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Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals
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Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals Seyed Alireza Ghorashi n, Amir Hossein Alinoori, Saeed Hajialigol Detector Group, Institute of Materials and Energy, Iranian Space Research Center, Isfahan, Iran
art ic l e i nf o
a b s t r a c t
Article history: Received 25 December 2013 Received in revised form 16 July 2014 Accepted 24 July 2014
Over the years, Ion Mobility Spectrometry (IMS) which refers to the techniques and cutting-edge instruments for characterizing of analytes by their gas phase mobility has been gaining popularity and validity among scientific researchers for detecting chemicals. This novel gas sensor with its high analytical speed, low detection limits, ease of use and ruggedness during transport has also become the dominant commercial technology in different industries. In spite of these paramount advantages, this detector has difficulty identifying matrix compounds. To overcome this problem a Gas Chromatograph (GC) can be used to introduce individual components of mixture into an IMS. The output signal of the hyphenated GC–IMS method is an extraordinary small, time-dependent current produced by mobile ions in atmospheric pressure. To exploit the qualitative and quantitative information hidden in this sensitive noisy signal, various signal processing methods have been nominated for spectrum filtration and improving signal-to-noise ratio (SNR). In the present paper an attempt is made to design and construct a GC–IMS instrument coupled with a Thermal Solid Sample Introduction (TSSI) module for injection of solid samples to capillary GC column. Moreover, various spectrum filtration methods for removing noise from GC–IMS signal would be investigated. The GC–IMS instrument used in this research was designed and constructed in Institute of Materials and Energy, Isfahan, Detector group and can be used as a chemical sensor for rapid detection of a broad range of chemical mixtures in many operational environments including on-board Volatile Organic Analyzer Sensor (VOAS) in space missions. The capability of constructed sensor for detection of complex mixtures has been proved by analyzing a mixture of three pesticides as test materials. Our proposed method for removing noise from the realtime TSSI–GC–IMS signal will be presented and its efficacy will be proved by offering real experiments. & 2014 Elsevier Ltd. All rights reserved.
Keywords: Chemical sensor Two dimensional Thermal solid sample introduction Gas chromatography–ion mobility spectrometry Computational methods Signal-to-noise ratio
1. Introduction Ion Mobility Spectrometry (IMS) as a popular and valid instrument has substantiated its potential to be a fast, sensitive, rugged and easily used gas sensor for detection of trace amounts of organic compounds [1–3]. The unique advantages of IMS make it a dominant commercial technology in different industries including on-site detection of explosives, drugs and warfare agents [4–9], bacteria and medical applications [10,11], pesticides in agriculture products [12,13], environmental monitoring [14], process control [15], rocket fuel leak detection [16], analyzing of wood species [17], and last, but perhaps the most interesting one is in both manned and robotic space explorations [18,19]. IMS is a gas-phase sensor that allows chemicals to be distinguished on the basis of their mass, charge, size and shape. It n Correspondence to: Institute of Materials and Energy, Iranian Space Research Center, 7th Km Imam Khomeini Street, P.O. Box: 81955/174, Isfahan, Iran. Tel.: þ 98 311 33222439; fax: þ 98 311 33222446. E-mail address: [email protected] (S.A. Ghorashi).
contains two fundamental regions namely ionization region and drift region as can be seen in Fig. 1. After ionizing chemical compounds in ionization region, the ion swarms are introduced into the drift tube through an electrical shutter grid which allows ion swarms to pass periodically. In the drift region, the ion swarms attain constant velocities through the homogenous electric field, called the drift velocity, and begin to separate according to their mobilities (Fig. 1). This process is done at ambient pressure in a gas usually air. Ions are neutralized upon collision with the detector at the end of drift region, causing an ionic current with a very small magnitude in the range from below pA to nA, which is amplified and converted into voltage. The total time of travel called drift time is a function of the detector length, electric field strength, drift gas (i.e., air or pure nitrogen), temperature, and atmospheric pressure. A plot of the detector response versus drift time is called a mobility spectrum and contains all the information provided by a mobility measurement. The ionization process in IMS is competitive and might lead to suppression of analytes in mixtures, as the species with higher electron or proton affinities are preferentially ionized. This
http://dx.doi.org/10.1016/j.mejo.2014.07.007 0026-2692/& 2014 Elsevier Ltd. All rights reserved.
Please cite this article as: S.A. Ghorashi, et al., Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals, Microelectron. J (2014), http://dx.doi.org/10.1016/j.mejo.2014.07.007i
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S.A. Ghorashi et al. / Microelectronics Journal ∎ (∎∎∎∎) ∎∎∎–∎∎∎
competitiveness also results in difficulties to quantify analytes, and usually stand-alone IMS is semi-quantitative at best. Therefore, stand-alone IMS is not ideally suited for the analysis of multicomponent mixtures or of analytes in complex matrices. Gas chromatography (GC) is probably the most widely known, used and accepted technique for the analysis of mixtures of volatile and semi-volatile chemicals. It is well suited for complex mixtures and samples in complex matrices, and is reasonably fast [20–22]. There are two possible methods to connect a capillary column to a mobility spectrometer including the attachment of the column to the drift tube on axis to the ion source with a concentric inner tube or through the side of the drift tube. Each method has its own advantages and drawbacks. The modern age for mobility measurement after capillary gas chromatography can be tracked back to the work of Baim and Hill [23]. IMS was further demonstrated as a
Fig. 1. Drift tube for ion mobility spectrometry. A voltage gradient is applied to the ions, as illustrated by the triangle. (A) Two different samples are introduced into the ionization region of the IMS instrument. (B) The two samples are ionized. (C) The ions enter the drift region when the shutter grid opens, flowing against the drift gas to reach the detector. Neutral samples do not enter the drift region.
selective and nonselective detector for GC in another report by Baim et al. [24]. DeBono et al. developed GC–IMS technique for separation of different groups of mixtures such as narcotics, explosives, lutidine isomers and pesticides [25,26]. Also in another study by them, rapid analyses of pesticides on imported fruits were performed by GC–IMS [27]. It is often desirable to gain instant information about the samples, either to draw direct analytical conclusions or to make a decision on whether more information is needed. In these cases, a method of generating analytical data immediately and at the site of the suspected problem would be of great value. On site analysis in real time or near real time is often referred to as field screening or field monitoring. The advantage of field screening with respect to time, money and personnel are obvious. The goal of developing field deployable analytical instruments is not to replace laboratory instruments, but to provide a method to increase the efficiency of sampling and laboratory analysis. Idealistically, a field screening instrument providing instantaneous sample information with high signal to noise ratio and resolution has the ability to identify and quantify analytes [28–30]. A prominent obstacle for developing GC–IMS as a field screening instrument would be sample preparation process especially for solid samples. The main aims are to increase sample throughput, improve the overall quality of the sample preparation procedures and decrease the required sample sizes, the use of organic solvents and sorbents, as well as the amount of waste. Thermal Solid Sample Introduction (TSSI) module, can be used as a fast and effective injection system to gas chromatograph without preparation of samples in laboratory. Fig. 2 depicts the configuration of TSSI–GC–IMS gas sensor for the determination of the complex mixtures of compounds. As mentioned before, because of the low duty cycle of the shutter grid ( 1%), the ionic current of IMS is very small in magnitude. Utilizing GC as an introduction system for IMS can exacerbate this problem. This reality results in a very small signalto-noise ratio (SNR) for the hyphenated analytical method (GC– IMS). At first glance, increasing the duty cycle can elevate the use of available ions and SNR; on the other hand, it degrades the resolution of IMS peak. One of the most prevalent methods to increase the SNR is to repeat a measurement many times and add all measurements which is called signal averaging (SA). In SA-IMS, the SNR response to the square root of the number of averages is linear. Another attempt was made with adding a gate at the end of the IMS cell that is called moving second gate (MSG) method. This combination of gates is able to pass ions into the enclosed drift region or to the collector as desired [23]. The restricted duty cycle of the shutter grid in the mentioned methods causes the inefficient use of available ions which contribute to the IMS signal. To overcome this problem and to
Fig. 2. The principal components of an IMS detector interfaced to a capillary gas chromatograph and thermal solid sample introduction system.
Please cite this article as: S.A. Ghorashi, et al., Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals, Microelectron. J (2014), http://dx.doi.org/10.1016/j.mejo.2014.07.007i
S.A. Ghorashi et al. / Microelectronics Journal ∎ (∎∎∎∎) ∎∎∎–∎∎∎
improve duty cycle, mathematical transforms were added to IMS hardware. Fourier Transform (FT) was the first experimental method which applied to a two-gate IMS to eliminate the noise and greatly improve spectral resolution [31]. The theoretical duty cycle of the FT-IMS would be about 25%; which should result in an enhancement factor of 5 for SNR, although the experiments revealed a 3-fold SNR improvement [32,33]. Later developments replaced the physical gate with an electronic external exit gate [34]. Overall, the experimental results of this method revealed the improved resolution and increased SNR compared to both the SAIMS and the FT-IMS designs. A more sophisticated technique for measuring ion mobility spectra can be feasible by impressing ion flow with a Temporal Switching Manner (TSM). By means of mathematical deconvolution like Hadamard Transform (HT), the signal can be analyzed and the unknown analyte can be recognized. An HT-IMS needs a hardware to apply a pseudorandom binary sequence to the ion gate, and then the encrypted raw data related to this sequence is collected and analyzed by entangled algorithms. The use of this semi-continuous technique can provide a roughly 5-fold SNR enhancement with a negligible reduction in resolution [35,36]. Notwithstanding, each of the aforementioned methods has its own pros and cons, the potential disadvantages of them including time consuming measurements, unnecessarily complicated and abstruse hardware, as well as high-throughput functions limit the attractiveness of these techniques for online spectrum measurements. In the present work an attempt is made to design and construct a home-made capillary GC for separation of mixtures of compounds with fast thermal behavior and minimal thermal degradation. Also a low flow IMS has designed for interfacing it to GC as a chemical gas detector. Moreover, an automated TSSI system constructed and coupled with system to form a TSSI–GC–IMS instrument which is well qualified for field screening applications. In addition, operative methods for removing noise and increasing SNR, while keeping hardware simplicity, for online monitoring would be presented.
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2. Methodology 2.1. Thermal solid sample introduction The TSSI consists of a sliding tray for placing a filter and desorber to volatilize the sample into a GC column through a heated six port valve. A preconcentrator sample loop traps volatile and non-volatile substances during the desorption cycle and release them by resistive heating into the analytical column. This method can be used to introduce volatile, semi-volatile and non-volatile organic compounds separately through vaporization of solid samples. TSSI provides a fast and easily applied method for direct detection with no additional solid sample preparation or extraction. 2.2. Fast gas chromatograph The goal of faster GC is to obtain the information required from a certain sample in a shorter time. Because of the limitations of narrow-bore columns, mega bore capillary columns (internal diameter 0.53 mm) are suitable for fast GC. Faster temperature programming is an attractive option for speeding up separations of fast GC. In faster chromatography, more peaks are generated per unit of time. 2.3. Ion mobility spectrometry The data regarded in this work were obtained by a home-made IMS which has been designed with a radioactive 63Ni source (St. Petersburg, Russia) in its ionization chamber. The device comprises the parts of a regular ion mobility spectrometer, namely, an ionization chamber, a shutter-grid, a separation chamber, an ion collector and voltage generators. A negative self-designed high voltage power supply provides the homogenous electric field in drift tube with the strength of
Fig. 3. Complete display of GC–IMS signal: (a) IMS spectrum, (b) chromatogram, and (c) related heatmap for raw data including drift time and retention time as the horizontal and vertical axes respectively.
Please cite this article as: S.A. Ghorashi, et al., Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals, Microelectron. J (2014), http://dx.doi.org/10.1016/j.mejo.2014.07.007i
S.A. Ghorashi et al. / Microelectronics Journal ∎ (∎∎∎∎) ∎∎∎–∎∎∎
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250 V/cm. The control system was implemented through sets of temperature and pressure controllers as well as flow meters (Delta Electronics, Autonics and Dwyer Instruments). The separated ions reach a Faraday plate of 35 mm in diameter and the signal is delivered to the computer. A data acquisition and analysis software were used for acquiring the signal of ions, as well as categorizing, denoising, analyzing and displaying them in an effective way. In a gas chromatograph column, mixture of compounds would be separated according to boiling points differences and polarities. Thus each compound leaves the column at a different time calling retention time. Plotting the intensity of IMS output versus drift time along with total ion versus retention time will provide a two dimensional plot and as a result the detection process would be performed more precisely (Fig. 3). 2.4. Noise reduction algorithms Two types of SA methods have been applied to raw data collected from a GC–IMS instrument to promote the clarity of online signal by minimizing its noise. In the common SA method, individual scanned signals (yk) are gathered and then summed with each other. That is " # yðt i Þ ¼
N
∑ yk ðt i Þ =N
k¼1
ð1Þ
where yk(ti) is the registered value of spectrum number k in time ti and N is the number of spectra which are contributed in averaging. This procedure would be executed successively and the oldest spectrum in the averaging process would be replaced with the new one before refreshing the monitored data in each stage. Indeed, the window of averaging has a constant length of N, but it moves along the spectra stream (Floating Window). Only in the first step of the FW-SA method, the waiting time for accumulating data is in the range of time needed for collecting N spectra, but for the other steps it is only the time of one spectrum. Because of this unique feature in the FW-SA method, the monitored signal would be refreshed in the range of milli seconds which is fast enough to achieve high-resolution chromatography. Another type of averaging, that is moving window averaging (WA), has been used in this research in an exclusive format. In the common WA method, the mean value of (2M þ1) data surrounding a referred point would be calculated. Namely " # yðt i Þ ¼
M
∑
j ¼ M
yðt i þ j Þ =ð2M þ 1Þ
mean value of the signal to the root-mean-square noise. This measurement is essentially empirical, and involves dividing the height of a relevant signal by the root-mean-square of the noise. However, in quantitative analyses, the area surrounded by the peak is a key parameter to estimate the concentration and power of the peak. Consequently, this work has been organized to use the ratio between peak area and nonpeak as an appropriate factor to calculate the SNR.
3. Results and discussion 3.1. Experimental data 3.1.1. TSSI–GC–IMS TSSI–GC–IMS used in this work has been made in Institute of Materials and Energy, Isfahan, Detector group. Fig. 4 reveals the constructed device which satisfies the requisites for a realistic and useful field screening instrument. 3.1.2. TSSI–GC–IMS results for mixture of pesticides A mixture of three pesticides including BHC, parathion and chlordane was introduced to the TSSI–GC–IMS system. The chemical sensor was operated in negative ion mode with a drift tube temperature and flow of 130 1C and 100 ml/min respectively. The TSSI operating conditions were as follows: desorber temperature 225 1C, transfer line temperature from desorber to valve 200 1C, valve temperature 200 1C, transfer line temperature from valve to GC 200 1C, and loop heating time 60 s. The GC temperature was isothermal in 175 1C. The resultant chromatogram has been shown in Fig. 5. As can be seen there are three peaks related to the analyzed pesticides, the first one for BHC, the second for parathion and the third for chlordane.
ð2Þ
where M defines the number of data before and after the referred point (y(ti)) which are contributed in averaging process. To develop this method, the following steps have been applied in the implementation of the SNR enhancement approach. First, the microstructure analysis of GC–IMS raw data would be scrutinized to understand the frequencies of noise repetition. Second, a multifunction moving window averaging (M-WA) algorithm is performed according to the temporal repetitio ns of noise explored from microstructure analysis. Third, an exceptional formula is applied to reinforce the peak magnitude and consequently boost the SNR. Finally, the standard moving window averaging is carried out.
Fig. 4. Commercial TSSI–GC–IMS made in Institute of Materials and Energy, Isfahan, Detector group.
2.5. Signal-to-noise ratio considerations Noise is often a limiting factor in the precision of instrumental measurements. Because of this influential effect, reducing the relative amount of noise or increasing the signal-to-noise ratio (SNR) is the eminent goal in the optimization of experimental variables. Generally, SNR has been defined as the proportion of the
Fig. 5. GC spectrum resulted from a mixture of pesticides.
Please cite this article as: S.A. Ghorashi, et al., Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals, Microelectron. J (2014), http://dx.doi.org/10.1016/j.mejo.2014.07.007i
S.A. Ghorashi et al. / Microelectronics Journal ∎ (∎∎∎∎) ∎∎∎–∎∎∎
Each peak in the GC spectrum provides a particular IMS spectrum and the combination of them give the capability of two dimensional identification of complex mixtures. The IMS spectra for these three pesticides have been shown in Fig. 6. On balance, with regard to the aforementioned experimental data, the potential of the constructed chemical sensor for detection of complex mixtures can be proved significantly. 3.2. Noise analysis For implementing a precise investigation of noise analysis, we focus on drift times between 14 ms and 20 ms. In this time interval we can find all of the effective noises that can disturb IMS signal analysis. Fig. 7 shows the raw data of GC–IMS signal in the mentioned time. We examined the raw data accurately and find a simple but effective way to remove the conquering noises in 15–16 ms drift times. In favor of microstructural analysis, we discovered that there is a noise which has a repetition frequency of 26 samples. This can be removed by averaging the samples with 26 distances. The results of this averaging can be seen in Fig. 7 in which the powerful noise has been reduced greatly (red spectrum). It is clear that this averaging method only removes the low frequency powerful noises and for removing the high frequency noises we need another method which is window averaging. Effectiveness of window averaging method depends on the window width and with selecting an optimum value for window width we can achieve a smooth signal. The combination of these two averaging methods can be recognized as a multifunction algorithm for removing low and high frequency noises and we have named it multifunction moving window averaging (M-WA) algorithm which has been implemented in Fig. 7 (black spectrum). 3.3. Online monitoring For evaluating the efficacy of our noise removing methods for online processing, drift time spectra measured with our home-made
5
GC–IMS were studied. The results of particular procedure used for processing of the IMS spectra in online monitoring are shown in Fig. 8. Online monitoring is one of the critical points in implementation of chemical detectors like IMS system. It needs to be fast, precise and reliable. The M-WA is a fast method and requires less computation time when it is compared with FW-SA procedure. The mentioned characteristics are unique enough to induce higher interest in utilizing this method in our study. On the other hand, for solving the problem of reducing the amplitude of the peak, an exceptional formula was applied in this case to strengthen the peak magnitude and finally make considerable improvement in SNR. In Fig. 9 we can find four spectra, the extremely noisy one is raw data (the blue spectrum) and the diagram which has been drawn on it, is the IMS measurement after M-WA method (the red spectrum). The last and perhaps the most exclusive part of noise reduction algorithm in this study would be the empowering of peak magnitude and decreasing the amplitude of noise. This can be done by powering
Fig. 7. Raw data of GC–IMS signal and the results of averaging method for removing noise; raw data (blue), effect of removing low frequency noise (red), effect of removing low and high frequency noise (black). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 6. IMS spectra for the analyzed pesticides.
Please cite this article as: S.A. Ghorashi, et al., Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals, Microelectron. J (2014), http://dx.doi.org/10.1016/j.mejo.2014.07.007i
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was performed according to the results of microstructural analysis. The last and perhaps the most exclusive part of noise reduction algorithm in this study was done by powering the M-WA output signal and normalizing which causes the empowering of peak magnitude and decreasing the amplitude of noise. Utilizing this procedure reduces the signal fluctuation considerably as well as facilitating the quantitative analysis.
References
Fig. 8. The results of M-WA method used for processing of the IMS spectra in online monitoring; raw data (blue), applying M-WA method (red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 9. The results of particular procedures and the exclusive noise reduction algorithm. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)
the M-WA output signal and normalizing it, that is yðt i Þ ¼ ½yM WA ðt i Þn =4095ðn 1Þ
ð3Þ
The denominator depends on the DAQ card resolution; in our work we have a 12 bit resolution A/D, therefore we write 4095 in the formula. The power n defines a threshold level in which the data level behind it would be weakened and the data above it would be magnified. Finally, for smoothing the denoised signal a standard moving window averaging with window width of 9 has been used. The resultant would be a great denoised spectrum which is completely reliable (the black spectrum in Fig. 9). It needs to be noticed that all of these calculations can be done quick enough to have an online monitoring of the IMS signal and consequently achieving a highresolution chromatography in the hyphenated GC–IMS technique.
4. Conclusion The designed and constructed TSSI–GC–IMS showed its potential to be used as a powerful system for field screening of organic samples. Inasmuch as the IMS signal is prone to be influenced by noise, utilizing an effective technique for denoising and improving SNR is an indispensable part of this analytical instrument. By applying a powerful computational technique in our work, the output would be an extremely low noise signal in contrast with the raw data. The proposed strategy uses a combination of different techniques for smoothing and denoising by means of various kinds of signal averaging methods. First of all, the frequency of noise repetition was identified by microstructural analysis of GC–IMS raw data. Then the M-WA algorithm which is formed by various types of signal averaging
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Please cite this article as: S.A. Ghorashi, et al., Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals, Microelectron. J (2014), http://dx.doi.org/10.1016/j.mejo.2014.07.007i
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Microelectronics Journal journal homepage: www.elsevier.com/locate/mejo
Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals Seyed Alireza Ghorashi n, Amir Hossein Alinoori, Saeed Hajialigol Detector Group, Institute of Materials and Energy, Iranian Space Research Center, Isfahan, Iran
art ic l e i nf o
a b s t r a c t
Article history: Received 25 December 2013 Received in revised form 16 July 2014 Accepted 24 July 2014
Over the years, Ion Mobility Spectrometry (IMS) which refers to the techniques and cutting-edge instruments for characterizing of analytes by their gas phase mobility has been gaining popularity and validity among scientific researchers for detecting chemicals. This novel gas sensor with its high analytical speed, low detection limits, ease of use and ruggedness during transport has also become the dominant commercial technology in different industries. In spite of these paramount advantages, this detector has difficulty identifying matrix compounds. To overcome this problem a Gas Chromatograph (GC) can be used to introduce individual components of mixture into an IMS. The output signal of the hyphenated GC–IMS method is an extraordinary small, time-dependent current produced by mobile ions in atmospheric pressure. To exploit the qualitative and quantitative information hidden in this sensitive noisy signal, various signal processing methods have been nominated for spectrum filtration and improving signal-to-noise ratio (SNR). In the present paper an attempt is made to design and construct a GC–IMS instrument coupled with a Thermal Solid Sample Introduction (TSSI) module for injection of solid samples to capillary GC column. Moreover, various spectrum filtration methods for removing noise from GC–IMS signal would be investigated. The GC–IMS instrument used in this research was designed and constructed in Institute of Materials and Energy, Isfahan, Detector group and can be used as a chemical sensor for rapid detection of a broad range of chemical mixtures in many operational environments including on-board Volatile Organic Analyzer Sensor (VOAS) in space missions. The capability of constructed sensor for detection of complex mixtures has been proved by analyzing a mixture of three pesticides as test materials. Our proposed method for removing noise from the realtime TSSI–GC–IMS signal will be presented and its efficacy will be proved by offering real experiments. & 2014 Elsevier Ltd. All rights reserved.
Keywords: Chemical sensor Two dimensional Thermal solid sample introduction Gas chromatography–ion mobility spectrometry Computational methods Signal-to-noise ratio
1. Introduction Ion Mobility Spectrometry (IMS) as a popular and valid instrument has substantiated its potential to be a fast, sensitive, rugged and easily used gas sensor for detection of trace amounts of organic compounds [1–3]. The unique advantages of IMS make it a dominant commercial technology in different industries including on-site detection of explosives, drugs and warfare agents [4–9], bacteria and medical applications [10,11], pesticides in agriculture products [12,13], environmental monitoring [14], process control [15], rocket fuel leak detection [16], analyzing of wood species [17], and last, but perhaps the most interesting one is in both manned and robotic space explorations [18,19]. IMS is a gas-phase sensor that allows chemicals to be distinguished on the basis of their mass, charge, size and shape. It n Correspondence to: Institute of Materials and Energy, Iranian Space Research Center, 7th Km Imam Khomeini Street, P.O. Box: 81955/174, Isfahan, Iran. Tel.: þ 98 311 33222439; fax: þ 98 311 33222446. E-mail address: [email protected] (S.A. Ghorashi).
contains two fundamental regions namely ionization region and drift region as can be seen in Fig. 1. After ionizing chemical compounds in ionization region, the ion swarms are introduced into the drift tube through an electrical shutter grid which allows ion swarms to pass periodically. In the drift region, the ion swarms attain constant velocities through the homogenous electric field, called the drift velocity, and begin to separate according to their mobilities (Fig. 1). This process is done at ambient pressure in a gas usually air. Ions are neutralized upon collision with the detector at the end of drift region, causing an ionic current with a very small magnitude in the range from below pA to nA, which is amplified and converted into voltage. The total time of travel called drift time is a function of the detector length, electric field strength, drift gas (i.e., air or pure nitrogen), temperature, and atmospheric pressure. A plot of the detector response versus drift time is called a mobility spectrum and contains all the information provided by a mobility measurement. The ionization process in IMS is competitive and might lead to suppression of analytes in mixtures, as the species with higher electron or proton affinities are preferentially ionized. This
http://dx.doi.org/10.1016/j.mejo.2014.07.007 0026-2692/& 2014 Elsevier Ltd. All rights reserved.
Please cite this article as: S.A. Ghorashi, et al., Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals, Microelectron. J (2014), http://dx.doi.org/10.1016/j.mejo.2014.07.007i
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competitiveness also results in difficulties to quantify analytes, and usually stand-alone IMS is semi-quantitative at best. Therefore, stand-alone IMS is not ideally suited for the analysis of multicomponent mixtures or of analytes in complex matrices. Gas chromatography (GC) is probably the most widely known, used and accepted technique for the analysis of mixtures of volatile and semi-volatile chemicals. It is well suited for complex mixtures and samples in complex matrices, and is reasonably fast [20–22]. There are two possible methods to connect a capillary column to a mobility spectrometer including the attachment of the column to the drift tube on axis to the ion source with a concentric inner tube or through the side of the drift tube. Each method has its own advantages and drawbacks. The modern age for mobility measurement after capillary gas chromatography can be tracked back to the work of Baim and Hill [23]. IMS was further demonstrated as a
Fig. 1. Drift tube for ion mobility spectrometry. A voltage gradient is applied to the ions, as illustrated by the triangle. (A) Two different samples are introduced into the ionization region of the IMS instrument. (B) The two samples are ionized. (C) The ions enter the drift region when the shutter grid opens, flowing against the drift gas to reach the detector. Neutral samples do not enter the drift region.
selective and nonselective detector for GC in another report by Baim et al. [24]. DeBono et al. developed GC–IMS technique for separation of different groups of mixtures such as narcotics, explosives, lutidine isomers and pesticides [25,26]. Also in another study by them, rapid analyses of pesticides on imported fruits were performed by GC–IMS [27]. It is often desirable to gain instant information about the samples, either to draw direct analytical conclusions or to make a decision on whether more information is needed. In these cases, a method of generating analytical data immediately and at the site of the suspected problem would be of great value. On site analysis in real time or near real time is often referred to as field screening or field monitoring. The advantage of field screening with respect to time, money and personnel are obvious. The goal of developing field deployable analytical instruments is not to replace laboratory instruments, but to provide a method to increase the efficiency of sampling and laboratory analysis. Idealistically, a field screening instrument providing instantaneous sample information with high signal to noise ratio and resolution has the ability to identify and quantify analytes [28–30]. A prominent obstacle for developing GC–IMS as a field screening instrument would be sample preparation process especially for solid samples. The main aims are to increase sample throughput, improve the overall quality of the sample preparation procedures and decrease the required sample sizes, the use of organic solvents and sorbents, as well as the amount of waste. Thermal Solid Sample Introduction (TSSI) module, can be used as a fast and effective injection system to gas chromatograph without preparation of samples in laboratory. Fig. 2 depicts the configuration of TSSI–GC–IMS gas sensor for the determination of the complex mixtures of compounds. As mentioned before, because of the low duty cycle of the shutter grid ( 1%), the ionic current of IMS is very small in magnitude. Utilizing GC as an introduction system for IMS can exacerbate this problem. This reality results in a very small signalto-noise ratio (SNR) for the hyphenated analytical method (GC– IMS). At first glance, increasing the duty cycle can elevate the use of available ions and SNR; on the other hand, it degrades the resolution of IMS peak. One of the most prevalent methods to increase the SNR is to repeat a measurement many times and add all measurements which is called signal averaging (SA). In SA-IMS, the SNR response to the square root of the number of averages is linear. Another attempt was made with adding a gate at the end of the IMS cell that is called moving second gate (MSG) method. This combination of gates is able to pass ions into the enclosed drift region or to the collector as desired [23]. The restricted duty cycle of the shutter grid in the mentioned methods causes the inefficient use of available ions which contribute to the IMS signal. To overcome this problem and to
Fig. 2. The principal components of an IMS detector interfaced to a capillary gas chromatograph and thermal solid sample introduction system.
Please cite this article as: S.A. Ghorashi, et al., Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals, Microelectron. J (2014), http://dx.doi.org/10.1016/j.mejo.2014.07.007i
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improve duty cycle, mathematical transforms were added to IMS hardware. Fourier Transform (FT) was the first experimental method which applied to a two-gate IMS to eliminate the noise and greatly improve spectral resolution [31]. The theoretical duty cycle of the FT-IMS would be about 25%; which should result in an enhancement factor of 5 for SNR, although the experiments revealed a 3-fold SNR improvement [32,33]. Later developments replaced the physical gate with an electronic external exit gate [34]. Overall, the experimental results of this method revealed the improved resolution and increased SNR compared to both the SAIMS and the FT-IMS designs. A more sophisticated technique for measuring ion mobility spectra can be feasible by impressing ion flow with a Temporal Switching Manner (TSM). By means of mathematical deconvolution like Hadamard Transform (HT), the signal can be analyzed and the unknown analyte can be recognized. An HT-IMS needs a hardware to apply a pseudorandom binary sequence to the ion gate, and then the encrypted raw data related to this sequence is collected and analyzed by entangled algorithms. The use of this semi-continuous technique can provide a roughly 5-fold SNR enhancement with a negligible reduction in resolution [35,36]. Notwithstanding, each of the aforementioned methods has its own pros and cons, the potential disadvantages of them including time consuming measurements, unnecessarily complicated and abstruse hardware, as well as high-throughput functions limit the attractiveness of these techniques for online spectrum measurements. In the present work an attempt is made to design and construct a home-made capillary GC for separation of mixtures of compounds with fast thermal behavior and minimal thermal degradation. Also a low flow IMS has designed for interfacing it to GC as a chemical gas detector. Moreover, an automated TSSI system constructed and coupled with system to form a TSSI–GC–IMS instrument which is well qualified for field screening applications. In addition, operative methods for removing noise and increasing SNR, while keeping hardware simplicity, for online monitoring would be presented.
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2. Methodology 2.1. Thermal solid sample introduction The TSSI consists of a sliding tray for placing a filter and desorber to volatilize the sample into a GC column through a heated six port valve. A preconcentrator sample loop traps volatile and non-volatile substances during the desorption cycle and release them by resistive heating into the analytical column. This method can be used to introduce volatile, semi-volatile and non-volatile organic compounds separately through vaporization of solid samples. TSSI provides a fast and easily applied method for direct detection with no additional solid sample preparation or extraction. 2.2. Fast gas chromatograph The goal of faster GC is to obtain the information required from a certain sample in a shorter time. Because of the limitations of narrow-bore columns, mega bore capillary columns (internal diameter 0.53 mm) are suitable for fast GC. Faster temperature programming is an attractive option for speeding up separations of fast GC. In faster chromatography, more peaks are generated per unit of time. 2.3. Ion mobility spectrometry The data regarded in this work were obtained by a home-made IMS which has been designed with a radioactive 63Ni source (St. Petersburg, Russia) in its ionization chamber. The device comprises the parts of a regular ion mobility spectrometer, namely, an ionization chamber, a shutter-grid, a separation chamber, an ion collector and voltage generators. A negative self-designed high voltage power supply provides the homogenous electric field in drift tube with the strength of
Fig. 3. Complete display of GC–IMS signal: (a) IMS spectrum, (b) chromatogram, and (c) related heatmap for raw data including drift time and retention time as the horizontal and vertical axes respectively.
Please cite this article as: S.A. Ghorashi, et al., Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals, Microelectron. J (2014), http://dx.doi.org/10.1016/j.mejo.2014.07.007i
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250 V/cm. The control system was implemented through sets of temperature and pressure controllers as well as flow meters (Delta Electronics, Autonics and Dwyer Instruments). The separated ions reach a Faraday plate of 35 mm in diameter and the signal is delivered to the computer. A data acquisition and analysis software were used for acquiring the signal of ions, as well as categorizing, denoising, analyzing and displaying them in an effective way. In a gas chromatograph column, mixture of compounds would be separated according to boiling points differences and polarities. Thus each compound leaves the column at a different time calling retention time. Plotting the intensity of IMS output versus drift time along with total ion versus retention time will provide a two dimensional plot and as a result the detection process would be performed more precisely (Fig. 3). 2.4. Noise reduction algorithms Two types of SA methods have been applied to raw data collected from a GC–IMS instrument to promote the clarity of online signal by minimizing its noise. In the common SA method, individual scanned signals (yk) are gathered and then summed with each other. That is " # yðt i Þ ¼
N
∑ yk ðt i Þ =N
k¼1
ð1Þ
where yk(ti) is the registered value of spectrum number k in time ti and N is the number of spectra which are contributed in averaging. This procedure would be executed successively and the oldest spectrum in the averaging process would be replaced with the new one before refreshing the monitored data in each stage. Indeed, the window of averaging has a constant length of N, but it moves along the spectra stream (Floating Window). Only in the first step of the FW-SA method, the waiting time for accumulating data is in the range of time needed for collecting N spectra, but for the other steps it is only the time of one spectrum. Because of this unique feature in the FW-SA method, the monitored signal would be refreshed in the range of milli seconds which is fast enough to achieve high-resolution chromatography. Another type of averaging, that is moving window averaging (WA), has been used in this research in an exclusive format. In the common WA method, the mean value of (2M þ1) data surrounding a referred point would be calculated. Namely " # yðt i Þ ¼
M
∑
j ¼ M
yðt i þ j Þ =ð2M þ 1Þ
mean value of the signal to the root-mean-square noise. This measurement is essentially empirical, and involves dividing the height of a relevant signal by the root-mean-square of the noise. However, in quantitative analyses, the area surrounded by the peak is a key parameter to estimate the concentration and power of the peak. Consequently, this work has been organized to use the ratio between peak area and nonpeak as an appropriate factor to calculate the SNR.
3. Results and discussion 3.1. Experimental data 3.1.1. TSSI–GC–IMS TSSI–GC–IMS used in this work has been made in Institute of Materials and Energy, Isfahan, Detector group. Fig. 4 reveals the constructed device which satisfies the requisites for a realistic and useful field screening instrument. 3.1.2. TSSI–GC–IMS results for mixture of pesticides A mixture of three pesticides including BHC, parathion and chlordane was introduced to the TSSI–GC–IMS system. The chemical sensor was operated in negative ion mode with a drift tube temperature and flow of 130 1C and 100 ml/min respectively. The TSSI operating conditions were as follows: desorber temperature 225 1C, transfer line temperature from desorber to valve 200 1C, valve temperature 200 1C, transfer line temperature from valve to GC 200 1C, and loop heating time 60 s. The GC temperature was isothermal in 175 1C. The resultant chromatogram has been shown in Fig. 5. As can be seen there are three peaks related to the analyzed pesticides, the first one for BHC, the second for parathion and the third for chlordane.
ð2Þ
where M defines the number of data before and after the referred point (y(ti)) which are contributed in averaging process. To develop this method, the following steps have been applied in the implementation of the SNR enhancement approach. First, the microstructure analysis of GC–IMS raw data would be scrutinized to understand the frequencies of noise repetition. Second, a multifunction moving window averaging (M-WA) algorithm is performed according to the temporal repetitio ns of noise explored from microstructure analysis. Third, an exceptional formula is applied to reinforce the peak magnitude and consequently boost the SNR. Finally, the standard moving window averaging is carried out.
Fig. 4. Commercial TSSI–GC–IMS made in Institute of Materials and Energy, Isfahan, Detector group.
2.5. Signal-to-noise ratio considerations Noise is often a limiting factor in the precision of instrumental measurements. Because of this influential effect, reducing the relative amount of noise or increasing the signal-to-noise ratio (SNR) is the eminent goal in the optimization of experimental variables. Generally, SNR has been defined as the proportion of the
Fig. 5. GC spectrum resulted from a mixture of pesticides.
Please cite this article as: S.A. Ghorashi, et al., Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals, Microelectron. J (2014), http://dx.doi.org/10.1016/j.mejo.2014.07.007i
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Each peak in the GC spectrum provides a particular IMS spectrum and the combination of them give the capability of two dimensional identification of complex mixtures. The IMS spectra for these three pesticides have been shown in Fig. 6. On balance, with regard to the aforementioned experimental data, the potential of the constructed chemical sensor for detection of complex mixtures can be proved significantly. 3.2. Noise analysis For implementing a precise investigation of noise analysis, we focus on drift times between 14 ms and 20 ms. In this time interval we can find all of the effective noises that can disturb IMS signal analysis. Fig. 7 shows the raw data of GC–IMS signal in the mentioned time. We examined the raw data accurately and find a simple but effective way to remove the conquering noises in 15–16 ms drift times. In favor of microstructural analysis, we discovered that there is a noise which has a repetition frequency of 26 samples. This can be removed by averaging the samples with 26 distances. The results of this averaging can be seen in Fig. 7 in which the powerful noise has been reduced greatly (red spectrum). It is clear that this averaging method only removes the low frequency powerful noises and for removing the high frequency noises we need another method which is window averaging. Effectiveness of window averaging method depends on the window width and with selecting an optimum value for window width we can achieve a smooth signal. The combination of these two averaging methods can be recognized as a multifunction algorithm for removing low and high frequency noises and we have named it multifunction moving window averaging (M-WA) algorithm which has been implemented in Fig. 7 (black spectrum). 3.3. Online monitoring For evaluating the efficacy of our noise removing methods for online processing, drift time spectra measured with our home-made
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GC–IMS were studied. The results of particular procedure used for processing of the IMS spectra in online monitoring are shown in Fig. 8. Online monitoring is one of the critical points in implementation of chemical detectors like IMS system. It needs to be fast, precise and reliable. The M-WA is a fast method and requires less computation time when it is compared with FW-SA procedure. The mentioned characteristics are unique enough to induce higher interest in utilizing this method in our study. On the other hand, for solving the problem of reducing the amplitude of the peak, an exceptional formula was applied in this case to strengthen the peak magnitude and finally make considerable improvement in SNR. In Fig. 9 we can find four spectra, the extremely noisy one is raw data (the blue spectrum) and the diagram which has been drawn on it, is the IMS measurement after M-WA method (the red spectrum). The last and perhaps the most exclusive part of noise reduction algorithm in this study would be the empowering of peak magnitude and decreasing the amplitude of noise. This can be done by powering
Fig. 7. Raw data of GC–IMS signal and the results of averaging method for removing noise; raw data (blue), effect of removing low frequency noise (red), effect of removing low and high frequency noise (black). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 6. IMS spectra for the analyzed pesticides.
Please cite this article as: S.A. Ghorashi, et al., Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals, Microelectron. J (2014), http://dx.doi.org/10.1016/j.mejo.2014.07.007i
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was performed according to the results of microstructural analysis. The last and perhaps the most exclusive part of noise reduction algorithm in this study was done by powering the M-WA output signal and normalizing which causes the empowering of peak magnitude and decreasing the amplitude of noise. Utilizing this procedure reduces the signal fluctuation considerably as well as facilitating the quantitative analysis.
References
Fig. 8. The results of M-WA method used for processing of the IMS spectra in online monitoring; raw data (blue), applying M-WA method (red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 9. The results of particular procedures and the exclusive noise reduction algorithm. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)
the M-WA output signal and normalizing it, that is yðt i Þ ¼ ½yM WA ðt i Þn =4095ðn 1Þ
ð3Þ
The denominator depends on the DAQ card resolution; in our work we have a 12 bit resolution A/D, therefore we write 4095 in the formula. The power n defines a threshold level in which the data level behind it would be weakened and the data above it would be magnified. Finally, for smoothing the denoised signal a standard moving window averaging with window width of 9 has been used. The resultant would be a great denoised spectrum which is completely reliable (the black spectrum in Fig. 9). It needs to be noticed that all of these calculations can be done quick enough to have an online monitoring of the IMS signal and consequently achieving a highresolution chromatography in the hyphenated GC–IMS technique.
4. Conclusion The designed and constructed TSSI–GC–IMS showed its potential to be used as a powerful system for field screening of organic samples. Inasmuch as the IMS signal is prone to be influenced by noise, utilizing an effective technique for denoising and improving SNR is an indispensable part of this analytical instrument. By applying a powerful computational technique in our work, the output would be an extremely low noise signal in contrast with the raw data. The proposed strategy uses a combination of different techniques for smoothing and denoising by means of various kinds of signal averaging methods. First of all, the frequency of noise repetition was identified by microstructural analysis of GC–IMS raw data. Then the M-WA algorithm which is formed by various types of signal averaging
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Please cite this article as: S.A. Ghorashi, et al., Signal-to-noise enhancement in TSSI–GC–IMS: Development of two dimensional sensor for detection of chemicals, Microelectron. J (2014), http://dx.doi.org/10.1016/j.mejo.2014.07.007i