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Sensitivity and selectivity of surface acoustic wave sensors for organic solvent vapour detection
587
Sensors und Actuators B, 7 (1992) 587-591
Sensitivity and selectivity of surface acoustic wave sensors for organic solvent vapour detection D. Amati, D. Am, N. Blom, M. Ehrat, J. Saunois and H. M. Widmer Analytical Research, Ciba - Geigy Ltd., CH-4002
Base1 (Switzerland)
Abstract Surface acoustic wave (SAW) devices have been coated with 20 organic polymers of different chemical nature. Their sensitivities, selectivities and response times towards gaseous organic solvents such as toluene, acetone, ethanol, methanol and dichlorbmethane in dry air have been determined. The best sensitivities and selectivities are obtained for toluene vapours. In order to compensate for the lack of selectivity, a pattern-recognition technique (partial least-squares regression, PLS) is applied to sensor responses obtained from ternary mixtures of analytes. The predictive properties of the model are discussed.
1. Introduction The mechanism of operation and the suitability of surface acoustic wave (SAW) devices for determining low analyte concentrations in the gas phase are well established [ 11. Their low cost and high sensitivity make these sensors very attractive for monitoring gaseous analytes in the atmosphere or in exhaust air. Amongst the general performance characteristics of analytical systems, such as stability, response time and reproducibility, the sensitivity and selectivity are two important features to be considered. For SAW devices, sensitivity and selectivity are achieved by coating them with a substance that interacts physically or chemically with the analyte of interest. Several coatings were inspected in that respect. If the analyte undergoes physisorption, interactions between the gas and the interface material are weak. The response is reversible, but a low selectivity is obtained [2]. In general, if a chemical reaction occurs between the two partners, a good selectivity is achieved, and partially or totally irreversible responses are observed. From these considerations, it can be deduced that the use of SAW sensors as an efficient analytical tool is actually strongly limited by the lack of selectivity or reversibility of their response. A method for solving the problem of poor selectivity consists of using an array of sensors in conjunction with pattern-recognition software [ 31. Experimental responses for ternary mixtures of 0925-4005/92/$5.00
toluene, acetone and dichloromethane obtained from three different coated sensors were recorded. The partial least-squares (PLS) regression method [4] was applied to experimental data and the predictive properties of the mathematical model were tested with the set of data used for calibration.
2. Experimental 2.1. SA W devices and coating deposition SAW devices operating at a fundamental resonance frequency of 71.6 MHz were purchased from Xensor Integration (Delft, Netherlands). The devices were of the dual delay-line oscillator type [5]. This kind of arrangement permits the influences of analyte gas temperature and pressure on the sensor response to be eliminated. Solutions of commercially available polymers were dissolved in suitable @vents, e.g., acetone (0.2 mg/ml). They were then sprayed with an air brush through a mask onto the space between the set of electrodes of one delay line (sample line), the second delay line (reference line) being left bare. The resonance frequency decrease of the sample delay line due to the coating ranged from 37 to 88 kHz. For the determination of sensitivities and selectivities, analyte vapour concentrations in the range 120 to 1300 ppm were used. Organic solvents were purchased as purissimum grade and were used without any further purification. @ 1992 -
Elsevier
Sequoia.
All rights
reserved
588
2.2. Instrumentation and software 2.2.1. Analyte vapour generation
180
To compare the performance of coated S A W devices for our set o f analytes, a gas generator capable of delivering well-known concentrations of the analyte(s) in dry air (carrier gas) was used [5].
140 ~120 w
2.2.2. Temperature control and frequency counter To ensure reliable results, the temperature o f the measuring cell was kept constant at 30.0 °C with a precision o f 0.01 °C ( R E X - G 9 type temperature controller). A three-channel universal frequency counter was used to record the frequency change o f the two delay lines as well as the difference frequency. The individual frequencies were measured every 3 s for an interval o f 0.3 s each. The total time for a run was 12 min.
'
' ~o
' ~o
' ~o
' ~o
I
ldoo
I
lioo
Fig. 1. Sensor responses for different concentrations of toluene ( 0 ) , acetone ( • ) and dichloromethane ( • ) vapours; coating, poly(epichlorohydrin); straight lines are linear regressions performed on experimental data.
also programmed in-house and was run on a microcomputer.
2.2.3. Software The gas generator, t h e temperature-control device and the frequency counter used for measurements were controlled by a computer. Software for data acquisition and data evaluation as well as for the control of all peripherals was developed inhouse. The algorithm for PLS analysis o f data was
3. Results and discussion The sensitivities obtained for our sets o f analytes and coatings are summarized in Table 1.
T A B L E 1. Absolute sensitivities of the coatings at 30 °C Coating
Qea (kHz)
Analyte ACET DCM ( 10 -3 Hz/ppm/kHz)
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.
Ethylene/vinylacetate copolymer 1,2 polybutadiene Chiorosulfonated polyethylene Polyamide resin Poly(epiehlorohydrin) Poly(methylhydrogen siloxane) Glycerol monooleate Poly(1-butene) Di-(2-ethylhexyl) phthalate Styrene/butylmethacrylate copolymer Epoxidized linseed oil Polycarbonate resin Poly(ethylene oxide) Poly(caprolactone) diol Polysulfone resin Hydroxybutyl methyl cellulose Poly(vinyl formal) Styrene/acrylonitrile copolymer Poly(acrylic acid) Hexatriacontane
69.5 87.8 59.2 68.4 72.2 47.7 36.9 41.3 62.0 66.8 78.3 74.3 59.9 78.1 84.1 62.6 64.4 66.8 40.1 72.3
0.38 0.18 0.27 0.20 0.57 0.33 0.27 0.17 0.11 0.13 0 0.34 ~ 0.17 0.04 0 0.58 a 0 0.18 0 0
0.54 0.25 0.23 0.29 0.38 0.29 0.31 0.22 0.25 0.36 0.21 0.98 a 0.35 0.18 0.47 a 0.96 ~ 0.13 0.44 0 0
ETOH
MEOH
TOL
0.43 0.11 0 0.56 0.31 0.24 0.55 0.22 0.13 0.36 0 0.08 0.35 0.16 0.10 0.89 ~ 0.10 0.16 0 0
0 0 0 0.26 0.14 0 0.22 0 0.11 0 0 0 0 0 0 0.60 0 0 0 0
7.22 5.83 5.37 a 4.72 4.56 4.48 3.63 2.70 2.55 2.00 a 1.69 1.51 1.32 1.06 0.52 ~ 0.47 ~ 0.36 0.31 a 0 0
aQ¢, frequency shift due to the mass of the coating; ACET, acetone; D C M , dichloromethane; ETOH, ethanol; M E O H , methanol; TOL, toluene. The response time of the sensors w a s lower than 1 min except for the results indicated with an ~, for which a response time between 1 and 5 min w a s observed.
589
(4 100
@I
Fig. 2. Typical experimental results. (a) Response time smaller than 1 min (analyte, toluene; coating, ethylene/vinylacetate copolymer). (b) Response time between 1 and 5 min (analyte, toluene; coating, chlorosulfonated polyethylene) A, frequency difference; B, reference resonance frequency; C, sample resonance frequency; I”, temperature.. The hatch-marks at the bottom of the Figures indicate the times when the sensor was exposed to the analyte.
Absolute sensitivities expressed in Hz/ppm per kHz of deposited polymer are reported in order to compare the performance of the coatings on an absolute basis. The best absolute sensitivities are obtained for toluene. Some coatings exhibit similar sensitivities for acetone, dichloromethane, ethanol
and methanol as for toluene (coatings 13, 15, 18), but in most cases their sensitivities are smaller by one order of magnitude or more (coatings 1 - 11). Coating sensitivities based on the mean absolute sensitivity over the twenty coatings can be classified as follows: toluene % dichloromethane
590
methanol z acetone > methanol. As an example of the linear concentration response generally observed with our sensors, we list here data acquired with a sensor coated with poly(epichlorohydrin) for several concentrations of toluene, acetone and dichloromethane in the ranges 55 to 500 ppm, 300 to 1200 ppm and 150 to 600 ppm, respectively (Fig. 1). Linear regressions performed on these data for each analyte give correlation factors Y of 0.9999, 0.9986 and 0.9990, respectively, indicating a linear behaviour of the sensor. Response times are usually lower than one minute, but slower responses from one to five minutes are also observed. These two types of typical experimental results are illustrated in Fig. 2(a) and (b). Table 2 gives a summary of the best coating performances for the set of analytes used. Assuming a noise level of 0.5 Hz and a signal-to-noise ratio of four, 4 ppm of toluene can be detected. The highest detection
limit of 111 ppm is obtained for methanol. Table 1 shows clearly that exclusive selectivity for, one analyte is never achieved. This means that the accurate detection and identification of a single chemical vapour of interest in the presence of multiple known or unknown interferences using one single coated sensor is precluded. A promising approach to solving such analytical problems is the use of pattern-recognition techniques in conjunction with an array of sensors of varying selectivity. Amongst the different possible existing techniques like multiple linear regression (MLR), principal component analysis (PCA) and PLS, the PLS regression method was chosen because the algorithm used for the calculations is the most complete and elegant one when prediction is important [4]. Consequently, sensor responses obtained from devices coated with ethylene/vinylacetate copolymer, 1,2 polybutadiene and poly( epichlorohydrin)
TABLE 2. Best sensitivities and selectivities obtained at 30 “C Analyte
Maximum absolute sensitivity (IO-’ Hz/ppm/kHz)
Maximum sensitivity ( Hz/ppm)
Maximum selectivity”
Detection limitb (ppm)
Toluene Dichloromethane Ethanol Acetone Methanol
7.22’ 0.983 0.894 0.5g4 0.604
0.502 0.073 0.056 0.036 0.018
I:532 I:123 I: 45 I: 46 1: 1.3’
4 27 35 56 Ill
“The indicated selectivities are calculated by comparing the responses of the five analytes for one coating. Zero responses are not considered. bCalculated on the basis of a noise level of 0.5 Hz and a signal-to-noise ratio of four. ‘Ethylene/vinylacetate copolymer. ‘I,2 polybutadiene, selectivity of toluene over ethanol. 3Polycarbonate resin, selectivity of dichloromethane over ethanol. 4Hydroxybutyl methyl cellulose. 5Poly(caprolactone) dial, selectivity of ethanol over acetone. 6Polycarbonate resin, selectivity of acetone over ethanol. ‘Polyamide resin, selectivity of methanol over acetone.
TABLE 3. PLS predictive properties for standard mixtures of toluene, acetone and dichloromethane Mixture
Toluene
,. Dichloromethane
Acetone
Cst”
c pred
difs W)
c P=d (mm)
(W
CS, (mm)
C P=d (mm)
d@
(mm)
Cst (mm)
d@
(ppm)
2 3 4 5 6
44.6 48.5 75.7 87.5 309.2 498.6
39.6 50.1 78.3 88.3 310.6 497.5
-11.7 3.3 3.4 0.9 0.5 -0.2
595.9 89.8 1010.7 1169.3 551.6 889.5
639.7 72.8 991.8 1160.1 547.5 894.9
7.4 - 18.9 - 1.9 -0.8 -0.7 0.6
286.1 311.1 485.2 80.7 264.8 427.0
308. I 314.8 464.5 85.4 237.6 444.2
7.7 1.2 -4.3 5.8 - 10.3 4.0
(PRESS) “2 (ppm)
2.6
1
21.2
W)
18.2
%, c,r,d, concentration of analyte in standard and that predicted by PLS, respectively; diff, difference between the standard and the predicted concentration; (PRESS) ‘12, square root of the -prediction sum of squares for each analyte.
591
for ternary mixtures of toluene, acetone and dichloromethane were recorded and the PLS regression method was applied to the experimental data. The concentrations of each analyte used for calibration in the six standard mixtures are given in Table 3 as well as the values predicted by the built-up PLS model. Good predictive properties are observed. The maximum difference between standard an,d predicted values is found for acetone in the second mixture ( - 18.9%). The square root of the prediction sum of squares ((PRESS) ‘12),which is best for toluene (2.6ppm), is close to the detection limit for each analyte.
4. Conclusions Our results at the surface Good results in conjunction be attributed
confirm that adsorption of an analyte of a coating leads to poor selectivity. obtained by using an array of sensors with the PLS regression method can to data featuring low noise levels,
linear coating responses for a single component and a good additivity of responses when several analytes adsorb competitively on the different coatings. The use of an array of sensors and pattern-recognition techniques could be a good way to compensate for the lack of selectivity.
References 1 H. Wohltjen, Mechanism of operation and design considerations for surface acoustic wave device vapour sensors, Sensors and Actuators, 5 (1984) 307-325. 2 M. S. Nieuwenhuizen and A. W. Barendsz, Processes involved at the chemical interface of a SAW chemosensor, Sensors and Actuatow, II (1987) 45-62. 3 H. V. Shurmer, J. W. Gardner and P. Corcoran, Intelligent vapour discrimination using a composite l2-element sensor array, Sensors and Actuators, 4 P. Geladi
Bf (1990)
2X-260.
Partial least-squares regression: a tutorial, Anal. Chitn. Acra, 185 (1986) I 17. 5 D. Am, N. Blom, K. Dubler-Steudle, N. Graber and H. M. Widmer, Surface acoustic wave gas sensors: applications in the chemical industry, Sensors and Actuators A, 25-27 (1991) 395-397.
and B. R. Kowalski,
Sensors und Actuators B, 7 (1992) 587-591
Sensitivity and selectivity of surface acoustic wave sensors for organic solvent vapour detection D. Amati, D. Am, N. Blom, M. Ehrat, J. Saunois and H. M. Widmer Analytical Research, Ciba - Geigy Ltd., CH-4002
Base1 (Switzerland)
Abstract Surface acoustic wave (SAW) devices have been coated with 20 organic polymers of different chemical nature. Their sensitivities, selectivities and response times towards gaseous organic solvents such as toluene, acetone, ethanol, methanol and dichlorbmethane in dry air have been determined. The best sensitivities and selectivities are obtained for toluene vapours. In order to compensate for the lack of selectivity, a pattern-recognition technique (partial least-squares regression, PLS) is applied to sensor responses obtained from ternary mixtures of analytes. The predictive properties of the model are discussed.
1. Introduction The mechanism of operation and the suitability of surface acoustic wave (SAW) devices for determining low analyte concentrations in the gas phase are well established [ 11. Their low cost and high sensitivity make these sensors very attractive for monitoring gaseous analytes in the atmosphere or in exhaust air. Amongst the general performance characteristics of analytical systems, such as stability, response time and reproducibility, the sensitivity and selectivity are two important features to be considered. For SAW devices, sensitivity and selectivity are achieved by coating them with a substance that interacts physically or chemically with the analyte of interest. Several coatings were inspected in that respect. If the analyte undergoes physisorption, interactions between the gas and the interface material are weak. The response is reversible, but a low selectivity is obtained [2]. In general, if a chemical reaction occurs between the two partners, a good selectivity is achieved, and partially or totally irreversible responses are observed. From these considerations, it can be deduced that the use of SAW sensors as an efficient analytical tool is actually strongly limited by the lack of selectivity or reversibility of their response. A method for solving the problem of poor selectivity consists of using an array of sensors in conjunction with pattern-recognition software [ 31. Experimental responses for ternary mixtures of 0925-4005/92/$5.00
toluene, acetone and dichloromethane obtained from three different coated sensors were recorded. The partial least-squares (PLS) regression method [4] was applied to experimental data and the predictive properties of the mathematical model were tested with the set of data used for calibration.
2. Experimental 2.1. SA W devices and coating deposition SAW devices operating at a fundamental resonance frequency of 71.6 MHz were purchased from Xensor Integration (Delft, Netherlands). The devices were of the dual delay-line oscillator type [5]. This kind of arrangement permits the influences of analyte gas temperature and pressure on the sensor response to be eliminated. Solutions of commercially available polymers were dissolved in suitable @vents, e.g., acetone (0.2 mg/ml). They were then sprayed with an air brush through a mask onto the space between the set of electrodes of one delay line (sample line), the second delay line (reference line) being left bare. The resonance frequency decrease of the sample delay line due to the coating ranged from 37 to 88 kHz. For the determination of sensitivities and selectivities, analyte vapour concentrations in the range 120 to 1300 ppm were used. Organic solvents were purchased as purissimum grade and were used without any further purification. @ 1992 -
Elsevier
Sequoia.
All rights
reserved
588
2.2. Instrumentation and software 2.2.1. Analyte vapour generation
180
To compare the performance of coated S A W devices for our set o f analytes, a gas generator capable of delivering well-known concentrations of the analyte(s) in dry air (carrier gas) was used [5].
140 ~120 w
2.2.2. Temperature control and frequency counter To ensure reliable results, the temperature o f the measuring cell was kept constant at 30.0 °C with a precision o f 0.01 °C ( R E X - G 9 type temperature controller). A three-channel universal frequency counter was used to record the frequency change o f the two delay lines as well as the difference frequency. The individual frequencies were measured every 3 s for an interval o f 0.3 s each. The total time for a run was 12 min.
'
' ~o
' ~o
' ~o
' ~o
I
ldoo
I
lioo
Fig. 1. Sensor responses for different concentrations of toluene ( 0 ) , acetone ( • ) and dichloromethane ( • ) vapours; coating, poly(epichlorohydrin); straight lines are linear regressions performed on experimental data.
also programmed in-house and was run on a microcomputer.
2.2.3. Software The gas generator, t h e temperature-control device and the frequency counter used for measurements were controlled by a computer. Software for data acquisition and data evaluation as well as for the control of all peripherals was developed inhouse. The algorithm for PLS analysis o f data was
3. Results and discussion The sensitivities obtained for our sets o f analytes and coatings are summarized in Table 1.
T A B L E 1. Absolute sensitivities of the coatings at 30 °C Coating
Qea (kHz)
Analyte ACET DCM ( 10 -3 Hz/ppm/kHz)
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.
Ethylene/vinylacetate copolymer 1,2 polybutadiene Chiorosulfonated polyethylene Polyamide resin Poly(epiehlorohydrin) Poly(methylhydrogen siloxane) Glycerol monooleate Poly(1-butene) Di-(2-ethylhexyl) phthalate Styrene/butylmethacrylate copolymer Epoxidized linseed oil Polycarbonate resin Poly(ethylene oxide) Poly(caprolactone) diol Polysulfone resin Hydroxybutyl methyl cellulose Poly(vinyl formal) Styrene/acrylonitrile copolymer Poly(acrylic acid) Hexatriacontane
69.5 87.8 59.2 68.4 72.2 47.7 36.9 41.3 62.0 66.8 78.3 74.3 59.9 78.1 84.1 62.6 64.4 66.8 40.1 72.3
0.38 0.18 0.27 0.20 0.57 0.33 0.27 0.17 0.11 0.13 0 0.34 ~ 0.17 0.04 0 0.58 a 0 0.18 0 0
0.54 0.25 0.23 0.29 0.38 0.29 0.31 0.22 0.25 0.36 0.21 0.98 a 0.35 0.18 0.47 a 0.96 ~ 0.13 0.44 0 0
ETOH
MEOH
TOL
0.43 0.11 0 0.56 0.31 0.24 0.55 0.22 0.13 0.36 0 0.08 0.35 0.16 0.10 0.89 ~ 0.10 0.16 0 0
0 0 0 0.26 0.14 0 0.22 0 0.11 0 0 0 0 0 0 0.60 0 0 0 0
7.22 5.83 5.37 a 4.72 4.56 4.48 3.63 2.70 2.55 2.00 a 1.69 1.51 1.32 1.06 0.52 ~ 0.47 ~ 0.36 0.31 a 0 0
aQ¢, frequency shift due to the mass of the coating; ACET, acetone; D C M , dichloromethane; ETOH, ethanol; M E O H , methanol; TOL, toluene. The response time of the sensors w a s lower than 1 min except for the results indicated with an ~, for which a response time between 1 and 5 min w a s observed.
589
(4 100
@I
Fig. 2. Typical experimental results. (a) Response time smaller than 1 min (analyte, toluene; coating, ethylene/vinylacetate copolymer). (b) Response time between 1 and 5 min (analyte, toluene; coating, chlorosulfonated polyethylene) A, frequency difference; B, reference resonance frequency; C, sample resonance frequency; I”, temperature.. The hatch-marks at the bottom of the Figures indicate the times when the sensor was exposed to the analyte.
Absolute sensitivities expressed in Hz/ppm per kHz of deposited polymer are reported in order to compare the performance of the coatings on an absolute basis. The best absolute sensitivities are obtained for toluene. Some coatings exhibit similar sensitivities for acetone, dichloromethane, ethanol
and methanol as for toluene (coatings 13, 15, 18), but in most cases their sensitivities are smaller by one order of magnitude or more (coatings 1 - 11). Coating sensitivities based on the mean absolute sensitivity over the twenty coatings can be classified as follows: toluene % dichloromethane
590
methanol z acetone > methanol. As an example of the linear concentration response generally observed with our sensors, we list here data acquired with a sensor coated with poly(epichlorohydrin) for several concentrations of toluene, acetone and dichloromethane in the ranges 55 to 500 ppm, 300 to 1200 ppm and 150 to 600 ppm, respectively (Fig. 1). Linear regressions performed on these data for each analyte give correlation factors Y of 0.9999, 0.9986 and 0.9990, respectively, indicating a linear behaviour of the sensor. Response times are usually lower than one minute, but slower responses from one to five minutes are also observed. These two types of typical experimental results are illustrated in Fig. 2(a) and (b). Table 2 gives a summary of the best coating performances for the set of analytes used. Assuming a noise level of 0.5 Hz and a signal-to-noise ratio of four, 4 ppm of toluene can be detected. The highest detection
limit of 111 ppm is obtained for methanol. Table 1 shows clearly that exclusive selectivity for, one analyte is never achieved. This means that the accurate detection and identification of a single chemical vapour of interest in the presence of multiple known or unknown interferences using one single coated sensor is precluded. A promising approach to solving such analytical problems is the use of pattern-recognition techniques in conjunction with an array of sensors of varying selectivity. Amongst the different possible existing techniques like multiple linear regression (MLR), principal component analysis (PCA) and PLS, the PLS regression method was chosen because the algorithm used for the calculations is the most complete and elegant one when prediction is important [4]. Consequently, sensor responses obtained from devices coated with ethylene/vinylacetate copolymer, 1,2 polybutadiene and poly( epichlorohydrin)
TABLE 2. Best sensitivities and selectivities obtained at 30 “C Analyte
Maximum absolute sensitivity (IO-’ Hz/ppm/kHz)
Maximum sensitivity ( Hz/ppm)
Maximum selectivity”
Detection limitb (ppm)
Toluene Dichloromethane Ethanol Acetone Methanol
7.22’ 0.983 0.894 0.5g4 0.604
0.502 0.073 0.056 0.036 0.018
I:532 I:123 I: 45 I: 46 1: 1.3’
4 27 35 56 Ill
“The indicated selectivities are calculated by comparing the responses of the five analytes for one coating. Zero responses are not considered. bCalculated on the basis of a noise level of 0.5 Hz and a signal-to-noise ratio of four. ‘Ethylene/vinylacetate copolymer. ‘I,2 polybutadiene, selectivity of toluene over ethanol. 3Polycarbonate resin, selectivity of dichloromethane over ethanol. 4Hydroxybutyl methyl cellulose. 5Poly(caprolactone) dial, selectivity of ethanol over acetone. 6Polycarbonate resin, selectivity of acetone over ethanol. ‘Polyamide resin, selectivity of methanol over acetone.
TABLE 3. PLS predictive properties for standard mixtures of toluene, acetone and dichloromethane Mixture
Toluene
,. Dichloromethane
Acetone
Cst”
c pred
difs W)
c P=d (mm)
(W
CS, (mm)
C P=d (mm)
d@
(mm)
Cst (mm)
d@
(ppm)
2 3 4 5 6
44.6 48.5 75.7 87.5 309.2 498.6
39.6 50.1 78.3 88.3 310.6 497.5
-11.7 3.3 3.4 0.9 0.5 -0.2
595.9 89.8 1010.7 1169.3 551.6 889.5
639.7 72.8 991.8 1160.1 547.5 894.9
7.4 - 18.9 - 1.9 -0.8 -0.7 0.6
286.1 311.1 485.2 80.7 264.8 427.0
308. I 314.8 464.5 85.4 237.6 444.2
7.7 1.2 -4.3 5.8 - 10.3 4.0
(PRESS) “2 (ppm)
2.6
1
21.2
W)
18.2
%, c,r,d, concentration of analyte in standard and that predicted by PLS, respectively; diff, difference between the standard and the predicted concentration; (PRESS) ‘12, square root of the -prediction sum of squares for each analyte.
591
for ternary mixtures of toluene, acetone and dichloromethane were recorded and the PLS regression method was applied to the experimental data. The concentrations of each analyte used for calibration in the six standard mixtures are given in Table 3 as well as the values predicted by the built-up PLS model. Good predictive properties are observed. The maximum difference between standard an,d predicted values is found for acetone in the second mixture ( - 18.9%). The square root of the prediction sum of squares ((PRESS) ‘12),which is best for toluene (2.6ppm), is close to the detection limit for each analyte.
4. Conclusions Our results at the surface Good results in conjunction be attributed
confirm that adsorption of an analyte of a coating leads to poor selectivity. obtained by using an array of sensors with the PLS regression method can to data featuring low noise levels,
linear coating responses for a single component and a good additivity of responses when several analytes adsorb competitively on the different coatings. The use of an array of sensors and pattern-recognition techniques could be a good way to compensate for the lack of selectivity.
References 1 H. Wohltjen, Mechanism of operation and design considerations for surface acoustic wave device vapour sensors, Sensors and Actuators, 5 (1984) 307-325. 2 M. S. Nieuwenhuizen and A. W. Barendsz, Processes involved at the chemical interface of a SAW chemosensor, Sensors and Actuatow, II (1987) 45-62. 3 H. V. Shurmer, J. W. Gardner and P. Corcoran, Intelligent vapour discrimination using a composite l2-element sensor array, Sensors and Actuators, 4 P. Geladi
Bf (1990)
2X-260.
Partial least-squares regression: a tutorial, Anal. Chitn. Acra, 185 (1986) I 17. 5 D. Am, N. Blom, K. Dubler-Steudle, N. Graber and H. M. Widmer, Surface acoustic wave gas sensors: applications in the chemical industry, Sensors and Actuators A, 25-27 (1991) 395-397.
and B. R. Kowalski,