Application of an automated interpretation system for infrared spectra

Application of an automated interpretation system for infrared spectra

285 Viirationrzl Spectroscopy, 4 (1993) 285-299 Elsevier Science Publishers B.V., Amsterdam Application of an automated interpretation for infrared ...

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285

Viirationrzl Spectroscopy, 4 (1993) 285-299 Elsevier Science Publishers B.V., Amsterdam

Application of an automated interpretation for infrared spectra Part I. Automated

system

qualitative analysis of polymers

J.A. de Koeijer, H.J. Luinge and J.H. van der Maas Analytical Molecular Spectrometry, University of Utrecht, P.O. Box 80083, 3508 TB Utrecht (Netherlands)

J.M. Chalmers and P.J. Tayler ICI PLC, Materials Research Centre, Wdton, Miaidlesbrough,Cleveland TS6 81E (UK) (Received 15th March 1992)

Abstract This paper describes the automated generation of interpretation rules for the characterization of aromatic polymers. Peak tables obtained from diffuse reflectance and attenuated total reflectance spectra are used to generate these rules. In order to improve the information content of the peak tables the role of second-order derivative spectra is investigated. Spectral windows, characteristic for a particular fragment, tend to become less specific due to an increase in the number of peaks. Application of interpretation rules obtained from homopolymers to spectra of copolymers and blends yields satisfactory results. Further improvement in the rule generation process is achieved by using mixture spectra. Keywords: Infrared spectrometry; Interpretation

rules; Knowledge base; Polymers; Spectral windows

Chemical and physical properties of polymers are strongly dependent on the amount and sequence of the repeating units present. By copolymerisation or blending of homopolymers compounds can be obtained with a range of desired properties. Characterization of (co)polymers and blends hence is an important objective in the polymer industry. For this purpose a wide range of spectrometric techniques is available [l] of which Fourier transform infrared (FT-IR) spectrometry is especially rewarding as the development of accessories (e.g., internal reflectance, Correspondence to: H.J. Luinge, Analytical Molecular Spectrometty, University of Utrecht, P.O. Box 80083, 3508 TB Utrecht (Netherlands). 0924-2031/93/$06.00

diffuse reflectance, specular reflectance, photoacoustic) has made it possible to obtain good quality spectra from almost any sample in its fabricated state. The objective of our study was to investigate the possibilities and limitations of automating the qualitative and quantitative characterisation of (co)polymers and blends by using infrared spectral data. This paper addresses the qualitative analysis, whereas in a subsequent paper [2] attention is focused on the quantitative aspects. Identification of components in mixtures has been performed previously by using (i) curve-fitting techniques 13-51 and (ii) knowledge-based systems [6-111. Methods based on curve-fitting require that the composite spectrum can be ap-

0 1993 - Elsevier Science Publishers B.V. All rights reserved

284

proximated by a linear combination of component spectra. These spectra are generally obtained by searching a library of references. The assumption of additivity holds well for vapourphase spectra, but may give rise to problems when dealing with solid mixtures such as polymer blends or copolymers. The ultimate results depend to a large extent on the amount of intermolecular interaction between the mixture components. Large interactions may give rise to significant vibrational shifts and/or intensity fluctuations. In knowledge-based systems the human interpretation process is more closely approached. By using interpretation rules (i.e., descriptions of spectrum-structure correlations) information on the composition of an unknown sample can be obtained. The knowledge necessary to devise such a system may be acquired from reference spectra, literature and/or directly from an expert spectrometrist. Only a few attempts have been made to determine the composition of mixtures using knowledge-based systems. Puskar et al. [6,71 adapted the PAIRS program [12], originally developed for the interpretation of pure compounds, into the PAWMI program for the analysis of environmental mixtures. The system was developed for recognition of the 62 most commonly identified organic compounds at hazardous waste sites. From the ten largest peaks in the spectrum of a pure compound rules are generated that are used to interpret mixture spectra. Band shifting caused by interactions are taken into account by using peak windows of different sizes. Each peak is given added importance if it is in a region of the spectrum in which there are few peaks in the other spectra in the training set. Furthermore, large peaks are given added significance. The interpretation results obtained are optimized by subtracting from each score the similarity between the corresponding spectrum and the highest-scoring spectrum. A statistical test is performed in order to determine the presence of other compounds. The IntIRpret program 181 was developed in order to cope with two limitations of the PAWMI system: the manual generation of rules and the

J.A. de Koeijer et al. / Vb. Spectrosc. 4 (1593) 285-299

use of peak location information only. Compared to PAWMI a 40% decrease in false positive results and a 24% decrease in false negative results was obtained. The MIXIR [lo] system applies knowledge about the infrared spectra of mixture components generated by the IRBASE program [9] from pure compound data to spectra of unknown mixtures. Compound-specific spectral descriptions are created interactively by assigning bands in the spectra of pure compounds knowing the functional groups which are present. Depending on the polarity of the functionalities spectral windows are created in order to account for possible band shifts. Initial window sizes are obtained from literature data. Interpretation of unknown mixture spectra occurs interactively. Recently, a knowledge-based system has been developed for the automated interpretation of infrared spectra (EXSPEC [131). The system contains a rule generation (ERGA [14]) and an interpretation module (SPINT [15]). Automated rule generation is accomplished by finding all compounds containing a specified substructure from a data base of coded structures followed by a search for characteristic spectral regions from a data base of corresponding spectral data. The most informative regions are stored as interpretation rules in a knowledge-base. Simultaneously three probabilities are calculated for each region. Two of these describe the likelihood that compounds absorb in the particular spectral region given the presence or absence of the fragment of interest. The third probability is related to the number of compounds in the data base containing the fragment. From these probabilities the likelihood that the structural unit is present in the unknown compound is calculated during the interpretation process. To account for possible band shifts and intensity variations a spectral region may be expanded with a fuzzy range in which the probability decays linearly to zero at the outer boundaries. The PAWMI, IntIRpret and IRBASE/ MIXIR system were all applied to liquid or vapour-phase mixtures. Rules were generated solely for recognition of pure components. In the current study use is made of solid polymer samples in which

287

LA. de Kaeijer et al. / Vii. Spectmc. 4 (1993) 285-299

0 Q 0

0

0

e

0

0 0

.o=i,

0

a B

2

ester

,_ . I

Ether imide

0

0 II

[email protected]

u

_;-

I_

-

.

.

c-o-_(cH,),-

::

o-

Substructure

. , _ .~ -~

Di(ay1 ether sulphone)

Di(atyl ether ketone)

Biiphenol-A

Di(aryl bnide)

Aryl imidc

AIkylene terephthalate

Aryl

&l-O-

carbollyl

Substructure name

-

-

Characteristic spectral windows for several structural frapents

TABLE

-

_,_^

obtained from DRIFT

1.00-0.86

0.70-0.57

1123-1115

_-^-I

603

608-

0.82-0.70

I-

0.81-0.69 0.64-0.54

1118-1113 823

1.00-0.86

1244-1238

831-

LOO-O.81

0.98-0.88 1490-148s

1155-1150

0.92-0.80 0.85-0.75

925 1658-1652

931-

0.79-0.66

0.88-0.50 832

1295-1274 849-

0.69-0.55 0.97-059

1082-1077 1174-1164

0X6-0.76

1086-1079 741- 735

0.73-0.62

0.79-0.65

1091-1077

1.00-0.90

1724-1719

0.78-0.65

l.co-O.&i

xX9-1245

1780-177s

0.98-0.88

1104-1099

1.00-0.83

1045-1019

726

0.86-0.58

1411-1405

731-

1.00-0.7s 0.81-0.64

1723-1714

1505-1486 1244-1225

0.97-0.59 0.97-0.52 l.cm-0.75

1174-1153 1164-1104

1.00-0.7s

1775-1654

818

670 827

819 545

924

-

_

-..

0.79-0.69

-d_A-

0.44-0.34

0.87-0.74

1247-1242 511 880

0.32-0.25

1299-1293

51788%

1.00-0.55

1.00-0.76

0.91-0.70

1.00-0.90

0.53-0.36

1.00-0.5s

0.99-0.28

0.54-0.34

0.99-0.28

0.77-0.67

0.80-0.70

0.42-0.30

0.60-0.28 0.97-0.59

l.cm-0.71

0.90-0.34

1.00-0.90

0.31-0.25

1489-1483

1152-1146

930-

1229-1222

1280-1275

557-

1162-1150

833-

1169-1161

833-

1712-1707

675-

568- 552 1378-1355

_

723

870

1720-1708

830-

728-

876-

0.52-0.42

0.74-0.53 1126-1118

0.70-0.42 1718-1710

0.80-0.48

0X4-0.53

0.91-0.37

1.00-0.70

1.00-0.28

l.cK-0.28

Intensity

1134-1118

1103-1088

x254-1244

1505-1485

1247-1222

1170-1148

1772-1648

(cm-‘)

Spectral range

(cm-‘)

ATR Intensity

Spectral range

spectra

DRIFI’

and ATR

I

LA.

de Koeijer

et aL /Vii.

Spectrosc.

4 (1993)

285-299

interactions may be more severe than in liquids or gases. The capabilities of EXSPEC to cope with these interactions are investigated here. Furthermore, as the system is also designed to identify components not present in the training set, EXSPEC is provided with classifying properties by generating rules for both homopolymer units and structural fragments of smaller size. EXSPEC currently processes data as peak tables containing position and intensity information. In this study a second-order derivative function is applied to the original data resulting in an increase in the number of peaks in order to facilitate the distinction of highly similar structural fragments. The results are compared with those obtained from the original data. Furthermore, both diffuse reflectance (DRIFT) and attenuated total reflectance (ATR) measurements are performed and compared with respect to interpretative potential.

EXPERIMENTAL

A total of 19 spectra of aromatic homopolymer samples was used for generating the knowledge bases. Of these, 15 were of different composition, and 4 represented a different crystallinity. An overview of the compounds is shown in Table 1. All spectra were recorded on a Perkin-Elmer 1720 FT-IR spectrometer. ATR spectra were obtained from film samples using a 60” KRS-5 crystal. The spectra were measured from 4000 to 450 cm-’ with a resolution of 2 cm-i, averaging 100 scans. The DRIFT measurements were performed using a Perkin-Elmer diffuse reflectance accessory, model PEDR 0186-0791. The samples were cast from dichloromethane solution onto finely ground dried Kl3r powder, or prepared using fine abrasive paper. DRIFT spectra were recorded from 4000 to 450 cm-’ with a resolution of 4 cm-i, averaging 256 scans, and transformed to log(l/R). All spectra were normalized on the strongest band. Second-order derivative spectra were calculated on a Perkin-Elmer 7500 data station, simultaneously applying a g-point quadratic polynomial smoothing function. Peak picking was performed with a threshold of 1% T

289

for the zero-order spectra and 5% T for the second-order derivative spectra. For the latter, a maximum band width of 20 cm-’ at the peak pick threshold was applied to prevent artifacts from being detected as bands. The rule generation program and the interpretation system were written in LPA MacProlog and installed on an Apple Macintosh II computer.

RESULTS AND DISCUSSION

Rule generation was performed using four different sets of peak tables obtained from DRIFT and ATR measurements with and without application of a second-order derivative function. The process consists of the following steps: (1) selection of a structural fragment for which rules are to be generated; (2) determination of spectral windows in which all compounds containing the fragment have an absorption; (3) sorting of the windows with respect to lower intensity boundary (the higher this value, the more likely the particular fragment can be detected at low concentrations); (4) expansion of the windows in order to account for band position and intensity variations in copolymers or blends; (5) determination of the specificity of the windows by counting the peaks of compounds not containing the fragment; (6) sorting of the windows with respect to specificity; (7) generation of rules based on the obtained spectral windows. Rules were generated for all structural fragments that appeared at least twice in the data set and for all homopolymers. The minimum window size was set to 5 cm-’ and 0.1 a.u. The windows were extended with a fuzzy region of 3 cm-’ and 1 a.u. in order to account for possible wavenumber shifts due to interactions and/or concentration-dependent intensity fluctuations. Unknown samples absorbing within the original window are assigned a high probability of containing the particular fragment depending on the specificity of the window. Within the fuzzy region the probability decreases linearly to zero upon moving towards the outer boundaries. A more detailed description of the rule generation program can be found in [14].

3

ester

spectral name

Ether imide

DKaryl ether sulphone)

DKaryl ether ketone)

Eliiphenol-A

DKaryl imide)

ANI imide

Alkyleoe. terephthalate

Awl

Aryl-O-

Carbonyl

Substructure

Characteristic

TABLE windows

0 I,

0

u

c-o-_(cH,),-

::

obtained

:: c-o-_(cH,),-

fragments

-_(cH*,,-O-E0

@

u0

-&

o-

for several structural Substructure Inteasity

0.89-0.36

1507-1486

0.94-0.22 0.21-0.11

1022-1015 1741-1733

0.55-0.15

13651353

854

0.49-0.29 0.29-0.19

879

SSS-

1516-1510

0.19-0.10

637

0.44-0.34

0.52-0.15

644-

1779-1774

8S9-

1.00-0.48

0.29-0.19

0.57-0.20

950

0.20-0.10

1330-1319

9Ss-

1226-1219

1.00-0.90

0.19-0.10

1263-12.56

92S

0.18-0.10

2970-2965

930-

0.41-0.31

0.47-0.24

1517-1511 1778-1773

0.48-0.18

0.42-0.32

1780-177s

888

0.74-0.58

893-

0.23-0.13

1243-1236

0.27-0.10

1616-1611

1413-1407

0.45-0.14

1351-1339

0.34-0.14

0.74-0.2.5

1412-1406

1714-1708

0.45-0.25

0.34-0.11

1341-1323

1714-1708

0.47-0.14

1.00-0.19

1017-1011 821

0.56-0.19

1118-1103

841-

0.34-0.11

1767-1648

1.00-0.27

1506-1485;

870 722

818

734

829

837-

924

SlS-

889-

885

510

884

879

1777-1772

1152-1146

12x37-1232

1489-1483

1163-1158

930-

1653-1647

564

569-

1081-1076

1160-1150

739-

1708-1703

1775-1769

829-

1716-1705

1778-1771

87572%

1716-1709

1410-1377

0.40-0.16 0.67-0.28

0.40-0.14

0.18-0.10

1.00-0.48

0.27-0.17

1.00-0.74

0.70-0.46

1.00-0.90

0.19-0.10

0.55-0.19

0.44-0.18

0.73-0.14

0.94-0.17

0.18-0.10 1.00-0.90

0.24-0.14

0.35-0.13 0.55-0.26

0.23-0.12

1.00-0.90

0.44-0.17

0.27-0.16

0.25-0.12

0.46-0.16

1.00-0.11

1016-1010

1716-1709

1.00-0.14

1169-1148

Intensity

0.46-0.12

range

1769-1647

Czrr-‘)

Spectral

(cm-‘)

rsnge

ATR

spectra

Spectral

derivative DRIFI

1490-1484

from second-order

J.A.de Ibeijeret al./Vi. Spectrosc. 4 (1993)285-299

291

d 8

2 a

JJI. de Koeijeret al. / VII. Spectrosc. 4 (1993) 285-299

292

Table 2 shows a number of characteristic spectral windows for several structural fragments as obtained from both DRIFT and ATR data. Generally, similar windows are obtained for a particular fragment with an apparent tendency towards lower wavenumber boundaries for ATR data as might be expected. Table 3 contains spectral windows for the same structural fragments based on second-order derivative data. Spectrum interpretation is performed by the SPINT module using a network of structural dependencies as shown in Fig. 1. All interpretation processes start at the root and proceed through the branches representing rules for structural units which increase in specificity. The propaga-

tion to the next branch is dependent on obtaining positive results at the previous decision stages. For each structural fragment the probability of its presence is calculated. An elaborate description is given in [151. Four knowledge bases were obtained for DRIFT and ATR spectra for both zero-order and second-order derivative data. Interpretation of the spectra used for rule generation yielded very good results. For each knowledge base an average number of 186 decisions was made with respect to the presence or absence of structural fragments. Recognition of all 67 structural fragments present was achieved without false negatives and only in two cases the presence of a

0.8 2

0.6

0 2

0.4

S 0.2

2000

1800

1600

1400

1200

1000

800

600

wavenumber (cm’) 1.0 0.8

-2 4

I-INTBRPmATION

0.6

5 1

0.99 ultem 0.98 Arykther 0.93 Aryl-imide 0.90 Bisphenol-A 0.72 Carbonyl

0.4

% 0.2

Timenoeded:Om.24~. 1200

800 wavenumber (cd)

1000

600

Fig. 2. Attenuated total reflectance spectrum, corresponding peak table as displayed by EXSPEC and interpretation results obtained from a film of Ultem 1000.

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IA de kbei_ieret aL /Vii. Spectrosc. 4 (1993) 285-299

In order to test the capabilities of the interpretation rules for detection of structural fragments in unknown blends and copolymers, the knowledge bases were applied to the spectra of a number of alkylene terephthalate blends and aryl ether sulphone copolymers. In all cases the sam-

carbonyl was suggested erroneously. Roth were due to an aromatic overtone in the spectral window assigned to a carbonyl. In Fig. 2 the interpretation of the peak table obtained from the ATR spectrum of a poly(ether imide) (Ultem 1000) is given as an example.

1

4

1400

1200

1000

800

600

wavenum?)cr (cm-’ ) Fig. 3. Diffuse reflectance spectra of (a) pure PBT, blends of (b) 89% PBT and 11% PET, (c) 65% PBT and 35% PET, (d) 10% PBT and 90% PET, and (e) pure PET. Characteristic peaks are marked with an arrow.

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J.A. de Kaeijer et al / Vii. Spectrosc. 4 (1993) 285-299

2ooo

1800

1600

1400

1200

1000

800 600 wavenumber (cm-‘) Fig. 4. Inverted second-order derivative diffuse reflectance spectra of (a) pure PBT, blends of(b) 89% PBT and 11% PET, (c) 65% PBT and 35% PET, (d) 10% PBT and 90% PET, and (e) pure PET. Characteristic peaks are marked with an arrow.

LA. de Koeder et al. /VT.

295

Spectrosc. 4 (1993) 285-299

TABLE 4

When using peak tables obtained from the second-order derivatives of the spectra (Fig. 41, the number of false negatives is reduced to two. Only at the lowest PET and PBT concentrations the detection of the respective homopolymers is not achieved. For PET this is caused by the decreasing intensity of the peak maximum in the region 1373-1368 cm-‘, whereas for PBT the disappearing peak maximum in the region 16771671 cm-’ is a major cause. Due to the fact that second-order derivative spectra generally yield more peak maxima than zero-order spectra, the number of interfering absorptions within the spectral windows increases. Therefore, the probability that a peak maximum is caused by a particular fragment is generally lower for second-order derivative spectra. This can be seen in Table 5 where detection of the PET and PBT units is accompanied with lower probabilities. By decreasing the window sizes this effect can be reduced. A second test was performed on a set of six copolymers of polfiaryl ether sulphone) (PES) and poly(ary1 ether ether sulphone) (PEES) with compositions ranging from 50 to 99% PES. Interpretation rules were obtained from DRIFT and ATR data both with and without applying a second-order derivative function. For both PES and PEES the six most informative spectral windows are used in interpretation rules. In Figs. 5 and 6 the DRIFT and ATR spectra of a number of PES-PEES copolymers is shown. Characteristic bands for PES and PEES are marked with an arrow. Interpretation results are given in Tables 6 and 7. As can be seen from

Characteristic spectral windows(in cm-‘) for PET and PBT DRIFT (zero-order derivative)

DRIFT (second-order derivative)

PET

PBT

PET

PBT

3433-3428 1046-1041 977- 971 1342-1337

2901-2895 1353-1348 1211-1205 1270-1265

1343-1337 1373-1368 1413-1407 1242-1236

2900-2894 2967-2961 1677-1671 1353-1348

ples were recognized correctly as consisting of alkylene terephthalate and aryl ether sulphone units, respectively. In Table 4 the four most characteristic spectral windows for both polfiethylene terephthalate) (PET) and polfibutylene terephthalate) (PBT) are shown as determined by the rule generator. DRIFT data and the corresponding second-order derivative spectra are compared. The spectrum-structure relations were used in order to detect the presence of PET and PBT in three PET-PBT blends with compositions of 11, 35 and 90% PET. Application of the interpretation module to the zero-order DRIFT spectra of the blends yielded five false negative results. PET could not be detected in the samples with 35 and 11% and PBT was not found in the 11, 35 and 90% PET samples. The main reason for the failure to detect PET at lower concentrations is the disappearing peak maximum in the region 10461041 cm-i. For PBT a similar cause can be found as with decreasing concentrations the peak maxima in the regions 1353-1348 cm-’ and 12111205 cm-’ are vanishing (Fig. 3).

TABLE 5 Probabilitiesfor the presence of PET and PBT obtained for DRIPT spectra of a number of blends Composition 100% PET 90% PET-lo% PBT 35% PET-65% PBT 11% PET-89% PBT 100% PBT

DRIFT (zero-order derivative)

DRIFI (second-order derivative)

PET

PBT

PET

PBT

0.99 0.99 0.01 0.01 0.01

0.01 0.01 0.01 0.01 0.98

0.77 0.76 0.69 0.19 0.12

0.01 0.01 0.65 0.79 0.79

J.A. de KMjer et al. / Vh. Spectrosc. 4 (I 993) 285-299

-II-II

(4

-

1400

1200

1000

800

waverumber

600 (cm’)

Fig. 5. Diffuse reflectance spectra of (a) pure PEES, copolymers of(b) 50% PEES and 50% PES, (c) 25% PEES and 75% PES, (d) 1% PEES and 99% PES, and (e) pure PES. Characteristic peaks are marked with an arrow.

these tables the probability for the presence of either of the homopolymers tends to decrease with decreasing concentration. This effect is due to the increasing distance of features in the copolymer spectra to the characteristic spectral windows. ATR data yield a somewhat higher

average probability for the presence of both PES (0.88) and PEES (0.67) than DRIFT data (0.69 and 0.55, respectively). Furthermore, it appears that using second-order derivative data gives a decrease in performance. For second-order derivative DRIlT data average probabilities of

LA. de i&&r et al. /Vii. S’ctmc.

4 (1993) 285-299

i

-_A (4

1 1

Fig. 6. Attenuated total reflectance spectra of (a) pure PEES, copolymers of (bl50% PEES and 50% PES, (c) 25% PEES and 754 PEG (d) 1% PEES and 99% PES, and (e) pure PES. Characteristic peaks are marked with an arrow.

JA. de Koeijer et al. / Vii. Spectrosc. 4 (1993) 285-299

298 TABLE 6

Probabilities for the presence of PES and PEES obtained for zero-order and second-order derivative DRIFT spectra of a number of copolymers DRIm

Composition 100% PES 99% PES-1% 89% PES-11% 80% PES-20% 75% PES-25% 60% PES-40% 50% PES-50% 100% PEES

PEES PEES PEES PEES PEES PEES

(zero-order derivative)

DRIFT (second-order derivative)

PES

PEES

PES

PEES

0.96 0.14 0.89 0.60 0.64 0.48 0.50 0.07

0.20 0.24 0.42 0.48 0.51 0.61 0.68 0.89

0.55 0.20 0.26 0.23 0.23 0.14 0.17 0.01

0.05 0.24 0.01 0.01 0.01 0.20 0.04 0.94

0.25 for PES and 0.21 for PEES are found whereas from second-order derivative ATR data values of 0.54 (PES) and 0.40 (PEES) are obtained. Again, this may be explained by the fact that the number of peaks in second-order derivative spectra increases compared to zero order data. Hence, the number of interfering absorptions in the spectral windows found by the rule generator increases. This will result in less specific rules and consequently lower probabilities. When PES-PEES copolymer spectra are included during rule generation, rules are obtained with better predicting properties for both PES and PEES. PEES, for instance, is well characterized by a spectral feature in the window 11921186 cm- ’ in the DRIFT spectrum and 11901185 cm-’ in the ATR spectrum caused by the

hydroquinone unit present in the polymer. Average probabilities for the presence of PES and PEES in all copolymers are 0.99 and 0.87 for both DRIFT and ATR data. Only in the 99% PES copolymer the presence of PEES is not detected by using either of the sampling techniques. It should be noted that it was not possible to detect the presence of PEES in the respective spectra by eye either. With rules obtained from the secondorder derivative data all samples are identified correctly (i.e., probability > OS), even the 99% PES sample. The improvement in performance obtained when using mixture spectra in the rule generation process is due to the fact that the spectral windows used in the interpretation rules are better tailored to the prediction of PES and PEES with

TABLE 7 Probabilities for the presence of PES and PEES obtained for zero-order and second-order derivative ATR spectra of a number of copolymers Composition

100% PES 99% PES-1% 89% PES-11% 80% PES-20% 75% PES-25% 60% PES-40% 50% PES-50% 100% PEES

PEES PEES PEES PEES PEES PEES

ATR (zero-order derivative)

ATR (second-order derivative)

PES

PEES

PES

PEES

0.94 0.91 0.90 0.80 0.88 0.92 0.79 0.10

0.29 0.38 0.58 0.41 0.76 0.76 0.84 0.93

0.88 0.61 0.69 0.01 0.66 0.47 0.48 0.44

0.08 0.24 0.42 0.21 0.45 0.41 0.52 0.56

299

J.A. de KoeiJer et al. /Vii. Spectrosc. 4 (1993) 285-299

to wavenumber and intensity the probabilities obtained are higher. Conclu.rions We

possible to and blends qualitatively from infrared spectral data using a knowledge-based approach. of fragment-specific spectral windows probabilities, can be generated automatically a training and stored a knowledge Application of knowledge base the origispectra yields good identification Applied to spectra of and blends) of the assignment to is perfect. Identification of the monomer units is more difficult, at low concentrations of particular unit. Mixture may used in rule generation to optimize the interfor recognition of

The authors are indebted to Mrs. W. Gaskin for recording the ATR spectra and to ICI PLC for funding this project.

it

for the respective fragments increasing number of absorptions. There is no marked difference between use of and ATR data. Future on further expansion of knowledge bases and improvement of rule generation

REFERENCES

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