Abstract

The composition of nontransparent polymers has been predicted from the fingerprint region in the mid-IR. The polymers were analyzed by the <u>H</u>orizontal <u>A</u>ttenuated <u>T</u>otal internal <u>R</u>eflection (HATR) FT-IR technique. The polymers were blends of three different master batches: (1) a polymer of ethylene with carbon black, (2) a co-polymer of ethylene and propylene monomers, and (3) an ethylene-propylene-diene elastomer. A calibration set was defined by use of mixture design. Partial least-squares (PLS) regression was used to calculate models for prediction of the relative concentrations of each master batch (one at a time). Second-derivated IR profiles normalized to 100% were used as predictive variables. Two alternative criteria were compared for optimizing the predictive ability of the calibration models: (1) squared prediction error of all the calibration samples, and (2) prediction error of replicated calibration samples of the centerpoint in the design only. The latter criterion turned out to be the more useful for the purpose of this study. This is because the centerpoint represents the target sample of the blending process. The one-component PLS models, suggested by the latter optimization criterion, gave predictions within 1% of the stated relative concentrations and with standard deviations from 0.5 to 1.3% for all three master batches.

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