl alcohol and hexanoic acid. The presence of significant amount of linalool along with nerolidol is also observed on the aroma extraction analysis of the Chinese Oolong teas. The main components of the brewed Sri Lanka clone DT-1 extract are linalool, linalool oxide (II and IV), benzyl alcohol and indole. Rawat et al., have characterized volatile components of Kangra orthodox black tea by two different extraction methods viz. simultaneous distillation extraction (SDE) and hydrodistillation, using GCMS [8]. They have reported that SDE extracts more volatile components from Kangra orthodox tea as compared to hydrodistillation methods. The VOC profile of tea obtained by simultaneous distillation extraction is dominated by terpenoids. Linalool oxide-II (furanoid) constitutes the highest amount (19.06%), followed by geraniol (16.21%) and linalool oxide-I (furanoid) (7.64%). The review reports indicate that linalool is by far the most important compound that defines the headspace of brewed, dry or extracted tea aroma. It is thus important to sense the linalool content in black orthodox tea for its quality estimation. Quartz crystal microbalance (QCM) sensors are very sensitive and are now increasingly being used as gas sensors. QCM sensors are made of a thin plate of quartz crystal blanks with metal electrodes on each side. When an alternating electrical excitation is applied to the electrodes, the deformation and relaxation of crystal faces occurs at natural resonance frequency which depends on the crystal dimensions, physical parameters and type of crystal cut. For sensing applications, QCM sensors are coated with appropriate analyte-sensitive coating. The target analyte is adsorbed on coating surface increasing the mass of QCM sensors and results in a change in its resonance frequency. According to Sauerbrey’s equation [9] the QCM resonance frequency decreases linearly with the adsorbed mass. The target gases with different molecular weights can be detected in terms of different frequency deviations. In other words, for different VOCs, differences in the values of deviations in resonance frequency can indicate the selectivity of sensors and the magnitude of deviations will indicate the sensitivity. In this paper, we investigate the development of a QCM sensor that can detect linalool. PEG is chosen as the adsorbent coating. PEG is a gas chromatographic material and is used extensively to sense aroma from fruits that contain significant amount of linalool
Development of sensor (electrostatic spray)
Gas delivery setup based on static headspace sampling (Single sensor)
Gas delivery setup based on dynamic headspace sampling (Sensor array)
Study of sensitivity and selectivity
Study of repeatability and reproducibility
Study of developed sensors with fresh tea samples
Fig. 1. Schematic of experimental process.
[10–13]. The works carried out is represented schematically in Fig. 1. The sensors were prepared using electrostatic spray method. The sensitivity, selectivity, repeatability and reproducibility of the developed sensors were studied. Finally, to validate the response with tea aroma, the developed sensors are exposed to five different orthodox tea samples with linalool content obtained through GCMS analysis. 2. Experimental procedures 2.1. Materials Polyethylene glycol 200 is procured from Merck Specialities Pvt. Ltd., Mumbai, India. Linalool, linalool oxide and trans-2-hexene1-al are collected from Aldrich, Germany. Geraniol is procured from National Chemicals, India. Five different varieties of orthodox tea samples were collected from Darjeeling Tea Research and Development Center (DTR&DC), Kurseong, India for validation of the developed sensors with tea samples. GCMS was performed at Tea Research Association, Tocklai Experimental Station, Jorhat, Assam, India with a PerkinElmer Clarus 500 GCMS model and a 60 m × 0.25 mm CP-Wax 52 CB capillary column from Agilent. Quantitative estimation of flavour compounds in GCMS instrument was done by the internal standard comparison technique using National Institute of Standards and Technology (NIST), Wiley and Maurer/Pfleger/Weber Drugs Mass spectral Libraries. The dichloromethane, caproic acid, sodium chloride, anhydrous sodium sulphate and GCMS standards for volatile flavour compounds were obtained from Sigma chemicals. 2.2. Sensor development An AT-cut 10 MHz Quartz crystal blank was coated with a gas chromatography-stationary phase material. PEG was chosen as the adsorbent coating. PEG was dissolved in chloroform with
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Teflon Chamber
Exhaust
Glass Syringe
Suction Pump Three way valve
Personal Computer and Data Acquisition
Counter (Agilent 53131 A)
Fig. 2. Measurement setup with single QCM sensor based on static headspace sampling.
concentration 0.17% (w/v) and then deposited on the silver electrodes of QCM (10 MHz, AT-Cut) using electrostatic spraying method [14]. In the electrostatic spray method, a solution of sensor-active material is loaded into a glass syringe. A high DC voltage of about 18 kV was applied between the needle and the electrode of the QCM. The high electric field at the tip of needle spreads out the droplets in a form of finely divided spray, yielding a uniform coating.
the test gas was injected through rubber septa into the air flow path from a side channel. The air flow carried the aroma molecules and facilitates adsorption of them onto the sensor surface. The air flow rate thus needs to be carefully controlled. The responses of all eight sensors were converted into voltages using electronic circuits. The resonance frequencies of the sensors were determined by counting the frequency of the voltage waveform. The real time frequency counting and data acquisition into a PC was performed using counter array of data acquisition card NI 6602.
2.3. Experimental setup Two types of measurement setup and odour delivery schemes were explored. Firstly, the response of PEG coated sensors were studied for sensitivity, selectivity and linearity. Secondly, the repeatability and reproducibility of sensors were studied. 2.3.1. Static headspace sampling with single sensor The measurement setup for the first part of the experiment was based on static headspace sampling method and is shown in Fig. 2. This measurement setup was used for the study of sensitivity and selectivity of developed sensors. The method of static headspace sampling is preferred as the measurement process is simple and free of environmental or experimental variations. The sensor chamber is a 100 ml Teflon chamber kept in a temperature controlled environment. The sample is injected into the sensor chamber using a glass syringe through a rubber septa attached to one of the arms of a three way valve. The gases were carefully dispersed in the chamber so that it affects the sensor in a short time with maximum impact. However, efforts were made to reduce the effect of syringe flow rate. The sampling duration was continued till the sensor response reached equilibrium with the target-gas environment. The QCM sensor was attached to a rigid base from which signal connections were taken to the counter instrument. The counter displays the current frequency of the sensors and also sends the readings to a personal computer for data-logging. After sampling, the sensors were purged for a short duration using a suction pump that exhausts the analyte gases outside the chamber. The purging was continued until the initial sensor baseline was recovered. In order to confirm the selectivity of developed sensor towards linalool, it was also exposed to other common tea volatiles like geraniol, trans-2-hexenal and linalool oxide. The sensitivity curves for three types of gases were studied and the selectivity of the sensor is discussed in Section 3. 2.3.2. Dynamic headspace sampling with sensor array In order to justify the reproducibility of sensor development process and repeatability of the sensors an array based measurement setup is conceived and implemented. The measurement setup for this part of the experiment is based on dynamic headspace sampling method and is presented in Fig. 3. The developed setup can support an array of up to eight sensors. The chamber volume is 500 ml. A suction pump attached to the sensor chamber continuously pulls in fresh air at a rate of 4 l/min. During sampling,
2.4. Study of reproducibility and repeatability for sensors For the study of reproducibility of sensors, eight quartz crystals were coated with PEG using electrostatic spray method and the final coating thickness kept constant by monitoring the change in frequency. After simultaneously exposing the sensor array to standard concentration of gases the frequency deviations were recorded. The relative standard deviations (RSD) of all eight sensors were calculated as the measure of reproducibility. For calculating repeatability, all the sensors are repeatedly exposed to same concentration of linalool vapour. The RSD of sensor responses for all the repeated applications is calculated. The average RSD considering all the eight sensors is the measure of repeatability. These measurements are carried out in experimental setup shown in Fig. 3.
2.5. Extraction and estimation of volatile flavour components (VFC) using GCMS 100 g of orthodox black tea was taken in a 3 l round bottomed flask, 1000 ml de-ionized boiling water was added to it along with 0.125 l caproic acid as internal standard. The mixture was steam distilled for about 45 min. To the steam distillate, 10 g of sodium chloride was added and the flavour components were sequentially extracted with dichloromethane with volumes of 80 ml, 40 ml and 40 ml respectively, consecutively thrice using a separating funnel. Anhydrous sodium sulphate of 10 g was then added to the extract for dehydration purpose and kept for 24 h. The dehydrated extract was then concentrated in Eyala CCA-1110 rotary evaporation concentrator. The solvent was further reduced under purified nitrogen atmosphere to 100 l. The concentrate thus obtained was injected to GCMS instrument. Helium was used as a carrier gas at a flow rate of 12 psi. The injector and detector temperature was set at 250 ◦ C and 300 ◦ C respectively. The column oven was temperature programmed from 60 ◦ C to 200 ◦ C by maintaining a 10 min isothermal condition at 60 ◦ C and 25 min at 200 ◦ C with an increment of 2 ◦ C/min. A runtime of 105 min was maintained. A number of peaks were obtained in the GCMS profile. Out of those peaks, few significant compounds were identified for study. The amount of each component was determined and expressed as the ratio of each peak area to that of the internal standard in the chromatogram.
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Exhaust
Suction Pump
Data Acquisition (NI PCI 6602)
Sensor Chamber and sensor array
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Personal Computer
Data analysis Air inlet
Gas injector channel Selector switch
Fig. 3. Measurement setup with eight QCM sensor array based on dynamic headspace sampling.
2.6. Study with orthodox black tea aroma using QCM sensors The gas delivery setup based on static headspace sampling was used for analysis of tea aroma with experimental conditions as decribed in Section 2.3.1. In total, five orthodox tea samples were procured from DTR&DC, Kurseong. They were labelled as S-1, S-2, S-3, S-4 and S-5. Each tea sample was kept in a 250 ml desiccator for about 15 min to generate sufficient headspace. 50 ml headspace aroma was then collected in a syringe from the desiccator and injected to the sensor chamber. The corresponding sensor responses were observed. 3. Results and discussion 3.1. Sensitivity and selectivity studies with static headspace system The sensor responses with linalool vapour were studied with the static headspace system. The QCM under study was placed in the chamber and the linalool concentration was varied from 10 to 900 ppm. The gases were kept confined in the chamber till it achieved an equilibrium state of interaction with the sensing layer. During the process, the frequency of the sensors decreased and stabilized after some time. The difference between initial and stabilized frequency was noted as information about gas concentration. The typical sensor response profile for 200 ppm linalool is presented in Fig. 4. It was observed that the response of the sensor stabilized after 20 s of application of the gas. The linear pattern of frequency drop is mainly attributed to the syringe flow rate. Once the injection ended at nearly 35 s, the equilibrium frequency was attained after 15 s. The 0 -5 -10
Frequency (Hz)
-15
sniffing cycles for a particular concentration was replicated three times and the averaged steady state deviations were recorded. It may be mentioned that excellent repeatability is observed among replicated readings. The sensitivity curves derived from averaged deviations are presented in Fig. 5. The sensitivity curve for linalool is presented in Fig. 5(a). The sensor produces highly linear response within the range of exposed concentration. The sensitivity obtained is 0.14 Hz/ppm, but linearity is very high as is evident from the high R2 value. As far as selectivity is concerned, the sensor responses are studied against other dominant vapours of tea, viz. geraniol, trans2-hexenal and linalool oxide. The sensitivity curves are presented from Fig. 5(b) through (d). It can be observed that the sensitivity of geraniol is lower than that of linalool. Though the sensitivity values are comparable, there is a difference in the offset term (1.0 for linalool and −0.519 for geraniol). These differences are manifested as differences in frequency deviations when both the sensors are exposed to same concentration e.g. 50 ppm of linalool, linalool oxide, geraniol and trans-2-hexenal vapours. Fig. 6 presents the bar plots of sensor responses to 50 ppm of each of the above mentioned vapours. Linalool has the highest response followed by geraniol. An interesting point that may be made from Fig. 6 is the lower response to linalool oxide. The reason may be the lower polarity of linalool oxide molecules due to more cyclic and symmetric molecular structure as compared to linalool. The absence of hydroxyl groups also reduces the polarity of this molecule. Hence, the behaviour of the sensors can have significant impact on the efficiency of qualitative discrimination among different types of tea aroma with various proportions of linalool and linalool oxide in its headspace. The response of trans2-hexenal is the lowest. This behaviour is also desirable, as the aroma of tea leaves gradually changes from grassy to sweet during tea manufacturing process. The sweet smell characterized by the presence of linalool is expected to be detected by such a sensor. It is however evident that there is a room for improvement as far as sensitivity is concerned. The application of gases under dynamic headspace system may help to increase the sensor responses and thus the sensitivity.
-20
3.2. Studies using dynamic headspace system -25 -30 -35 -40 10
20
30 Time (seconds)
40
50
60
Fig. 4. Response profile for 200 ppm linalool vapour using static headspace sampling.
3.2.1. Sensitivity Experiments were conducted with a dynamic headspace sampling set up as shown in Fig. 4. The air flow rate was set at 4 l/min. In this method an array of eight PEG sensors was exposed to linalool vapour with four standard concentrations, namely 30, 50, 70 and 100 ppm. Typical responses of one such sensor with 30, 50 and 100 ppm are presented in Fig. 7. The drop in QCM resonance frequency is observed during sampling phase when gas molecules adsorb on the sensor surface. Increased frequency deviations are observed for concentrations
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Fig. 5. (a) Sensitivity curve for linalool vapour using static headspace sampling. (b) Sensitivity curve for geraniol using static headspace sampling. (c) Sensitivity curve for trans-2-hexenal using static headspace sampling. (d) Sensitivity curve for linalool oxide using static headspace sampling.
of 50 and 100 ppm. The responses are recorded and the sensitivity curve is drawn out as shown in Fig. 8(a). Each point in the plot is an average of 5 repeated readings. Highly linear responses as indicated by R2 of 0.974 are obtained over a range of 30–100 ppm. The sensitivity obtained as 0.3 Hz/ppm is an improvement over static sampling system. The sensitivity curves for geraniol and trans-2-hexenal is also presented in Fig. 8(b) and (c) respectively. The sensitivity obtained for geraniol following same analysis procedure is 0.213 Hz/ppm while that for trans2-hexenal is 0.210 Hz/ppm. It can again be observed that the sensors are most selective to linalool, followed by geraniol and then trans-2-hexenal.
3.2.2. Repeatability and reproducibility The repeatability of the sensors were tested with 25 repeated applications of 30 ppm linalool. The repeatability is expressed in terms of relative standard deviations (RSD) among the responses from repeated trials. It has been observed that the best repeatability obtained is nearly 14.4% (RSD) and the average repeatability considering all eight sensors is 14.98% (RSD). However, it was observed that more repeatable responses were obtained at higher concentrations. For establishing the reproducibility of sensor preparation process, eight quartz crystals were coated with PEG and simultaeneously subjected to volatiles of linalool. The RSD of responses over
9
0
8
A -5 Frequency deviation (Hz)
Frequency deviation (Hz)
7 6 5 4 3
-10
-15
B
-20
C
-25
2 -30
1 0
10
Trans-2-hexenal
Linalool Oxide
Geraniol
Linalool
20
30
40
50 60 70 Time (seconds)
80
90
100
110
Test gases Fig. 6. Response of sensors at 50 ppm of volatiles.
Fig. 7. Frequency deviation pattern for QCM sensors in dynamic sampling system when exposed to (a) 30 ppm, (b) 50 ppm and (c) 100 ppm of linalool.
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Fig. 8. (a) Sensitivity curve for linalool using dynamic headspace sampling. (b) Sensitivity curve for geraniol using dynamic headspace sampling. (c) Sensitivity curve for trans-2-hexenal using dynamic headspace sampling.
Table 2 Repeatability and reproducibility measures for the developed sensors. Concentration 30 ppm 50 ppm 100 ppm
Repeatability (RSD %)
Reproducibility (RSD %)
14.98 8.95 13.4
14.94 11.81 9.01
eight sensors were calculated. Average reproducibility obtained for 25 repeated trials is 14.94% (RSD). Table 2 presents the average values of repeatability and reproducibility including eight sensors. It can be observed that better reproducibility value expressed in RSD of 9% is obtained. This indicates that the reponses of sensors do not vary much even when they are prepared again and again. Thus, one sensor can be easily replaced by the other one without much change in sensitivity.
3.4. Study with orthodox black tea aroma using static headspace technique Fig. 9 shows the QCM sensor profiles after application of different orthodox tea samples. It can be observed that each of the tea samples produce distinct frequency profiles. The response reaches equilibrium within 90 s of the application of tea aroma. The average frequency deviations of three trials obtained for each tea sample with their respective linalool content estimated by GCMS is presented in Table 4. Fig. 10 presents the bar-plot comparison of responses obtained with the developed QCM sensors and the GCMS
0 -10
3.3. Estimation of major volatile flavour components by GSMS analysis The peaks obtained in the GCMS profile were identified by mass-spectroscopy, out of which a few significant volatile components are presented in Table 3. The amount of each component is expressed as the ratio of each peak area to that of the internal standard in the chromatogram. It may be observed that besides linalool, linalool oxide, benzyl alcohol, benzeneethanol and geranic acid significantly contributes to the aroma headspace. Samples S1–S-5 are arranged according to increasing percentage of linalool in aroma headspace. The headspace of S-1 is composed of lowest proportion of linalool and highest percentage of linalool oxide, followed by that for benzyl alcohol and benzeneethanol. Highest proportion of linalool can be observed in S-5.
Frequency deviation (Hz)
-20 -30 -40 -50 -60 -70 S-1 S-2 S-3 S-4 S-5
-80 -90 10
20
30
40
50
60
70
80
90
Time (seconds) Fig. 9. QCM sensor profiles for different DTR&DC tea samples.
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Table 3 Presence of important flavour compounds in tea samples determined through GCMS. Volatile flavoury compounds (VFC)
S-1
S-2
S-3
S-4
S-5
Correlation with QCM response
2-Hexenal, (E)Linalool oxide Linalool Phenylacetaldehyde Benzyl alcohol Geraniol Benzeneethanol Nonanic acid Geranic acid
0.22 3.38 0.07 0.00 1.47 0.00 0.83 0.07 0.34
0.15 1.47 0.95 0.02 0.00 0.89 0.88 0.09 0.35
0.01 0.01 1.14 0.28 0.87 0.39 0.97 0.29 0.34
0.04 0.00 1.16 0.28 1.72 0.10 0.61 0.17 0.64
0.08 0.21 3.55 0.01 1.49 0.00 0.89 0.05 0.33
−0.59 −0.76 0.98 0.03 0.22 −0.21 0.06 −0.10 0.07
Table 4 QCM and GCMS responses for linalool. Tea samples
QCM deviations (Hz)
GCMS estimations for linalool
S-1 S-2 S-3 S-4 S-5
10.13 27.02 31.63 36.20 61.31
0.07 0.95 1.14 1.16 3.55
estimations of linalool. The values along the vertical axis is range normalized between 0.01 and 1. The names of tea samples are arranged according to the increasing values of their linalool content along the horizontal axis. It may be observed that the response of QCM sensors increases along with the increase of linalool content. The correlation coefficient is 0.98 between GCMS estimations and QCM frequency deviations as shown by the scatter plot of Fig. 11. In order to examine the extent of cross-sensitivity of other gases in tea aroma, the correlation coefficients between their GCMS estimations and QCM frequency deviations are reported in Table 3. Interestingly, It may be observed while the highest positive correlation of 0.98 is obtained for linalool, large negative values of −0.76 is obtained for linalool oxide. This may be explained based on the fact that, linalool oxide is obtained after oxidation of linalool. In other words, increase of linalool oxide comes at the cost of decrease in linalool itself. However, tea sample S-1 having lowest amount of linalool produces lowest frequency deviation despite the highest presence of linalool oxide. This indicates a satisfactory rejection for linalool oxide. The content of 2-hexenal generates a correlation of −0.59 with the QCM response. During tea processing, the characteristic aroma of tea leaves changes from grassy to sweet. This is
1 QCM Deviations (Hz) GC Estimates
0.9
Normalised QCM frequency deviations (Hz)
y = 0.95*x + 0.097 R = 0.98 1
0.8
0.6
0.4
0.2
0
-0.2 -0.2
0
0.2
0.4
0.6
0.8
1
1.2
Normalised GCMS estimates of linalool Fig. 11. Scatter plot of QCM response and GCMS estimates for linalool in tea samples.
caused due to the increase of linalool along with the decrease of 2hexenal during tea processing. The proportion of 2-hexenal is also found to be highest in S-1. In the light of above discussions it can be stated that during tea processing, the generation of linalool bears negative correlation with both 2-hexenal and linalool oxide. The correlation with other volatile flavour compounds listed in Table 3 is quite insignificant indicating negligible cross-sensitivity towards other competing gases. The developed sensors can provide a low cost instrumental means to detect linalool in black tea. The sensors can be particularly useful for evaluation of Darjeeling orthodox tea samples as their headspace is characterized by higher linalool content [15]. 4. Conclusion
Normalized responses
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
S-1
S-2
S-3
S-4
S-5
Tea samples Fig. 10. Comparison of QCM response and GCMS estimates for linalool in tea samples.
The development of a QCM sensor for linalool is discussed in this work. The response of the sensors are studied with standard concentration of linalool vapour and the sensitivity is sufficient for electronic detection. It has been found that the sensors are somewhat selective towards linalool as compared to geraniol, followed by a weak response towards linalool oxide and responds very weakly towards trans-2-hexenal. The sensitivity has been improved using dynamic headspace sampling method and significant improvement in the response intensities are obtained. The repeatability and reproducibility measure, expressed in terms of RSD, have been obtained below 15%. The performance of the sensors were tested with orthodox tea samples. It is found that the QCM frequency deviations have a satisfactory correlation with gas chromatographic estimations for linalool. Sufficient rejection is obtained for competing gases like 2-hexenal and linalool oxide. It can be thus be concluded that such a sensor can be used for an array based sensing in an electronic nose. As the Indian aromatic tea has a
P. Sharma et al. / Sensors and Actuators B 190 (2014) 318–325
large amount of linalool in its headspace, the developed sensor can be an appropriate choice for devising methodologies for making qualitative differentiation among various types of tea aroma and subsequent grading. Acknowledgements This work is supported by Department of Science & Technology, University Grants Commission, Government of India and DST-CSIR Sensor Hub programme at CGCRI Kolkata (GAP 03/32). The authors are thankful to tea research association (TRA), Tocklai experimental station, Jorhat, Assam for providing infrastructural support and Mr. Udhav Das, research fellow, TRA for his help in biochemical estimations with tea samples. The authors are extremely thankful to the esteemed reviewers for their comments to improve the quality of work. References [1] G.W. Sanderson, H.N. Grahar, On the formation of black tea aroma, J. Agric. Food Chem. 21 (1973) 576–585. [2] P.K. Mahanta, R. Singh, Flavour components of assam and darjeeling teas in relation to agropractices and processing, in: Proceedings of the International Conference on R&D in Tea, 1990, pp. 148–153. [3] H. Guth, W. Grosch, Identification of potent odourants in static headspace samples of green and black tea powders on the basis of aroma extract dilution analysis (AEDA), Flavour Fragr. J. 8 (1993) 173–178. [4] H.-D. Belitz, W. Grosch, P. Schieberle, Food Chemistry, 4th ed., Springer-Verlag, Berlin, Heidelberg, 2009, pp. 350. [5] H. Masuda, K. Kumazawa, The change in the flavor of green and black tea drinks by the retorting process, in: Caffeinated Beverages; ACS Symposium Series 754, American Chemical Society, Washington, DC, 2000, pp. 337–346. [6] S. Christian, S. Peter, Characterization of the key aroma compounds in the beverage prepared from darjeeling black tea: quantitative differences between tea leaves and infusion, J. Agric. Food Chem. 54 (2006) 916–924. [7] M. Kawakami, S.N. Ganguly, J. Banerjee, A. Kobayashi, Aroma composition of oolong tea and black tea by brewed extraction method and characterizing compounds of darjeeling tea aroma, J. Agric. Food Chem. 43 (1995) 200–207. [8] R. Rawat, A. Gulati, G.D.K. Babu, R. Acharya, V.K. Kaul, B. Singh, Characterization of volatile components of Kangra orthodox black tea by gas chromatography–mass spectrometry, J. Agric. Food Chem. 105 (2007) 229–235. [9] G. Sauerbrey, The use of quartz oscillators for weighing thin layers and for microweighing, Z. Phys. 155 (1959) 206–222. ˜ [10] S. Munoz-Aguirre, A. Yoshino, T. Nakamoto, T. Moriizumi, Odor approximation of fruit flavors using a QCM odor sensing system, Sens. Actuators B: Chem. 123 (2007) 1101–1106. [11] U. Herrmann, T. Jonischkeit, J. Bargon, U. Hahn, Q. Yi Li, C.A. Schalley, E. Vogel, F. Vögtle, Monitoring apple flavor by use of quartz microbalances, Anal. Bioanal. Chem. 372 (2002) 611–614. [12] T. Yamanaka, R. Matsumoto, T. Nakamoto, Study of apple flavor using odor recorder with five components, Sens. Actuators B: Chem. 89 (2003) 112–119.
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Biographies Prolay Sharma received his M.Tech degree in instrumentation and electronics engineering in 2006 from Jadavpur University, Kolkata, India. He is currently perusing his PhD degree at the Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, India. His research interests include electronic nose, quartz crystal microbalance sensor, pattern recognition and signal processing. Arunangshu Ghosh received his M.Tech degree in instrumentation and electronics engineering in 2005 from Jadavpur University, Kolkata, India. He is currently perusing his PhD degree at the Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, India. His research interests include electronic tongue, machine olfaction, Quartz crystal microbalance sensors and pattern recognition. Bipan Tudu received his PhD degree in the year 2011 from Jadavpur University, Kolkata, India. He is currently an associate professor with the Department of Instrumentation and Electronics Engineering, Jadavpur University. His main research interest includes pattern recognition, artificial intelligence, machine olfaction and the electronic tongue. Lakshi Prasad Bhuyan is currently with the Biochemistry department at the Tea Research Association, Tocklai Experimental Station, Jorhat, Assam, India. He is a PhD and his research interests are in the fields of flavour chemistry, tea biochemistry, tea enzymology and quality improvement of tea. Pradip Tamuly is currently the head of biochemistry in the Tea Research Association, Tocklai Experimental Station, Jorhat, Assam, India. He is a PhD and his research interests are in the fields of tea bio-chemistry, flavour chemistry and tea processing science. Nabarun Bhattacharyya received his PhD degree in the year 2008 from Jadavpur University, Kolkata, India. He is currently an associate director with the Centre for the Development of Advanced Computing (CDAC), Kolkata, India. His research areas include agrielectronics, machine olfaction, soft computing and pattern recognition. Rajib Bandyopadhyay received the PhD degree from Jadavpur University, Kolkata, India, in 2001. He is currently a professor with the Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata. His research interests include the fields of machine olfaction, electronic tongue and intelligent systems. Anutosh Chatterjee received the Ph.D. degree from Calcutta University, Kolkata, India, in 1975. He is currently associated with the Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata. His research interests include the fields of electronic nose and tongue, molecular spectroscopy, NMR, EPR and NQR-based instrumentation systems.