Spectral Inter-Conversion Analysis of Conformational Defects inPolyethylene Crystals

LIPING ZHANG, SHIN WATANABE, ISAO NODA, and YUQING WU*Grain and Oil Food Processing Key Laboratory of Jilin Province, Jilin Business and Technology College, No. 1606, Haoyue Road, Changchun,

130062, China (L.Z.); Procter & Gamble International Operations Pte Ltd, 238A Thomson Road, #14-01 Novena Square Tower A, Singapore

307684 (S.W.); The Procter & Gamble Company, 8611 Beckett Road, West Chester, Ohio 45069 (I.N.); and State Key Lab for SupramolecularStructure and Material, Jilin University, Changchun 130012, China (Y.W.)

Conformational discrimination of polyethylene crystals during a heating

process was carried out using a novel spectral inter-conversion analysis

based on the combination of near-infrared (NIR) and mid-infrared (mid-

IR) spectroscopy. Two mid-IR and two NIR spectral ranges relating to

different conformational sequences were explored at 710–752 cm�1 and

4300–4362 cm�1 (corresponding to the orthorhombic), and 1328–1374

cm�1 (relating to the defect) and 4235–4295 cm�1 (corresponding to the

unconfirmed conformation). Five partial least squares-2 (PLS-2) models

were built based on these four infrared spectral ranges and were used to

make the basis for spectral inter-conversion analysis. By comparing the

conformational disordering induced by thermal stimulus, the attribution

of an obscure NIR band at 4265 cm�1 was confirmed. The temperature

dependency of the band at 4265 cm�1 in the NIR range was very similar to

that of the bands at 1368, 1353, and 1341 cm�1 in the mid-IR range,

indicating they had a common spectral origin. The band intensity at 4265

cm�1 was also extracted from the predicted spectra and compared with

the intensity of the measured spectra. The excellent matching in the trend

of intensity changes with temperature further demonstrated the same

vibrational origin of the NIR band at 4265 cm�1 with respect to the mid-

IR bands at 1368, 1353, and 1341 cm�1. The quantitative comparison of

the prediction deviation values between different multivariate spectral

models further validate the assignment of the band at 4265 cm�1 to the

conformational defect sequence formed in the heating process.

Index Headings: Conformational defects; Polyethylene; Spectral inter-

conversion; Extraction of spectral information; Partial least squares;

PLS; Mid-infrared spectroscopy; Near-infrared spectroscopy.

INTRODUCTION

The structural clarification of polyethylene (PE) has longbeen a subject of scientific interest due to its essentialimportance both in fundamental polymer science and in thepolymer industry. Given the fact that the performance of thepolymer, such as tensile strength, fatigue life, and thermalbehaviors, relates closely to the changes in the aggregation andconformation state of molecules in the crystalline andamorphous phases, a number of investigations on the structuralchanges of PE during heating have been carried out over thepast two decades.1–7

Among the thermally induced structural changes of PE, thephase transition within the crystallite has long been ofparticular interest. Under high temperature or pressure, PEcrystallizes into an orthorhombic phase through a partiallydisordered hexagonal phase, in which the polymeric chainslose the ordered all-trans conformational sequence.1–4 Suchcrystallization through the hexagonal phase yields high-quality

orthorhombic crystals. In contrast, a direct crystallizationalways leads to the formation of disordered regions in thecrystal, where the gauche defects (chain-folding) are ob-served.3,4

Various experimental methods, such as X-ray diffrac-tion,8–10 X-ray imaging,11,12 differential scanning calorimetry(DSC),13–15 and transmission electron microscopy (TEM),16,17

have been utilized to reveal the microscopic structure of semi-crystalline polymers and their crystallization process. Theinvestigation of the conformational transition of polymers isoften carried out using spectroscopic techniques, especiallyRaman scattering18,19 and infrared spectroscopy.20–22 Themonomer sequences in crystalline, amorphous, and intermedi-ate phases could be distinguished separately in the vibrationalspectra. Because of the advantages of mid-infrared (mid-IR)over near-infrared (NIR) spectroscopy in terms of well-established band assignments and relative ease of spectralinterpretation, mid-IR has traditionally seen much greaterapplication in the study of polymers. However, transmissionmeasurement of intense mid-IR bands often creates experi-mental limitations, requiring more skills and techniques in thesample preparation, especially when one studies phasetransition behavior of PE under physical tension.20 ThoughRaman spectral measurements do not have such limitations,other deficiencies, such as less than satisfactory signal-to-noiseratio (S/N) or background interference, also restrict its wideapplication in exploring the phase transition of PE. In contrast,the relatively low absorption coefficient often makes measure-ment by near-infrared (NIR) spectroscopy a preferred tech-nique over mid-IR or Raman spectroscopy. Limitation in thespectral band attribution and clarification, in turn, restricts itsprogress in the study of polymers, especially in phase transitionanalysis.

With the rapid progress in NIR instrumentation, especially incombination with emerging spectral analysis techniques suchas chemometrics23–29 and two-dimensional correlation spec-troscopy (2D-COS),30–32 remarkable advances have been madeover the last two decades. NIR spectroscopy has beenextensively used for the quantitative characterization ofpolymers, including polyethylene. For example, Rohe et al.developed a transmission sensor for NIR spectroscopy to beused during in-line polymer extrusion.23 PLS analysisdemonstrated that the system was suitable for harsh industrialenvironments and could improve the extrusion processes.Watari et al. reported the combined application of NIRspectroscopy and chemometrics for the on-line monitoring ofthe density of linear low-density polyethylene (LLDPE).24 Satoet al. reported the establishment of an on-line monitoringsystem that could predict the physical properties of PE bymeans of NIR spectroscopy and PLS data analysis.27 However,

Received 5 July 2011; accepted 26 September 2011.* Author to whom correspondence should be sent. E-mail: yqwu@jlu.edu.cn.DOI: 10.1366/11-06401

Volume 65, Number 12, 2011 APPLIED SPECTROSCOPY 14030003-7028/11/6512-1403$2.00/0

� 2011 Society for Applied Spectroscopy

FIG. 1. Mid-IR spectra in the 600–1550 cm�1 region of (a) HDPE, (b)LLDPE, and (c) LDPE, which were measured in the temperature range from 30to 135 8C with an interval of 5 8C.

FIG. 2. NIR spectra in the 4000–4500 cm�1 region of (a) HDPE, (b) LLDPE,and (c) LDPE, which were measured in the temperature range from 30 to 1358C with an interval of 5 8C.

1404 Volume 65, Number 12, 2011

detailed descriptions of the structural changes during the phasetransition of polyethylene have seldom been reported sofar.28,29 Watanabe et al. reported the potential of NIRspectroscopy in investigating the structural and phase transitionbehavior of PE crystal and identified the NIR bands that arecharacteristic of the regular orthorhombic phase, conforma-tionally disordered hexagonal phase, and amorphous phase.28

In recent years, with the advance of 2D hetero-spectralcorrelation analysis, NIR and mid-IR spectroscopy in combi-nation have been applied to structural analyses, exploringconformational changes of polymers.29,32–34 By using mid-IR–NIR 2D hetero-spectral correlation analysis, Sun et al.investigated the physico-chemical mechanism and changingsequence of molecular groups in poly(N-isopropyl acrylamide)PNiPAM. By using a similar technique, Watanabe et al. alsorevealed that the intensity of an NIR band at 4265 cm�1

changed in the same way as that of the mid-IR bands in thespectral range of 1390–1240 cm�1 and thus attributed the NIRband to the formation of conformational defect sequences inthe crystalline PE during the heating process.29 The traditional

combined application of mid-IR and NIR spectroscopy can befurther assisted by multivariate data analysis methods.Successful applications of such an approach have already beenfound in quality analysis in the food industry, such as theprocessing of dairy products, vegetables, and meat.35–37 Forexample, Cuadrado et al. carried out a study of the statisticalcharacteristics of several enological parameters of wine bycombining the measurement of NIR and mid-IR spectra andapplying partial least squares multivariate analysis.37 Despitethe promising success in the quality prediction (e.g., compo-nent prediction), relatively little has been reported on the jointapplication of the multiple spectral measurement, and espe-cially the inter-conversion of thus-obtained spectral data, forthe discrimination of the obscure NIR spectral bands ofcomplex macromolecules.38–41 In our previous reports, weexplored the joint application of mid-IR and NIR combinedwith PLS on the multivariate estimation between mid-IR andNIR spectra for hexafluoroisopropanol–water mixtures. Thepower of this approach is demonstrated in successfullydiscerning generally weak and highly overlapped NIR spectralbands.39 The thermally induced changes in the weak interactionof PNiPAM are also estimated by the spectral inter-conversionscheme.40,41

In this study, we identify the bands due to the orthorhombic,amorphous, and hexagonal phases by measuring the NIRspectra of thermally treated PE samples as well as the polarizedNIR spectra of uniaxially oriented PE. By using the spectralinter-conversion strategy, we will demonstrate the capability ofNIR spectroscopy to explore the phase transition betweenorthorhombic and hexagonal phases in PE.

EXPERIMENTAL

Sample Preparation. Three PE samples with differentstructures and crystallinities were used in this study. All thesamples were common industrial-grade PE samples that wereoriginally supplied in the form of pellets. In order to adjust thethermal history of each sample and to prepare film samples forNIR measurements, PE pellet samples were sandwichedbetween a pair of polyimide films and melted at approximately10 8C higher than their melting temperature, Tm. The sampleswere kept for more than 10 minutes at the same temperatureunder a pressure of approximately 2 MPa using a Shindo hotpress YS-5. The samples were then cooled to ambienttemperature at �2.0 8C/min using a Mettler FP800. Thespectroscopic studies were completed within one week ofsample preparation.

Spectral Measurements. Near-infrared spectral measure-ments of PE samples were performed with a spectral resolutionof 2 cm�1 by means of a Thermo Nexus 870 FT-IR/NIRspectrometer. An MCT detector and an extended KBr beam

FIG. 3. Loading plots for the first factor of PLS-2 regression between (a)1000–1500 cm�1 and (b) 4000–4500 cm�1 spectral regions for all the samplesat 30 to 136 8C.

TABLE I. PLS-2 models used in the present study.

Samples

X variables Y variables

30, 32, 34, . . ., and 136 8C

Model I 1328 - 1374 cm�1 4235 - 4295 cm�1

Model I’ 4235 - 4295 cm�1 1328 - 1374 cm�1

Model II 710 - 752 cm�1 4235 - 4295 cm�1

Model II’ 4235 - 4295 cm�1 710 - 752 cm�1

Model III 1328 - 1374 cm�1 4300 - 4362 cm�1

APPLIED SPECTROSCOPY 1405

splitter were used for the simultaneous measurements of bothNIR (5000–4000 cm�1) and mid-IR (4000–1200 cm�1) spectra.A total of 32 spectra were measured for approximatedimensions of 10 3 10 3 0.3 mm3 of the specimens. Thesample was sandwiched between a pair of CaF2 plates (30 3 153 4 mm) and then placed in a heating block. The temperaturewas controlled by a CHINO temperature controller while itssensor was directly inserted into the sample set. In thetemperature-dependent measurements, temperature was in-creased at a constant rate of 2 8C/min up to 100 8C and then1 8C/min to the melt.

Data Analysis. In order to correct the baseline shift of theNIR spectra, multiple scattering correction (MSC) was appliedby using Unscrambler 7.01 (CAMO Software) with the fullMSC selection.

Partial Least Squares-2 Modeling. The PLS-2 modeling ofmultiple mid-IR and NIR absorbance spectra was performed byusing the PLS-2 routine of Unscrambler 7.01. The data weremean-centered and full cross-validation was used for modelcalibration and prediction. The spectra that were not used in thePLS-2 model were selected for further prediction.

RESULTS AND DISCUSSION

Temperature-Dependent Mid-Infrared and Near-Infra-red Spectra of Three Polyethylene Samples During theHeating Process. Figure 1 shows the temperature-dependentmid-IR spectra in the 600–1500 cm�1 region of the three typesof PE samples representing (a) HDPE, (b) LLDPE, and (c)LDPE obtained during the heating process in the temperaturerange from 30 to 135 8C. As seen in the spectrum collected at30 8C, there are two strong peaks at 730 and 1472 cm�1, whichcorrespond to the crystalline bands ascribed to the orthorhom-bic form of the polymer.20 As the temperature increases, theintensity of the bands decreases up to the melting point, whichin turn is compensated by an increase in the intensity of bandsat 1308, 1341, 1353, and 1368 cm�1 assignable, respectively,to gtg’, eg, gg’, and gtg conformational defect sequences.29

Figure 2 shows the temperature-dependent NIR spectra inthe 4000–4500 cm�1 region of the same three types of PEsamples, (a) HDPE, (b) LLDPE, and (c) LDPE, collected in thetemperature range from 30 to 135 8C. The bands at 4322 cm�1

and 4355 cm�1 are ascribed to the combinations of mas(CH2) þd(CH2) and ms(CH2) þ d(CH2) and are characteristic of theorthorhombic phases.29 Similar to the temperature-dependentmid-IR spectra, the intensity of the two bands decreases withtemperature increasing up to each melting point, indicating thedisordering of the orthorhombic crystalline structure.

Correlation Analysis of the Mid-Infrared and Near-Infrared Spectral Bands of Polyethylene Sample by PartialLeast Squares-2 Calibration. PLS-2 calibration was per-formed on the mid-IR and NIR spectra in the range 650–1500and 4000–4500 cm�1 collected during in temperature regionfrom 30 to 136 8C. Figures 3a and 3b plot the loadings of thefirst factor for the mid-IR and NIR spectra. The loadings of thefirst factor demonstrate which wavenumber predominantlyinfluences the model42 and accordingly show the spectralregions where the mid-IR bands correlate strongly with theNIR bands. The positive bands at 720, 730, and 1472 cm�1 andthe negative band at 1350 cm�1 present in the mid-IR spectralregions of Fig. 3a essentially originate from the crystalline andthe conformational defect sequences of PE, respectively.Meanwhile, strong positive bands at 4250 and 4323cm�1 wereobserved in the NIR spectral region (Fig. 3b), indicating that allthese bands had strong correlations with the correspondingmid-IR bands. However, it is difficult to find the exacthomologous pair of mid-IR and NIR bands only by means ofthe PLS-2 calibration. It is noteworthy that the band positionsin Fig. 3 were somewhat different from those found in either ofthe original spectra, as the result of the co-contribution of thewhole spectral dataset to the loadings plots.

Building and Validation of the PLS-2 RegressionModels. As discussed in our previous work,39–41 it oftenbecomes possible to predict the absorbance values of one set ofspectra from the other by using partial least squaresmultivariate regression models. We call this operation spectralinter-conversion. Most importantly, better spectral predictioncan be achieved between the bands originating from the sameor closely related fundamental models, which will be veryhelpful in better understanding and simplifying the complexNIR spectra of polymers. In the previous paper,29 the band at4265 cm�1 was ascribed to the conformational defectsequences of PE because of its similarity in the temperaturedependency against that of mid-IR conformational bands in the

TABLE II. Results of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) of PLS-2 models.

Models

RMSEPa RMSECa

HDPE LLDPE LDPE HDPE LLDPE LDPE

Model I 6.54 3 10�5 4.22 3 10�5 9.04 3 10�6 4.21 3 10�5 2.10 3 10�5 6.41 3 10�6

1353 ! 4265b

(Def. ! ?)Model II 1.16 3 10�4 7.09 3 10�5 2.21 3 10�5 9.45 3 10�5 3.25 3 10�5 1.68 3 10�5

730 ! 4265b

(Orth. ! ?)Model I’ 7.64 3 10�5 2.68 3 10�3 9.55 3 10�5 5.26 3 10�5 1.62 3 10�3 6.00 3 10�5

4265 ! 1353c

(? ! Def.)Model II’ 2.21 3 10�2 3.91 3 10�3 4.13 3 10�3 1.91 3 10�2 2.45 3 10�3 2.70 3 10�3

4265 ! 730d

(? ! Orth.)Model III 9.82 3 10�5 9.60 3 10�5 3.08 3 10�5 6.08 3 10�5 5.17 3 10�5 2.19 3 10�5

1353 ! 4320e

(Def. ! Orth.)

a The results show the values for PC1 and the Y variables at 4265 cm�1 (b), 1353 cm�1 (c), 730 cm�1 (d), and 4320 cm�1 (e), respectively.

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FIG. 4. Predicted NIR spectra (solid line) vs. measured spectra (dashed line) inthe 4240–4290 cm�1 region, based on Model I, of (a) HDPE, (b) LLDPE, and(c) LDPE in the temperature range from 35 to 135 8C.

FIG. 5. Predicted mid-IR spectra (solid line) vs. measured spectra (dashedline) in the 1330–1370 cm�1 region, based on Model I’, of (a) HDPE, (b)LLDPE, and (c) LDPE in the temperature range from 35 to 135 8C.

APPLIED SPECTROSCOPY 1407

range 1328–1374 cm�1. In the present study, five differentPLS-2 models (named Model I, Model I’, Model II, Model II’,and Model III, see Table I) were built based on the spectral datacollected in the mid-IR and NIR ranges relating to theconformational disordering of PE crystals induced by thermalstimulus. All the models were built based on the temperature-dependent mid-IR and NIR spectra measured at temperaturesbetween 30 and 136 8C to ensure consistency in thecomparison. Model I and Model I’ were built between thespectral range of 1328–1374 cm�1 (center at 1368, 1353, and1341 cm�1) and 4235–4295 cm�1 (center at 4265 cm�1), whichmost likely had the same origin, i.e., conformational defectsequence.29 To provide contrast, the other three models werealso built between two spectral ranges, which may havedifferent origins. Model II and Model II’ were built between thespectral ranges of 725–752 cm�1 (centering at 730 cm�1, due tothe orthorhombic conformation) and 4235–4295 cm�1; whileModel III was built between the spectral ranges of 1328–1374cm�1 and 4300–4362 cm�1 (centering at 4342 cm�1 due toorthorhombic conformation).

Partial least squares prediction is sensitive to the number ofcomponents (latent variables) used in the model.40 The optimalnumber of latent variables in PLS-2 is determined by the fullcross-validation in the present study. The most efficient andsimplest measurement of the uncertainty of the futurepredictions is the root mean square error of prediction(RMSEP). This value (one for each response) measures theaveraged uncertainty that can be expected when predicting Y-values from new samples and is expressed in the same units asthe Y-variable.40 The results of the root mean square error ofcalibration (RMSEC) and RMSEP that were computed by theUnscrambler software are shown in Table II, which lists thevalues for the first principle component (PC1) and the Yvariables at 4265, 1353, 730, and 4320 cm�1. The smallervalues of both RMSEC and RMSEP of Model I in comparisonwith those of Model II, for all three kinds of PE samples,indicate that much less uncertainty exists in the prediction ofthe spectral behavior of the NIR band at 4265 cm�1 by Model I;the cases are similar for Model I’ and Model II’, which impliesthat the band at 4265 cm�1 may correlate more closely to theband at 1353 cm�1 than that at 730 cm�1. In other words, theNIR band at 4265 cm�1 may have a similar origin as the bandat 1353 cm�1, which was ascribed to the conformational defectsequence formed in the heating process. Also, the smallervalues of RMSEC and RMSEP for Model I in comparison withthose for Model III suggest less uncertainty in the prediction byModel I, illustrating the higher correlation between the mid-IRband at 1350 and the NIR band at 4265 cm�1 than that at 4320cm�1.

Inter-conversion Between Mid-Infrared and Near-Infra-red Spectra of Three Polyethylene Samples During theHeating Process. Model I was first used for a new spectral datamatrix Xo collected in the mid-IR range (1328–1374 cm�1),which was measured at temperatures between 31 and 135 8C,to predict the desired corresponding new NIR spectral matrixYo (4235–4295 cm�1). Plotting the predicted spectra versus themeasured reference spectra was a convenient way to illustratethe validity of such a prediction.39 Figure 4 shows the predictedNIR spectra based on Model I and the corresponding measuredspectra of (a) HDPE, (b) LLDPE, and (c) LDPE in thetemperature range from 35 to 135 8C. The two corresponding

FIG. 6. Temperature dependence of the absorbance of band at 4265 cm�1

corresponding to (a) HDPE, (b) LLDPE, and (c) LDPE in the temperaturerange from 35 to 135 8C for the predicted spectra (�) based on Model I’ andmeasured spectra (*).

1408 Volume 65, Number 12, 2011

spectra at each temperature are very close to each other,especially with an absorption band around 4253 cm�1 at 358C.

Analogously, when we treated an NIR spectral data as the X-matrix and a mid-IR spectral data as the Y-matrix, we obtaineda reverse regression model of Model I, termed Model I’. ModelI’ was thus used for a new spectral data matrix, Xo, collected inthe NIR range (4235–4295 cm�1) at temperatures between 31and 135 8C, to predict the desired corresponding new mid-IRspectral matrix Yo (1328–1374 cm�1). Figure 5 shows the

predicted mid-IR spectra by using Model I’ and thecorresponding measured spectra of (a) HDPE, (b) LLDPE,and (c) LDPE. Similar to the prediction result of Model I, thetwo corresponding spectra at each temperature were very closeto each other, concerning the band shape, peak intensity, andposition.

The apparent success of spectral inter-conversion based onModel I’ is very interesting from a practical point of view. Themeasurement of NIR spectra is often more straightforward thanthe measurement of their mid-IR counterparts, especially forthick real-world plastics samples encountered in industriallaboratories. The potential advantage of reconstructing well-resolved and often better-understood mid-IR spectra directlyfrom the easily obtainable NIR spectra should be obvious. Thisrather attractive practical utility is one of the driving forcesbehind the current study.

The successful mutual prediction results of Model I andModel I’ indicate that the temperature dependency of the bandat 4265 cm�1 in the NIR range is very similar to that of thebands at 1368, 1353, and 1341 cm�1 in the mid-IR range. Inother words, these bands correlate closely to each other, andthey most likely share the same spectral origin.

In addition to the contrast of the temperature-dependent mid-IR and NIR spectra between the predicted and measuredspectra, the specific intensity value of the band at 4265 cm�1

was also extracted from the predicted spectra and comparedwith that of the measured ones. The change in intensity of thisband with temperature is shown in Fig. 6. It can be seen fromFigs. 6a and 6b that the intensity of the NIR band at 4265 cm�1

increases abruptly as the temperature approaches Tm for HDPEand LLDPE, indicating that the thermally induced conforma-tional disordering of these two polymers undergoes a distinct‘‘on-set’’ process.29 In contrast, the increment of the bandintensity for LDPE was quite different, and it seems that theconformational disordering of this polymer is a much moregradual process. Nevertheless, distinct and well-correlatedpatterns can be seen for each polymer from the plot of the bandintensity trend between the measured and predicted spectra.The result indicates that the behavior of the NIR band at 4265cm�1 can be predicted exactly by the PLS model built on thisband and the bands at 1368, 1353, and 1341 cm�1 in the mid-IR range. This conclusion further confirms that all these bandshave the same origin. In other words, the assignment for themid-IR bands at 1368 cm�1, 1353 cm�1, and 1341 cm�1 (as theconformational defect sequences) also applies equally well tothat of the NIR band at 4265 cm�1.

To further validate the band assignment at 4265 cm�1,additional PLS models (see Table I) were built to evaluate thecorrelation between the above NIR band and other bands in themid-IR range. For example, Model II and Model II’ were builtbetween the bands at 4235–4295 cm�1 (centered at 4265 cm�1)and the bands at 710–752 cm�1 (centered at 730 cm�1,

FIG. 7. Predicted NIR and mid-IR spectra (solid line) vs. the measured spectra(dashed line) in (a) the 4240–4290 cm�1 region and (b) the 710–750 cm�1

region, based on Model II and Model II’, of LDPE in the temperature rangefrom 35 to 135 8C.

TABLE III. Relative mean square error (RMSE) of the predicted absorbance value.

Model I Model II Model I’ Model II’ Model III

1353!4265 730!4265 4265!1353 4265!730 1353!4320

Def.!? Orth.!? ?!Def. ?!Orth. Def.!Orth.

HDPE 6.37 3 10�3 6.14 3 10�2 8.65 3 10�3 3.14 3 10�2 1.06 3 10�2

LLDPE 1.03 3 10�2 1.38 3 10�2 1.07 3 10�2 1.61 3 10�2 1. 99 3 10�2

LDPE 2.62 3 10�2 3.75 3 10�2 4.48 3 10�3 1.26 3 10�2 3.69 3 10�2

APPLIED SPECTROSCOPY 1409

attributed to the orthorhombic crystalline conformationalsequence). The predicted NIR and mid-IR spectra of LDPEin the temperature range from 35 to 135 8C, based on these twomodels, are shown in Fig. 7.

Though it seems that the plots of the predicted spectra agreereasonably well with those of the measured spectra in both Fig.5 and Fig. 7, the relative mean square error (or RMSE40) valuesfor HDPE calculated for Model II and Model II’ are 6.14 310�2 and 3.14 3 10�2, which are much larger than the values of6.37 3 10�3 and 8.65 3 10�3 obtained for Model I and Model I’(see Table III). The results indicate that the spectral inter-conversion based on Model II is less precise (or predictive)compared to that of Model I. According to the principle ofmultivariate PLS prediction, better spectral prediction can beachieved between the bands originating from the same orclosely related vibration modes, which will be especiallyhelpful in better understanding the complex NIR spectra ofpolymers and other macromolecular systems.39 So it isreasonable to conclude that the NIR band at 4265 cm�1

correlates more closely to the bands at 1368 cm�1, 1353 cm�1,and 1341 cm�1 than the band at 730 cm�1, which suggests thatthis NIR band should be assigned to the conformational defectsequence during the heating. Therefore, by comparing theprediction deviation values (RMSE) between different PLSmodels, one can also distinguish the complex NIR bands.

Table III lists the RMSE values of a total of seven PLSmodels. Besides the above-mentioned Model II and Model II’,the prediction deviation of Model III (based on the spectralrange that corresponds to the conformational defect andorthorhombic crystalline contribution) is also larger than thatof Model I, suggesting that the mid-IR band at 1353 cm�1

correlates more closely to the NIR band at 4265 cm�1 than thatat 4320 cm�1. The result again confirms the attribution of theband at 4265 cm�1 as a conformational defect sequence. Ofnote is that this conclusion is also in good accord with theresults of the 2D correlation spectral method that was used inour previous paper.29

CONCLUSION

In the present study, we have demonstrated the conforma-tional discrimination of polyethylene crystal during itsthermally induced structural changes by extraction of spectralinformation from NIR spectra through the corresponding mid-IR spectra by means of spectral inter-conversion analysis. Weexplored two mid-IR and two NIR spectral ranges, attributed todifferent conformational sequences, respectively, 710–752cm�1 and 4300–4362 cm�1 (corresponding to the orthorhom-bic), 1328–1374 cm�1 (relating to the defect), and 4235–4295cm�1 (relating to the unconfirmed conformation). Based on theabsorbance data collected in these four infrared spectral ranges,five PLS-2 models were built to make the conformationaldiscrimination by the spectral inter-conversion scheme. By thequalitative contrast, namely, shape-comparing the predictedspectra with the measured spectra, along with comparing thepredicted deviation RMSE values between the differentmodels, it became possible to distinguish the conformationaldisordering induced by thermal stimulus. The attribution of aNIR band at 4265 cm�1 is further confirmed. The ability to beable to reconstruct well-resolved and often better-understoodmid-IR spectra directly from the more easily obtainable NIRspectra is attractive, especially for thick real-world plasticssamples often encountered in industrial laboratories. The

extraction of spectral information by means of the inter-conversion for Model I and Model I’ indicates that thetemperature dependency of the band at 4265 cm�1 in the NIRrange is very similar to that of the bands at 1368, 1353, and1341 cm�1 in the mid-IR range, indicating that they have thesimilar spectral origin. The band intensity at 4265 cm�1 wasalso extracted from the predicted spectra based on Model I andcompared with that of the measured ones. The excellentmatching in the intensity changing tendency with temperatureonce again demonstrates the same origin of this NIR band andthe above-mentioned mid-IR ones. The much smaller predicteddeviation values resulting from Model I (or Model I’) relative tothose from Model II (or Model II’) and Model III indicate thehigh correlation of the NIR band at 4265 cm�1 and those mid-IR bands at 1368, 1353, and 1341 cm�1, which furtherconfirms the attribution of the band at 4265 cm�1 to theconformational defect sequence formed in the heating process.

ACKNOWLEDGMENTS

The financial support from the Project of NSFC (No. 20934002) is gratefullyacknowledged by L.Z. and Y.W. Also, L. Z. thanks the ‘‘11th five-year plan’’ ofthe science research project from the education department of Jilin province(No. 2010225).

1. D. C. Bassett, B. A. Khalifa, and B. Turner, Nature 239, 371 (1972).2. D. C. Bassett, S. Block, and G. J. Piermarini, J. Appl. Phys. 45, 4146

(1974).3. M. Hikosaka, S. Minomura, and T. Seto, Jpn. J. Appl. Phys. 19, 1763

(1980).4. M. Hikosaka, K. Tsukijima, S. Rastogi, and A. Keller, Polymer 33, 2502

(1992).5. S. Watanabe, N. Sano, I. Noda, and Y. Ozaki, J. Phys. Chem. B 113, 3385

(2009).6. P. Bernazzania and R. F. Sanchez, Eur. Phys. J. E: Soft Matter. 26, 427

(2008).7. G. Rojas, B. Inci, Y. Wei, and K. B. Wagener, J. Am. Chem. Soc. 131,

17376 (2009).8. J. M. Schultz, E. W. Fisher, O. Schaumburg, and H. A. Zachmann, J.

Polym. Sci. Polym. Phys. Ed. 18, 239 (1980).9. H. Matsuda, T. Aoike, T. Yamanobe, and T. Komoto, Polymer 42, 5013

(2001).10. L. Fontana, D. Q. Vinh, M. Santoro, and S. Scandolo, Phys. Rev. B 75,

174112 (2007).11. K. Tashiro, S. Kariyo, A. Nishimori, T. Fujii, S. Saragai, and S. Nakamoto,

et al., J. Polym. Sci., Part B Polym. Phys. 40, 495 (2002).12. M. Prodanovic, W. B. Lindquist, and R. S. Seright, J. Colloid Interface Sci.

298, 282 (2006).13. H. Matsuda, T. Aoike, T. Yamanobe, and T. Komoto, Polymer 42, 5013

(2001).14. H. Uehara, T. Yamanobe, and T. Komoto, Macromolecules 33, 4861

(2000).15. H. Kakuda, T. Okada, M. Otsuka, Y. Katsumoto, and T. Hasegawa, Anal.

Bioanal. Chem. 393, 367 (2009).16. H. Uehara, T. Yamanobe, and T. Komoto, Macromolecules 33, 4861

(2000).17. M. Kakiage, M. Sekiya, T. Yamanobe, T. Komoto, S. Sasaki, S.

Murakami, and H. Uehara, J. Phys. Chem. B 112, 5311 (2008).18. J. Kim, Y. Kim, and H. Chung, Talanta 83, 879 (2011).19. M. Kim, J. Noh, and H. Chung, Anal. Chim. Acta 632, 122 (2009).20. Y. Xiao, L. Yan, P. Zhang, N. Zhu, L. Chen, and P. He, et al., J. Appl.

Polym. Sci. 100, 4835 (2006).21. L. Fontana, M. Santoro, R. Bini, D. Q. Vinh, and S. Scandolo, J. Chem.

Phys. 133, 204502 (2010).22. M. Cossar, J. Teh, A. Kivisto, and J. Mackenzie, Appl. Spectrosc. 59, 300

(2005).23. T. Rohe, W. Becker, S. Kolle, N. Eisenreich, and P. Eyerer, Talanta 50,

283 (1999).24. M. Watari, H. Higashiyama, N. Mitsui, M. Tomo, and Y. Ozaki, Appl.

Spectrosc. 58, 248 (2004).25. T. Nagata, M. Ohshima, and M. Tanigaki, Polym. Eng. Sci, 40, 1107

(2000).

1410 Volume 65, Number 12, 2011

26. R. Reshadat, S. Desa, S. Joseph, M. Mehra, N. Stoev, and S. T. Balke,Appl. Spectrosc. 53, 1412 (1999).

27. H. Sato, M. Shimoyama, T. Kamiya, T. Amari, S. Sasic, and T. Ninomiya,et al., J. Near Infrared Spectrosc. 11, 309 (2003).

28. S. Watanabe, J. Dybal, K. Tashiro, and Y. Ozaki, Polymer 47, 2010(2006).

29. S. Watanabe, I. Noda, and Y. Ozaki, Polymer 49, 774 (2008).30. S. Sasic, H. Sato, M. Shimoyama, and Y. Ozaki, Analyst 130, 652 (2005).31. Y. Guo, B. Sun, and P. Wu, Langmuir 24, 5521 (2008).32. P. Wu, Y. Yang, and H. W. Siesler, Polymer 42, 10181 (2001).33. B. Sun, Y. Lin, and P. Wu, Appl. Spectrosc. 61, 765 (2007).34. P. Wu and H. W. Siesler, J. Mol. Struct. 521, 37 (2000).

35. R. Quilitzsch and M. Baranska, H. Schulz, and E. Hoberg, J. Appl. Bot.Food Qual. 79, 163 (2005).

36. R. Karoui, A. M. Mouazen, E. Dufour, L. Pillonel, and E. Schaller, et al.,Eur. Food Res. Technol. 223, 44 (2006).

37. M. U. Cuadrado, M. D. Luque de Castro, P. M. Perez Juan, and M. A.Gomez-Nieto, Talanta 66, 218 (2005).

38. R. Lew and S. T. Balke, Appl. Spectrosc. 47, 1747 (1993).39. L. Zhang, I. Noda, B. Czarnik-Matusewicz, and Y. Wu, Anal. Sci. 23, 901

(2007).40. L. Zhang, I. Noda, and Y. Wu, Appl. Spectrosc. 63, 112 (2009).41. L. Zhang, I. Noda, and Y. Wu, Appl. Spectrosc. 63, 63 (2009).42. R. Lew and S. T. Balke, Appl. Spectrosc. 47, 47 (1993).

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